From 96244f4e5b2e57e23349367c83aefde434b372fe Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Fri, 22 Aug 2025 14:45:17 -0700 Subject: [PATCH 01/28] echo variables --- src/kernelbot/api/api_utils.py | 298 ++++++++++++++++-- src/kernelbot/api/main.py | 235 +++++--------- src/libkernelbot/backend.py | 30 +- src/libkernelbot/db_types.py | 5 + src/libkernelbot/leaderboard_db.py | 47 ++- .../20250822_01_UtXzl-website-submission.py | 13 + unit-tests/test_leaderboard_db.py | 28 ++ 7 files changed, 458 insertions(+), 198 deletions(-) create mode 100644 src/migrations/20250822_01_UtXzl-website-submission.py diff --git a/src/kernelbot/api/api_utils.py b/src/kernelbot/api/api_utils.py index b5cda480..db25eef1 100644 --- a/src/kernelbot/api/api_utils.py +++ b/src/kernelbot/api/api_utils.py @@ -1,6 +1,13 @@ +import asyncio +from dataclasses import asdict +from datetime import datetime +from re import sub +import time +from typing import Any, Optional, Tuple import requests -from fastapi import HTTPException - +from fastapi import HTTPException, UploadFile +from fastapi import BackgroundTasks +import json from kernelbot.env import env from libkernelbot.backend import KernelBackend from libkernelbot.consts import SubmissionMode @@ -12,9 +19,13 @@ Text, ) from libkernelbot.submission import SubmissionRequest, prepare_submission +from src.kernelbot.api.main import simple_rate_limit +from src.libkernelbot.leaderboard_db import LeaderboardDB -async def _handle_discord_oauth(code: str, redirect_uri: str) -> tuple[str, str]: +async def _handle_discord_oauth( + code: str, redirect_uri: str +) -> tuple[str, str]: """Handles the Discord OAuth code exchange and user info retrieval.""" client_id = env.CLI_DISCORD_CLIENT_ID client_secret = env.CLI_DISCORD_CLIENT_SECRET @@ -22,11 +33,17 @@ async def _handle_discord_oauth(code: str, redirect_uri: str) -> tuple[str, str] user_api_url = "https://discord.com/api/users/@me" if not client_id: - raise HTTPException(status_code=500, detail="Discord client ID not configured.") + raise HTTPException( + status_code=500, detail="Discord client ID not configured." + ) if not client_secret: - raise HTTPException(status_code=500, detail="Discord client secret not configured.") + raise HTTPException( + status_code=500, detail="Discord client secret not configured." + ) if not token_url: - raise HTTPException(status_code=500, detail="Discord token URL not configured.") + raise HTTPException( + status_code=500, detail="Discord token URL not configured." + ) token_data = { "client_id": client_id, @@ -69,13 +86,16 @@ async def _handle_discord_oauth(code: str, redirect_uri: str) -> tuple[str, str] if not user_id or not user_name: raise HTTPException( - status_code=500, detail="Failed to retrieve user ID or username from Discord." + status_code=500, + detail="Failed to retrieve user ID or username from Discord.", ) return user_id, user_name -async def _handle_github_oauth(code: str, redirect_uri: str) -> tuple[str, str]: +async def _handle_github_oauth( + code: str, redirect_uri: str +) -> tuple[str, str]: """Handles the GitHub OAuth code exchange and user info retrieval.""" client_id = env.CLI_GITHUB_CLIENT_ID client_secret = env.CLI_GITHUB_CLIENT_SECRET @@ -84,9 +104,13 @@ async def _handle_github_oauth(code: str, redirect_uri: str) -> tuple[str, str]: user_api_url = "https://api.github.com/user" if not client_id: - raise HTTPException(status_code=500, detail="GitHub client ID not configured.") + raise HTTPException( + status_code=500, detail="GitHub client ID not configured." + ) if not client_secret: - raise HTTPException(status_code=500, detail="GitHub client secret not configured.") + raise HTTPException( + status_code=500, detail="GitHub client secret not configured." + ) token_data = { "client_id": client_id, @@ -98,7 +122,9 @@ async def _handle_github_oauth(code: str, redirect_uri: str) -> tuple[str, str]: headers = {"Accept": "application/json"} # Request JSON response for token try: - token_response = requests.post(token_url, data=token_data, headers=headers) + token_response = requests.post( + token_url, data=token_data, headers=headers + ) token_response.raise_for_status() except requests.exceptions.RequestException as e: raise HTTPException( @@ -125,33 +151,20 @@ async def _handle_github_oauth(code: str, redirect_uri: str) -> tuple[str, str]: ) from e user_json = user_response.json() - user_id = str(user_json.get("id")) # GitHub ID is integer, convert to string for consistency + user_id = str( + user_json.get("id") + ) # GitHub ID is integer, convert to string for consistency user_name = user_json.get("login") # GitHub uses 'login' for username if not user_id or not user_name: raise HTTPException( - status_code=500, detail="Failed to retrieve user ID or username from GitHub." + status_code=500, + detail="Failed to retrieve user ID or username from GitHub.", ) return user_id, user_name -async def _run_submission( - submission: SubmissionRequest, mode: SubmissionMode, backend: KernelBackend -): - try: - req = prepare_submission(submission, backend) - except Exception as e: - raise HTTPException(status_code=400, detail=str(e)) from e - - if len(req.gpus) != 1: - raise HTTPException(status_code=400, detail="Invalid GPU type") - - reporter = MultiProgressReporterAPI() - sub_id, results = await backend.submit_full(req, mode, reporter) - return results, [rep.get_message() + "\n" + rep.long_report for rep in reporter.runs] - - class MultiProgressReporterAPI(MultiProgressReporter): def __init__(self): self.runs = [] @@ -183,3 +196,230 @@ async def display_report(self, title: str, report: RunResultReport): elif isinstance(part, Log): self.long_report += f"\n\n## {part.header}:\n" self.long_report += f"```\n{part.content}```" + + +async def to_submission_info( + user_info: Any, + submission_mode: str, + file: UploadFile, + leaderboard_name: str, + gpu_type: str, + db_context: LeaderboardDB, +) -> tuple[SubmissionRequest, SubmissionMode]: + user_name = user_info["user_name"] + user_id = user_info["user_id"] + + try: + submission_mode_enum: SubmissionMode = SubmissionMode( + submission_mode.lower() + ) + except ValueError: + raise HTTPException( + status_code=400, + detail=f"Invalid submission mode value: '{submission_mode}'", + ) from None + + if submission_mode_enum in [SubmissionMode.PROFILE]: + raise HTTPException( + status_code=400, + detail="Profile submissions are not currently supported via API", + ) + + allowed_modes = [ + SubmissionMode.TEST, + SubmissionMode.BENCHMARK, + SubmissionMode.LEADERBOARD, + ] + if submission_mode_enum not in allowed_modes: + raise HTTPException( + status_code=400, + detail=f"Submission mode '{submission_mode}' is not supported for this endpoint", + ) + + try: + with db_context as db: + leaderboard_item = db.get_leaderboard(leaderboard_name) + gpus = leaderboard_item.get("gpu_types", []) + if gpu_type not in gpus: + supported_gpus = ", ".join(gpus) if gpus else "None" + raise HTTPException( + status_code=400, + detail=f"GPU type '{gpu_type}' is not supported for " + f"leaderboard '{leaderboard_name}'. Supported GPUs: {supported_gpus}", + ) + except HTTPException: + raise + except Exception as e: + raise HTTPException( + status_code=500, + detail=f"Internal server error while validating leaderboard/GPU: {e}", + ) from e + + try: + submission_content = await file.read() + if not submission_content: + raise HTTPException( + status_code=400, + detail="Empty file submitted. Please provide a file with code.", + ) + if len(submission_content) > 1_000_000: + raise HTTPException( + status_code=413, + detail="Submission file is too large (limit: 1MB).", + ) + + except HTTPException: + raise + except Exception as e: + raise HTTPException( + status_code=400, detail=f"Error reading submission file: {e}" + ) from e + + try: + submission_code = submission_content.decode("utf-8") + submission_request = SubmissionRequest( + code=submission_code, + file_name=file.filename or "submission.py", + user_id=user_id, + user_name=user_name, + gpus=[gpu_type], + leaderboard=leaderboard_name, + ) + except UnicodeDecodeError: + raise HTTPException( + status_code=400, + detail="Failed to decode submission file content as UTF-8.", + ) from None + except Exception as e: + raise HTTPException( + status_code=500, + detail=f"Internal server error creating submission request: {e}", + ) from e + + return submission_request, submission_mode_enum + + +def json_serializer(obj): + """JSON serializer for objects not serializable by default json code""" + if isinstance(obj, (datetime.datetime, datetime.date, datetime.time)): + return obj.isoformat() + raise TypeError(f"Type {type(obj)} not serializable") + + +async def _run_submission( + submission: SubmissionRequest, + mode: SubmissionMode, + backend: KernelBackend, + submission_id: Optional[int] = None, +): + try: + req = prepare_submission(submission, backend) + except Exception as e: + raise HTTPException(status_code=400, detail=str(e)) from e + + if len(req.gpus) != 1: + raise HTTPException(status_code=400, detail="Invalid GPU type") + + reporter = MultiProgressReporterAPI() + sub_id, results = await backend.submit_full( + req, mode, reporter, submission_id + ) + return ( + results, + [rep.get_message() + "\n" + rep.long_report for rep in reporter.runs], + sub_id, + ) + + +def start_detached_run( + submission_request: SubmissionRequest, + submission_mode_enum: SubmissionMode, + backend: KernelBackend, + db: "LeaderboardDB", + background_tasks: BackgroundTasks, +) -> int: + """Starts a submission in the background and returns the submission id immediately.""" + + # create submission id, so that it can be return to client before task is started + with db: + req = submission_request + sub_id = db.create_submission( + leaderboard=req.leaderboard, + file_name=req.file_name, + code=req.code, + user_id=req.user_id, + time=datetime.now(), + user_name=req.user_name, + ) + + # makes the task run in the background + background_tasks.add_task( + _run_submission, + submission_request, + submission_mode_enum, + backend, + db, + sub_id, + ) + return sub_id + + +async def sse_stream_submission( + submission_request: SubmissionRequest, + submission_mode_enum: SubmissionMode, + backend: KernelBackend, +): + start_time = time.time() + task: asyncio.Task | None = None + try: + task = asyncio.create_task( + _run_submission(submission_request, submission_mode_enum, backend) + ) + + while not task.done(): + elapsed_time = time.time() - start_time + yield ( + "event: status\n" + f"data: {json.dumps({'status': 'processing','elapsed_time': round(elapsed_time, 2)}, default=json_serializer)}\n\n" + ) + try: + await asyncio.wait_for(asyncio.shield(task), timeout=15.0) + except asyncio.TimeoutError: + continue + except asyncio.CancelledError: + yield ( + "event: error\n" + f"data: {json.dumps({'status': 'error','detail': 'Submission cancelled'}, default=json_serializer)}\n\n" + ) + return + + result, reports = await task + result_data = { + "status": "success", + "results": [asdict(r) for r in result], + "reports": reports, + } + yield "event: result\n" f"data: {json.dumps(result_data, default=json_serializer)}\n\n" + + except HTTPException as http_exc: + error_data = { + "status": "error", + "detail": http_exc.detail, + "status_code": http_exc.status_code, + } + yield "event: error\n" f"data: {json.dumps(error_data, default=json_serializer)}\n\n" + except Exception as e: + error_type = type(e).__name__ + error_data = { + "status": "error", + "detail": f"An unexpected error occurred: {error_type}", + "raw_error": str(e), + } + yield "event: error\n" f"data: {json.dumps(error_data, default=json_serializer)}\n\n" + finally: + if task and not task.done(): + task.cancel() + try: + await task + except asyncio.CancelledError: + pass diff --git a/src/kernelbot/api/main.py b/src/kernelbot/api/main.py index 2848d383..eda249c7 100644 --- a/src/kernelbot/api/main.py +++ b/src/kernelbot/api/main.py @@ -5,18 +5,20 @@ import os import time from dataclasses import asdict -from typing import Annotated, Optional +from typing import Annotated, Any, Optional, Tuple -from fastapi import Depends, FastAPI, Header, HTTPException, Request, UploadFile +from fastapi import Depends, FastAPI, Header, HTTPException, Request, UploadFile, BackgroundTasks from fastapi.responses import JSONResponse, StreamingResponse + from libkernelbot.backend import KernelBackend from libkernelbot.consts import SubmissionMode -from libkernelbot.leaderboard_db import LeaderboardRankedEntry +from libkernelbot.leaderboard_db import LeaderboardDB, LeaderboardRankedEntry from libkernelbot.submission import SubmissionRequest -from libkernelbot.utils import KernelBotError +from libkernelbot.utils import KernelBotError, +from src.libkernelbot.db_types import IdentityType -from .api_utils import _handle_discord_oauth, _handle_github_oauth, _run_submission +from .api_utils import _handle_discord_oauth, _handle_github_oauth, sse_stream_submission, start_detached_run, to_submission_info # yes, we do want ... = Depends() in function signatures # ruff: noqa: B008 @@ -24,19 +26,12 @@ app = FastAPI() -def json_serializer(obj): - """JSON serializer for objects not serializable by default json code""" - if isinstance(obj, (datetime.datetime, datetime.date, datetime.time)): - return obj.isoformat() - raise TypeError(f"Type {type(obj)} not serializable") - backend_instance: KernelBackend = None _last_action = time.time() _submit_limiter = asyncio.Semaphore(3) - async def simple_rate_limit(): """ A very primitive rate limiter. This function returns at most @@ -223,70 +218,48 @@ async def cli_auth(auth_provider: str, code: str, state: str, db_context=Depends "is_reset": is_reset, } - -async def _stream_submission_response( - submission_request: SubmissionRequest, - submission_mode_enum: SubmissionMode, - backend: KernelBackend, -): - start_time = time.time() - task: asyncio.Task | None = None - try: - task = asyncio.create_task( - _run_submission( - submission_request, - submission_mode_enum, - backend, - ) +async def validate_user_header( + x_web_auth_id: Optional[str] = Header(None, alias="X-Web-Auth-Id"), + x_popcorn_cli_id: Optional[str] = Header(None, alias="X-Popcorn-Cli-Id"), + db_context: LeaderboardDB =Depends(get_db), +) -> Any: + """ + Validate either X-Web-Auth-Id or X-Popcorn-Cli-Id and return the associated user id. + Prefers X-Web-Auth-Id if both are provided. + """ + token = x_web_auth_id or x_popcorn_cli_id + if not token: + raise HTTPException( + status_code=400, + detail="Missing X-Web-Auth-Id or X-Popcorn-Cli-Id header", ) - while not task.done(): - elapsed_time = time.time() - start_time - yield f"event: status\ndata: {json.dumps({'status': 'processing', - 'elapsed_time': round(elapsed_time, 2)}, - default=json_serializer)}\n\n" - - try: - await asyncio.wait_for(asyncio.shield(task), timeout=15.0) - except asyncio.TimeoutError: - continue - except asyncio.CancelledError: - yield f"event: error\ndata: {json.dumps( - {'status': 'error', 'detail': 'Submission cancelled'}, - default=json_serializer)}\n\n" - return - - result, reports = await task - result_data = { - "status": "success", - "results": [asdict(r) for r in result], - "reports": reports, - } - yield f"event: result\ndata: {json.dumps(result_data, default=json_serializer)}\n\n" - - except HTTPException as http_exc: - error_data = { - "status": "error", - "detail": http_exc.detail, - "status_code": http_exc.status_code, - } - yield f"event: error\ndata: {json.dumps(error_data, default=json_serializer)}\n\n" + if x_web_auth_id: + token = x_web_auth_id + id_type = IdentityType.WEB + elif x_popcorn_cli_id: + token = x_popcorn_cli_id + id_type = IdentityType.CLI + else: + raise HTTPException( + status_code=400, + detail="Missing header must be eother X-Web-Auth-Id or X-Popcorn-Cli-Id header", + ) + try: + with db_context as db: + user_info = db.validate_identity(token, id_type) except Exception as e: - error_type = type(e).__name__ - error_data = { - "status": "error", - "detail": f"An unexpected error occurred: {error_type}", - "raw_error": str(e), - } - yield f"event: error\ndata: {json.dumps(error_data, default=json_serializer)}\n\n" - finally: - if task and not task.done(): - task.cancel() - try: - await task - except asyncio.CancelledError: - pass + raise HTTPException( + status_code=500, + detail=f"Database error during validation: {e}", + ) from e + if not user_info: + raise HTTPException( + status_code=401, + detail="Invalid or unauthorized auth header", + ) + return user_info @app.post("/{leaderboard_name}/{gpu_type}/{submission_mode}") async def run_submission( # noqa: C901 @@ -316,94 +289,48 @@ async def run_submission( # noqa: C901 StreamingResponse: A streaming response containing the status and results of the submission. """ await simple_rate_limit() - user_name = user_info["user_name"] - user_id = user_info["user_id"] - - try: - submission_mode_enum: SubmissionMode = SubmissionMode(submission_mode.lower()) - except ValueError: - raise HTTPException( - status_code=400, detail=f"Invalid submission mode value: '{submission_mode}'" - ) from None - - if submission_mode_enum in [SubmissionMode.PROFILE]: - raise HTTPException( - status_code=400, detail="Profile submissions are not currently supported via API" - ) - - allowed_modes = [ - SubmissionMode.TEST, - SubmissionMode.BENCHMARK, - SubmissionMode.LEADERBOARD, - ] - if submission_mode_enum not in allowed_modes: - raise HTTPException( - status_code=400, - detail=f"Submission mode '{submission_mode}' is not supported for this endpoint", - ) - - try: - with db_context as db: - leaderboard_item = db.get_leaderboard(leaderboard_name) - gpus = leaderboard_item.get("gpu_types", []) - if gpu_type not in gpus: - supported_gpus = ", ".join(gpus) if gpus else "None" - raise HTTPException( - status_code=400, - detail=f"GPU type '{gpu_type}' is not supported for " - f"leaderboard '{leaderboard_name}'. Supported GPUs: {supported_gpus}", - ) - except HTTPException: - raise - except Exception as e: - raise HTTPException( - status_code=500, - detail=f"Internal server error while validating leaderboard/GPU: {e}", - ) from e - - try: - submission_content = await file.read() - if not submission_content: - raise HTTPException( - status_code=400, detail="Empty file submitted. Please provide a file with code." - ) - if len(submission_content) > 1_000_000: - raise HTTPException( - status_code=413, detail="Submission file is too large (limit: 1MB)." - ) - - except HTTPException: - raise - except Exception as e: - raise HTTPException(status_code=400, detail=f"Error reading submission file: {e}") from e - - try: - submission_code = submission_content.decode("utf-8") - submission_request = SubmissionRequest( - code=submission_code, - file_name=file.filename or "submission.py", - user_id=user_id, - user_name=user_name, - gpus=[gpu_type], - leaderboard=leaderboard_name, - ) - except UnicodeDecodeError: - raise HTTPException( - status_code=400, detail="Failed to decode submission file content as UTF-8." - ) from None - except Exception as e: - raise HTTPException( - status_code=500, detail=f"Internal server error creating submission request: {e}" - ) from e - - generator = _stream_submission_response( + submission_request,submission_mode_enum = await to_submission_info( + user_info, + submission_mode, + file, + leaderboard_name, + gpu_type, + db_context) + + generator = sse_stream_submission( submission_request=submission_request, submission_mode_enum=submission_mode_enum, backend=backend_instance, ) - return StreamingResponse(generator, media_type="text/event-stream") +@app.post("submission/{leaderboard_name}/{gpu_type}/{submission_mode}") +async def run_submission_v2( # noqa: C901 + leaderboard_name: str, + gpu_type: str, + submission_mode: str, + file: UploadFile, + user_info: Annotated[dict, Depends(validate_user_header)], + db_context=Depends(get_db), + background_tasks: BackgroundTasks = Depends() +) -> Any: + + await simple_rate_limit() + + submission_request,submission_mode_enum = await to_submission_info( + user_info, + submission_mode, + file, + leaderboard_name, + gpu_type, + db_context) + + sub_id = start_detached_run(submission_request, submission_mode_enum, backend_instance, db_context, background_tasks) + return JSONResponse( + status_code=202, + content={"id": sub_id, "status": "accepted"}, + ) + @app.get("/leaderboards") async def get_leaderboards(db_context=Depends(get_db)): diff --git a/src/libkernelbot/backend.py b/src/libkernelbot/backend.py index 3874b142..2441a366 100644 --- a/src/libkernelbot/backend.py +++ b/src/libkernelbot/backend.py @@ -1,6 +1,8 @@ import asyncio import copy from datetime import datetime +from os import stat +from re import sub from types import SimpleNamespace from typing import Optional @@ -53,18 +55,25 @@ def register_launcher(self, launcher: Launcher): self.launcher_map[gpu.value] = launcher async def submit_full( - self, req: ProcessedSubmissionRequest, mode: SubmissionMode, reporter: MultiProgressReporter + self, req: ProcessedSubmissionRequest, mode: SubmissionMode, reporter: MultiProgressReporter, + pre_sub_id: Optional[int] = None ): - with self.db as db: - sub_id = db.create_submission( - leaderboard=req.leaderboard, - file_name=req.file_name, - code=req.code, - user_id=req.user_id, - time=datetime.now(), - user_name=req.user_name, - ) + """ + pre_sub_id is used to pass the submission id which is created beforehand. + """ + if pre_sub_id is not None: + sub_id = pre_sub_id + else: + with self.db as db: + sub_id = db.create_submission( + leaderboard=req.leaderboard, + file_name=req.file_name, + code=req.code, + user_id=req.user_id, + time=datetime.now(), + user_name=req.user_name, + ) selected_gpus = [get_gpu_by_name(gpu) for gpu in req.gpus] try: @@ -103,7 +112,6 @@ async def submit_full( finally: with self.db as db: db.mark_submission_done(sub_id) - return sub_id, results async def submit_leaderboard( # noqa: C901 diff --git a/src/libkernelbot/db_types.py b/src/libkernelbot/db_types.py index e8715fa2..0a03ec52 100644 --- a/src/libkernelbot/db_types.py +++ b/src/libkernelbot/db_types.py @@ -1,10 +1,15 @@ # This file provides TypeDict definitions for the return types we get from database queries import datetime +from enum import Enum from typing import TYPE_CHECKING, List, NotRequired, Optional, TypedDict if TYPE_CHECKING: from libkernelbot.task import LeaderboardTask +class IdentityType(str, Enum): + CLI = "cli" + WEB = "web" + UNKNOWN = "unknown" class LeaderboardItem(TypedDict): id: int diff --git a/src/libkernelbot/leaderboard_db.py b/src/libkernelbot/leaderboard_db.py index c322ab68..8b7b17bc 100644 --- a/src/libkernelbot/leaderboard_db.py +++ b/src/libkernelbot/leaderboard_db.py @@ -1,11 +1,11 @@ import dataclasses import datetime import json -from typing import Dict, List, Optional +from typing import Any, Dict, List, Optional import psycopg2 -from libkernelbot.db_types import LeaderboardItem, LeaderboardRankedEntry, RunItem, SubmissionItem +from libkernelbot.db_types import IdentityType, LeaderboardItem, LeaderboardRankedEntry, RunItem, SubmissionItem from libkernelbot.run_eval import CompileResult, RunResult, SystemInfo from libkernelbot.task import LeaderboardDefinition, LeaderboardTask from libkernelbot.utils import ( @@ -16,7 +16,6 @@ logger = setup_logging(__name__) - class LeaderboardDB: def __init__( self, host: str, database: str, user: str, password: str, port: str, url: str, ssl_mode: str @@ -235,6 +234,45 @@ def delete_leaderboard(self, leaderboard_name: str, force: bool = False): logger.exception("Could not delete leaderboard %s.", leaderboard_name, exc_info=e) raise KernelBotError(f"Could not delete leaderboard `{leaderboard_name}`.") from e + def validate_identity( + self, + identifier: str, + id_type: IdentityType, + ) -> Optional[dict[str, str]]: + """ + Validate an identity (CLI or Web) and return {user_id, user_name} if found. + + Args: + identifier: The identifier value (CLI ID or Web Auth ID). + id_type: IdentityType enum (IdentityType.CLI or IdentityType.WEB). + + Returns: + Optional[dict[str, str]]: {"user_id": ..., "user_name": ...} if valid; else None. + """ + where_by_type = { + IdentityType.CLI: ("cli_id = %s AND cli_valid = TRUE", "CLI ID"), + IdentityType.WEB: ("web_auth_id = %s", "WEB AUTH ID"), + } + + where_clause, human_label = where_by_type[id_type] + + try: + self.cursor.execute( + f""" + SELECT id, user_name + FROM leaderboard.user_info + WHERE {where_clause} + """, + (identifier,), + ) + row = self.cursor.fetchone() + return {"user_id": row[0], "user_name": row[1], "id_type":id_type.value} if row else None + except psycopg2.Error as e: + self.connection.rollback() + logger.exception("Error validating %s %s", human_label, identifier, exc_info=e) + raise KernelBotError(f"Error validating {human_label}") from e + + def create_submission( self, leaderboard: str, @@ -243,7 +281,7 @@ def create_submission( code: str, time: datetime.datetime, user_name: str = None, - ) -> Optional[int]: + ) -> int: try: # check if we already have the code self.cursor.execute( @@ -305,6 +343,7 @@ def create_submission( ), ) submission_id = self.cursor.fetchone()[0] + submission_id = int(submission_id) assert submission_id is not None self.connection.commit() return submission_id diff --git a/src/migrations/20250822_01_UtXzl-website-submission.py b/src/migrations/20250822_01_UtXzl-website-submission.py new file mode 100644 index 00000000..ab1d3b29 --- /dev/null +++ b/src/migrations/20250822_01_UtXzl-website-submission.py @@ -0,0 +1,13 @@ +""" +website_submission +""" + +from yoyo import step + +__depends__ = {'20250728_01_Q3jso-fix-code-table'} + +steps = [ + step( + "ALTER TABLE leaderboard.user_info ADD COLUMN IF NOT EXISTS web_auth_id VARCHAR(255) DEFAULT NULL;" + ) +] diff --git a/unit-tests/test_leaderboard_db.py b/unit-tests/test_leaderboard_db.py index 939f69b9..3c419557 100644 --- a/unit-tests/test_leaderboard_db.py +++ b/unit-tests/test_leaderboard_db.py @@ -5,6 +5,7 @@ import time import pytest +from libkernelbot.db_types import IdentityType from test_report import sample_compile_result, sample_run_result, sample_system_info from test_task import task_directory @@ -431,6 +432,33 @@ def test_leaderboard_submission_ranked(database, submit_leaderboard): }, ] +def test_validate_identity_web_auth_happy_path(database, submit_leaderboard): + with database as db: + db.cursor.execute( + """ + INSERT INTO leaderboard.user_info (id, user_name, web_auth_id) + VALUES (%s, %s, %s) + """, + (f"1234", f"sara_jojo","2345" ), + ) + user_info = db.validate_identity("2345",IdentityType.WEB) + assert user_info["user_id"] =="1234" + assert user_info["user_name"] =="sara_jojo" + assert user_info["id_type"] ==IdentityType.WEB.value + +def test_validate_identity_web_auth_missing(database, submit_leaderboard): + with database as db: + db.cursor.execute( + """ + INSERT INTO leaderboard.user_info (id, user_name) + VALUES (%s, %s) + """, + (f"1234", f"sara_jojo"), + ) + res = db.validate_identity("2345",IdentityType.WEB) + assert res is None + + def test_leaderboard_submission_deduplication(database, submit_leaderboard): """validate that identical submission codes are added just once""" From d555364556c2ec1a066c0f8852205dae9ce7105f Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Fri, 22 Aug 2025 14:48:59 -0700 Subject: [PATCH 02/28] echo variables --- src/kernelbot/api/api_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/kernelbot/api/api_utils.py b/src/kernelbot/api/api_utils.py index db25eef1..ab52b5a5 100644 --- a/src/kernelbot/api/api_utils.py +++ b/src/kernelbot/api/api_utils.py @@ -1,6 +1,6 @@ import asyncio from dataclasses import asdict -from datetime import datetime +import datetime from re import sub import time from typing import Any, Optional, Tuple From 423963de205bad1f341b85b0b31ecebedde4b5e2 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Fri, 22 Aug 2025 15:04:57 -0700 Subject: [PATCH 03/28] echo variables --- src/kernelbot/api/api_utils.py | 13 ++++++------- src/libkernelbot/backend.py | 2 -- src/libkernelbot/leaderboard_db.py | 10 ++++++++-- .../20250822_01_UtXzl-website-submission.py | 3 ++- unit-tests/test_leaderboard_db.py | 6 +++--- 5 files changed, 19 insertions(+), 15 deletions(-) diff --git a/src/kernelbot/api/api_utils.py b/src/kernelbot/api/api_utils.py index ab52b5a5..57b764bb 100644 --- a/src/kernelbot/api/api_utils.py +++ b/src/kernelbot/api/api_utils.py @@ -1,13 +1,13 @@ import asyncio -from dataclasses import asdict import datetime -from re import sub +import json import time -from typing import Any, Optional, Tuple +from dataclasses import asdict +from typing import Any, Optional + import requests -from fastapi import HTTPException, UploadFile -from fastapi import BackgroundTasks -import json +from fastapi import BackgroundTasks, HTTPException, UploadFile + from kernelbot.env import env from libkernelbot.backend import KernelBackend from libkernelbot.consts import SubmissionMode @@ -19,7 +19,6 @@ Text, ) from libkernelbot.submission import SubmissionRequest, prepare_submission -from src.kernelbot.api.main import simple_rate_limit from src.libkernelbot.leaderboard_db import LeaderboardDB diff --git a/src/libkernelbot/backend.py b/src/libkernelbot/backend.py index 2441a366..4cee7b8d 100644 --- a/src/libkernelbot/backend.py +++ b/src/libkernelbot/backend.py @@ -1,8 +1,6 @@ import asyncio import copy from datetime import datetime -from os import stat -from re import sub from types import SimpleNamespace from typing import Optional diff --git a/src/libkernelbot/leaderboard_db.py b/src/libkernelbot/leaderboard_db.py index 8b7b17bc..0d3f69a7 100644 --- a/src/libkernelbot/leaderboard_db.py +++ b/src/libkernelbot/leaderboard_db.py @@ -1,11 +1,17 @@ import dataclasses import datetime import json -from typing import Any, Dict, List, Optional +from typing import Dict, List, Optional import psycopg2 -from libkernelbot.db_types import IdentityType, LeaderboardItem, LeaderboardRankedEntry, RunItem, SubmissionItem +from libkernelbot.db_types import ( + IdentityType, + LeaderboardItem, + LeaderboardRankedEntry, + RunItem, + SubmissionItem, +) from libkernelbot.run_eval import CompileResult, RunResult, SystemInfo from libkernelbot.task import LeaderboardDefinition, LeaderboardTask from libkernelbot.utils import ( diff --git a/src/migrations/20250822_01_UtXzl-website-submission.py b/src/migrations/20250822_01_UtXzl-website-submission.py index ab1d3b29..03bd9192 100644 --- a/src/migrations/20250822_01_UtXzl-website-submission.py +++ b/src/migrations/20250822_01_UtXzl-website-submission.py @@ -8,6 +8,7 @@ steps = [ step( - "ALTER TABLE leaderboard.user_info ADD COLUMN IF NOT EXISTS web_auth_id VARCHAR(255) DEFAULT NULL;" + "ALTER TABLE leaderboard.user_info " + "ADD COLUMN IF NOT EXISTS web_auth_id VARCHAR(255) DEFAULT NULL;" ) ] diff --git a/unit-tests/test_leaderboard_db.py b/unit-tests/test_leaderboard_db.py index 3c419557..1f1cf6a7 100644 --- a/unit-tests/test_leaderboard_db.py +++ b/unit-tests/test_leaderboard_db.py @@ -5,11 +5,11 @@ import time import pytest -from libkernelbot.db_types import IdentityType from test_report import sample_compile_result, sample_run_result, sample_system_info from test_task import task_directory from libkernelbot import leaderboard_db +from libkernelbot.db_types import IdentityType from libkernelbot.utils import KernelBotError DATABASE_URL = "postgresql://postgres:postgres@localhost:5433/clusterdev" @@ -439,7 +439,7 @@ def test_validate_identity_web_auth_happy_path(database, submit_leaderboard): INSERT INTO leaderboard.user_info (id, user_name, web_auth_id) VALUES (%s, %s, %s) """, - (f"1234", f"sara_jojo","2345" ), + ("1234", "sara_jojo","2345" ), ) user_info = db.validate_identity("2345",IdentityType.WEB) assert user_info["user_id"] =="1234" @@ -453,7 +453,7 @@ def test_validate_identity_web_auth_missing(database, submit_leaderboard): INSERT INTO leaderboard.user_info (id, user_name) VALUES (%s, %s) """, - (f"1234", f"sara_jojo"), + ("1234", "sara_jojo"), ) res = db.validate_identity("2345",IdentityType.WEB) assert res is None From 486b5330bd238b65b2a1d5d26a48fcd4305f63e7 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Fri, 22 Aug 2025 19:46:26 -0700 Subject: [PATCH 04/28] echo variables --- data.sql | 9194 +++++++++++++++++ pyproject.toml | 2 +- requirements.txt | 3 +- seed.sql | 21 + src/discord_cluster_manager.egg-info/PKG-INFO | 408 + .../SOURCES.txt | 59 + .../dependency_links.txt | 1 + .../requires.txt | 22 + .../top_level.txt | 4 + src/kernelbot/api/api_utils.py | 27 +- src/kernelbot/api/main.py | 129 +- src/libkernelbot/backend.py | 43 +- src/libkernelbot/leaderboard_db.py | 22 + .../20250822_01_UtXzl-website-submission.py | 10 +- submission.py | 1 + unit-tests/conftest.py | 24 + unit-tests/test_leaderboard_db.py | 21 +- unit-tests/test_submission.py | 81 +- unit-tests/test_validate_user_header.py | 70 + 19 files changed, 10056 insertions(+), 86 deletions(-) create mode 100644 data.sql create mode 100644 seed.sql create mode 100644 src/discord_cluster_manager.egg-info/PKG-INFO create mode 100644 src/discord_cluster_manager.egg-info/SOURCES.txt create mode 100644 src/discord_cluster_manager.egg-info/dependency_links.txt create mode 100644 src/discord_cluster_manager.egg-info/requires.txt create mode 100644 src/discord_cluster_manager.egg-info/top_level.txt create mode 100644 submission.py create mode 100644 unit-tests/conftest.py create mode 100644 unit-tests/test_validate_user_header.py diff --git a/data.sql b/data.sql new file mode 100644 index 00000000..76a4633c --- /dev/null +++ b/data.sql @@ -0,0 +1,9194 @@ +-- +-- PostgreSQL database dump +-- + +-- Dumped from database version 16.8 (Ubuntu 16.8-1.pgdg22.04+1) +-- Dumped by pg_dump version 16.8 (Ubuntu 16.8-1.pgdg22.04+1) + +SET statement_timeout = 0; +SET lock_timeout = 0; +SET idle_in_transaction_session_timeout = 0; +SET client_encoding = 'UTF8'; +SET standard_conforming_strings = on; +SELECT pg_catalog.set_config('search_path', '', false); +SET check_function_bodies = false; +SET xmloption = content; +SET client_min_messages = warning; +SET row_security = off; + +-- +-- Name: leaderboard; Type: SCHEMA; Schema: -; Owner: - +-- + +CREATE SCHEMA leaderboard; + + +SET default_tablespace = ''; + +SET default_table_access_method = heap; + +-- +-- Name: code_files; Type: TABLE; Schema: leaderboard; Owner: - +-- + +CREATE TABLE leaderboard.code_files ( + id integer NOT NULL, + code text NOT NULL, + hash text GENERATED ALWAYS AS (encode(sha256((code)::bytea), 'hex'::text)) STORED +); + + +-- +-- Name: code_files_id_seq; Type: SEQUENCE; Schema: leaderboard; Owner: - +-- + +CREATE SEQUENCE leaderboard.code_files_id_seq + AS integer + START WITH 1 + INCREMENT BY 1 + NO MINVALUE + NO MAXVALUE + CACHE 1; + + +-- +-- Name: code_files_id_seq; Type: SEQUENCE OWNED BY; Schema: leaderboard; Owner: - +-- + +ALTER SEQUENCE leaderboard.code_files_id_seq OWNED BY leaderboard.code_files.id; + + +-- +-- Name: gpu_type; Type: TABLE; Schema: leaderboard; Owner: - +-- + +CREATE TABLE leaderboard.gpu_type ( + leaderboard_id integer NOT NULL, + gpu_type text NOT NULL +); + + +-- +-- Name: leaderboard; Type: TABLE; Schema: leaderboard; Owner: - +-- + +CREATE TABLE leaderboard.leaderboard ( + id integer NOT NULL, + name text NOT NULL, + deadline timestamp with time zone NOT NULL, + task jsonb NOT NULL, + creator_id bigint DEFAULT '-1'::integer NOT NULL, + forum_id bigint NOT NULL, + secret_seed bigint DEFAULT floor((random() * ('2147483648'::bigint)::double precision)) NOT NULL, + description text NOT NULL +); + + +-- +-- Name: leaderboard_id_seq; Type: SEQUENCE; Schema: leaderboard; Owner: - +-- + +CREATE SEQUENCE leaderboard.leaderboard_id_seq + AS integer + START WITH 1 + INCREMENT BY 1 + NO MINVALUE + NO MAXVALUE + CACHE 1; + + +-- +-- Name: leaderboard_id_seq; Type: SEQUENCE OWNED BY; Schema: leaderboard; Owner: - +-- + +ALTER SEQUENCE leaderboard.leaderboard_id_seq OWNED BY leaderboard.leaderboard.id; + + +-- +-- Name: runs; Type: TABLE; Schema: leaderboard; Owner: - +-- + +CREATE TABLE leaderboard.runs ( + id integer NOT NULL, + submission_id integer NOT NULL, + start_time timestamp with time zone NOT NULL, + end_time timestamp with time zone NOT NULL, + mode text NOT NULL, + secret boolean NOT NULL, + runner text NOT NULL, + score numeric, + passed boolean NOT NULL, + compilation jsonb, + meta jsonb, + result jsonb, + system_info jsonb NOT NULL +); + + +-- +-- Name: runs_id_seq; Type: SEQUENCE; Schema: leaderboard; Owner: - +-- + +CREATE SEQUENCE leaderboard.runs_id_seq + AS integer + START WITH 1 + INCREMENT BY 1 + NO MINVALUE + NO MAXVALUE + CACHE 1; + + +-- +-- Name: runs_id_seq; Type: SEQUENCE OWNED BY; Schema: leaderboard; Owner: - +-- + +ALTER SEQUENCE leaderboard.runs_id_seq OWNED BY leaderboard.runs.id; + + +-- +-- Name: submission; Type: TABLE; Schema: leaderboard; Owner: - +-- + +CREATE TABLE leaderboard.submission ( + id integer NOT NULL, + leaderboard_id integer NOT NULL, + file_name text NOT NULL, + user_id text NOT NULL, + code_id integer NOT NULL, + submission_time timestamp with time zone NOT NULL, + done boolean DEFAULT false +); + + +-- +-- Name: submission_id_seq; Type: SEQUENCE; Schema: leaderboard; Owner: - +-- + +CREATE SEQUENCE leaderboard.submission_id_seq + AS integer + START WITH 1 + INCREMENT BY 1 + NO MINVALUE + NO MAXVALUE + CACHE 1; + + +-- +-- Name: submission_id_seq; Type: SEQUENCE OWNED BY; Schema: leaderboard; Owner: - +-- + +ALTER SEQUENCE leaderboard.submission_id_seq OWNED BY leaderboard.submission.id; + + +-- +-- Name: user_info; Type: TABLE; Schema: leaderboard; Owner: - +-- + +CREATE TABLE leaderboard.user_info ( + id text NOT NULL, + user_name text +); + + +-- +-- Name: code_files id; Type: DEFAULT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.code_files ALTER COLUMN id SET DEFAULT nextval('leaderboard.code_files_id_seq'::regclass); + + +-- +-- Name: leaderboard id; Type: DEFAULT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.leaderboard ALTER COLUMN id SET DEFAULT nextval('leaderboard.leaderboard_id_seq'::regclass); + + +-- +-- Name: runs id; Type: DEFAULT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.runs ALTER COLUMN id SET DEFAULT nextval('leaderboard.runs_id_seq'::regclass); + + +-- +-- Name: submission id; Type: DEFAULT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.submission ALTER COLUMN id SET DEFAULT nextval('leaderboard.submission_id_seq'::regclass); + + +-- +-- Data for Name: code_files; Type: TABLE DATA; Schema: leaderboard; Owner: - +-- + +COPY leaderboard.code_files (id, code) FROM stdin; +13 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +14 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +24 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +25 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +39 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +40 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +41 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +211 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +53 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +56 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +83 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +102 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +101 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1608 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +105 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +125 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +126 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +127 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +147 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +159 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +905 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +146 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1459 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +153 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +161 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +907 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +172 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +184 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +806 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +290 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +807 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1274 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +929 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +372 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +373 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1275 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +392 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1276 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1278 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +423 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1361 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +429 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +435 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +442 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +445 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1362 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1363 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1364 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1365 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1366 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1367 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1368 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1369 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1370 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +507 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +572 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +513 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +521 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +524 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +526 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +534 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +535 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1371 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +578 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1372 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1373 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1374 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1375 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1376 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +709 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +716 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +717 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1377 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1378 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +743 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +744 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1379 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1390 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1391 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1393 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +793 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +794 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +890 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +891 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +820 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +821 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1553 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1554 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1555 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1556 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1601 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1049 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1078 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1135 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1117 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1143 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1144 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1146 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1149 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1161 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1192 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1602 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1603 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1604 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1605 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1606 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1609 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1610 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1611 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1612 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1613 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1614 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1615 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1616 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1617 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +22 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1270 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1277 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1299 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1300 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1301 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1322 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1321 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1324 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1353 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1354 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1392 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1410 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1422 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1411 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +298 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +299 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +300 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +301 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +23 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1539 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1543 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +26 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1588 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1607 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +302 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +303 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +29 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +30 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1661 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1663 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +304 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +305 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +31 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +32 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +33 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +15 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +306 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +16 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +291 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +292 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +293 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +294 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +279 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +295 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +296 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +307 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +353 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +354 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +355 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +356 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +357 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +358 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +379 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +380 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +381 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +382 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +383 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +384 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +385 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +386 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +387 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +388 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +389 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +390 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +391 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +72 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +970 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +393 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +5 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +394 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +395 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +396 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +397 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +400 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +401 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +402 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +17 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +18 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +19 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +20 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +21 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +34 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +35 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +36 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +37 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +38 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +46 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +54 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +55 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +58 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +59 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +60 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +403 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +404 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +405 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +406 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +407 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +408 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +409 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +410 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +411 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +412 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +413 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +414 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +308 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +415 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +417 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +418 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +421 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +425 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +309 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +426 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +427 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +431 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +433 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +432 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +310 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +437 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +438 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +439 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +440 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +61 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +62 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +63 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +64 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +65 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +71 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +66 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +67 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +68 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +69 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +70 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1644 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +962 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +447 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +448 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +449 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +451 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +452 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +453 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +454 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +455 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +456 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1645 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +457 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +458 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +459 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +460 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +461 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +462 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +463 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +464 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +465 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +466 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +467 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +468 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +469 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +470 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +471 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +472 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +473 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +474 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +475 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +476 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +477 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +478 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +479 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +480 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +482 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +483 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +484 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +485 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +486 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +487 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +488 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +490 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +489 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +491 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +492 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +493 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +495 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +497 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +499 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +500 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +976 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +536 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +537 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +538 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +539 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +540 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +541 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +542 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +543 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +544 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +545 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +546 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +547 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +548 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +549 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +550 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +551 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +552 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +553 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +554 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +555 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +556 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +557 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +558 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +559 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +560 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +561 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +562 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +563 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +564 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +565 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +566 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +567 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +568 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +569 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +570 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +963 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +571 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +573 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +574 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +575 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +576 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +577 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +245 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +579 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +580 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +581 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +582 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +583 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +584 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +585 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +586 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +587 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +588 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +589 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +590 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +591 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +592 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +594 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +595 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +596 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +597 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +598 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +599 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +600 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +601 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +603 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +604 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +605 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +606 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +607 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +608 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +609 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +611 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +610 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +613 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +612 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +614 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +615 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +616 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +617 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +618 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +619 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +620 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +621 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +622 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +623 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +624 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +625 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +626 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +627 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +628 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +629 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +630 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +636 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +631 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +632 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +633 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +634 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +635 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +637 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +638 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +639 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +640 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +641 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +642 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +643 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +644 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +645 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +646 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +648 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +647 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +649 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +650 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +651 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +652 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +653 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +654 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +656 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +655 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +657 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +658 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +659 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +660 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +661 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +662 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +663 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +664 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +665 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +666 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +668 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +667 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +669 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +670 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +671 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +672 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +673 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +674 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +675 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +676 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +677 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +11 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +73 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +74 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +75 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +76 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +678 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +679 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +680 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +681 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +682 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +683 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +684 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +685 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +686 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +687 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +688 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +689 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +691 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +690 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +692 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +693 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +694 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +696 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +695 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +697 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +698 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +700 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +964 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +971 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +972 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +699 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +701 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +702 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +703 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +704 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +705 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +722 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +706 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +707 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +708 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +710 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +723 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +711 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +712 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +714 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +718 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +721 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +724 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +725 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +726 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +727 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +728 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +729 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +730 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +731 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +732 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +733 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +734 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +735 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +736 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +737 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +738 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +77 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +78 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +79 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +80 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +82 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +81 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +6 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +85 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +84 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +86 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +87 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +89 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +90 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +92 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +93 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +95 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +96 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +97 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +98 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +100 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +745 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +746 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +747 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +748 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +749 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +750 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +751 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +752 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +753 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +754 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +755 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +756 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +757 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +758 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +759 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +760 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +761 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +762 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +965 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +763 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +764 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +765 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +766 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +767 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +768 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +769 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +770 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +771 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +772 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +773 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +774 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +775 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +130 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +776 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +777 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +778 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +103 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +106 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +107 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +108 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +109 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +110 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +111 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +114 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +115 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +132 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +116 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +117 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +119 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +120 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +121 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +122 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +123 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +124 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +129 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +131 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +134 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +954 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +968 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +779 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +780 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +781 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +783 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +784 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +785 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +786 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +805 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +787 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +788 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +803 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +802 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +148 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +150 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +151 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +152 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +154 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +156 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +157 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +158 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +804 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +808 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +809 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +810 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +811 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +812 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +813 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +814 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +815 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +816 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +817 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +818 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +819 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +822 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +826 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +827 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +828 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +829 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +830 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +831 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +832 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +833 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +834 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +835 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +836 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +837 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +838 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +839 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +840 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +841 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +842 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +843 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +844 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +845 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +846 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +847 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +848 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +849 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +850 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +851 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +852 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +854 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +853 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +855 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +857 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +856 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +859 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +858 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +860 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +861 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +862 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +863 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +864 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +160 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +782 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +865 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +866 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +867 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +868 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +869 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +870 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +871 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +872 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +873 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +874 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +875 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +876 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +877 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +878 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +879 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +880 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +881 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +882 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +883 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +966 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +884 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +885 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +886 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +887 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +888 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +889 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +892 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +893 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +894 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +895 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +896 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +897 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +898 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +899 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +900 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +901 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +902 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +903 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +904 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +906 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +179 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +908 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +909 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +910 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +911 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +912 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +913 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +914 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +915 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +916 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +917 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +918 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +919 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +920 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +921 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +922 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +923 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +924 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +164 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +165 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +166 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +167 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +168 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +169 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +925 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +926 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +927 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +928 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +930 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +931 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +932 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +934 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +935 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +936 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +937 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +938 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +939 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +940 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +941 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +942 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +943 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +944 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +945 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +946 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +947 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +948 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +949 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +950 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +951 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +952 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +953 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +955 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +956 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +957 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +974 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +958 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +959 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +960 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +961 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +967 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +973 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +975 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +978 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +979 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +980 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +991 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +992 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +993 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +994 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +995 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +996 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +997 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +998 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +999 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1000 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1001 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1002 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1003 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1004 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1005 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1006 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1007 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1008 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1009 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1010 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1011 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1012 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1013 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1014 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1015 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1016 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +170 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +171 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +173 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +174 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +176 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +177 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +178 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1017 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1018 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1019 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1020 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1021 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1022 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1023 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1024 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1025 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1027 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1028 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1029 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1030 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1031 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1032 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1033 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1034 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1035 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1036 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1037 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1038 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1039 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1040 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1041 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1042 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1043 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1044 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1045 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1046 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1047 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1048 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1050 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1051 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1052 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1053 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1054 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1055 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1056 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1057 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1058 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1059 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1060 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1061 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1062 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1079 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1080 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1081 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1082 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1083 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1084 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1085 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +181 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +182 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +185 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +186 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +187 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +188 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +190 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +191 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +192 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +193 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +194 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +195 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +196 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1086 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1087 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1088 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1089 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1090 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1091 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1092 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1093 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1094 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1095 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1096 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1101 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1102 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1103 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1104 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1105 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1106 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1107 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1108 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1109 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1110 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1111 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1112 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1113 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1114 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1115 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1116 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1118 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1119 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1120 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1121 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1124 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1125 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1126 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1127 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1128 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1129 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +197 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1130 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1131 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1132 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1133 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1134 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1136 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1140 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1141 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1151 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1152 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1153 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1154 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1155 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1156 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1157 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1158 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1159 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1162 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1163 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1166 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1167 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1168 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1169 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1170 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1171 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1172 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1173 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1174 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1175 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1176 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +213 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1177 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1178 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1179 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1180 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1181 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1182 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1183 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1184 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1185 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1186 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1187 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1188 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1189 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1190 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1191 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1193 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1194 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1195 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1196 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1197 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1198 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1199 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1200 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1201 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1202 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +225 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1203 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +198 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +199 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1267 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1268 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1269 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1271 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1272 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1279 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1280 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1281 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1282 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1283 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1284 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1285 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1286 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1287 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1288 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1289 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1290 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1291 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1292 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1293 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1294 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1295 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1296 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1297 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1298 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1323 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1355 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1356 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1357 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1358 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1359 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1360 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +244 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1380 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1381 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1383 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1384 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1385 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1386 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1387 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1388 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1389 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1394 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1395 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1396 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1397 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +200 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +201 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +202 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +203 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +204 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +205 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +206 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +207 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +208 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +209 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +210 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +212 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1412 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1413 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1414 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1415 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1416 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1417 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1418 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +216 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1419 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1420 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1421 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1423 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +217 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1424 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1453 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1454 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1455 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +218 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1456 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1457 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1458 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +331 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1273 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1460 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1461 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1462 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1463 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +219 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1464 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1465 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1466 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1467 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +220 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1468 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1469 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1470 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1471 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +221 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1472 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1473 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1474 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1475 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +222 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1476 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1477 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1478 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1479 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +223 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1480 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1481 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1482 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +214 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +215 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +224 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1483 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1487 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1488 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1489 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1490 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1491 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1497 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1498 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1545 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1546 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1499 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1500 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1501 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1502 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1547 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1548 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1503 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1504 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1505 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1506 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1549 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1550 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1507 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1509 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1510 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1511 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +226 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1512 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1513 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1514 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1515 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +227 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1516 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1517 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1518 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1519 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +228 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1520 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1521 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1522 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1523 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +229 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1524 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1525 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1526 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1527 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +230 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1528 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1529 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1530 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1531 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +231 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1532 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1533 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1534 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1535 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +232 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1536 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1537 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1538 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1540 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1541 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1542 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1544 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +233 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +234 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +235 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +236 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +237 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +238 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +239 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +240 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +241 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +242 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +243 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1551 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1552 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1557 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1558 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1559 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1560 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1561 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1562 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1563 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1564 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1565 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1566 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +10 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1567 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1568 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1569 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1570 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1571 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1572 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1573 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1574 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1575 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1576 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1577 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1578 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +246 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +247 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +248 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +249 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +250 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +251 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +252 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +253 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +254 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +255 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +256 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +257 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +969 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +977 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +258 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +259 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +260 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1579 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1580 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1581 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1582 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1583 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1584 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1585 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1586 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1587 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1589 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1590 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1591 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1592 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1593 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1594 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1595 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1596 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1597 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1598 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1599 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1600 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +311 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +312 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +313 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +314 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +315 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +316 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +317 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +318 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +319 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +320 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +321 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +322 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +323 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +324 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +325 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +326 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +327 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +328 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +329 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +330 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1618 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1619 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1620 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1621 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +332 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +333 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +334 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +335 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +336 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +337 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +338 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +339 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +340 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +341 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1622 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1623 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1624 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1625 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1626 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1627 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1628 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1629 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1630 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1631 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1632 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1633 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1634 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1646 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1647 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1648 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1649 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1650 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1651 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1652 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1653 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1654 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1655 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1656 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +359 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1657 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1658 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1659 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1660 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1664 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1665 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1667 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1669 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +343 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1670 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1671 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1672 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1673 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +344 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1674 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1675 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1676 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1677 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +345 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1678 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1679 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1680 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1681 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +346 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1682 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1683 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1684 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1685 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +342 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +347 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +348 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +349 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +350 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +351 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +352 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1686 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1687 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1635 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1636 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1637 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1642 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1638 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +261 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +262 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +263 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +264 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +265 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +266 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +267 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +268 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +269 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +270 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +271 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +272 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +273 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +274 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +275 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +276 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +277 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +278 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +280 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +281 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +282 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +283 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +284 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +285 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +286 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +287 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +288 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +289 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +297 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1643 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +360 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +361 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +362 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +363 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +364 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +365 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +366 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +367 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +368 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +369 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +370 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +371 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +376 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +377 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +378 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +593 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1639 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1640 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1641 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +\. + + +-- +-- Data for Name: gpu_type; Type: TABLE DATA; Schema: leaderboard; Owner: - +-- + +COPY leaderboard.gpu_type (leaderboard_id, gpu_type) FROM stdin; +339 H100 +339 A100 +339 T4 +339 L4 +340 H100 +340 A100 +340 T4 +340 L4 +341 H100 +341 A100 +341 T4 +341 L4 +342 H100 +342 A100 +342 T4 +342 L4 +343 H100 +343 A100 +343 T4 +343 L4 +\. + + +-- +-- Data for Name: leaderboard; Type: TABLE DATA; Schema: leaderboard; Owner: - +-- + +COPY leaderboard.leaderboard (id, name, deadline, task, creator_id, forum_id, secret_seed, description) FROM stdin; +339 conv2d 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "from typing import TypedDict, TypeVar, Tuple\\nimport torch\\n\\ninput_t = TypeVar(\\"input_t\\", bound=Tuple[torch.Tensor, torch.Tensor])\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor)\\n\\n\\nclass TestSpec(TypedDict):\\n size: int\\n kernelsize: int\\n channels: int\\n batch: int\\n seed: int ", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "from utils import make_match_reference\\nimport torch\\nimport torch.nn.functional as F\\nfrom task import input_t, output_t\\n\\nclass DisableCuDNNTF32:\\n def __init__(self):\\n self.allow_tf32 = torch.backends.cudnn.allow_tf32\\n self.deterministic = torch.backends.cudnn.deterministic\\n pass\\n\\n def __enter__(self):\\n torch.backends.cudnn.allow_tf32 = False\\n torch.backends.cudnn.deterministic = True\\n return self\\n\\n def __exit__(self, exc_type, exc_value, traceback):\\n torch.backends.cudnn.allow_tf32 = self.allow_tf32\\n torch.backends.cudnn.deterministic = self.deterministic\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n \\"\\"\\"\\n Reference implementation of 2D convolution using PyTorch.\\n Args:\\n data: Tuple of (input tensor, kernel tensor)\\n Returns:\\n Output tensor after convolution\\n \\"\\"\\"\\n with DisableCuDNNTF32():\\n input_tensor, kernel = data\\n return F.conv2d(\\n input_tensor, \\n kernel,\\n\\n # No padding and no striding\\n # TODO: Can revisit this in future problems\\n stride=1,\\n padding=0\\n )\\n\\n\\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\\n \\"\\"\\"\\n Generates random input and kernel tensors.\\n Returns:\\n Tuple of (input tensor, kernel tensor)\\n \\"\\"\\"\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n \\n # Generate input tensor: [batch, in_channels, height, width]\\n input_tensor = torch.randn(\\n batch, channels, size, size,\\n device='cuda', \\n dtype=torch.float32, \\n generator=gen\\n ).contiguous()\\n \\n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\\n # Here we use same number of output channels as input channels for simplicity\\n kernel = torch.randn(\\n channels, channels, kernelsize, kernelsize,\\n device='cuda',\\n dtype=torch.float32,\\n generator=gen\\n ).contiguous()\\n \\n return (input_tensor, kernel)\\n\\n\\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)", "submission.py": "@SUBMISSION@"}, "tests": [{"seed": 4242, "size": 32, "batch": 1, "channels": 16, "kernelsize": 4}, {"seed": 5236, "size": 32, "batch": 2, "channels": 16, "kernelsize": 4}, {"seed": 1001, "size": 64, "batch": 1, "channels": 32, "kernelsize": 4}, {"seed": 5531, "size": 64, "batch": 2, "channels": 32, "kernelsize": 8}, {"seed": 9173, "size": 128, "batch": 1, "channels": 64, "kernelsize": 8}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"seed": 54352, "size": 128, "batch": 4, "channels": 64, "kernelsize": 8}, {"seed": 93246, "size": 128, "batch": 4, "channels": 64, "kernelsize": 16}, {"seed": 6256, "size": 256, "batch": 2, "channels": 128, "kernelsize": 16}, {"seed": 8841, "size": 256, "batch": 2, "channels": 128, "kernelsize": 16}, {"seed": 6252, "size": 256, "batch": 1, "channels": 128, "kernelsize": 32}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279714277527582 1828004782 Implement a 2D convolution kernel that matches the reference implementation.\nThe kernel should perform 2D convolution with the given specifications\nWe will benchmark different sizes, kernel sizes, channels and batch sizes but they will all \nbe even numbers with the exception of batch size which can sometimes be 1\nWe assume no padding and striding and instead vary the size of the input and kernel,\nnumber of channels, and batch size.\n\nInput: Tuple of (input_tensor, kernel)\n - input_tensor: 4D tensor of shape (batch, channels, height, width) with arbitrary values\n - kernel: 4D tensor of shape (channels, channels, kernelsize, kernelsize) with arbitrary values\nOutput: 4D tensor of shape (batch, channels, height-kernelsize+1, width-kernelsize+1) with convolved values\n +340 grayscale 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "from typing import TypedDict, TypeVar\\nimport torch\\n\\ninput_t = TypeVar(\\"input_t\\", bound=torch.Tensor) # Input will be (H, W, 3) RGB tensor\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor) # Output will be (H, W) grayscale tensor\\n\\nclass TestSpec(TypedDict):\\n size: int # Size of the square image (H=W)\\n seed: int ", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "from utils import make_match_reference\\nimport torch\\nfrom task import input_t, output_t\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n \\"\\"\\"\\n Reference implementation of RGB to grayscale conversion using PyTorch.\\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\\n \\n Args:\\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\\n Returns:\\n Grayscale tensor of shape (H, W) with values in [0, 1]\\n \\"\\"\\"\\n # Standard RGB to Grayscale coefficients\\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \\n device=data.device, \\n dtype=data.dtype)\\n return torch.sum(data * weights, dim=-1)\\n\\n\\ndef generate_input(size: int, seed: int) -> input_t:\\n \\"\\"\\"\\n Generates random RGB image tensor of specified size.\\n Returns:\\n Tensor of shape (size, size, 3) with values in [0, 1]\\n \\"\\"\\"\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n return torch.rand(size, size, 3, \\n device='cuda', \\n dtype=torch.float32, \\n generator=gen).contiguous()\\n\\n\\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\\n", "submission.py": "@SUBMISSION@"}, "tests": [{"seed": 1001, "size": 128}, {"seed": 5531, "size": 256}, {"seed": 9173, "size": 512}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"seed": 54352, "size": 512}, {"seed": 93246, "size": 1024}, {"seed": 6256, "size": 2048}, {"seed": 8841, "size": 4096}, {"seed": 6252, "size": 8192}, {"seed": 54352, "size": 16384}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279718383878165 1067465556 Implement an RGB to grayscale conversion kernel that matches the reference implementation.\nThe kernel should convert square RGB images with even sizes to grayscale using the standard coefficients:\nY = 0.2989 R + 0.5870 G + 0.1140 B\n\nInput: RGB tensor of shape (H, W, 3) with values in [0, 1]\nOutput: Grayscale tensor of shape (H, W) with values in [0, 1]\n +341 histogram 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "from typing import TypedDict, TypeVar\\nimport torch\\n\\ninput_t = TypeVar(\\"input_t\\", bound=torch.Tensor)\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor)\\n\\nclass TestSpec(TypedDict):\\n size: int\\n seed: int\\n contention: int\\n\\n", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "from utils import verbose_allequal\\nimport torch\\nfrom task import input_t, output_t\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n \\"\\"\\"\\n Reference implementation of histogram using PyTorch.\\n Args:\\n data: tensor of shape (size,)\\n Returns:\\n Tensor containing bin counts\\n \\"\\"\\"\\n # Count values in each bin\\n return torch.bincount(data, minlength=256)\\n\\n\\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\\n \\"\\"\\"\\n Generates random input tensor for histogram.\\n\\n Args:\\n size: Size of the input tensor (must be multiple of 16)\\n contention: float in [0, 100], specifying the percentage of identical values\\n seed: Random seed\\n Returns:\\n The input tensor with values in [0, 255]\\n \\"\\"\\"\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n \\n # Generate integer values between 0 and 256\\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\\n\\n # make one value appear quite often, increasing the chance for atomic contention\\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\\n data[evil_loc] = evil_value\\n\\n return data.contiguous()\\n\\n\\ndef check_implementation(data, output):\\n expected = ref_kernel(data)\\n reasons = verbose_allequal(output, expected)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n", "submission.py": "@SUBMISSION@"}, "tests": [{"seed": 9991, "size": 5120, "contention": 10}, {"seed": 2105, "size": 7840, "contention": 10}, {"seed": 9999, "size": 30080, "contention": 10}, {"seed": 4254, "size": 30080, "contention": 90}, {"seed": 1212, "size": 100000, "contention": 10}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"seed": 6252, "size": 1310720, "contention": 10}, {"seed": 8841, "size": 2621440, "contention": 10}, {"seed": 3411, "size": 2621440, "contention": 40}, {"seed": 8753, "size": 2621440, "contention": 90}, {"seed": 6252, "size": 5242880, "contention": 10}, {"seed": 8841, "size": 10485760, "contention": 10}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279720996929577 186241933 Implement a histogram kernel that counts the number of elements falling into each bin across the specified range.\nThe minimum and maximum values of the range are fixed to 0 and 100 respectively.\nAll sizes are multiples of 16 and the number of bins is set to the size of the input tensor divided by 16.\n\nInput:\n - data: a tensor of shape (size,)\n +342 matmul 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "import torch\\nfrom typing import TypeVar, TypedDict\\n\\ninput_t = TypeVar(\\"input_t\\", bound=tuple[torch.Tensor, torch.Tensor])\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor)\\n\\nclass TestSpec(TypedDict):\\n m: int\\n n: int\\n k: int\\n seed: int\\n", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "import torch\\nfrom task import input_t, output_t\\nfrom utils import make_match_reference\\n\\n\\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\\n a.uniform_(0, 1, generator=gen)\\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\\n b.uniform_(0, 1, generator=gen)\\n return (a, b)\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n a, b = data\\n return a @ b\\n\\n\\ncheck_implementation = make_match_reference(ref_kernel)\\n", "submission.py": "@SUBMISSION@"}, "tests": [{"k": 64, "m": 64, "n": 64, "seed": 53124}, {"k": 128, "m": 128, "n": 128, "seed": 3321}, {"k": 256, "m": 256, "n": 256, "seed": 1200}, {"k": 32, "m": 32, "n": 512, "seed": 32523}, {"k": 64, "m": 64, "n": 1024, "seed": 4327}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"k": 128, "m": 128, "n": 128, "seed": 43214}, {"k": 256, "m": 256, "n": 256, "seed": 423011}, {"k": 512, "m": 512, "n": 512, "seed": 123456}, {"k": 1024, "m": 1024, "n": 1024, "seed": 1029}, {"k": 2048, "m": 2048, "n": 2048, "seed": 75342}, {"k": 1024, "m": 1024, "n": 1536, "seed": 321}, {"k": 2048, "m": 2048, "n": 3072, "seed": 32412}, {"k": 4096, "m": 4096, "n": 5120, "seed": 123456}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279724021026939 818466997 Implement a custom matmul function that matches the reference implementation.\nThe function should handle a tuple of input tensors and apply matmul\nThe shapes of all outer and inner dimensions of tensors are multiples of 16\n +343 prefixsum 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "from typing import TypedDict, TypeVar\\nimport torch\\n\\ninput_t = TypeVar(\\"input_t\\", bound=torch.Tensor)\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor)\\n\\nclass TestSpec(TypedDict):\\n size: int\\n seed: int ", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "from utils import match_reference\\nimport torch\\nfrom task import input_t, output_t\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n \\"\\"\\"\\n Reference implementation of inclusive prefix sum using PyTorch.\\n Args:\\n data: Input tensor to compute prefix sum on\\n Returns:\\n Tensor containing the inclusive prefix sum\\n \\"\\"\\"\\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\\n\\n\\ndef generate_input(size: int, seed: int) -> input_t:\\n \\"\\"\\"\\n Generates random input tensor.\\n Returns:\\n Tensor to compute prefix sum on\\n \\"\\"\\"\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\\n\\n\\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\\n# The tolerance is scaled by the square root of the input size\\ndef check_implementation(data: input_t, output: output_t) -> str:\\n # Then get the size for scaling the tolerance\\n n = data.numel()\\n \\n scale_factor = n ** 0.5 # Square root of input size\\n rtol = 1e-5 * scale_factor\\n atol = 1e-5 * scale_factor\\n\\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\\n", "submission.py": "@SUBMISSION@"}, "tests": [{"seed": 4242, "size": 1023}, {"seed": 5236, "size": 1024}, {"seed": 1001, "size": 1025}, {"seed": 5531, "size": 2048}, {"seed": 9173, "size": 4096}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"seed": 12345, "size": 262144}, {"seed": 67890, "size": 524288}, {"seed": 13579, "size": 1048576}, {"seed": 24680, "size": 2097152}, {"seed": 35791, "size": 4194304}, {"seed": 46802, "size": 8388608}, {"seed": 57913, "size": 16777216}, {"seed": 68024, "size": 33554432}, {"seed": 79135, "size": 67108864}, {"seed": 80246, "size": 134217728}, {"seed": 91357, "size": 268435456}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279728202874890 2028323019 Implement an inclusive prefix sum (scan) kernel that matches the reference implementation.\nThe kernel should compute the cumulative sum of all elements up to each position.\nBecause of numerical instability, the tolerance is scaled by the square root of the input size.\n\nInput:\n- `data`: A 1D tensor of size `n`\nOutput:\n- `output`: A 1D tensor of size `n`\n +\. + + +-- +-- Data for Name: runs; Type: TABLE DATA; Schema: leaderboard; Owner: - +-- + +COPY leaderboard.runs (id, submission_id, start_time, end_time, mode, secret, runner, score, passed, compilation, meta, result, system_info) FROM stdin; +124 19 2025-02-23 10:35:45.16001-08 2025-02-23 09:11:28.790124-08 test f H100 \N t \N \N \N {} +125 19 2025-02-23 10:03:43.960257-08 2025-02-23 10:50:24.236204-08 benchmark f H100 \N t \N \N \N {} +126 19 2025-02-23 09:41:46.565827-08 2025-02-23 09:15:24.849129-08 leaderboard f H100 0.006118796 t \N \N \N {} +204 36 2025-02-23 12:17:55.397587-08 2025-02-23 12:19:38.507867-08 benchmark f A100 \N t \N \N \N {} +946 278 2025-02-24 14:44:53.850686-08 2025-02-24 14:00:11.854228-08 test f L4 \N t \N \N \N {} +205 36 2025-02-23 12:02:10.362107-08 2025-02-23 11:12:04.114552-08 benchmark f L4 \N t \N \N \N {} +206 36 2025-02-23 11:00:03.586181-08 2025-02-23 11:24:17.467616-08 benchmark f T4 \N t \N \N \N {} +207 37 2025-02-23 11:17:23.516624-08 2025-02-23 11:31:53.132068-08 test t H100 \N t \N \N \N {} +5225 1609 2025-03-06 07:56:36.142637-08 2025-03-06 06:19:05.979588-08 benchmark f T4 \N f \N \N \N {} +208 37 2025-02-23 11:26:57.890844-08 2025-02-23 11:52:24.509388-08 benchmark t H100 \N t \N \N \N {} +209 37 2025-02-23 12:35:01.552626-08 2025-02-23 11:29:30.650285-08 leaderboard t H100 0.012353844333333334 t \N \N \N {} +210 37 2025-02-23 11:15:04.963959-08 2025-02-23 11:05:51.028191-08 test f A100 \N t \N \N \N {} +3564 1210 2025-02-28 11:17:29.229928-08 2025-02-28 10:53:05.57324-08 leaderboard t A100 0.0030784606666666666 t \N \N \N {} +211 37 2025-02-23 12:02:46.792172-08 2025-02-23 11:06:28.086792-08 benchmark f A100 \N t \N \N \N {} +212 37 2025-02-23 12:10:16.775337-08 2025-02-23 11:11:59.763695-08 leaderboard f A100 0.018901413333333332 t \N \N \N {} +213 37 2025-02-23 11:16:17.316289-08 2025-02-23 11:02:40.657232-08 test f L4 \N t \N \N \N {} +214 37 2025-02-23 11:03:49.41155-08 2025-02-23 11:59:30.243303-08 benchmark f L4 \N t \N \N \N {} +215 37 2025-02-23 12:39:40.437807-08 2025-02-23 11:20:57.604522-08 leaderboard f L4 0.018757093833333332 t \N \N \N {} +217 37 2025-02-23 12:34:49.874051-08 2025-02-23 11:12:01.835801-08 benchmark t T4 \N t \N \N \N {} +218 37 2025-02-23 12:40:43.031117-08 2025-02-23 11:29:51.197423-08 leaderboard t T4 0.03179523833333333 t \N \N \N {} +219 37 2025-02-23 11:14:46.019525-08 2025-02-23 11:54:59.698254-08 test t A100 \N t \N \N \N {} +221 37 2025-02-23 11:25:33.405411-08 2025-02-23 12:18:02.508921-08 leaderboard t A100 0.018848922333333334 t \N \N \N {} +222 37 2025-02-23 11:58:16.947847-08 2025-02-23 12:21:51.3522-08 test t L4 \N t \N \N \N {} +223 37 2025-02-23 10:58:25.479183-08 2025-02-23 11:47:34.593766-08 benchmark t L4 \N t \N \N \N {} +224 37 2025-02-23 10:49:41.805569-08 2025-02-23 11:56:17.24445-08 leaderboard t L4 0.01939468366666667 t \N \N \N {} +226 37 2025-02-23 12:22:30.275578-08 2025-02-23 11:48:36.047752-08 benchmark f T4 \N t \N \N \N {} +227 37 2025-02-23 12:40:05.515776-08 2025-02-23 10:58:56.313103-08 leaderboard f T4 0.030903404 t \N \N \N {} +228 37 2025-02-23 11:04:15.184419-08 2025-02-23 11:00:17.817738-08 test f H100 \N t \N \N \N {} +229 37 2025-02-23 12:35:53.114693-08 2025-02-23 10:57:25.119918-08 benchmark f H100 \N t \N \N \N {} +230 37 2025-02-23 11:23:12.875657-08 2025-02-23 10:51:41.667717-08 leaderboard f H100 0.012353275333333334 t \N \N \N {} +231 38 2025-02-23 11:50:25.46655-08 2025-02-23 11:45:34.668208-08 test f T4 \N f \N \N \N {} +232 39 2025-02-23 12:32:41.932731-08 2025-02-23 10:59:56.084677-08 test f T4 \N f \N \N \N {} +259 54 2025-02-23 13:17:30.33764-08 2025-02-23 13:46:28.669109-08 test t T4 \N t \N \N \N {} +233 40 2025-02-23 11:29:02.58055-08 2025-02-23 12:36:20.107203-08 test f T4 \N f \N \N \N {} +234 41 2025-02-23 11:36:32.996867-08 2025-02-23 12:51:08.480576-08 test f T4 \N f \N \N \N {} +240 47 2025-02-23 11:50:55.177428-08 2025-02-23 12:30:28.807619-08 test f T4 \N f \N \N \N {} +241 48 2025-02-23 13:23:28.109642-08 2025-02-23 11:57:04.295155-08 test f T4 \N f \N \N \N {} +242 49 2025-02-23 11:53:10.397813-08 2025-02-23 12:03:03.783486-08 test f T4 \N f \N \N \N {} +1276 427 2025-02-24 22:41:37.072571-08 2025-02-24 23:30:11.670458-08 test t H100 \N t \N \N \N {} +246 53 2025-02-23 13:05:55.652535-08 2025-02-23 13:14:14.362137-08 benchmark f T4 \N t \N \N \N {} +254 54 2025-02-23 11:58:10.69175-08 2025-02-23 12:34:56.201415-08 benchmark f L4 \N t \N \N \N {} +255 54 2025-02-23 12:05:59.80503-08 2025-02-23 12:05:35.639007-08 leaderboard f L4 0.01764701466666667 t \N \N \N {} +256 54 2025-02-23 13:16:11.769925-08 2025-02-23 13:35:56.018887-08 test f A100 \N t \N \N \N {} +257 54 2025-02-23 12:06:54.552569-08 2025-02-23 12:27:58.339221-08 benchmark f A100 \N t \N \N \N {} +258 54 2025-02-23 11:52:48.189654-08 2025-02-23 13:04:58.541644-08 leaderboard f A100 0.0032710223333333333 t \N \N \N {} +260 54 2025-02-23 13:00:18.36019-08 2025-02-23 12:08:18.086439-08 benchmark t T4 \N t \N \N \N {} +261 54 2025-02-23 12:48:01.153003-08 2025-02-23 13:08:30.274831-08 leaderboard t T4 0.017266412 t \N \N \N {} +262 54 2025-02-23 12:11:01.579205-08 2025-02-23 11:58:34.491578-08 test t H100 \N t \N \N \N {} +263 54 2025-02-23 13:16:28.788531-08 2025-02-23 13:00:17.556954-08 benchmark t H100 \N t \N \N \N {} +264 54 2025-02-23 12:04:25.422389-08 2025-02-23 13:13:19.248136-08 leaderboard t H100 0.001458645875 t \N \N \N {} +265 54 2025-02-23 12:31:12.419543-08 2025-02-23 13:09:45.606598-08 test f T4 \N t \N \N \N {} +266 54 2025-02-23 12:40:46.985253-08 2025-02-23 13:34:24.451784-08 benchmark f T4 \N t \N \N \N {} +267 54 2025-02-23 13:25:58.537497-08 2025-02-23 12:54:43.007543-08 leaderboard f T4 0.01723258033333333 t \N \N \N {} +269 54 2025-02-23 12:20:20.316186-08 2025-02-23 13:13:12.18701-08 benchmark t L4 \N t \N \N \N {} +270 54 2025-02-23 13:11:14.571589-08 2025-02-23 12:58:35.784449-08 leaderboard t L4 0.017926125272727272 t \N \N \N {} +271 54 2025-02-23 12:33:04.814186-08 2025-02-23 12:38:40.126485-08 test f H100 \N t \N \N \N {} +272 54 2025-02-23 13:50:03.91953-08 2025-02-23 13:25:15.447661-08 benchmark f H100 \N t \N \N \N {} +273 54 2025-02-23 13:10:29.552929-08 2025-02-23 13:26:17.441886-08 leaderboard f H100 0.0010422076999999999 t \N \N \N {} +274 55 2025-02-23 13:01:35.147956-08 2025-02-23 13:02:44.097619-08 test f T4 \N t \N \N \N {} +275 56 2025-02-23 12:44:20.029345-08 2025-02-23 14:21:48.402951-08 test f T4 \N f \N \N \N {} +276 57 2025-02-23 12:54:37.579254-08 2025-02-23 13:41:28.569725-08 test f T4 \N t \N \N \N {} +314 77 2025-02-23 14:22:31.876974-08 2025-02-23 13:58:03.675484-08 test t H100 \N t \N \N \N {} +345 91 2025-02-23 18:28:50.540756-08 2025-02-23 18:30:32.69747-08 test f H100 \N f \N \N \N {} +277 58 2025-02-23 14:27:11.52301-08 2025-02-23 12:39:45.802969-08 benchmark f H100 \N t \N \N \N {} +278 59 2025-02-23 14:36:38.592486-08 2025-02-23 14:35:07.207052-08 test f T4 \N f \N \N \N {} +279 60 2025-02-23 13:55:19.127155-08 2025-02-23 14:12:12.400517-08 benchmark f T4 \N f \N \N \N {} +280 61 2025-02-23 13:18:07.578575-08 2025-02-23 14:55:51.403557-08 test f T4 \N f \N \N \N {} +320 78 2025-02-23 14:11:00.190481-08 2025-02-23 15:16:48.880961-08 test f H100 \N t \N \N \N {} +321 78 2025-02-23 14:03:20.036503-08 2025-02-23 14:23:37.916867-08 benchmark f H100 \N t \N \N \N {} +322 78 2025-02-23 15:36:20.056643-08 2025-02-23 15:34:57.100941-08 leaderboard f H100 0.001428631625 t \N \N \N {} +324 79 2025-02-23 15:02:01.218849-08 2025-02-23 15:47:08.188349-08 benchmark f H100 \N t \N \N \N {} +325 79 2025-02-23 14:41:07.330032-08 2025-02-23 15:38:15.89614-08 leaderboard f H100 0.0014191957142857144 t \N \N \N {} +326 79 2025-02-23 14:41:13.012481-08 2025-02-23 15:16:33.618694-08 test t H100 \N t \N \N \N {} +328 79 2025-02-23 14:27:28.406633-08 2025-02-23 15:30:01.543516-08 leaderboard t H100 0.0014211721666666666 t \N \N \N {} +329 80 2025-02-23 15:07:57.673532-08 2025-02-23 14:03:31.284187-08 test t H100 \N t \N \N \N {} +330 80 2025-02-23 13:57:07.60172-08 2025-02-23 13:55:16.031154-08 benchmark t H100 \N t \N \N \N {} +331 80 2025-02-23 14:45:52.647508-08 2025-02-23 14:51:00.631073-08 leaderboard t H100 0.0014186101666666668 t \N \N \N {} +333 80 2025-02-23 15:21:15.153944-08 2025-02-23 14:32:10.166808-08 benchmark f H100 \N t \N \N \N {} +334 80 2025-02-23 15:11:41.57439-08 2025-02-23 14:59:52.063767-08 leaderboard f H100 0.0014430232 t \N \N \N {} +4353 1425 2025-03-01 19:49:30.705637-08 2025-03-01 19:52:46.274107-08 leaderboard t A100 0.003362815 t \N \N \N {} +341 87 2025-02-23 18:32:19.084216-08 2025-02-23 18:24:30.655121-08 test f L4 \N f \N \N \N {} +342 88 2025-02-23 18:14:30.40463-08 2025-02-23 17:59:25.451214-08 test f H100 \N f \N \N \N {} +343 89 2025-02-23 17:38:34.140327-08 2025-02-23 17:53:30.915891-08 test f H100 \N f \N \N \N {} +344 90 2025-02-23 18:21:47.199663-08 2025-02-23 17:59:54.220257-08 test f H100 \N f \N \N \N {} +409 115 2025-02-23 19:35:25.852763-08 2025-02-23 18:57:39.125632-08 test f T4 \N t \N \N \N {} +575 162 2025-02-24 04:19:29.964217-08 2025-02-24 03:34:20.053499-08 benchmark t L4 \N t \N \N \N {} +346 92 2025-02-23 17:54:16.477409-08 2025-02-23 18:50:22.606457-08 test f H100 \N f \N \N \N {} +347 93 2025-02-23 18:25:21.117234-08 2025-02-23 18:07:32.76191-08 test t H100 \N t \N \N \N {} +348 93 2025-02-23 18:44:24.03074-08 2025-02-23 17:50:35.187274-08 benchmark t H100 \N t \N \N \N {} +349 93 2025-02-23 18:52:30.803751-08 2025-02-23 18:08:51.653673-08 leaderboard t H100 0.011944822666666665 t \N \N \N {} +350 93 2025-02-23 17:33:45.219692-08 2025-02-23 18:09:19.103073-08 test f H100 \N t \N \N \N {} +127 19 2025-02-23 09:52:43.386488-08 2025-02-23 09:04:44.918935-08 test t H100 \N t \N \N \N {} +128 19 2025-02-23 10:19:49.22057-08 2025-02-23 10:18:08.314956-08 benchmark t H100 \N t \N \N \N {} +129 19 2025-02-23 09:19:50.98444-08 2025-02-23 09:12:23.909584-08 leaderboard t H100 0.006109189 t \N \N \N {} +351 93 2025-02-23 18:02:58.08203-08 2025-02-23 19:27:48.608126-08 benchmark f H100 \N t \N \N \N {} +352 93 2025-02-23 17:35:41.478368-08 2025-02-23 17:43:26.596456-08 leaderboard f H100 0.0121398512 t \N \N \N {} +353 94 2025-02-23 18:01:28.888692-08 2025-02-23 18:25:10.085679-08 test t H100 \N t \N \N \N {} +355 94 2025-02-23 19:24:29.747081-08 2025-02-23 17:41:44.084312-08 leaderboard t H100 0.011895692333333334 t \N \N \N {} +356 94 2025-02-23 18:29:05.574286-08 2025-02-23 19:30:40.100513-08 test f H100 \N t \N \N \N {} +357 94 2025-02-23 19:00:06.086828-08 2025-02-23 19:27:29.302545-08 benchmark f H100 \N t \N \N \N {} +360 96 2025-02-23 18:52:27.921108-08 2025-02-23 19:32:10.278467-08 benchmark f H100 \N t \N \N \N {} +362 97 2025-02-23 18:43:11.382309-08 2025-02-23 18:32:53.162246-08 benchmark t H100 \N t \N \N \N {} +363 97 2025-02-23 18:08:22.457997-08 2025-02-23 18:01:35.677657-08 leaderboard t H100 0.0015710386666666668 t \N \N \N {} +364 97 2025-02-23 18:00:30.642191-08 2025-02-23 19:31:22.809425-08 test f H100 \N t \N \N \N {} +365 97 2025-02-23 19:12:44.951064-08 2025-02-23 17:52:24.96366-08 benchmark f H100 \N t \N \N \N {} +366 97 2025-02-23 18:34:04.98843-08 2025-02-23 19:32:44.684641-08 leaderboard f H100 0.0015707563333333333 t \N \N \N {} +370 99 2025-02-23 19:47:48.997469-08 2025-02-23 19:43:46.393992-08 leaderboard t H100 0.0014419603333333333 t \N \N \N {} +419 123 2025-02-23 20:03:41.74647-08 2025-02-23 19:48:21.795119-08 test f L4 \N t \N \N \N {} +372 99 2025-02-23 19:38:48.275786-08 2025-02-23 18:15:04.56295-08 benchmark f H100 \N t \N \N \N {} +373 99 2025-02-23 19:12:46.215077-08 2025-02-23 18:59:54.371176-08 leaderboard f H100 0.001431599 t \N \N \N {} +374 100 2025-02-23 19:55:52.569939-08 2025-02-23 19:27:50.142717-08 benchmark f H100 \N t \N \N \N {} +375 101 2025-02-23 18:47:52.05672-08 2025-02-23 18:49:29.177973-08 benchmark f H100 \N f \N \N \N {} +376 103 2025-02-23 19:50:51.240681-08 2025-02-23 19:03:51.348191-08 benchmark f T4 \N t \N \N \N {} +402 107 2025-02-23 19:56:20.721626-08 2025-02-23 19:44:53.872926-08 test f H100 \N f \N \N \N {} +381 104 2025-02-23 18:11:03.43025-08 2025-02-23 18:08:57.029995-08 test t A100 \N t \N \N \N {} +382 104 2025-02-23 18:46:23.802647-08 2025-02-23 18:27:06.148219-08 benchmark t A100 \N t \N \N \N {} +383 104 2025-02-23 20:01:50.376892-08 2025-02-23 19:53:06.069857-08 leaderboard t A100 0.0017416949090909091 t \N \N \N {} +384 104 2025-02-23 18:19:47.990746-08 2025-02-23 19:28:16.772556-08 test f A100 \N t \N \N \N {} +386 104 2025-02-23 18:12:18.644326-08 2025-02-23 19:34:19.590964-08 leaderboard f A100 0.001762813 t \N \N \N {} +387 104 2025-02-23 19:49:03.622017-08 2025-02-23 18:55:44.844842-08 test t H100 \N t \N \N \N {} +388 104 2025-02-23 19:24:20.239368-08 2025-02-23 19:08:05.533919-08 benchmark t H100 \N t \N \N \N {} +389 104 2025-02-23 20:02:50.775118-08 2025-02-23 19:19:38.596389-08 leaderboard t H100 0.001051997 t \N \N \N {} +390 104 2025-02-23 19:55:47.925265-08 2025-02-23 18:30:53.620534-08 test f T4 \N t \N \N \N {} +391 104 2025-02-23 19:19:44.079247-08 2025-02-23 19:10:46.577196-08 benchmark f T4 \N t \N \N \N {} +392 104 2025-02-23 19:52:00.493107-08 2025-02-23 19:03:38.169538-08 leaderboard f T4 0.009289433666666666 t \N \N \N {} +393 104 2025-02-23 18:24:57.464605-08 2025-02-23 20:02:29.61335-08 test f L4 \N t \N \N \N {} +582 167 2025-02-24 03:48:55.014484-08 2025-02-24 05:19:01.867475-08 test f H100 \N f \N \N \N {} +3574 1220 2025-02-28 13:45:47.445988-08 2025-02-28 13:55:19.067931-08 benchmark f A100 \N t \N \N \N {} +605 172 2025-02-24 05:12:35.084465-08 2025-02-24 03:53:41.553176-08 leaderboard f T4 0.017926890833333334 t \N \N \N {} +607 172 2025-02-24 05:35:11.303477-08 2025-02-24 04:35:10.682311-08 benchmark f L4 \N t \N \N \N {} +608 172 2025-02-24 05:14:02.764614-08 2025-02-24 05:32:57.132512-08 leaderboard f L4 0.017603741333333332 t \N \N \N {} +616 174 2025-02-24 04:08:35.184962-08 2025-02-24 04:11:19.105749-08 benchmark f A100 \N t \N \N \N {} +617 174 2025-02-24 04:45:50.307802-08 2025-02-24 04:49:07.034774-08 leaderboard f A100 0.003163628 t \N \N \N {} +618 174 2025-02-24 05:05:10.997982-08 2025-02-24 04:21:07.467177-08 test t H100 \N t \N \N \N {} +619 174 2025-02-24 04:49:07.394184-08 2025-02-24 05:36:35.84385-08 benchmark t H100 \N t \N \N \N {} +620 174 2025-02-24 03:56:43.871806-08 2025-02-24 05:06:06.267042-08 leaderboard t H100 0.0014537886666666667 t \N \N \N {} +621 174 2025-02-24 04:11:26.341666-08 2025-02-24 05:44:14.091621-08 test t A100 \N t \N \N \N {} +622 174 2025-02-24 05:03:36.072136-08 2025-02-24 04:51:58.063144-08 benchmark t A100 \N t \N \N \N {} +623 174 2025-02-24 04:20:01.163633-08 2025-02-24 03:57:19.043219-08 leaderboard t A100 0.0031645186666666666 t \N \N \N {} +690 217 2025-02-24 07:33:26.518857-08 2025-02-24 07:07:15.672567-08 test f A100 \N f \N \N \N {} +630 174 2025-02-24 04:46:32.413079-08 2025-02-24 05:37:46.353976-08 test f T4 \N t \N \N \N {} +631 174 2025-02-24 04:08:35.774251-08 2025-02-24 04:13:23.290244-08 benchmark f T4 \N t \N \N \N {} +632 174 2025-02-24 05:26:47.831146-08 2025-02-24 05:29:05.035813-08 leaderboard f T4 0.02047257833333333 t \N \N \N {} +3277 1144 2025-02-28 05:40:20.632509-08 2025-02-28 06:23:09.782086-08 leaderboard f A100 0.0030818813333333334 t \N \N \N {} +680 211 2025-02-24 06:16:43.935048-08 2025-02-24 05:24:09.037312-08 benchmark t H100 \N t \N \N \N {} +681 211 2025-02-24 06:20:50.131308-08 2025-02-24 06:19:33.50419-08 leaderboard t H100 0.00343199925 t \N \N \N {} +682 211 2025-02-24 06:04:20.742817-08 2025-02-24 06:51:37.437888-08 test f H100 \N t \N \N \N {} +725 231 2025-02-24 08:33:34.394553-08 2025-02-24 07:52:30.592827-08 test f T4 \N t \N \N \N {} +683 211 2025-02-24 05:48:41.277846-08 2025-02-24 06:20:29.451233-08 benchmark f H100 \N t \N \N \N {} +684 211 2025-02-24 05:40:28.316963-08 2025-02-24 05:24:29.585484-08 leaderboard f H100 0.0034547503333333335 t \N \N \N {} +685 212 2025-02-24 07:16:11.487533-08 2025-02-24 05:49:31.110402-08 benchmark f H100 \N t \N \N \N {} +707 225 2025-02-24 07:34:13.86609-08 2025-02-24 09:25:38.673749-08 leaderboard t A100 0.010140606949999999 t \N \N \N {} +713 229 2025-02-24 08:36:51.707477-08 2025-02-24 09:19:35.205825-08 leaderboard t A100 0.004042308666666667 t \N \N \N {} +715 229 2025-02-24 08:59:14.990632-08 2025-02-24 08:40:06.19623-08 benchmark f A100 \N t \N \N \N {} +1002 289 2025-02-24 16:17:52.085682-08 2025-02-24 15:49:52.549542-08 leaderboard t T4 0.017548657333333332 t \N \N \N {} +1004 289 2025-02-24 15:49:21.702779-08 2025-02-24 15:56:44.290148-08 benchmark f H100 \N t \N \N \N {} +1005 289 2025-02-24 16:30:57.115003-08 2025-02-24 16:53:11.026432-08 leaderboard f H100 0.0014277393999999998 t \N \N \N {} +1006 289 2025-02-24 17:08:54.259849-08 2025-02-24 16:01:18.083591-08 test f T4 \N t \N \N \N {} +1012 289 2025-02-24 15:50:44.700014-08 2025-02-24 16:13:26.646399-08 test t L4 \N t \N \N \N {} +2411 812 2025-02-26 11:55:00.140651-08 2025-02-26 12:25:31.245914-08 test t T4 \N f \N \N \N {} +1474 491 2025-02-25 04:38:47.881649-08 2025-02-25 06:09:17.695829-08 test f A100 \N t \N \N \N {} +1477 492 2025-02-25 05:11:27.321383-08 2025-02-25 05:35:34.773378-08 test t H100 \N t \N \N \N {} +1507 504 2025-02-25 07:20:39.230369-08 2025-02-25 07:49:03.830451-08 test f A100 \N t \N \N \N {} +1018 290 2025-02-24 17:13:55.431038-08 2025-02-24 16:41:27.422869-08 test f A100 \N f \N \N \N {} +1019 290 2025-02-24 16:59:40.514483-08 2025-02-24 15:54:21.408908-08 test t H100 \N f \N \N \N {} +1020 290 2025-02-24 16:35:12.120375-08 2025-02-24 17:02:25.401972-08 test t A100 \N f \N \N \N {} +1329 443 2025-02-25 01:04:39.585597-08 2025-02-25 02:46:51.917328-08 benchmark f A100 \N t \N \N \N {} +1330 443 2025-02-25 01:21:21.17285-08 2025-02-25 02:34:21.089915-08 leaderboard f A100 0.0031880928 t \N \N \N {} +1032 291 2025-02-24 17:02:48.537924-08 2025-02-24 16:42:54.273152-08 test t H100 \N t \N \N \N {} +1033 291 2025-02-24 17:18:28.789252-08 2025-02-24 17:23:51.01374-08 benchmark t H100 \N t \N \N \N {} +6109 1847 2025-03-11 00:39:56.642025-07 2025-03-11 00:31:13.424537-07 benchmark f A100 \N f \N \N \N {} +1034 291 2025-02-24 17:27:21.544186-08 2025-02-24 16:17:02.165474-08 leaderboard t H100 0.0014878653333333333 t \N \N \N {} +1035 291 2025-02-24 16:34:37.159795-08 2025-02-24 16:06:45.714048-08 test t L4 \N t \N \N \N {} +1047 291 2025-02-24 17:36:54.22773-08 2025-02-24 17:03:45.859127-08 test f L4 \N t \N \N \N {} +1480 492 2025-02-25 06:19:11.982018-08 2025-02-25 06:17:28.318413-08 test f H100 \N t \N \N \N {} +1036 291 2025-02-24 17:54:34.398457-08 2025-02-24 16:41:28.568693-08 benchmark t L4 \N t \N \N \N {} +1037 291 2025-02-24 16:38:12.362104-08 2025-02-24 16:19:38.183305-08 leaderboard t L4 0.017247674666666667 t \N \N \N {} +1038 291 2025-02-24 16:24:02.713537-08 2025-02-24 16:41:50.09925-08 test f H100 \N t \N \N \N {} +1039 291 2025-02-24 17:42:26.593225-08 2025-02-24 17:17:44.711243-08 benchmark f H100 \N t \N \N \N {} +1040 291 2025-02-24 17:17:51.955383-08 2025-02-24 17:24:28.626898-08 leaderboard f H100 0.001511652 t \N \N \N {} +1379 462 2025-02-25 04:30:12.6402-08 2025-02-25 03:17:09.15611-08 benchmark f H100 \N t \N \N \N {} +1042 291 2025-02-24 17:37:57.142995-08 2025-02-24 17:18:33.682684-08 benchmark t T4 \N t \N \N \N {} +1043 291 2025-02-24 17:05:51.220243-08 2025-02-24 17:10:08.13623-08 leaderboard t T4 0.017371874333333332 t \N \N \N {} +1044 291 2025-02-24 17:06:11.858432-08 2025-02-24 16:33:50.751909-08 test f T4 \N t \N \N \N {} +1045 291 2025-02-24 16:16:44.982001-08 2025-02-24 16:31:49.425344-08 benchmark f T4 \N t \N \N \N {} +1046 291 2025-02-24 17:12:14.548228-08 2025-02-24 16:41:40.82905-08 leaderboard f T4 0.017367958 t \N \N \N {} +1380 463 2025-02-25 03:45:25.261332-08 2025-02-25 04:20:54.913035-08 benchmark f H100 \N t \N \N \N {} +2380 802 2025-02-26 12:30:03.429377-08 2025-02-26 11:34:34.394213-08 benchmark f A100 \N f \N \N \N {} +1048 291 2025-02-24 16:19:24.170092-08 2025-02-24 17:06:26.862153-08 benchmark f L4 \N t \N \N \N {} +1049 291 2025-02-24 15:56:27.762751-08 2025-02-24 17:05:19.405224-08 leaderboard f L4 0.017399107333333334 t \N \N \N {} +1050 292 2025-02-24 17:52:15.055926-08 2025-02-24 17:44:34.698152-08 test f A100 \N t \N \N \N {} +1051 292 2025-02-24 16:22:47.842013-08 2025-02-24 17:43:41.94016-08 benchmark f A100 \N t \N \N \N {} +1052 292 2025-02-24 17:08:31.329041-08 2025-02-24 16:04:23.906272-08 leaderboard f A100 0.0031465863333333334 t \N \N \N {} +1381 464 2025-02-25 04:30:12.200923-08 2025-02-25 04:46:17.323875-08 test f H100 \N t \N \N \N {} +1510 504 2025-02-25 07:50:24.939578-08 2025-02-25 07:14:47.363201-08 test t A100 \N t \N \N \N {} +1053 292 2025-02-24 17:46:58.491665-08 2025-02-24 17:43:14.530104-08 test t A100 \N t \N \N \N {} +1054 292 2025-02-24 16:28:07.677678-08 2025-02-24 17:17:05.064075-08 benchmark t A100 \N t \N \N \N {} +1055 292 2025-02-24 17:20:28.611387-08 2025-02-24 16:25:56.736329-08 leaderboard t A100 0.003155266 t \N \N \N {} +1056 292 2025-02-24 16:22:44.412346-08 2025-02-24 16:35:31.252286-08 test t L4 \N t \N \N \N {} +1057 292 2025-02-24 17:51:06.9878-08 2025-02-24 16:57:56.1698-08 benchmark t L4 \N t \N \N \N {} +1384 464 2025-02-25 04:01:57.357806-08 2025-02-25 04:32:07.486918-08 test t H100 \N t \N \N \N {} +1485 495 2025-02-25 07:02:18.828551-08 2025-02-25 05:31:13.315806-08 test t A100 \N f \N \N \N {} +1058 292 2025-02-24 16:12:14.175483-08 2025-02-24 16:19:24.281562-08 leaderboard t L4 0.017181366666666666 t \N \N \N {} +1059 292 2025-02-24 16:45:08.313765-08 2025-02-24 16:24:12.371964-08 test f H100 \N t \N \N \N {} +1060 292 2025-02-24 16:57:23.927277-08 2025-02-24 17:45:53.131411-08 benchmark f H100 \N t \N \N \N {} +1387 465 2025-02-25 03:17:52.9791-08 2025-02-25 04:50:46.975041-08 test t H100 \N t \N \N \N {} +1063 292 2025-02-24 17:43:12.741558-08 2025-02-24 16:54:56.492661-08 benchmark t T4 \N t \N \N \N {} +1064 292 2025-02-24 16:36:30.649022-08 2025-02-24 16:31:15.555557-08 leaderboard t T4 0.016662345666666665 t \N \N \N {} +6110 1848 2025-03-11 02:24:40.439817-07 2025-03-11 01:49:27.022226-07 benchmark f A100 \N f \N \N \N {} +1065 292 2025-02-24 17:01:31.487312-08 2025-02-24 16:08:17.982794-08 test f T4 \N t \N \N \N {} +1066 292 2025-02-24 17:34:24.686206-08 2025-02-24 16:15:54.00558-08 benchmark f T4 \N t \N \N \N {} +1067 292 2025-02-24 16:25:45.909582-08 2025-02-24 17:35:16.325804-08 leaderboard f T4 0.016692271666666664 t \N \N \N {} +1089 300 2025-02-24 17:15:39.013548-08 2025-02-24 18:07:57.788624-08 test f A100 \N t \N \N \N {} +1390 465 2025-02-25 04:07:56.363414-08 2025-02-25 03:08:55.772199-08 test f H100 \N t \N \N \N {} +1069 292 2025-02-24 16:33:24.005735-08 2025-02-24 17:00:37.977793-08 benchmark f L4 \N t \N \N \N {} +1070 292 2025-02-24 16:11:38.035638-08 2025-02-24 16:35:28.298125-08 leaderboard f L4 0.01725526833333333 t \N \N \N {} +1072 292 2025-02-24 17:49:21.420511-08 2025-02-24 17:39:52.392614-08 benchmark t H100 \N t \N \N \N {} +1073 292 2025-02-24 17:39:03.403534-08 2025-02-24 16:41:35.838976-08 leaderboard t H100 0.00143393225 t \N \N \N {} +1074 293 2025-02-24 16:28:54.883242-08 2025-02-24 17:35:23.368271-08 test f H100 \N t \N \N \N {} +1075 294 2025-02-24 17:12:03.460856-08 2025-02-24 17:11:25.091501-08 benchmark f H100 \N t \N \N \N {} +1076 295 2025-02-24 17:45:00.760378-08 2025-02-24 18:03:30.527064-08 test f H100 \N t \N \N \N {} +3899 1300 2025-03-01 04:59:30.357371-08 2025-03-01 05:19:06.000963-08 benchmark f H100 \N t \N \N \N {} +1083 297 2025-02-24 18:12:12.906661-08 2025-02-24 16:31:51.879538-08 test f A100 \N t \N \N \N {} +1087 300 2025-02-24 17:00:42.01574-08 2025-02-24 17:08:59.120585-08 benchmark t A100 \N t \N \N \N {} +1088 300 2025-02-24 16:54:53.189438-08 2025-02-24 17:03:28.128475-08 leaderboard t A100 0.014111941 t \N \N \N {} +1090 300 2025-02-24 18:18:13.575515-08 2025-02-24 17:44:59.217622-08 benchmark f A100 \N t \N \N \N {} +1101 304 2025-02-24 17:40:45.55189-08 2025-02-24 17:01:05.496887-08 test t H100 \N t \N \N \N {} +1102 304 2025-02-24 18:50:24.116927-08 2025-02-24 18:27:17.991007-08 benchmark t H100 \N t \N \N \N {} +1103 304 2025-02-24 18:56:45.352747-08 2025-02-24 17:39:36.014601-08 leaderboard t H100 0.0036164646666666665 t \N \N \N {} +1104 304 2025-02-24 17:14:31.119701-08 2025-02-24 18:21:08.729398-08 test f T4 \N t \N \N \N {} +1108 304 2025-02-24 17:14:00.044903-08 2025-02-24 17:35:39.490324-08 benchmark t T4 \N t \N \N \N {} +1109 304 2025-02-24 18:01:06.49365-08 2025-02-24 17:46:19.984475-08 leaderboard t T4 0.02827727433333333 t \N \N \N {} +1110 304 2025-02-24 18:55:51.538374-08 2025-02-24 18:30:10.634801-08 test f H100 \N t \N \N \N {} +1112 304 2025-02-24 17:35:18.104482-08 2025-02-24 17:19:54.790912-08 leaderboard f H100 0.003638923 t \N \N \N {} +1115 304 2025-02-24 17:17:06.648489-08 2025-02-24 18:55:20.002642-08 leaderboard t L4 0.027880181666666667 t \N \N \N {} +1116 304 2025-02-24 18:01:36.171802-08 2025-02-24 17:39:40.210678-08 test f L4 \N t \N \N \N {} +1117 304 2025-02-24 17:16:05.293543-08 2025-02-24 17:09:24.002204-08 benchmark f L4 \N t \N \N \N {} +1149 322 2025-02-24 20:09:50.646883-08 2025-02-24 20:09:27.027041-08 benchmark f A100 \N t \N \N \N {} +1150 323 2025-02-24 19:44:17.381273-08 2025-02-24 18:40:51.316233-08 test f H100 \N f \N \N \N {} +1513 505 2025-02-25 06:40:15.410176-08 2025-02-25 07:37:48.715184-08 test f A100 \N t \N \N \N {} +1151 324 2025-02-24 18:48:04.557654-08 2025-02-24 19:42:55.295313-08 benchmark f A100 \N t \N \N \N {} +1152 325 2025-02-24 19:59:18.76177-08 2025-02-24 18:45:50.840944-08 benchmark f A100 \N f \N \N \N {} +1153 326 2025-02-24 18:52:24.053553-08 2025-02-24 19:29:33.646058-08 benchmark f A100 \N t \N \N \N {} +1154 327 2025-02-24 20:15:12.831932-08 2025-02-24 18:46:28.80301-08 benchmark f A100 \N t \N \N \N {} +1165 333 2025-02-24 19:59:10.567094-08 2025-02-24 19:43:21.331396-08 benchmark f A100 \N f \N \N \N {} +1156 329 2025-02-24 20:00:37.855323-08 2025-02-24 19:49:02.655715-08 test f H100 \N t \N \N \N {} +1157 329 2025-02-24 19:16:48.278346-08 2025-02-24 18:35:28.873095-08 benchmark f H100 \N t \N \N \N {} +1158 329 2025-02-24 19:19:35.133313-08 2025-02-24 19:39:48.264665-08 leaderboard f H100 0.0015907006666666668 t \N \N \N {} +1159 329 2025-02-24 20:06:20.184176-08 2025-02-24 19:17:10.273748-08 test t H100 \N t \N \N \N {} +1395 467 2025-02-25 04:01:56.308441-08 2025-02-25 03:40:45.970012-08 test f A100 \N f \N \N \N {} +1162 330 2025-02-24 20:15:53.048254-08 2025-02-24 19:40:58.672546-08 benchmark f A100 \N t \N \N \N {} +1163 331 2025-02-24 19:39:33.765253-08 2025-02-24 20:18:32.015656-08 benchmark f A100 \N f \N \N \N {} +1164 332 2025-02-24 19:57:11.008009-08 2025-02-24 19:45:28.206315-08 test f A100 \N f \N \N \N {} +1396 468 2025-02-25 03:56:27.738846-08 2025-02-25 04:21:51.589822-08 test f A100 \N t \N \N \N {} +1170 338 2025-02-24 20:00:52.228565-08 2025-02-24 19:06:41.353851-08 benchmark f A100 \N f \N \N \N {} +1185 353 2025-02-24 20:23:22.920373-08 2025-02-24 20:31:23.388252-08 benchmark f A100 \N f \N \N \N {} +1172 340 2025-02-24 20:30:32.647454-08 2025-02-24 19:20:53.458575-08 benchmark f A100 \N t \N \N \N {} +1173 341 2025-02-24 19:18:26.427276-08 2025-02-24 20:25:12.965728-08 benchmark f A100 \N t \N \N \N {} +1174 342 2025-02-24 19:16:50.848989-08 2025-02-24 20:26:36.909914-08 benchmark f A100 \N f \N \N \N {} +1399 468 2025-02-25 03:54:55.679208-08 2025-02-25 04:15:06.87232-08 test t A100 \N t \N \N \N {} +1516 505 2025-02-25 07:18:07.594758-08 2025-02-25 07:03:51.966033-08 test t A100 \N t \N \N \N {} +1178 346 2025-02-24 20:46:35.583472-08 2025-02-24 19:47:02.016449-08 benchmark f A100 \N f \N \N \N {} +1179 347 2025-02-24 20:01:38.196152-08 2025-02-24 19:54:49.601721-08 benchmark f A100 \N f \N \N \N {} +1402 469 2025-02-25 03:40:45.982253-08 2025-02-25 03:31:06.961948-08 test f A100 \N t \N \N \N {} +1182 350 2025-02-24 19:20:16.154751-08 2025-02-24 20:48:25.71245-08 benchmark f A100 \N t \N \N \N {} +1183 351 2025-02-24 20:45:34.464741-08 2025-02-24 20:18:14.635844-08 benchmark f H100 \N t \N \N \N {} +1184 352 2025-02-24 20:01:05.338551-08 2025-02-24 20:52:19.857137-08 test f A100 \N f \N \N \N {} +1405 469 2025-02-25 04:58:27.346875-08 2025-02-25 04:51:26.433509-08 test t A100 \N t \N \N \N {} +1519 506 2025-02-25 06:47:13.929749-08 2025-02-25 06:42:22.498667-08 test t H100 \N t \N \N \N {} +1188 358 2025-02-24 19:37:31.122358-08 2025-02-24 20:18:57.150066-08 benchmark f A100 \N f \N \N \N {} +1189 360 2025-02-24 21:06:56.386392-08 2025-02-24 19:27:17.523239-08 benchmark f A100 \N f \N \N \N {} +1190 363 2025-02-24 20:45:31.277208-08 2025-02-24 20:01:17.753967-08 benchmark f A100 \N f \N \N \N {} +1408 470 2025-02-25 03:59:58.320768-08 2025-02-25 04:28:37.828048-08 test t A100 \N t \N \N \N {} +1193 365 2025-02-24 19:43:43.570899-08 2025-02-24 19:39:23.906226-08 benchmark f A100 \N t \N \N \N {} +6140 1882 2025-03-11 10:55:44.95308-07 2025-03-11 11:05:57.417109-07 test t T4 \N f \N \N \N {} +1194 365 2025-02-24 20:08:47.222036-08 2025-02-24 19:30:56.207701-08 leaderboard f A100 0.01920636041666667 t \N \N \N {} +1195 365 2025-02-24 19:51:58.749971-08 2025-02-24 19:45:07.726733-08 test t A100 \N t \N \N \N {} +1196 365 2025-02-24 19:38:48.837266-08 2025-02-24 19:34:14.850955-08 benchmark t A100 \N t \N \N \N {} +1197 365 2025-02-24 21:18:06.454034-08 2025-02-24 20:41:46.182401-08 leaderboard t A100 0.023418701 t \N \N \N {} +1411 470 2025-02-25 03:23:48.300357-08 2025-02-25 04:14:39.867996-08 test f A100 \N t \N \N \N {} +1201 369 2025-02-24 19:57:06.809954-08 2025-02-24 19:38:21.922123-08 benchmark f A100 \N f \N \N \N {} +1202 370 2025-02-24 20:42:28.895887-08 2025-02-24 20:44:35.166507-08 benchmark f A100 \N f \N \N \N {} +1205 373 2025-02-24 20:31:46.327908-08 2025-02-24 19:52:32.554037-08 benchmark f A100 \N f \N \N \N {} +1251 412 2025-02-24 21:58:13.257224-08 2025-02-24 20:59:40.847628-08 benchmark f A100 \N t \N \N \N {} +1252 413 2025-02-24 21:02:53.363826-08 2025-02-24 21:32:32.418367-08 test f A100 \N t \N \N \N {} +1253 413 2025-02-24 21:45:44.88428-08 2025-02-24 22:03:57.186711-08 benchmark f A100 \N t \N \N \N {} +1254 413 2025-02-24 22:46:22.934333-08 2025-02-24 22:52:04.198254-08 leaderboard f A100 0.0008381501 t \N \N \N {} +1261 416 2025-02-24 22:58:19.495213-08 2025-02-24 21:04:11.500619-08 benchmark f H100 \N t \N \N \N {} +1262 416 2025-02-24 21:17:50.838915-08 2025-02-24 22:04:53.900724-08 leaderboard f H100 0.00182382483 t \N \N \N {} +1267 418 2025-02-24 23:14:01.289913-08 2025-02-24 23:08:07.113865-08 test f H100 \N f \N \N \N {} +1268 419 2025-02-24 22:02:19.904283-08 2025-02-24 22:45:33.136654-08 test f H100 \N f \N \N \N {} +1269 420 2025-02-24 22:11:27.988828-08 2025-02-24 22:20:38.794633-08 test f L4 \N f \N \N \N {} +1270 421 2025-02-24 22:44:39.054132-08 2025-02-24 23:23:37.763687-08 test f H100 \N f \N \N \N {} +1285 431 2025-02-25 00:40:59.712906-08 2025-02-25 00:18:36.054996-08 benchmark f A100 \N t \N \N \N {} +1286 432 2025-02-25 00:31:05.092152-08 2025-02-25 00:20:51.202659-08 test f A100 \N f \N \N \N {} +1287 432 2025-02-25 00:49:38.550792-08 2025-02-25 00:46:23.249092-08 test t A100 \N f \N \N \N {} +1288 433 2025-02-25 00:12:52.435038-08 2025-02-25 00:43:33.913224-08 test t A100 \N f \N \N \N {} +1289 433 2025-02-25 00:41:52.056466-08 2025-02-25 01:46:10.51578-08 test f A100 \N f \N \N \N {} +1290 434 2025-02-25 02:07:55.489435-08 2025-02-25 00:38:51.721901-08 test f A100 \N f \N \N \N {} +1300 437 2025-02-25 01:42:35.344955-08 2025-02-25 00:18:58.076587-08 test f A100 \N t \N \N \N {} +1301 438 2025-02-25 00:52:08.957799-08 2025-02-25 01:01:30.640546-08 test f A100 \N t \N \N \N {} +1306 438 2025-02-25 02:17:28.708296-08 2025-02-25 00:43:42.436098-08 leaderboard t A100 0.00319022725 t \N \N \N {} +1307 439 2025-02-25 00:35:26.857477-08 2025-02-25 00:40:59.59712-08 test t A100 \N t \N \N \N {} +1312 439 2025-02-25 02:25:43.593928-08 2025-02-25 00:33:11.310668-08 leaderboard f A100 0.018465732104166667 t \N \N \N {} +1313 440 2025-02-25 00:40:22.163117-08 2025-02-25 02:26:51.813453-08 test t A100 \N f \N \N \N {} +1345 447 2025-02-25 01:34:33.910168-08 2025-02-25 03:08:17.172342-08 test f A100 \N t \N \N \N {} +1314 440 2025-02-25 01:49:18.063405-08 2025-02-25 01:27:01.624065-08 test f A100 \N f \N \N \N {} +1315 441 2025-02-25 02:02:15.063829-08 2025-02-25 01:35:47.494284-08 test t A100 \N t \N \N \N {} +1316 441 2025-02-25 01:37:10.608604-08 2025-02-25 01:46:29.023721-08 benchmark t A100 \N f \N \N \N {} +1335 445 2025-02-25 02:43:54.759243-08 2025-02-25 01:44:21.434113-08 leaderboard f A100 0.0030836436666666664 t \N \N \N {} +1336 445 2025-02-25 02:54:21.021597-08 2025-02-25 01:51:35.634885-08 test t A100 \N t \N \N \N {} +1346 448 2025-02-25 02:33:47.252483-08 2025-02-25 02:35:35.888545-08 test f H100 \N t \N \N \N {} +1434 476 2025-02-25 04:00:22.852373-08 2025-02-25 04:04:45.593426-08 test t A100 \N f \N \N \N {} +1339 446 2025-02-25 02:36:06.477421-08 2025-02-25 03:07:01.814655-08 test f H100 \N t \N \N \N {} +1340 446 2025-02-25 01:30:46.736236-08 2025-02-25 01:35:56.032794-08 benchmark f H100 \N t \N \N \N {} +1341 446 2025-02-25 02:50:19.546464-08 2025-02-25 03:05:51.854793-08 leaderboard f H100 0.001413967 t \N \N \N {} +1342 446 2025-02-25 01:57:52.59555-08 2025-02-25 03:11:12.62416-08 test t H100 \N t \N \N \N {} +1343 446 2025-02-25 02:54:39.59271-08 2025-02-25 01:59:29.726735-08 benchmark t H100 \N t \N \N \N {} +1344 446 2025-02-25 02:39:25.860644-08 2025-02-25 02:13:36.717111-08 leaderboard t H100 0.001406711 t \N \N \N {} +1347 448 2025-02-25 02:43:41.717721-08 2025-02-25 03:26:33.446347-08 benchmark f H100 \N t \N \N \N {} +1348 448 2025-02-25 02:24:25.941676-08 2025-02-25 01:44:32.522029-08 leaderboard f H100 0.0014466804444444445 t \N \N \N {} +1349 448 2025-02-25 02:46:47.800926-08 2025-02-25 02:27:20.46965-08 test t H100 \N t \N \N \N {} +1350 448 2025-02-25 03:06:45.439062-08 2025-02-25 02:01:51.335675-08 benchmark t H100 \N t \N \N \N {} +1351 448 2025-02-25 03:30:09.49455-08 2025-02-25 03:12:41.990012-08 leaderboard t H100 0.0014756132222222221 t \N \N \N {} +1352 449 2025-02-25 03:33:12.39421-08 2025-02-25 02:23:52.923798-08 benchmark f H100 \N f \N \N \N {} +1435 476 2025-02-25 04:55:28.037485-08 2025-02-25 03:57:08.314649-08 test f A100 \N f \N \N \N {} +1555 513 2025-02-25 07:41:24.90562-08 2025-02-25 07:02:07.563164-08 test t A100 \N t \N \N \N {} +2909 1016 2025-02-27 00:36:25.1576-08 2025-02-27 00:50:15.121869-08 benchmark f L4 \N t \N \N \N {} +2910 1016 2025-02-27 00:33:26.148508-08 2025-02-27 01:43:46.236307-08 leaderboard f L4 0.017180370666666667 t \N \N \N {} +3041 1088 2025-02-27 14:56:34.384661-08 2025-02-27 15:37:21.179849-08 benchmark f A100 \N t \N \N \N {} +3369 1163 2025-02-28 10:09:00.6989-08 2025-02-28 09:38:40.385253-08 leaderboard t T4 0.01692768366666667 t \N \N \N {} +1400 468 2025-02-25 04:45:59.134192-08 2025-02-25 03:14:40.329073-08 benchmark t A100 \N t \N \N \N {} +1401 468 2025-02-25 03:36:58.192612-08 2025-02-25 04:35:55.042696-08 leaderboard t A100 0.003100401 t \N \N \N {} +2919 1023 2025-02-27 02:46:56.557824-08 2025-02-27 03:30:51.397045-08 test t A100 \N t \N \N \N {} +2920 1023 2025-02-27 02:54:26.545327-08 2025-02-27 03:57:32.864122-08 benchmark t A100 \N t \N \N \N {} +2921 1023 2025-02-27 04:31:43.865249-08 2025-02-27 03:08:56.104014-08 leaderboard t A100 0.00009803284313725489 t \N \N \N {} +3120 1110 2025-02-28 01:35:44.444997-08 2025-02-28 03:00:13.974579-08 leaderboard t A100 0.00310022 t \N \N \N {} +3559 1210 2025-02-28 11:35:52.32336-08 2025-02-28 10:56:38.662439-08 test f A100 \N t \N \N \N {} +1403 469 2025-02-25 04:31:28.4595-08 2025-02-25 03:33:06.99222-08 benchmark f A100 \N t \N \N \N {} +2381 803 2025-02-26 11:33:33.487376-08 2025-02-26 12:57:20.418702-08 benchmark f A100 \N f \N \N \N {} +2427 833 2025-02-26 13:42:17.786461-08 2025-02-26 12:30:47.6523-08 benchmark f H100 \N t \N \N \N {} +3009 1074 2025-02-27 13:15:17.144176-08 2025-02-27 12:29:52.339984-08 leaderboard t A100 0.008153932666666667 t \N \N \N {} +2952 1039 2025-02-27 09:30:20.903906-08 2025-02-27 09:13:37.115048-08 benchmark f A100 \N t \N \N \N {} +3010 1074 2025-02-27 13:59:33.48938-08 2025-02-27 13:27:19.116278-08 test f A100 \N t \N \N \N {} +3011 1074 2025-02-27 14:02:47.636628-08 2025-02-27 13:32:31.614474-08 benchmark f A100 \N t \N \N \N {} +3012 1074 2025-02-27 13:15:20.480402-08 2025-02-27 12:20:44.401517-08 leaderboard f A100 0.008157396666666667 t \N \N \N {} +3085 1104 2025-02-28 01:59:18.600676-08 2025-02-28 02:22:56.347954-08 test f A100 \N t \N \N \N {} +3094 1106 2025-02-28 02:25:58.750131-08 2025-02-28 03:06:28.944884-08 test t A100 \N t \N \N \N {} +3100 1107 2025-02-28 01:26:20.879799-08 2025-02-28 02:25:12.920428-08 test f A100 \N t \N \N \N {} +1478 492 2025-02-25 04:23:20.449898-08 2025-02-25 04:58:19.421573-08 benchmark t H100 \N t \N \N \N {} +1479 492 2025-02-25 04:51:19.751745-08 2025-02-25 05:39:17.407689-08 leaderboard t H100 0.0014122803333333333 t \N \N \N {} +2953 1040 2025-02-27 09:36:18.815464-08 2025-02-27 08:40:35.471157-08 test f A100 \N t \N \N \N {} +2954 1040 2025-02-27 08:26:37.101243-08 2025-02-27 09:01:30.532817-08 benchmark f A100 \N t \N \N \N {} +2955 1040 2025-02-27 09:41:18.891429-08 2025-02-27 08:04:00.37954-08 leaderboard f A100 0.003193513 t \N \N \N {} +3013 1075 2025-02-27 13:11:51.024355-08 2025-02-27 13:17:55.679639-08 test f A100 \N f \N \N \N {} +3103 1107 2025-02-28 03:07:10.741712-08 2025-02-28 02:19:44.946765-08 test t A100 \N t \N \N \N {} +3225 1127 2025-02-28 05:28:19.551054-08 2025-02-28 05:56:14.735613-08 leaderboard f A100 0.0030895356666666663 t \N \N \N {} +1481 492 2025-02-25 04:54:31.873992-08 2025-02-25 04:22:13.298402-08 benchmark f H100 \N t \N \N \N {} +1482 492 2025-02-25 06:14:51.357942-08 2025-02-25 06:18:57.491972-08 leaderboard f H100 0.001430011 t \N \N \N {} +1524 506 2025-02-25 06:33:04.067626-08 2025-02-25 07:23:14.188914-08 leaderboard f H100 0.0014538050909090909 t \N \N \N {} +1525 507 2025-02-25 07:34:25.667422-08 2025-02-25 06:18:53.214067-08 test t A100 \N f \N \N \N {} +1526 507 2025-02-25 08:04:00.305064-08 2025-02-25 07:53:47.234207-08 test f A100 \N f \N \N \N {} +1527 508 2025-02-25 06:51:53.729029-08 2025-02-25 06:49:15.739902-08 benchmark f A100 \N f \N \N \N {} +1528 509 2025-02-25 07:57:51.169726-08 2025-02-25 08:03:52.639663-08 benchmark f A100 \N t \N \N \N {} +3160 1117 2025-02-28 01:57:36.630356-08 2025-02-28 02:15:23.444492-08 test t A100 \N t \N \N \N {} +1531 512 2025-02-25 07:27:21.471591-08 2025-02-25 07:32:23.967466-08 test t A100 \N t \N \N \N {} +130 20 2025-02-23 09:47:46.962051-08 2025-02-23 10:49:22.323496-08 test f H100 \N t \N \N \N {} +1532 512 2025-02-25 07:07:12.686135-08 2025-02-25 06:51:42.064245-08 benchmark t A100 \N t \N \N \N {} +1533 512 2025-02-25 07:35:12.160809-08 2025-02-25 06:23:12.621053-08 leaderboard t A100 0.0033605953333333337 t \N \N \N {} +1534 512 2025-02-25 07:40:57.831066-08 2025-02-25 07:21:40.9266-08 test f A100 \N t \N \N \N {} +1535 512 2025-02-25 07:49:49.262012-08 2025-02-25 07:10:31.438503-08 benchmark f A100 \N t \N \N \N {} +1536 512 2025-02-25 06:32:25.921122-08 2025-02-25 07:25:06.894207-08 leaderboard f A100 0.0033895406666666667 t \N \N \N {} +1537 512 2025-02-25 06:24:52.920212-08 2025-02-25 06:36:53.942491-08 test t H100 \N t \N \N \N {} +1538 512 2025-02-25 06:30:33.771888-08 2025-02-25 07:19:58.296436-08 benchmark t H100 \N t \N \N \N {} +1558 513 2025-02-25 07:20:20.352133-08 2025-02-25 07:48:40.674871-08 test f A100 \N t \N \N \N {} +3263 1142 2025-02-28 06:26:36.269643-08 2025-02-28 06:34:10.748084-08 test f A100 \N t \N \N \N {} +2020 694 2025-02-25 14:25:32.099879-08 2025-02-25 14:05:01.976545-08 test f H100 \N f \N \N \N {} +1576 516 2025-02-25 07:10:56.831342-08 2025-02-25 07:01:47.567348-08 test f A100 \N t \N \N \N {} +1577 516 2025-02-25 08:23:01.443137-08 2025-02-25 07:25:44.603998-08 benchmark f A100 \N t \N \N \N {} +1578 516 2025-02-25 08:13:13.657148-08 2025-02-25 07:31:55.453583-08 leaderboard f A100 0.0031859834 t \N \N \N {} +1580 518 2025-02-25 08:07:50.629405-08 2025-02-25 07:17:48.243079-08 benchmark f T4 \N t \N \N \N {} +1582 519 2025-02-25 07:03:06.655132-08 2025-02-25 07:54:35.221308-08 benchmark t H100 \N t \N \N \N {} +1583 519 2025-02-25 08:20:50.0298-08 2025-02-25 08:34:10.546647-08 leaderboard t H100 0.0014114776666666667 t \N \N \N {} +1584 519 2025-02-25 08:01:59.762518-08 2025-02-25 08:40:03.53953-08 test f H100 \N t \N \N \N {} +1585 519 2025-02-25 06:48:35.700356-08 2025-02-25 07:22:01.937122-08 benchmark f H100 \N t \N \N \N {} +1586 519 2025-02-25 07:48:14.082024-08 2025-02-25 07:23:02.722501-08 leaderboard f H100 0.0014109543333333332 t \N \N \N {} +1587 520 2025-02-25 06:50:50.074767-08 2025-02-25 06:52:07.567587-08 test f A100 \N t \N \N \N {} +1588 521 2025-02-25 07:04:03.01671-08 2025-02-25 08:36:16.326265-08 benchmark f A100 \N t \N \N \N {} +1603 532 2025-02-25 08:01:48.392638-08 2025-02-25 07:55:14.350257-08 test t A100 \N t \N \N \N {} +1604 532 2025-02-25 08:04:01.52357-08 2025-02-25 08:48:53.380739-08 benchmark t A100 \N t \N \N \N {} +1605 532 2025-02-25 08:07:29.187473-08 2025-02-25 07:30:38.019107-08 leaderboard t A100 0.0030833873333333335 t \N \N \N {} +1606 533 2025-02-25 07:13:57.228578-08 2025-02-25 09:02:45.282064-08 test t H100 \N t \N \N \N {} +1607 533 2025-02-25 08:46:17.241842-08 2025-02-25 08:46:34.390308-08 benchmark t H100 \N t \N \N \N {} +1608 533 2025-02-25 07:15:43.847706-08 2025-02-25 07:17:56.713485-08 leaderboard t H100 0.0014808887368421052 t \N \N \N {} +1614 536 2025-02-25 07:40:05.156643-08 2025-02-25 08:50:38.71485-08 benchmark f H100 \N t \N \N \N {} +1615 537 2025-02-25 08:20:34.71729-08 2025-02-25 08:00:07.524271-08 test f A100 \N t \N \N \N {} +1626 539 2025-02-25 08:31:18.319762-08 2025-02-25 07:36:37.562316-08 benchmark t H100 \N t \N \N \N {} +1627 539 2025-02-25 07:45:22.622109-08 2025-02-25 07:26:16.545192-08 leaderboard t H100 0.0014020573333333333 t \N \N \N {} +1628 540 2025-02-25 08:39:12.579698-08 2025-02-25 08:21:54.313518-08 test f H100 \N t \N \N \N {} +1629 540 2025-02-25 09:06:18.861595-08 2025-02-25 09:06:08.76981-08 benchmark f H100 \N t \N \N \N {} +1630 540 2025-02-25 07:42:40.881925-08 2025-02-25 09:24:44.220023-08 leaderboard f H100 0.0014151046666666667 t \N \N \N {} +1631 540 2025-02-25 09:11:16.420021-08 2025-02-25 08:59:13.13714-08 test t H100 \N t \N \N \N {} +1683 557 2025-02-25 08:58:37.853947-08 2025-02-25 09:17:12.880606-08 test f A100 \N t \N \N \N {} +1642 543 2025-02-25 09:29:08.780743-08 2025-02-25 07:49:54.770844-08 benchmark t A100 \N t \N \N \N {} +1643 543 2025-02-25 08:08:43.291503-08 2025-02-25 07:45:38.670153-08 leaderboard t A100 0.003091805 t \N \N \N {} +1644 543 2025-02-25 08:16:39.851047-08 2025-02-25 09:04:53.563147-08 test f A100 \N t \N \N \N {} +1645 543 2025-02-25 08:33:33.890389-08 2025-02-25 09:25:02.242877-08 benchmark f A100 \N t \N \N \N {} +1647 544 2025-02-25 08:10:30.369306-08 2025-02-25 07:43:45.870981-08 test f A100 \N f \N \N \N {} +1648 544 2025-02-25 07:53:54.16523-08 2025-02-25 08:33:04.35208-08 test t A100 \N f \N \N \N {} +1649 544 2025-02-25 08:33:21.506269-08 2025-02-25 08:07:38.323301-08 test t T4 \N f \N \N \N {} +1650 544 2025-02-25 09:15:52.74522-08 2025-02-25 08:12:39.641754-08 test f T4 \N f \N \N \N {} +1651 545 2025-02-25 08:25:31.259943-08 2025-02-25 08:02:07.523474-08 test f A100 \N t \N \N \N {} +1976 679 2025-02-25 12:53:34.216219-08 2025-02-25 13:15:06.928515-08 test t T4 \N t \N \N \N {} +1710 571 2025-02-25 09:19:18.026341-08 2025-02-25 09:52:30.405634-08 test t T4 \N f \N \N \N {} +1711 571 2025-02-25 08:33:43.295723-08 2025-02-25 10:06:35.053909-08 test f T4 \N f \N \N \N {} +1726 579 2025-02-25 10:12:23.270121-08 2025-02-25 09:22:50.331776-08 test f A100 \N f \N \N \N {} +1734 585 2025-02-25 08:29:31.038561-08 2025-02-25 08:26:58.367928-08 test f A100 \N f \N \N \N {} +1735 583 2025-02-25 08:35:16.843418-08 2025-02-25 09:05:45.380553-08 test t H100 \N t \N \N \N {} +1736 583 2025-02-25 10:11:22.418499-08 2025-02-25 09:25:54.155768-08 benchmark t H100 \N t \N \N \N {} +1737 583 2025-02-25 08:51:06.601652-08 2025-02-25 09:10:23.575069-08 leaderboard t H100 0.0000772660625 t \N \N \N {} +2810 981 2025-02-26 23:47:22.315813-08 2025-02-26 23:04:15.576922-08 leaderboard f H100 0.00149895725 t \N \N \N {} +3817 1285 2025-03-01 03:36:02.152194-08 2025-03-01 03:19:46.959566-08 test f A100 \N t \N \N \N {} +4806 1487 2025-03-02 14:19:46.759466-08 2025-03-02 12:30:42.83975-08 test f T4 \N t \N \N \N {} +1784 613 2025-02-25 11:14:58.236656-08 2025-02-25 10:52:55.040195-08 test f A100 \N t \N \N \N {} +1785 614 2025-02-25 10:45:46.882457-08 2025-02-25 10:39:52.677212-08 benchmark f H100 \N t \N \N \N {} +2355 791 2025-02-26 08:55:33.742418-08 2025-02-26 07:47:49.701265-08 test f H100 \N f \N \N \N {} +1786 615 2025-02-25 10:56:29.193673-08 2025-02-25 09:37:14.897702-08 benchmark f H100 \N f \N \N \N {} +1787 616 2025-02-25 10:39:54.716283-08 2025-02-25 10:38:23.837561-08 test f A100 \N t \N \N \N {} +1788 616 2025-02-25 10:53:22.333552-08 2025-02-25 10:30:34.088241-08 benchmark f A100 \N t \N \N \N {} +1789 616 2025-02-25 11:11:25.986403-08 2025-02-25 10:31:45.03736-08 leaderboard f A100 0.000205736 t \N \N \N {} +1790 616 2025-02-25 09:41:53.090321-08 2025-02-25 10:12:39.131059-08 test t A100 \N t \N \N \N {} +1796 616 2025-02-25 09:49:54.963655-08 2025-02-25 11:04:32.101396-08 test t T4 \N t \N \N \N {} +2775 966 2025-02-26 20:12:59.070745-08 2025-02-26 19:39:08.158654-08 leaderboard f A100 0.014324531 t \N \N \N {} +1792 616 2025-02-25 10:01:38.707534-08 2025-02-25 11:11:50.587762-08 leaderboard t A100 0.00017068230769230768 t \N \N \N {} +1793 616 2025-02-25 10:03:00.704862-08 2025-02-25 09:45:03.774932-08 test f H100 \N t \N \N \N {} +1794 616 2025-02-25 10:53:30.74314-08 2025-02-25 11:05:56.056638-08 benchmark f H100 \N t \N \N \N {} +1795 616 2025-02-25 11:17:45.875715-08 2025-02-25 10:39:10.578077-08 leaderboard f H100 0.00012224712903225807 t \N \N \N {} +2356 791 2025-02-26 07:29:55.883929-08 2025-02-26 08:47:28.665781-08 test t H100 \N f \N \N \N {} +1798 616 2025-02-25 09:55:41.682206-08 2025-02-25 10:56:48.765016-08 leaderboard t T4 0.000549977 t \N \N \N {} +1799 616 2025-02-25 09:35:29.329871-08 2025-02-25 10:19:26.68501-08 test f T4 \N t \N \N \N {} +1800 616 2025-02-25 10:37:31.462732-08 2025-02-25 10:39:06.329009-08 benchmark f T4 \N t \N \N \N {} +1801 616 2025-02-25 10:49:46.717275-08 2025-02-25 09:34:27.810838-08 leaderboard f T4 0.0007055456666666667 t \N \N \N {} +2357 792 2025-02-26 09:14:17.578297-08 2025-02-26 08:39:13.532029-08 test t H100 \N f \N \N \N {} +1803 616 2025-02-25 10:02:17.879749-08 2025-02-25 11:05:44.711116-08 benchmark t H100 \N t \N \N \N {} +1804 616 2025-02-25 11:14:14.091418-08 2025-02-25 10:00:08.773034-08 leaderboard t H100 0.00012245449019607842 t \N \N \N {} +1805 616 2025-02-25 10:57:07.382054-08 2025-02-25 10:21:18.841312-08 test f L4 \N t \N \N \N {} +1806 616 2025-02-25 11:25:11.990406-08 2025-02-25 10:09:51.776687-08 benchmark f L4 \N t \N \N \N {} +2358 792 2025-02-26 08:54:16.016762-08 2025-02-26 08:39:38.92267-08 test f H100 \N f \N \N \N {} +1810 616 2025-02-25 11:00:52.381329-08 2025-02-25 11:23:28.247671-08 leaderboard t L4 0.00021845146153846154 t \N \N \N {} +1811 617 2025-02-25 10:55:46.819723-08 2025-02-25 10:55:23.275903-08 benchmark f H100 \N f \N \N \N {} +1812 618 2025-02-25 10:21:57.018191-08 2025-02-25 10:55:18.464595-08 benchmark f A100 \N t \N \N \N {} +2370 797 2025-02-26 10:53:12.943723-08 2025-02-26 09:33:24.539346-08 test f A100 \N f \N \N \N {} +1816 620 2025-02-25 10:05:33.623324-08 2025-02-25 10:33:30.515341-08 leaderboard t A100 0.00309566 t \N \N \N {} +1817 620 2025-02-25 11:21:18.896922-08 2025-02-25 10:03:28.563169-08 test f A100 \N t \N \N \N {} +1818 620 2025-02-25 11:29:14.645796-08 2025-02-25 09:47:07.54021-08 benchmark f A100 \N t \N \N \N {} +1819 620 2025-02-25 10:07:21.891596-08 2025-02-25 11:09:15.984274-08 leaderboard f A100 0.0030797906666666665 t \N \N \N {} +2415 817 2025-02-26 12:03:48.353023-08 2025-02-26 12:27:23.72571-08 benchmark f H100 \N f \N \N \N {} +1821 621 2025-02-25 09:54:58.090582-08 2025-02-25 11:30:23.907082-08 benchmark f A100 \N t \N \N \N {} +138 23 2025-02-23 11:54:58.067969-08 2025-02-23 12:08:04.435299-08 test f A100 \N t \N \N \N {} +147 23 2025-02-23 11:38:07.488367-08 2025-02-23 11:53:30.778579-08 test t T4 \N t \N \N \N {} +156 23 2025-02-23 10:30:22.436806-08 2025-02-23 10:14:59.268017-08 test f L4 \N t \N \N \N {} +1353 450 2025-02-25 02:01:19.511091-08 2025-02-25 01:50:23.554828-08 benchmark f H100 \N f \N \N \N {} +1822 621 2025-02-25 10:17:37.410428-08 2025-02-25 10:36:03.237286-08 leaderboard f A100 0.00011739522222222221 t \N \N \N {} +4809 1487 2025-03-02 13:45:28.37586-08 2025-03-02 12:40:42.351849-08 benchmark t L4 \N f \N \N \N {} +2226 725 2025-02-25 14:54:19.929968-08 2025-02-25 15:47:46.340338-08 benchmark f A100 \N t \N \N \N {} +2228 727 2025-02-25 20:35:49.008461-08 2025-02-25 21:27:48.607545-08 test f H100 \N f \N \N \N {} +2230 729 2025-02-25 21:17:44.501223-08 2025-02-25 20:34:01.289359-08 test f H100 \N f \N \N \N {} +2234 733 2025-02-25 21:21:37.259904-08 2025-02-25 22:02:04.25938-08 benchmark f H100 \N t \N \N \N {} +2235 734 2025-02-25 22:33:25.794507-08 2025-02-25 22:37:59.655817-08 test f H100 \N f \N \N \N {} +4810 1487 2025-03-02 14:06:07.69494-08 2025-03-02 14:05:29.275996-08 test f L4 \N t \N \N \N {} +2237 736 2025-02-25 23:19:21.988232-08 2025-02-25 23:45:39.636835-08 benchmark f A100 \N t \N \N \N {} +2238 737 2025-02-25 23:00:41.307289-08 2025-02-25 23:14:23.811188-08 benchmark f A100 \N t \N \N \N {} +2239 738 2025-02-25 23:48:44.965108-08 2025-02-25 23:42:33.619927-08 benchmark f A100 \N f \N \N \N {} +2240 739 2025-02-25 23:19:53.648595-08 2025-02-25 23:40:49.676196-08 benchmark f A100 \N f \N \N \N {} +2272 766 2025-02-26 04:44:38.752744-08 2025-02-26 03:37:57.159249-08 test f A100 \N t \N \N \N {} +2244 743 2025-02-26 00:35:45.969202-08 2025-02-26 00:32:30.806442-08 benchmark f A100 \N f \N \N \N {} +3587 1229 2025-02-28 14:32:10.95012-08 2025-02-28 14:44:22.261204-08 test f H100 \N t \N \N \N {} +2246 745 2025-02-26 00:37:33.931264-08 2025-02-26 00:40:26.964338-08 test f H100 \N f \N \N \N {} +2247 746 2025-02-26 01:55:19.252796-08 2025-02-26 00:36:07.797743-08 test f H100 \N f \N \N \N {} +2248 747 2025-02-26 01:15:55.844133-08 2025-02-26 01:53:31.828353-08 test f H100 \N f \N \N \N {} +2250 749 2025-02-26 00:25:17.403977-08 2025-02-26 00:24:56.829005-08 benchmark f H100 \N t \N \N \N {} +2256 755 2025-02-26 02:22:45.950588-08 2025-02-26 02:07:54.180517-08 benchmark f H100 \N f \N \N \N {} +2257 756 2025-02-26 03:00:14.310747-08 2025-02-26 02:46:46.937635-08 benchmark f H100 \N t \N \N \N {} +2258 757 2025-02-26 01:10:17.629744-08 2025-02-26 01:41:31.08645-08 benchmark f H100 \N t \N \N \N {} +2263 762 2025-02-26 02:18:34.242484-08 2025-02-26 02:55:39.021981-08 test t H100 \N t \N \N \N {} +2291 770 2025-02-26 04:07:08.486179-08 2025-02-26 03:34:41.389378-08 test f A100 \N t \N \N \N {} +2264 762 2025-02-26 02:58:11.327721-08 2025-02-26 03:46:33.348626-08 benchmark t H100 \N t \N \N \N {} +2266 762 2025-02-26 02:37:45.731387-08 2025-02-26 03:44:38.02645-08 test f H100 \N t \N \N \N {} +2267 762 2025-02-26 03:39:25.066238-08 2025-02-26 02:14:19.098169-08 benchmark f H100 \N t \N \N \N {} +2268 762 2025-02-26 02:23:03.127111-08 2025-02-26 02:36:35.552634-08 leaderboard f H100 0.0014381206666666667 t \N \N \N {} +2269 763 2025-02-26 02:45:57.978469-08 2025-02-26 02:49:10.932866-08 benchmark f H100 \N f \N \N \N {} +2270 765 2025-02-26 04:02:02.067979-08 2025-02-26 03:15:12.820422-08 test f A100 \N f \N \N \N {} +2271 765 2025-02-26 03:46:07.247621-08 2025-02-26 03:33:23.013529-08 test t A100 \N f \N \N \N {} +2273 766 2025-02-26 04:26:50.535168-08 2025-02-26 04:37:20.190686-08 benchmark f A100 \N t \N \N \N {} +2276 766 2025-02-26 03:57:01.450513-08 2025-02-26 04:19:48.460438-08 benchmark t A100 \N t \N \N \N {} +2277 766 2025-02-26 04:23:34.32179-08 2025-02-26 04:37:21.779651-08 leaderboard t A100 0.0008035725 t \N \N \N {} +2278 767 2025-02-26 04:59:10.716734-08 2025-02-26 04:56:31.615231-08 test f A100 \N t \N \N \N {} +2279 767 2025-02-26 04:04:53.620256-08 2025-02-26 03:40:43.285038-08 benchmark f A100 \N t \N \N \N {} +2811 981 2025-02-26 23:20:55.588139-08 2025-02-26 23:02:56.11656-08 test t H100 \N t \N \N \N {} +3820 1285 2025-03-01 02:46:38.245389-08 2025-03-01 02:36:19.519197-08 test t A100 \N t \N \N \N {} +2283 767 2025-02-26 03:48:47.510918-08 2025-02-26 04:12:24.590357-08 leaderboard t A100 0.0008112885 t \N \N \N {} +2285 769 2025-02-26 03:32:55.537334-08 2025-02-26 04:57:18.17064-08 test t A100 \N t \N \N \N {} +2286 769 2025-02-26 03:54:10.03714-08 2025-02-26 03:53:44.323045-08 benchmark t A100 \N t \N \N \N {} +2287 769 2025-02-26 04:13:17.84753-08 2025-02-26 04:35:13.277401-08 leaderboard t A100 0.0008150356 t \N \N \N {} +2288 769 2025-02-26 03:53:13.370089-08 2025-02-26 03:34:52.714707-08 test f A100 \N t \N \N \N {} +2289 769 2025-02-26 04:13:58.330006-08 2025-02-26 03:53:06.926282-08 benchmark f A100 \N t \N \N \N {} +2290 769 2025-02-26 04:11:02.675153-08 2025-02-26 04:13:02.665399-08 leaderboard f A100 0.0008101136666666666 t \N \N \N {} +2292 770 2025-02-26 03:47:42.434385-08 2025-02-26 03:50:45.275328-08 benchmark f A100 \N t \N \N \N {} +2293 770 2025-02-26 03:53:00.59453-08 2025-02-26 04:00:41.911017-08 leaderboard f A100 0.0008105805 t \N \N \N {} +2294 770 2025-02-26 04:01:53.351812-08 2025-02-26 03:50:07.775814-08 test t A100 \N t \N \N \N {} +2295 770 2025-02-26 03:11:56.96532-08 2025-02-26 04:01:17.104616-08 benchmark t A100 \N t \N \N \N {} +2296 770 2025-02-26 03:30:49.241367-08 2025-02-26 04:03:05.950494-08 leaderboard t A100 0.00080491525 t \N \N \N {} +2297 771 2025-02-26 04:33:09.624984-08 2025-02-26 04:02:12.069329-08 benchmark f A100 \N t \N \N \N {} +2298 772 2025-02-26 04:59:38.93896-08 2025-02-26 04:38:25.02308-08 benchmark f A100 \N t \N \N \N {} +2299 773 2025-02-26 04:20:34.328694-08 2025-02-26 03:54:56.809911-08 benchmark f A100 \N t \N \N \N {} +2304 774 2025-02-26 03:57:56.984318-08 2025-02-26 04:38:02.103282-08 benchmark t A100 \N t \N \N \N {} +2305 774 2025-02-26 03:44:38.635658-08 2025-02-26 05:02:38.874989-08 leaderboard t A100 0.00311144 t \N \N \N {} +2306 775 2025-02-26 04:12:31.181055-08 2025-02-26 04:59:52.882918-08 benchmark f A100 \N t \N \N \N {} +2307 776 2025-02-26 04:20:56.019237-08 2025-02-26 03:52:11.190759-08 benchmark f A100 \N t \N \N \N {} +2329 784 2025-02-26 07:42:05.262849-08 2025-02-26 07:36:19.042812-08 test f A100 \N f \N \N \N {} +2311 778 2025-02-26 03:30:09.435566-08 2025-02-26 04:06:02.956045-08 leaderboard t A100 0.003092609 t \N \N \N {} +2312 778 2025-02-26 05:23:38.077187-08 2025-02-26 03:53:55.424873-08 test f A100 \N t \N \N \N {} +2326 783 2025-02-26 08:05:37.792029-08 2025-02-26 08:12:28.335942-08 test f A100 \N t \N \N \N {} +2327 783 2025-02-26 08:39:42.131724-08 2025-02-26 08:09:32.991225-08 benchmark f A100 \N t \N \N \N {} +2328 783 2025-02-26 08:30:37.407938-08 2025-02-26 07:35:49.357033-08 leaderboard f A100 0.003227151 t \N \N \N {} +2331 785 2025-02-26 07:27:47.482468-08 2025-02-26 08:17:53.94964-08 test f A100 \N t \N \N \N {} +2332 785 2025-02-26 07:37:11.087311-08 2025-02-26 08:15:07.866294-08 benchmark f A100 \N t \N \N \N {} +2333 785 2025-02-26 07:59:24.368247-08 2025-02-26 08:20:21.480809-08 leaderboard f A100 0.004367405666666667 t \N \N \N {} +2337 786 2025-02-26 08:22:04.365539-08 2025-02-26 08:24:47.375813-08 test f A100 \N f \N \N \N {} +2338 786 2025-02-26 08:35:45.345849-08 2025-02-26 08:14:30.033798-08 test t A100 \N f \N \N \N {} +2339 787 2025-02-26 07:22:18.968697-08 2025-02-26 08:22:46.178651-08 test t A100 \N f \N \N \N {} +2346 789 2025-02-26 07:08:50.231065-08 2025-02-26 08:49:16.966065-08 test f A100 \N t \N \N \N {} +3844 1289 2025-03-01 02:58:23.301247-08 2025-03-01 03:47:22.156604-08 test t A100 \N t \N \N \N {} +2344 789 2025-02-26 08:30:43.202837-08 2025-02-26 08:53:02.953547-08 benchmark t A100 \N t \N \N \N {} +2345 789 2025-02-26 08:54:31.507554-08 2025-02-26 08:18:29.611102-08 leaderboard t A100 0.00322034475 t \N \N \N {} +2349 790 2025-02-26 07:27:15.296498-08 2025-02-26 07:45:31.770943-08 test t H100 \N t \N \N \N {} +2350 790 2025-02-26 07:27:09.22196-08 2025-02-26 08:40:58.669769-08 benchmark t H100 \N t \N \N \N {} +2351 790 2025-02-26 07:38:50.506814-08 2025-02-26 07:39:57.059049-08 leaderboard t H100 0.0015428555 t \N \N \N {} +2352 790 2025-02-26 07:14:10.99536-08 2025-02-26 08:11:26.301088-08 test f H100 \N t \N \N \N {} +2353 790 2025-02-26 07:55:02.506428-08 2025-02-26 08:30:47.285542-08 benchmark f H100 \N t \N \N \N {} +2354 790 2025-02-26 08:58:21.825582-08 2025-02-26 08:36:23.311282-08 leaderboard f H100 0.00153994575 t \N \N \N {} +2362 794 2025-02-26 09:16:23.731727-08 2025-02-26 09:45:35.701403-08 test f A100 \N f \N \N \N {} +2363 794 2025-02-26 10:05:30.642869-08 2025-02-26 10:06:11.654779-08 test f H100 \N f \N \N \N {} +2364 794 2025-02-26 08:54:28.852181-08 2025-02-26 10:36:05.947891-08 test f L4 \N f \N \N \N {} +2369 796 2025-02-26 09:04:59.633587-08 2025-02-26 08:58:58.997794-08 test f H100 \N f \N \N \N {} +3269 1143 2025-02-28 05:49:04.471481-08 2025-02-28 05:33:52.950669-08 test t A100 \N t \N \N \N {} +3272 1143 2025-02-28 07:24:05.395179-08 2025-02-28 06:21:30.549804-08 test f A100 \N t \N \N \N {} +2389 806 2025-02-26 11:59:47.526877-08 2025-02-26 13:27:08.118395-08 benchmark f H100 \N t \N \N \N {} +131 21 2025-02-23 10:54:12.413903-08 2025-02-23 11:03:24.456859-08 benchmark f H100 \N t \N \N \N {} +132 22 2025-02-23 11:18:52.406962-08 2025-02-23 11:21:40.34417-08 test f H100 \N t \N \N \N {} +133 22 2025-02-23 11:10:07.454357-08 2025-02-23 11:27:41.266157-08 benchmark f H100 \N t \N \N \N {} +134 22 2025-02-23 11:11:08.332204-08 2025-02-23 09:56:47.514659-08 leaderboard f H100 0.007636646 t \N \N \N {} +135 22 2025-02-23 11:15:29.967609-08 2025-02-23 10:36:22.679826-08 test t H100 \N t \N \N \N {} +136 22 2025-02-23 10:30:49.187464-08 2025-02-23 10:54:58.140967-08 benchmark t H100 \N t \N \N \N {} +137 22 2025-02-23 10:01:17.414414-08 2025-02-23 10:57:17.950859-08 leaderboard t H100 0.007676687 t \N \N \N {} +6139 1882 2025-03-11 10:43:46.751248-07 2025-03-11 11:39:34.207773-07 test f T4 \N f \N \N \N {} +139 23 2025-02-23 11:00:36.681319-08 2025-02-23 11:20:38.884145-08 benchmark f A100 \N t \N \N \N {} +140 23 2025-02-23 11:47:46.303484-08 2025-02-23 10:27:15.204326-08 leaderboard f A100 0.010159300357142857 t \N \N \N {} +141 23 2025-02-23 11:55:55.792415-08 2025-02-23 11:05:18.699548-08 test t A100 \N t \N \N \N {} +142 23 2025-02-23 10:45:14.3074-08 2025-02-23 10:59:33.198898-08 benchmark t A100 \N t \N \N \N {} +143 23 2025-02-23 11:17:34.462052-08 2025-02-23 10:26:24.612757-08 leaderboard t A100 0.010135129449999999 t \N \N \N {} +144 23 2025-02-23 10:09:53.746-08 2025-02-23 11:09:01.710035-08 test f H100 \N t \N \N \N {} +145 23 2025-02-23 11:23:51.944416-08 2025-02-23 11:35:42.525714-08 benchmark f H100 \N t \N \N \N {} +146 23 2025-02-23 11:15:27.676586-08 2025-02-23 12:03:10.988718-08 leaderboard f H100 0.006125231666666667 t \N \N \N {} +148 23 2025-02-23 11:13:16.041704-08 2025-02-23 11:50:47.350172-08 benchmark t T4 \N t \N \N \N {} +149 23 2025-02-23 11:28:20.433165-08 2025-02-23 10:28:06.298908-08 leaderboard t T4 0.048027397 t \N \N \N {} +150 23 2025-02-23 11:18:42.410803-08 2025-02-23 10:31:01.372052-08 test t H100 \N t \N \N \N {} +151 23 2025-02-23 11:58:30.811996-08 2025-02-23 11:21:35.60515-08 benchmark t H100 \N t \N \N \N {} +152 23 2025-02-23 11:11:00.271834-08 2025-02-23 11:00:53.563642-08 leaderboard t H100 0.006109236666666667 t \N \N \N {} +153 23 2025-02-23 10:26:41.850092-08 2025-02-23 10:39:34.799916-08 test f T4 \N t \N \N \N {} +2390 807 2025-02-26 12:40:07.882315-08 2025-02-26 13:29:00.67918-08 benchmark f A100 \N t \N \N \N {} +2391 808 2025-02-26 12:44:03.203064-08 2025-02-26 12:20:13.654261-08 benchmark f H100 \N t \N \N \N {} +2392 809 2025-02-26 11:46:24.444642-08 2025-02-26 13:37:16.547993-08 benchmark f H100 \N t \N \N \N {} +2393 811 2025-02-26 11:51:23.557392-08 2025-02-26 12:37:24.914401-08 benchmark f H100 \N f \N \N \N {} +4811 1487 2025-03-02 13:46:45.410776-08 2025-03-02 13:08:51.056877-08 benchmark f L4 \N f \N \N \N {} +3847 1290 2025-03-01 04:03:32.270689-08 2025-03-01 02:32:19.138733-08 test f A100 \N t \N \N \N {} +2394 810 2025-02-26 13:25:37.482593-08 2025-02-26 12:59:08.470332-08 benchmark f H100 \N f \N \N \N {} +2395 812 2025-02-26 13:33:28.597174-08 2025-02-26 12:57:21.570896-08 test f H100 \N t \N \N \N {} +2396 812 2025-02-26 12:34:20.532251-08 2025-02-26 12:31:27.053945-08 benchmark f H100 \N t \N \N \N {} +2397 812 2025-02-26 12:19:54.717591-08 2025-02-26 12:10:14.47041-08 leaderboard f H100 0.0032161647200000002 t \N \N \N {} +2398 812 2025-02-26 13:18:48.216215-08 2025-02-26 11:52:47.39111-08 test f A100 \N t \N \N \N {} +2401 812 2025-02-26 12:08:17.632371-08 2025-02-26 12:08:36.634538-08 test t A100 \N t \N \N \N {} +2402 812 2025-02-26 13:29:27.61967-08 2025-02-26 13:25:55.587367-08 benchmark t A100 \N t \N \N \N {} +2403 812 2025-02-26 13:21:36.142662-08 2025-02-26 12:50:20.677716-08 leaderboard t A100 0.00698412748 t \N \N \N {} +2404 812 2025-02-26 13:13:30.239955-08 2025-02-26 12:39:13.840154-08 test f L4 \N f \N \N \N {} +2405 812 2025-02-26 12:25:57.063676-08 2025-02-26 12:24:58.010138-08 test t H100 \N t \N \N \N {} +2410 812 2025-02-26 12:20:36.785307-08 2025-02-26 12:48:06.635821-08 test f T4 \N f \N \N \N {} +2435 839 2025-02-26 14:18:21.87914-08 2025-02-26 14:18:15.815334-08 benchmark t H100 \N t \N \N \N {} +2436 839 2025-02-26 14:10:30.590633-08 2025-02-26 14:11:00.415518-08 leaderboard t H100 0.0014024583333333332 t \N \N \N {} +2441 845 2025-02-26 14:15:04.422652-08 2025-02-26 14:13:19.625716-08 test t L4 \N t \N \N \N {} +2447 852 2025-02-26 14:16:39.200008-08 2025-02-26 13:25:34.941047-08 benchmark f A100 \N t \N \N \N {} +2442 845 2025-02-26 13:50:40.358584-08 2025-02-26 13:07:51.38911-08 benchmark t L4 \N t \N \N \N {} +2451 856 2025-02-26 12:43:06.254876-08 2025-02-26 13:32:18.217747-08 benchmark f A100 \N t \N \N \N {} +2452 858 2025-02-26 13:00:26.844507-08 2025-02-26 14:38:31.646993-08 test f T4 \N f \N \N \N {} +2453 859 2025-02-26 14:36:40.012139-08 2025-02-26 13:10:56.467322-08 benchmark f H100 \N t \N \N \N {} +2455 860 2025-02-26 13:23:16.833722-08 2025-02-26 14:41:34.259251-08 benchmark f A100 \N t \N \N \N {} +2462 864 2025-02-26 13:11:46.57133-08 2025-02-26 14:39:28.851851-08 test f T4 \N f \N \N \N {} +2463 865 2025-02-26 13:11:01.636695-08 2025-02-26 14:32:14.927023-08 test f A100 \N t \N \N \N {} +2464 865 2025-02-26 13:30:02.44975-08 2025-02-26 14:19:35.875255-08 benchmark f A100 \N t \N \N \N {} +2465 865 2025-02-26 13:09:06.850344-08 2025-02-26 14:04:10.639691-08 leaderboard f A100 0.0068088155999999995 t \N \N \N {} +2466 865 2025-02-26 14:08:58.695846-08 2025-02-26 12:57:46.38922-08 test t A100 \N t \N \N \N {} +3385 1166 2025-02-28 10:20:27.28906-08 2025-02-28 10:10:11.161724-08 leaderboard t L4 0.017129881333333333 t \N \N \N {} +2481 870 2025-02-26 13:59:09.266137-08 2025-02-26 13:07:02.894219-08 leaderboard f H100 0.001470785 t \N \N \N {} +2820 983 2025-02-26 23:04:13.026351-08 2025-02-26 23:16:23.959588-08 test f T4 \N t \N \N \N {} +2508 872 2025-02-26 13:21:09.133466-08 2025-02-26 13:53:51.09567-08 benchmark f A100 \N t \N \N \N {} +2509 872 2025-02-26 14:26:03.179438-08 2025-02-26 13:48:36.711846-08 leaderboard f A100 0.003223094 t \N \N \N {} +3286 1147 2025-02-28 07:23:54.538307-08 2025-02-28 06:54:57.696014-08 benchmark f L4 \N t \N \N \N {} +2511 872 2025-02-26 13:29:54.390732-08 2025-02-26 14:56:13.502248-08 benchmark f H100 \N t \N \N \N {} +2512 872 2025-02-26 14:00:24.384-08 2025-02-26 14:52:47.576219-08 leaderboard f H100 0.0014829895714285714 t \N \N \N {} +2513 872 2025-02-26 14:08:21.032655-08 2025-02-26 14:48:45.337774-08 test f T4 \N t \N \N \N {} +2514 872 2025-02-26 13:43:03.545363-08 2025-02-26 13:27:16.423676-08 benchmark f T4 \N t \N \N \N {} +2515 872 2025-02-26 14:53:04.043695-08 2025-02-26 14:31:15.017571-08 leaderboard f T4 0.016977648666666668 t \N \N \N {} +3297 1148 2025-02-28 05:56:14.732913-08 2025-02-28 06:35:38.237057-08 benchmark t T4 \N t \N \N \N {} +2516 872 2025-02-26 13:59:35.326154-08 2025-02-26 14:28:54.006791-08 test t T4 \N t \N \N \N {} +2517 872 2025-02-26 14:10:22.48064-08 2025-02-26 13:05:44.349918-08 benchmark t T4 \N t \N \N \N {} +2518 872 2025-02-26 13:38:47.072409-08 2025-02-26 13:18:53.541371-08 leaderboard t T4 0.016951106 t \N \N \N {} +2519 872 2025-02-26 14:41:30.759683-08 2025-02-26 13:51:40.998977-08 test f L4 \N t \N \N \N {} +2520 872 2025-02-26 13:15:09.29921-08 2025-02-26 13:07:54.264512-08 benchmark f L4 \N t \N \N \N {} +3181 1120 2025-02-28 02:42:01.210008-08 2025-02-28 02:43:08.325982-08 test t A100 \N t \N \N \N {} +154 23 2025-02-23 11:57:05.139267-08 2025-02-23 11:09:15.68083-08 benchmark f T4 \N t \N \N \N {} +180 27 2025-02-23 10:28:14.91215-08 2025-02-23 11:48:21.706378-08 test t T4 \N t \N \N \N {} +2521 872 2025-02-26 14:43:07.037109-08 2025-02-26 14:45:02.826917-08 leaderboard f L4 0.017294490333333332 t \N \N \N {} +2522 872 2025-02-26 14:50:44.169022-08 2025-02-26 14:42:28.113826-08 test t L4 \N t \N \N \N {} +2523 872 2025-02-26 13:56:52.894596-08 2025-02-26 13:28:48.739095-08 benchmark t L4 \N t \N \N \N {} +2527 875 2025-02-26 14:23:38.227767-08 2025-02-26 13:30:18.365385-08 test t L4 \N t \N \N \N {} +3589 1229 2025-02-28 13:21:19.11789-08 2025-02-28 14:31:58.395368-08 leaderboard f H100 0.0011892495 t \N \N \N {} +2532 875 2025-02-26 14:59:13.072405-08 2025-02-26 13:27:06.641704-08 leaderboard f H100 0.001465262 t \N \N \N {} +2533 875 2025-02-26 14:47:12.530795-08 2025-02-26 13:13:21.249202-08 test f A100 \N t \N \N \N {} +2534 875 2025-02-26 13:48:48.551741-08 2025-02-26 13:28:31.146854-08 benchmark f A100 \N t \N \N \N {} +2535 875 2025-02-26 14:39:27.031045-08 2025-02-26 13:25:27.404117-08 leaderboard f A100 0.0032578983333333335 t \N \N \N {} +3312 1150 2025-02-28 07:23:23.622707-08 2025-02-28 06:09:54.030373-08 benchmark f H100 \N t \N \N \N {} +2537 875 2025-02-26 14:32:13.549304-08 2025-02-26 14:39:07.302029-08 benchmark t A100 \N t \N \N \N {} +2548 875 2025-02-26 14:56:04.001163-08 2025-02-26 13:42:23.904365-08 test f L4 \N t \N \N \N {} +2549 875 2025-02-26 14:18:16.659538-08 2025-02-26 13:23:18.958744-08 benchmark f L4 \N t \N \N \N {} +2550 875 2025-02-26 14:22:09.462839-08 2025-02-26 14:54:20.343262-08 leaderboard f L4 0.01742031 t \N \N \N {} +2551 877 2025-02-26 13:57:57.39587-08 2025-02-26 13:12:36.270032-08 test f L4 \N t \N \N \N {} +2573 877 2025-02-26 13:16:45.52631-08 2025-02-26 14:52:12.270412-08 test t T4 \N t \N \N \N {} +3399 1172 2025-02-28 10:30:05.610744-08 2025-02-28 09:16:34.458793-08 test f L4 \N f \N \N \N {} +2553 877 2025-02-26 13:10:29.354581-08 2025-02-26 14:37:38.257685-08 leaderboard f L4 0.01741081066666667 t \N \N \N {} +2554 877 2025-02-26 13:58:10.825604-08 2025-02-26 14:00:37.261867-08 test t L4 \N t \N \N \N {} +2555 877 2025-02-26 13:48:17.730063-08 2025-02-26 14:50:07.298178-08 benchmark t L4 \N t \N \N \N {} +2556 877 2025-02-26 13:50:16.824546-08 2025-02-26 14:44:02.236307-08 leaderboard t L4 0.017350969 t \N \N \N {} +3353 1157 2025-02-28 07:00:17.816299-08 2025-02-28 08:05:40.861-08 test t A100 \N t \N \N \N {} +2559 877 2025-02-26 14:12:21.70114-08 2025-02-26 14:49:53.732127-08 benchmark f A100 \N t \N \N \N {} +2560 877 2025-02-26 13:13:01.61476-08 2025-02-26 14:52:53.675129-08 leaderboard f A100 0.00324053 t \N \N \N {} +2561 877 2025-02-26 14:17:12.652893-08 2025-02-26 14:33:51.585787-08 test f H100 \N t \N \N \N {} +3356 1157 2025-02-28 07:54:22.893197-08 2025-02-28 07:34:43.645784-08 test f A100 \N t \N \N \N {} +2563 877 2025-02-26 13:56:06.528311-08 2025-02-26 15:06:14.803986-08 leaderboard f H100 0.001457541 t \N \N \N {} +2564 877 2025-02-26 14:47:01.068968-08 2025-02-26 14:04:16.625491-08 test t H100 \N t \N \N \N {} +2565 877 2025-02-26 13:58:40.178869-08 2025-02-26 15:00:37.290202-08 benchmark t H100 \N t \N \N \N {} +2566 877 2025-02-26 14:22:11.994956-08 2025-02-26 14:57:41.541904-08 leaderboard t H100 0.00144549375 t \N \N \N {} +3359 1158 2025-02-28 06:54:49.380912-08 2025-02-28 07:18:43.60823-08 test f T4 \N f \N \N \N {} +3859 1292 2025-03-01 03:27:40.255676-08 2025-03-01 02:36:03.541477-08 test f A100 \N t \N \N \N {} +2568 877 2025-02-26 14:12:07.205591-08 2025-02-26 13:51:47.845088-08 benchmark t A100 \N t \N \N \N {} +2569 877 2025-02-26 13:57:24.028157-08 2025-02-26 14:47:07.033256-08 leaderboard t A100 0.0032292653333333334 t \N \N \N {} +2570 877 2025-02-26 14:16:41.930867-08 2025-02-26 13:09:12.487972-08 test f T4 \N t \N \N \N {} +2571 877 2025-02-26 14:36:17.713292-08 2025-02-26 14:26:39.900745-08 benchmark f T4 \N t \N \N \N {} +2572 877 2025-02-26 15:02:18.460758-08 2025-02-26 14:58:57.369637-08 leaderboard f T4 0.01811966666666667 t \N \N \N {} +3406 1174 2025-02-28 09:14:03.047294-08 2025-02-28 09:16:54.531135-08 leaderboard t L4 0.017114292333333333 t \N \N \N {} +2575 877 2025-02-26 13:12:49.74724-08 2025-02-26 14:37:18.913817-08 leaderboard t T4 0.01813283333333333 t \N \N \N {} +2576 878 2025-02-26 13:16:27.296503-08 2025-02-26 14:27:29.207894-08 benchmark f T4 \N f \N \N \N {} +2577 879 2025-02-26 14:34:25.671098-08 2025-02-26 13:45:30.958405-08 benchmark f T4 \N t \N \N \N {} +2578 881 2025-02-26 13:39:45.596644-08 2025-02-26 14:01:02.933021-08 test f T4 \N f \N \N \N {} +2579 881 2025-02-26 14:04:26.768148-08 2025-02-26 13:45:26.952914-08 test f L4 \N f \N \N \N {} +3408 1175 2025-02-28 10:29:09.88226-08 2025-02-28 09:14:06.803313-08 test f L4 \N f \N \N \N {} +3862 1292 2025-03-01 03:25:13.486827-08 2025-03-01 02:53:16.099437-08 test t A100 \N t \N \N \N {} +2581 882 2025-02-26 13:33:10.645023-08 2025-02-26 13:55:42.912415-08 test f L4 \N t \N \N \N {} +2582 883 2025-02-26 14:19:49.686171-08 2025-02-26 14:38:27.107218-08 benchmark f L4 \N t \N \N \N {} +2583 883 2025-02-26 13:51:19.440545-08 2025-02-26 14:09:42.760292-08 benchmark f H100 \N t \N \N \N {} +2584 883 2025-02-26 14:39:09.643525-08 2025-02-26 13:31:21.985198-08 benchmark f A100 \N t \N \N \N {} +3418 1178 2025-02-28 10:35:09.967262-08 2025-02-28 11:06:54.18143-08 test f L4 \N f \N \N \N {} +3865 1293 2025-03-01 02:35:22.489765-08 2025-03-01 04:14:08.608387-08 test f A100 \N t \N \N \N {} +2587 884 2025-02-26 13:22:21.621679-08 2025-02-26 13:44:27.093032-08 benchmark f A100 \N t \N \N \N {} +2588 884 2025-02-26 14:52:31.919133-08 2025-02-26 13:38:05.904058-08 leaderboard f A100 0.003262141 t \N \N \N {} +2589 885 2025-02-26 13:31:31.720937-08 2025-02-26 13:36:09.135751-08 test f H100 \N t \N \N \N {} +2607 884 2025-02-26 15:03:02.758478-08 2025-02-26 14:21:28.592486-08 test t T4 \N t \N \N \N {} +3419 1178 2025-02-28 09:14:52.41289-08 2025-02-28 09:52:57.968186-08 test t L4 \N f \N \N \N {} +2592 884 2025-02-26 14:47:15.785668-08 2025-02-26 14:38:49.447224-08 test f L4 \N t \N \N \N {} +2062 700 2025-02-25 13:08:14.95001-08 2025-02-25 15:03:46.466768-08 test f A100 \N f \N \N \N {} +4223 1394 2025-03-01 14:57:35.411891-08 2025-03-01 15:35:59.507097-08 benchmark t A100 \N t \N \N \N {} +4247 1402 2025-03-01 17:04:55.972494-08 2025-03-01 17:47:24.376431-08 test t H100 \N t \N \N \N {} +4228 1395 2025-03-01 17:18:07.852776-08 2025-03-01 15:48:31.55626-08 benchmark f A100 \N t \N \N \N {} +4229 1396 2025-03-01 16:29:41.366025-08 2025-03-01 17:24:30.840608-08 test f H100 \N t \N \N \N {} +4717 1477 2025-03-02 08:12:18.55485-08 2025-03-02 06:22:18.597758-08 test f A100 \N t \N \N \N {} +4231 1398 2025-03-01 17:25:09.232586-08 2025-03-01 17:18:31.325671-08 test t A100 \N t \N \N \N {} +4232 1398 2025-03-01 16:28:07.719269-08 2025-03-01 15:58:31.010676-08 benchmark t A100 \N t \N \N \N {} +4233 1398 2025-03-01 15:43:44.691938-08 2025-03-01 17:19:39.809228-08 leaderboard t A100 0.0031178923333333333 t \N \N \N {} +4234 1398 2025-03-01 16:39:23.354655-08 2025-03-01 15:51:38.701412-08 test f H100 \N t \N \N \N {} +4253 1403 2025-03-01 17:26:34.106395-08 2025-03-01 17:42:47.155658-08 test t H100 \N t \N \N \N {} +4236 1398 2025-03-01 16:06:23.791952-08 2025-03-01 17:26:44.856408-08 leaderboard f H100 0.0014146966666666668 t \N \N \N {} +4237 1398 2025-03-01 15:36:12.31163-08 2025-03-01 15:33:41.234566-08 test t H100 \N t \N \N \N {} +4238 1398 2025-03-01 16:56:17.454711-08 2025-03-01 17:19:38.557978-08 benchmark t H100 \N t \N \N \N {} +4239 1398 2025-03-01 16:47:15.049127-08 2025-03-01 16:11:47.093143-08 leaderboard t H100 0.0014016626666666667 t \N \N \N {} +4378 1432 2025-03-01 22:51:39.742514-08 2025-03-01 22:23:37.282462-08 benchmark f T4 \N t \N \N \N {} +4242 1399 2025-03-01 16:22:07.45004-08 2025-03-01 16:01:06.543886-08 benchmark f A100 \N f \N \N \N {} +4243 1400 2025-03-01 17:07:07.130708-08 2025-03-01 16:24:06.325488-08 benchmark f H100 \N t \N \N \N {} +4244 1400 2025-03-01 17:29:39.977791-08 2025-03-01 17:02:08.939787-08 benchmark f A100 \N f \N \N \N {} +4346 1425 2025-03-01 18:21:27.980051-08 2025-03-01 19:51:56.286091-08 benchmark t L4 \N t \N \N \N {} +4347 1425 2025-03-01 18:37:42.52065-08 2025-03-01 19:22:48.263204-08 leaderboard t L4 0.01815886933333333 t \N \N \N {} +4348 1425 2025-03-01 18:57:46.285303-08 2025-03-01 20:03:56.603557-08 test f A100 \N t \N \N \N {} +4349 1425 2025-03-01 19:34:06.836256-08 2025-03-01 19:17:45.64278-08 benchmark f A100 \N t \N \N \N {} +4350 1425 2025-03-01 19:38:27.662128-08 2025-03-01 18:55:37.052703-08 leaderboard f A100 0.0033818667999999997 t \N \N \N {} +4352 1425 2025-03-01 18:49:23.803275-08 2025-03-01 19:23:11.101795-08 benchmark t A100 \N t \N \N \N {} +4357 1425 2025-03-01 19:41:41.606451-08 2025-03-01 18:23:09.857404-08 test t H100 \N t \N \N \N {} +4358 1425 2025-03-01 20:09:08.19207-08 2025-03-01 19:19:21.809996-08 benchmark t H100 \N t \N \N \N {} +4359 1425 2025-03-01 19:59:58.903988-08 2025-03-01 19:30:42.092627-08 leaderboard t H100 0.0015028730833333333 t \N \N \N {} +4361 1425 2025-03-01 18:31:13.881994-08 2025-03-01 18:26:52.74436-08 benchmark f T4 \N t \N \N \N {} +4362 1425 2025-03-01 20:12:38.573176-08 2025-03-01 19:33:01.456733-08 leaderboard f T4 0.01936384925 t \N \N \N {} +4363 1425 2025-03-01 19:42:14.310793-08 2025-03-01 18:41:20.723626-08 test f L4 \N t \N \N \N {} +4389 1440 2025-03-02 00:03:48.502169-08 2025-03-01 23:51:41.439127-08 test f A100 \N f \N \N \N {} +4390 1440 2025-03-02 00:04:14.366803-08 2025-03-01 22:49:12.463788-08 test f L4 \N f \N \N \N {} +4391 1440 2025-03-02 00:19:06.804988-08 2025-03-01 22:28:37.519544-08 test t T4 \N f \N \N \N {} +155 23 2025-02-23 11:57:27.550237-08 2025-02-23 11:56:29.734882-08 leaderboard f T4 0.048176454 t \N \N \N {} +157 23 2025-02-23 11:12:21.468647-08 2025-02-23 10:43:21.975416-08 benchmark f L4 \N t \N \N \N {} +4392 1440 2025-03-01 22:48:12.01349-08 2025-03-01 23:58:44.155125-08 test f T4 \N f \N \N \N {} +4877 1500 2025-03-03 10:04:01.91414-08 2025-03-03 08:30:53.107105-08 test f A100 \N t \N \N \N {} +4394 1440 2025-03-01 22:21:22.693848-08 2025-03-01 22:51:41.663873-08 test t H100 \N f \N \N \N {} +4395 1441 2025-03-01 23:58:46.89894-08 2025-03-01 23:53:44.591271-08 test f H100 \N f \N \N \N {} +4396 1441 2025-03-01 22:28:23.815345-08 2025-03-01 23:22:03.532582-08 test t L4 \N f \N \N \N {} +4397 1441 2025-03-01 23:53:44.616031-08 2025-03-01 23:38:33.596759-08 test t A100 \N f \N \N \N {} +4884 1505 2025-03-03 12:56:34.606368-08 2025-03-03 12:53:18.783025-08 test t A100 \N f \N \N \N {} +4399 1441 2025-03-01 23:03:04.456378-08 2025-03-02 00:06:04.885856-08 test f T4 \N f \N \N \N {} +4400 1441 2025-03-01 23:24:40.888023-08 2025-03-01 22:59:35.615921-08 test t H100 \N f \N \N \N {} +4401 1441 2025-03-02 00:02:25.303815-08 2025-03-01 22:57:40.957396-08 test t T4 \N f \N \N \N {} +4402 1441 2025-03-01 22:31:27.972284-08 2025-03-02 00:14:26.267738-08 test f L4 \N f \N \N \N {} +5107 1565 2025-03-04 18:00:00.576218-08 2025-03-04 18:32:07.019701-08 test f A100 \N t \N \N \N {} +4411 1443 2025-03-01 23:33:39.745676-08 2025-03-02 00:11:02.528027-08 test f L4 \N t \N \N \N {} +4412 1443 2025-03-02 01:02:06.237344-08 2025-03-01 23:53:38.020406-08 benchmark f L4 \N t \N \N \N {} +4413 1443 2025-03-02 00:45:38.730106-08 2025-03-02 00:36:30.242811-08 leaderboard f L4 0.009132959666666666 t \N \N \N {} +4418 1443 2025-03-01 23:15:31.653046-08 2025-03-02 00:20:24.722124-08 benchmark t A100 \N t \N \N \N {} +4419 1443 2025-03-01 23:11:05.373713-08 2025-03-02 00:24:01.197231-08 leaderboard t A100 0.002639421 t \N \N \N {} +4420 1443 2025-03-02 00:51:28.235424-08 2025-03-01 23:57:03.643851-08 test f A100 \N t \N \N \N {} +4421 1443 2025-03-02 00:14:40.806085-08 2025-03-02 00:04:05.289386-08 benchmark f A100 \N t \N \N \N {} +4422 1443 2025-03-01 23:14:33.6022-08 2025-03-02 00:33:33.808058-08 leaderboard f A100 0.0022384285652173913 t \N \N \N {} +4426 1443 2025-03-02 00:44:05.280092-08 2025-03-02 00:15:14.019635-08 test t H100 \N t \N \N \N {} +4427 1443 2025-03-01 23:59:15.284401-08 2025-03-02 00:58:48.422971-08 benchmark t H100 \N t \N \N \N {} +4428 1443 2025-03-02 00:11:17.701257-08 2025-03-01 23:26:04.825333-08 leaderboard t H100 0.00150569175 t \N \N \N {} +4429 1443 2025-03-01 23:25:46.014191-08 2025-03-02 00:05:37.324677-08 test f T4 \N t \N \N \N {} +2776 965 2025-02-26 21:06:31.43475-08 2025-02-26 19:53:09.101962-08 test t A100 \N t \N \N \N {} +4482 1445 2025-03-01 23:50:49.419996-08 2025-03-02 00:57:24.575893-08 leaderboard t L4 0.009311355333333333 t \N \N \N {} +4483 1446 2025-03-01 23:44:24.497614-08 2025-03-02 00:35:56.595882-08 test f A100 \N t \N \N \N {} +4484 1447 2025-03-01 23:27:17.550566-08 2025-03-02 01:12:31.419414-08 test f H100 \N t \N \N \N {} +4485 1447 2025-03-02 00:26:50.566555-08 2025-03-02 00:25:59.636108-08 benchmark f H100 \N f \N \N \N {} +4487 1447 2025-03-01 23:39:57.57287-08 2025-03-02 00:18:06.18322-08 benchmark t A100 \N f \N \N \N {} +2045 697 2025-02-25 13:02:11.887058-08 2025-02-25 13:35:15.729017-08 test f T4 \N f \N \N \N {} +2047 698 2025-02-25 14:51:14.379308-08 2025-02-25 14:49:23.452588-08 test f T4 \N t \N \N \N {} +2791 974 2025-02-26 20:28:12.007924-08 2025-02-26 21:26:15.641402-08 leaderboard t A100 0.003154194 t \N \N \N {} +3509 1201 2025-02-28 11:18:27.367438-08 2025-02-28 11:48:52.303198-08 test f A100 \N t \N \N \N {} +4498 1447 2025-03-01 23:16:26.107227-08 2025-03-02 00:47:04.910932-08 test t L4 \N t \N \N \N {} +4499 1447 2025-03-02 00:01:54.884825-08 2025-03-01 23:38:49.704891-08 benchmark t L4 \N f \N \N \N {} +4891 1507 2025-03-03 12:40:00.041984-08 2025-03-03 13:14:35.944127-08 test t A100 \N t \N \N \N {} +4504 1448 2025-03-01 23:24:02.182135-08 2025-03-01 23:40:44.693368-08 benchmark t A100 \N t \N \N \N {} +4505 1448 2025-03-02 00:40:33.995417-08 2025-03-02 00:56:55.060033-08 leaderboard t A100 0.00524246372 t \N \N \N {} +4506 1449 2025-03-02 00:45:08.313002-08 2025-03-02 00:16:16.737716-08 test t H100 \N t \N \N \N {} +4507 1449 2025-03-01 23:29:42.540346-08 2025-03-01 23:39:28.57237-08 benchmark t H100 \N t \N \N \N {} +4508 1449 2025-03-02 01:19:24.4957-08 2025-03-01 23:51:56.634988-08 leaderboard t H100 0.0033840856666666665 t \N \N \N {} +4509 1449 2025-03-02 00:12:06.024757-08 2025-03-02 01:10:54.396657-08 test f H100 \N t \N \N \N {} +4510 1449 2025-03-02 01:18:17.542284-08 2025-03-02 00:19:32.525502-08 benchmark f H100 \N t \N \N \N {} +4511 1449 2025-03-01 23:57:24.368485-08 2025-03-02 00:13:37.996217-08 leaderboard f H100 0.00341148875 t \N \N \N {} +4512 1450 2025-03-02 00:45:11.616534-08 2025-03-02 00:48:49.228002-08 test t H100 \N t \N \N \N {} +4513 1450 2025-03-01 23:40:22.279655-08 2025-03-02 00:00:22.363114-08 benchmark t H100 \N t \N \N \N {} +4899 1508 2025-03-03 13:37:04.374969-08 2025-03-03 13:09:34.935444-08 leaderboard t H100 0.007641404666666667 t \N \N \N {} +4517 1450 2025-03-02 00:03:53.293716-08 2025-03-02 00:25:33.705236-08 leaderboard t L4 0.009105309 t \N \N \N {} +4518 1450 2025-03-02 00:49:38.190529-08 2025-03-02 00:22:20.570554-08 test f A100 \N t \N \N \N {} +4519 1450 2025-03-02 00:11:10.810461-08 2025-03-02 01:03:13.770329-08 benchmark f A100 \N t \N \N \N {} +4520 1450 2025-03-02 01:08:07.36635-08 2025-03-02 00:17:23.477428-08 leaderboard f A100 0.0018727816666666668 t \N \N \N {} +4524 1450 2025-03-02 01:17:14.573469-08 2025-03-02 00:17:34.147369-08 test t T4 \N t \N \N \N {} +4525 1450 2025-03-02 00:43:27.056954-08 2025-03-01 23:43:04.43667-08 benchmark t T4 \N t \N \N \N {} +4526 1450 2025-03-02 00:53:05.519328-08 2025-03-02 00:06:06.503136-08 leaderboard t T4 0.009121147333333334 t \N \N \N {} +4527 1450 2025-03-01 23:54:26.363976-08 2025-03-02 01:19:17.019076-08 test t A100 \N t \N \N \N {} +4528 1450 2025-03-02 01:18:02.912247-08 2025-03-01 23:49:49.929737-08 benchmark t A100 \N t \N \N \N {} +4529 1450 2025-03-01 23:35:24.197844-08 2025-03-02 01:11:23.44255-08 leaderboard t A100 0.0017849657777777778 t \N \N \N {} +4530 1450 2025-03-02 00:54:36.280862-08 2025-03-01 23:35:47.462129-08 test f T4 \N t \N \N \N {} +4531 1450 2025-03-02 01:00:08.84832-08 2025-03-02 00:06:08.090762-08 benchmark f T4 \N t \N \N \N {} +4532 1450 2025-03-01 23:55:16.426583-08 2025-03-02 00:04:54.110158-08 leaderboard f T4 0.009106582333333333 t \N \N \N {} +4533 1450 2025-03-02 01:11:28.694794-08 2025-03-02 00:06:59.909067-08 test f L4 \N t \N \N \N {} +4534 1450 2025-03-02 00:11:02.404367-08 2025-03-02 01:17:42.734039-08 benchmark f L4 \N t \N \N \N {} +6713 2081 2025-03-15 02:46:39.538367-07 2025-03-15 03:50:32.068412-07 test t A100 \N t \N \N \N {} +4537 1451 2025-03-02 00:52:04.139941-08 2025-03-02 00:11:49.100971-08 benchmark t H100 \N t \N \N \N {} +4538 1451 2025-03-02 00:51:58.06123-08 2025-03-02 00:02:39.242368-08 leaderboard t H100 0.001079664 t \N \N \N {} +4539 1451 2025-03-02 00:06:02.121158-08 2025-03-02 00:00:09.264632-08 test f H100 \N t \N \N \N {} +4540 1451 2025-03-02 00:23:19.306421-08 2025-03-02 00:51:05.732006-08 benchmark f H100 \N t \N \N \N {} +4541 1451 2025-03-01 23:45:53.396285-08 2025-03-02 01:03:28.077189-08 leaderboard f H100 0.0010786196666666668 t \N \N \N {} +4626 1459 2025-03-02 06:58:07.444981-08 2025-03-02 06:44:02.224251-08 test t A100 \N t \N \N \N {} +4627 1459 2025-03-02 06:21:17.548115-08 2025-03-02 06:15:21.551657-08 benchmark t A100 \N t \N \N \N {} +4628 1459 2025-03-02 07:19:36.161701-08 2025-03-02 06:24:21.742016-08 leaderboard t A100 0.0030925023333333336 t \N \N \N {} +4629 1459 2025-03-02 05:52:24.785964-08 2025-03-02 05:52:16.34119-08 test f A100 \N t \N \N \N {} +4630 1459 2025-03-02 06:37:13.167125-08 2025-03-02 05:59:45.399577-08 benchmark f A100 \N t \N \N \N {} +4631 1459 2025-03-02 05:50:19.68331-08 2025-03-02 05:29:11.389842-08 leaderboard f A100 0.003070814 t \N \N \N {} +4635 1460 2025-03-02 07:14:27.918333-08 2025-03-02 05:44:05.344296-08 test f A100 \N t \N \N \N {} +4636 1460 2025-03-02 06:43:52.831925-08 2025-03-02 07:13:44.23175-08 benchmark f A100 \N t \N \N \N {} +4637 1460 2025-03-02 06:16:46.167909-08 2025-03-02 06:00:38.936259-08 leaderboard f A100 0.0030692296666666665 t \N \N \N {} +4638 1461 2025-03-02 06:03:46.169582-08 2025-03-02 06:31:43.401529-08 test t A100 \N t \N \N \N {} +4639 1461 2025-03-02 06:35:00.966361-08 2025-03-02 05:55:09.655918-08 benchmark t A100 \N t \N \N \N {} +4640 1461 2025-03-02 05:53:37.416513-08 2025-03-02 06:54:43.324146-08 leaderboard t A100 0.003078079 t \N \N \N {} +4644 1462 2025-03-02 06:49:33.296679-08 2025-03-02 06:48:19.278536-08 test f A100 \N t \N \N \N {} +4645 1462 2025-03-02 05:51:40.609401-08 2025-03-02 05:54:32.467256-08 benchmark f A100 \N t \N \N \N {} +4646 1462 2025-03-02 07:10:09.012521-08 2025-03-02 06:39:52.753547-08 leaderboard f A100 0.0030936686666666667 t \N \N \N {} +4647 1462 2025-03-02 06:21:07.074149-08 2025-03-02 06:19:05.973852-08 test t A100 \N t \N \N \N {} +4648 1462 2025-03-02 06:22:56.946678-08 2025-03-02 06:32:18.964173-08 benchmark t A100 \N t \N \N \N {} +4649 1462 2025-03-02 06:57:59.888395-08 2025-03-02 06:30:20.934248-08 leaderboard t A100 0.0030776453333333335 t \N \N \N {} +4652 1463 2025-03-02 06:00:11.005396-08 2025-03-02 05:45:35.797748-08 leaderboard t A100 0.0030704546666666666 t \N \N \N {} +4654 1463 2025-03-02 07:23:29.318291-08 2025-03-02 06:15:05.549905-08 benchmark f A100 \N t \N \N \N {} +158 23 2025-02-23 10:46:27.961353-08 2025-02-23 10:54:07.890706-08 leaderboard f L4 0.043783646333333336 t \N \N \N {} +159 23 2025-02-23 11:24:25.855339-08 2025-02-23 11:24:36.858525-08 test t L4 \N t \N \N \N {} +160 23 2025-02-23 11:23:10.687318-08 2025-02-23 10:51:27.096721-08 benchmark t L4 \N t \N \N \N {} +161 23 2025-02-23 10:17:17.903963-08 2025-02-23 11:20:35.401154-08 leaderboard t L4 0.04378879433333334 t \N \N \N {} +162 24 2025-02-23 11:04:17.242792-08 2025-02-23 12:05:18.328255-08 test f T4 \N f \N \N \N {} +163 25 2025-02-23 11:29:26.781421-08 2025-02-23 11:38:33.754711-08 test f H100 \N t \N \N \N {} +164 25 2025-02-23 11:46:38.661863-08 2025-02-23 11:39:19.931063-08 test f A100 \N t \N \N \N {} +165 25 2025-02-23 11:44:46.673883-08 2025-02-23 12:23:21.640415-08 test f T4 \N t \N \N \N {} +4655 1463 2025-03-02 05:44:44.87667-08 2025-03-02 06:39:01.814288-08 leaderboard f A100 0.003075862 t \N \N \N {} +4656 1464 2025-03-02 06:24:06.602618-08 2025-03-02 06:25:40.742865-08 test t A100 \N t \N \N \N {} +4657 1464 2025-03-02 06:25:44.512665-08 2025-03-02 06:07:00.018787-08 benchmark t A100 \N t \N \N \N {} +4658 1464 2025-03-02 07:24:38.102391-08 2025-03-02 07:03:30.28672-08 leaderboard t A100 0.0030908703333333335 t \N \N \N {} +4662 1465 2025-03-02 06:45:59.862943-08 2025-03-02 06:27:15.188637-08 test f A100 \N t \N \N \N {} +4663 1465 2025-03-02 06:12:13.396238-08 2025-03-02 06:13:12.261244-08 benchmark f A100 \N t \N \N \N {} +4664 1465 2025-03-02 05:44:57.969997-08 2025-03-02 07:15:25.843464-08 leaderboard f A100 0.0031012606666666665 t \N \N \N {} +4665 1465 2025-03-02 07:22:35.937837-08 2025-03-02 07:15:34.451037-08 test t A100 \N t \N \N \N {} +4666 1465 2025-03-02 05:41:53.096577-08 2025-03-02 06:59:11.932007-08 benchmark t A100 \N t \N \N \N {} +4667 1465 2025-03-02 07:22:50.191152-08 2025-03-02 06:20:27.38764-08 leaderboard t A100 0.0031609141666666665 t \N \N \N {} +4671 1466 2025-03-02 06:48:49.912925-08 2025-03-02 06:36:05.088369-08 test f A100 \N t \N \N \N {} +4672 1466 2025-03-02 07:00:40.126712-08 2025-03-02 07:05:41.314194-08 benchmark f A100 \N t \N \N \N {} +4673 1466 2025-03-02 07:03:50.192296-08 2025-03-02 06:21:41.476403-08 leaderboard f A100 0.0030736896666666664 t \N \N \N {} +4674 1467 2025-03-02 07:34:10.787696-08 2025-03-02 07:29:26.411023-08 test t A100 \N t \N \N \N {} +4675 1467 2025-03-02 06:56:49.136222-08 2025-03-02 06:41:21.315023-08 benchmark t A100 \N t \N \N \N {} +4807 1487 2025-03-02 12:30:33.450976-08 2025-03-02 13:35:13.770124-08 benchmark f T4 \N f \N \N \N {} +4693 1470 2025-03-02 06:12:06.283994-08 2025-03-02 06:51:42.391905-08 benchmark t A100 \N t \N \N \N {} +4694 1470 2025-03-02 06:33:53.917995-08 2025-03-02 07:33:01.914128-08 leaderboard t A100 0.004008321333333334 t \N \N \N {} +4698 1471 2025-03-02 07:29:03.787601-08 2025-03-02 06:41:20.283599-08 test f A100 \N t \N \N \N {} +4699 1471 2025-03-02 05:48:12.197045-08 2025-03-02 06:12:43.42567-08 benchmark f A100 \N t \N \N \N {} +4700 1471 2025-03-02 06:56:34.879652-08 2025-03-02 07:44:48.551731-08 leaderboard f A100 0.0031009833333333335 t \N \N \N {} +4701 1471 2025-03-02 06:30:38.128029-08 2025-03-02 07:27:12.350551-08 test t A100 \N t \N \N \N {} +4702 1471 2025-03-02 07:44:43.71905-08 2025-03-02 07:20:57.056431-08 benchmark t A100 \N t \N \N \N {} +4703 1471 2025-03-02 05:52:40.876763-08 2025-03-02 06:18:11.15908-08 leaderboard t A100 0.003077995 t \N \N \N {} +4708 1476 2025-03-02 07:04:38.833526-08 2025-03-02 06:22:07.053844-08 test t A100 \N t \N \N \N {} +4711 1476 2025-03-02 06:32:53.010168-08 2025-03-02 08:13:38.448364-08 test f A100 \N t \N \N \N {} +4712 1476 2025-03-02 07:07:03.075398-08 2025-03-02 06:39:50.920146-08 benchmark f A100 \N t \N \N \N {} +4713 1476 2025-03-02 07:29:16.863809-08 2025-03-02 08:09:32.549873-08 leaderboard f A100 0.003081995 t \N \N \N {} +4714 1477 2025-03-02 08:12:43.521171-08 2025-03-02 07:24:21.691154-08 test t A100 \N t \N \N \N {} +4715 1477 2025-03-02 08:12:18.641473-08 2025-03-02 08:16:06.720104-08 benchmark t A100 \N t \N \N \N {} +4716 1477 2025-03-02 07:47:17.510849-08 2025-03-02 06:38:36.379617-08 leaderboard t A100 0.0030823416666666665 t \N \N \N {} +4720 1478 2025-03-02 06:43:15.887395-08 2025-03-02 07:01:09.324629-08 test f A100 \N t \N \N \N {} +4721 1478 2025-03-02 06:19:24.519466-08 2025-03-02 07:02:12.8161-08 benchmark f A100 \N t \N \N \N {} +4722 1478 2025-03-02 07:24:47.923184-08 2025-03-02 06:22:30.821228-08 leaderboard f A100 0.0030851883333333336 t \N \N \N {} +4723 1478 2025-03-02 06:21:50.565071-08 2025-03-02 06:38:29.276371-08 test t A100 \N t \N \N \N {} +4724 1478 2025-03-02 07:55:40.137891-08 2025-03-02 08:07:32.643622-08 benchmark t A100 \N t \N \N \N {} +4725 1478 2025-03-02 08:04:19.082753-08 2025-03-02 06:39:54.506806-08 leaderboard t A100 0.0030785193333333336 t \N \N \N {} +4728 1479 2025-03-02 08:14:46.908587-08 2025-03-02 07:03:37.792816-08 leaderboard f A100 0.0030867813333333334 t \N \N \N {} +4729 1479 2025-03-02 06:45:56.767886-08 2025-03-02 08:16:10.271116-08 test t A100 \N t \N \N \N {} +4730 1479 2025-03-02 07:53:43.730007-08 2025-03-02 06:49:41.785807-08 benchmark t A100 \N t \N \N \N {} +4731 1479 2025-03-02 07:11:52.419771-08 2025-03-02 07:23:15.061576-08 leaderboard t A100 0.0031140623333333335 t \N \N \N {} +4732 1480 2025-03-02 07:09:43.554747-08 2025-03-02 06:58:05.009739-08 test t A100 \N t \N \N \N {} +4733 1480 2025-03-02 07:52:32.541906-08 2025-03-02 07:38:29.663474-08 benchmark t A100 \N t \N \N \N {} +4734 1480 2025-03-02 07:44:10.008433-08 2025-03-02 08:17:23.134924-08 leaderboard t A100 0.0030920546666666666 t \N \N \N {} +4738 1481 2025-03-02 07:04:34.701596-08 2025-03-02 07:21:13.788761-08 test f A100 \N t \N \N \N {} +4739 1481 2025-03-02 06:49:36.00208-08 2025-03-02 07:31:10.469625-08 benchmark f A100 \N t \N \N \N {} +4740 1481 2025-03-02 07:47:10.568157-08 2025-03-02 08:09:45.89037-08 leaderboard f A100 0.003085828 t \N \N \N {} +4741 1481 2025-03-02 06:48:53.469551-08 2025-03-02 07:03:55.679567-08 test t A100 \N t \N \N \N {} +4742 1481 2025-03-02 06:31:13.795781-08 2025-03-02 06:49:28.737931-08 benchmark t A100 \N t \N \N \N {} +4743 1481 2025-03-02 08:15:11.812141-08 2025-03-02 06:37:00.374019-08 leaderboard t A100 0.0030922536666666664 t \N \N \N {} +4744 1482 2025-03-02 07:43:03.723058-08 2025-03-02 07:35:15.071326-08 test f T4 \N f \N \N \N {} +4745 1483 2025-03-02 07:31:56.372241-08 2025-03-02 08:05:39.164579-08 test f T4 \N t \N \N \N {} +4746 1484 2025-03-02 08:35:02.123221-08 2025-03-02 07:56:53.596064-08 test f T4 \N t \N \N \N {} +4747 1484 2025-03-02 07:06:16.362073-08 2025-03-02 07:58:54.271757-08 benchmark f T4 \N t \N \N \N {} +4748 1484 2025-03-02 07:48:44.592479-08 2025-03-02 06:54:14.32109-08 leaderboard f T4 0.07661455133333332 t \N \N \N {} +4749 1484 2025-03-02 08:43:32.528981-08 2025-03-02 07:50:46.63591-08 test t T4 \N t \N \N \N {} +4750 1484 2025-03-02 07:57:18.393313-08 2025-03-02 07:47:01.612778-08 benchmark t T4 \N t \N \N \N {} +4751 1484 2025-03-02 07:42:07.868143-08 2025-03-02 08:02:04.953244-08 leaderboard t T4 0.07684545833333332 t \N \N \N {} +6735 2086 2025-03-15 04:33:46.257775-07 2025-03-15 04:41:06.722991-07 test t A100 \N t \N \N \N {} +4755 1485 2025-03-02 11:37:55.155684-08 2025-03-02 12:30:52.129299-08 test t A100 \N t \N \N \N {} +4756 1485 2025-03-02 11:54:47.977319-08 2025-03-02 11:49:51.955954-08 benchmark t A100 \N t \N \N \N {} +4757 1485 2025-03-02 12:35:43.476034-08 2025-03-02 13:11:14.518517-08 leaderboard t A100 0.0017045449166666667 t \N \N \N {} +4758 1485 2025-03-02 12:19:44.247676-08 2025-03-02 12:41:13.158847-08 test f H100 \N t \N \N \N {} +4762 1485 2025-03-02 13:26:36.984272-08 2025-03-02 13:09:50.663429-08 benchmark t H100 \N t \N \N \N {} +4763 1485 2025-03-02 11:57:05.793115-08 2025-03-02 12:51:08.493618-08 leaderboard t H100 0.0010300953333333333 t \N \N \N {} +4765 1485 2025-03-02 13:18:29.547704-08 2025-03-02 11:53:12.069821-08 benchmark f T4 \N t \N \N \N {} +4766 1485 2025-03-02 12:34:18.620178-08 2025-03-02 12:01:46.474428-08 leaderboard f T4 0.013093859 t \N \N \N {} +4770 1485 2025-03-02 11:28:06.060656-08 2025-03-02 12:33:39.575643-08 test t L4 \N t \N \N \N {} +4771 1485 2025-03-02 12:38:32.300929-08 2025-03-02 12:35:21.086611-08 benchmark t L4 \N t \N \N \N {} +4772 1485 2025-03-02 11:38:38.993577-08 2025-03-02 12:03:06.955576-08 leaderboard t L4 0.009067796666666666 t \N \N \N {} +4782 1486 2025-03-02 13:23:24.636755-08 2025-03-02 13:02:48.120735-08 leaderboard t L4 0.01330954 t \N \N \N {} +4783 1486 2025-03-02 11:52:32.464719-08 2025-03-02 11:51:42.704104-08 test t T4 \N t \N \N \N {} +4784 1486 2025-03-02 12:54:34.061003-08 2025-03-02 13:23:05.121934-08 benchmark t T4 \N f \N \N \N {} +4785 1486 2025-03-02 13:03:39.801457-08 2025-03-02 13:50:21.033648-08 test t H100 \N t \N \N \N {} +4786 1486 2025-03-02 13:02:54.20138-08 2025-03-02 13:03:49.524039-08 benchmark t H100 \N t \N \N \N {} +4787 1486 2025-03-02 13:37:42.944284-08 2025-03-02 13:04:38.530285-08 leaderboard t H100 0.014144686666666666 t \N \N \N {} +166 25 2025-02-23 11:29:44.547374-08 2025-02-23 11:45:47.371586-08 test f L4 \N t \N \N \N {} +6741 2087 2025-03-15 03:27:43.798435-07 2025-03-15 04:38:01.97182-07 test t A100 \N t \N \N \N {} +4789 1486 2025-03-02 13:07:40.863559-08 2025-03-02 12:56:43.445817-08 benchmark f T4 \N f \N \N \N {} +4790 1486 2025-03-02 11:54:48.604048-08 2025-03-02 13:06:33.970638-08 test f H100 \N t \N \N \N {} +4791 1486 2025-03-02 13:12:20.061089-08 2025-03-02 13:06:35.664868-08 benchmark f H100 \N t \N \N \N {} +4792 1486 2025-03-02 12:46:36.012816-08 2025-03-02 13:51:06.73015-08 leaderboard f H100 0.014137576333333334 t \N \N \N {} +4793 1486 2025-03-02 11:56:43.712458-08 2025-03-02 12:12:52.126902-08 test f L4 \N t \N \N \N {} +4794 1486 2025-03-02 12:57:19.285267-08 2025-03-02 13:40:00.434892-08 benchmark f L4 \N t \N \N \N {} +6744 2087 2025-03-15 03:03:55.336213-07 2025-03-15 04:21:27.642744-07 test f A100 \N t \N \N \N {} +4796 1487 2025-03-02 13:56:03.952211-08 2025-03-02 14:17:06.867401-08 test t H100 \N t \N \N \N {} +2058 700 2025-02-25 13:35:50.559263-08 2025-02-25 14:57:49.798923-08 test t A100 \N f \N \N \N {} +4944 1522 2025-03-03 14:24:33.347813-08 2025-03-03 14:26:01.708268-08 test t H100 \N t \N \N \N {} +4946 1522 2025-03-03 15:27:11.77608-08 2025-03-03 14:42:48.21655-08 leaderboard t H100 0.00110717921 t \N \N \N {} +4948 1524 2025-03-03 14:06:57.97848-08 2025-03-03 15:53:35.131501-08 test f A100 \N t \N \N \N {} +4949 1524 2025-03-03 14:35:09.882957-08 2025-03-03 14:20:11.280882-08 benchmark f A100 \N t \N \N \N {} +6769 2097 2025-03-15 04:27:49.490385-07 2025-03-15 03:27:02.099035-07 test f A100 \N t \N \N \N {} +4957 1524 2025-03-03 14:23:59.058716-08 2025-03-03 15:25:20.637415-08 test f H100 \N t \N \N \N {} +4958 1524 2025-03-03 14:03:34.405543-08 2025-03-03 14:18:35.717577-08 benchmark f H100 \N t \N \N \N {} +4959 1524 2025-03-03 14:09:29.703995-08 2025-03-03 15:30:42.011483-08 leaderboard f H100 0.00140554825 t \N \N \N {} +5255 1613 2025-03-06 07:12:38.56695-08 2025-03-06 08:33:42.642582-08 leaderboard f L4 0.01713484 t \N \N \N {} +4981 1533 2025-03-03 23:32:30.171269-08 2025-03-03 22:37:21.705286-08 benchmark f L4 \N f \N \N \N {} +5018 1541 2025-03-04 09:09:22.632315-08 2025-03-04 09:22:15.724438-08 test f T4 \N f \N \N \N {} +4982 1533 2025-03-03 22:41:27.291617-08 2025-03-03 22:26:36.666409-08 test t L4 \N t \N \N \N {} +4983 1533 2025-03-03 22:10:42.942356-08 2025-03-03 23:52:58.998599-08 benchmark t L4 \N f \N \N \N {} +4984 1534 2025-03-03 22:26:12.301319-08 2025-03-03 22:26:15.332411-08 test t A100 \N t \N \N \N {} +4985 1534 2025-03-03 22:36:43.072753-08 2025-03-03 23:30:03.866041-08 benchmark t A100 \N t \N \N \N {} +4986 1534 2025-03-03 22:14:27.365325-08 2025-03-03 23:53:50.401219-08 leaderboard t A100 0.001491775 t \N \N \N {} +4997 1534 2025-03-03 23:13:33.43142-08 2025-03-03 22:46:25.592457-08 benchmark t H100 \N t \N \N \N {} +4998 1534 2025-03-03 23:34:45.888365-08 2025-03-03 22:22:19.090656-08 leaderboard t H100 0.001050694 t \N \N \N {} +4999 1534 2025-03-03 23:33:14.778803-08 2025-03-03 23:11:24.883939-08 test t T4 \N t \N \N \N {} +5004 1534 2025-03-03 22:52:44.48133-08 2025-03-03 22:20:58.315866-08 leaderboard f T4 0.013578895 t \N \N \N {} +5005 1534 2025-03-03 22:45:45.446291-08 2025-03-03 23:14:25.105517-08 test t L4 \N t \N \N \N {} +5006 1534 2025-03-03 23:46:05.132215-08 2025-03-03 23:00:38.449509-08 benchmark t L4 \N t \N \N \N {} +5007 1534 2025-03-03 23:25:45.105749-08 2025-03-03 22:52:16.954981-08 leaderboard t L4 0.00906405 t \N \N \N {} +5022 1545 2025-03-04 14:58:30.960919-08 2025-03-04 14:52:33.273284-08 test t A100 \N t \N \N \N {} +5023 1545 2025-03-04 14:51:10.938222-08 2025-03-04 15:28:30.503521-08 benchmark t A100 \N t \N \N \N {} +5074 1551 2025-03-04 16:47:22.476681-08 2025-03-04 17:10:56.60229-08 test f H100 \N t \N \N \N {} +5024 1545 2025-03-04 13:51:12.047062-08 2025-03-04 14:35:53.536936-08 leaderboard t A100 0.00004836930555555555 t \N \N \N {} +5025 1545 2025-03-04 14:21:17.803519-08 2025-03-04 15:31:14.61315-08 test f A100 \N t \N \N \N {} +5026 1545 2025-03-04 13:52:48.051537-08 2025-03-04 15:31:03.104837-08 benchmark f A100 \N t \N \N \N {} +5028 1545 2025-03-04 15:34:09.122729-08 2025-03-04 14:42:01.686509-08 test t H100 \N t \N \N \N {} +5034 1545 2025-03-04 14:29:25.07288-08 2025-03-04 14:33:25.56239-08 test t T4 \N t \N \N \N {} +5029 1545 2025-03-04 15:35:11.114668-08 2025-03-04 14:50:01.633693-08 benchmark t H100 \N t \N \N \N {} +5030 1545 2025-03-04 14:15:23.548104-08 2025-03-04 14:34:05.166616-08 leaderboard t H100 0.00004719919 t \N \N \N {} +5031 1545 2025-03-04 13:53:27.796904-08 2025-03-04 13:50:34.783512-08 test f H100 \N t \N \N \N {} +5157 1571 2025-03-04 23:08:49.976878-08 2025-03-04 22:42:28.36004-08 benchmark f A100 \N t \N \N \N {} +5158 1571 2025-03-04 22:35:15.21-08 2025-03-04 22:34:20.752683-08 leaderboard f A100 0.0014883393333333334 t \N \N \N {} +5159 1571 2025-03-04 23:12:18.343342-08 2025-03-04 23:12:10.298764-08 test f H100 \N t \N \N \N {} +5160 1571 2025-03-04 22:52:31.849847-08 2025-03-04 22:15:55.305164-08 benchmark f H100 \N t \N \N \N {} +5161 1571 2025-03-04 21:54:25.033079-08 2025-03-04 23:19:13.810944-08 leaderboard f H100 0.0010518306666666667 t \N \N \N {} +5162 1571 2025-03-04 23:26:27.704888-08 2025-03-04 21:45:35.062949-08 test t H100 \N t \N \N \N {} +2066 702 2025-02-25 14:01:35.67269-08 2025-02-25 14:13:03.658618-08 test f H100 \N t \N \N \N {} +2422 827 2025-02-26 13:49:48.149073-08 2025-02-26 13:42:33.751386-08 benchmark f H100 \N f \N \N \N {} +5622 1716 2025-03-09 10:46:42.607602-07 2025-03-09 09:07:56.188107-07 benchmark t T4 \N f \N \N \N {} +5623 1716 2025-03-09 09:31:22.907917-07 2025-03-09 09:56:01.467636-07 test f T4 \N t \N \N \N {} +5624 1716 2025-03-09 10:16:02.064243-07 2025-03-09 10:32:48.331759-07 benchmark f T4 \N f \N \N \N {} +5625 1717 2025-03-09 10:13:52.307292-07 2025-03-09 09:42:55.90134-07 test f T4 \N t \N \N \N {} +5626 1717 2025-03-09 10:41:28.462205-07 2025-03-09 10:30:33.37398-07 benchmark f T4 \N t \N \N \N {} +5627 1717 2025-03-09 09:58:14.43763-07 2025-03-09 09:29:59.555675-07 leaderboard f T4 0.01689148266666667 t \N \N \N {} +5628 1717 2025-03-09 08:54:15.401227-07 2025-03-09 09:01:20.665735-07 test t T4 \N t \N \N \N {} +5633 1718 2025-03-09 10:16:16.764055-07 2025-03-09 08:57:06.957134-07 leaderboard t T4 0.02135399933333333 t \N \N \N {} +5660 1723 2025-03-09 10:03:33.814323-07 2025-03-09 10:04:32.576403-07 leaderboard f T4 0.016605525666666666 t \N \N \N {} +5661 1723 2025-03-09 10:47:11.568642-07 2025-03-09 09:35:57.762614-07 test t T4 \N t \N \N \N {} +5662 1723 2025-03-09 10:00:24.122137-07 2025-03-09 09:14:18.616382-07 benchmark t T4 \N t \N \N \N {} +5683 1726 2025-03-09 09:19:42.062291-07 2025-03-09 09:47:18.455329-07 benchmark f L4 \N t \N \N \N {} +5684 1726 2025-03-09 09:35:37.887021-07 2025-03-09 10:36:27.46401-07 leaderboard f L4 0.017259138 t \N \N \N {} +5685 1727 2025-03-09 10:09:36.558925-07 2025-03-09 09:36:26.692942-07 test f L4 \N t \N \N \N {} +5692 1728 2025-03-09 09:54:55.125187-07 2025-03-09 09:30:43.343027-07 benchmark f A100 \N t \N \N \N {} +5693 1728 2025-03-09 10:44:16.533423-07 2025-03-09 10:12:57.412213-07 leaderboard f A100 0.0026361306666666663 t \N \N \N {} +5694 1729 2025-03-09 10:18:27.24795-07 2025-03-09 11:00:29.021145-07 test t H100 \N t \N \N \N {} +5780 1750 2025-03-09 22:31:41.177478-07 2025-03-09 22:32:40.296083-07 leaderboard t H100 0.0015017373333333333 t \N \N \N {} +5783 1751 2025-03-09 22:33:46.42896-07 2025-03-09 22:29:10.948791-07 leaderboard t L4 0.017186112666666666 t \N \N \N {} +5789 1752 2025-03-09 22:09:13.460918-07 2025-03-09 22:30:06.841849-07 leaderboard f T4 0.016641214666666668 t \N \N \N {} +5791 1752 2025-03-09 21:25:43.431271-07 2025-03-09 22:03:39.205218-07 benchmark t T4 \N t \N \N \N {} +5792 1752 2025-03-09 22:06:34.639731-07 2025-03-09 22:03:04.943777-07 leaderboard t T4 0.016605053666666664 t \N \N \N {} +5890 1783 2025-03-10 11:42:15.635873-07 2025-03-10 12:18:31.181049-07 test f T4 \N f \N \N \N {} +5891 1784 2025-03-10 12:42:17.820815-07 2025-03-10 12:42:44.878659-07 test f T4 \N f \N \N \N {} +5892 1784 2025-03-10 13:03:34.982484-07 2025-03-10 11:10:33.398792-07 test t T4 \N f \N \N \N {} +6112 1851 2025-03-11 02:01:12.667219-07 2025-03-11 01:10:58.721364-07 test f A100 \N f \N \N \N {} +5899 1788 2025-03-10 12:51:42.965452-07 2025-03-10 11:53:10.996306-07 test t T4 \N f \N \N \N {} +5900 1788 2025-03-10 11:22:13.20187-07 2025-03-10 11:32:35.572673-07 test f T4 \N f \N \N \N {} +5901 1789 2025-03-10 13:02:58.074539-07 2025-03-10 13:17:21.023123-07 test f T4 \N f \N \N \N {} +5902 1789 2025-03-10 12:01:52.164022-07 2025-03-10 11:27:33.462883-07 test t T4 \N f \N \N \N {} +5903 1790 2025-03-10 11:26:13.302143-07 2025-03-10 12:50:52.591159-07 test t T4 \N f \N \N \N {} +5915 1796 2025-03-10 12:15:59.801508-07 2025-03-10 13:02:17.771902-07 test t T4 \N f \N \N \N {} +5916 1796 2025-03-10 12:57:44.974492-07 2025-03-10 12:53:59.035234-07 test f T4 \N f \N \N \N {} +5944 1809 2025-03-10 13:50:04.063532-07 2025-03-10 14:02:36.910964-07 test t A100 \N t \N \N \N {} +5917 1797 2025-03-10 12:47:21.898424-07 2025-03-10 12:35:53.990658-07 test f T4 \N f \N \N \N {} +5918 1797 2025-03-10 11:54:01.992015-07 2025-03-10 13:25:49.093367-07 test t T4 \N f \N \N \N {} +5919 1798 2025-03-10 12:02:54.007464-07 2025-03-10 12:00:35.023294-07 test f T4 \N f \N \N \N {} +5920 1798 2025-03-10 11:53:48.460819-07 2025-03-10 12:54:41.600572-07 test t T4 \N f \N \N \N {} +5921 1799 2025-03-10 12:01:32.892869-07 2025-03-10 12:01:20.064834-07 test f T4 \N f \N \N \N {} +5945 1809 2025-03-10 13:30:52.742-07 2025-03-10 13:35:00.53151-07 benchmark t A100 \N t \N \N \N {} +2067 702 2025-02-25 13:08:25.421143-08 2025-02-25 13:34:01.386083-08 benchmark f H100 \N t \N \N \N {} +2068 702 2025-02-25 13:42:25.465302-08 2025-02-25 13:31:29.0514-08 leaderboard f H100 0.001931559125 t \N \N \N {} +167 26 2025-02-23 12:21:33.030404-08 2025-02-23 11:41:55.949378-08 benchmark f A100 \N t \N \N \N {} +168 26 2025-02-23 11:54:48.8754-08 2025-02-23 11:19:18.094078-08 benchmark f H100 \N t \N \N \N {} +169 26 2025-02-23 11:53:04.673811-08 2025-02-23 11:52:00.921431-08 benchmark f T4 \N t \N \N \N {} +170 26 2025-02-23 10:45:00.538357-08 2025-02-23 11:33:31.309144-08 benchmark f L4 \N t \N \N \N {} +5969 1818 2025-03-10 13:34:50.62569-07 2025-03-10 13:50:19.381297-07 leaderboard f T4 0.007938620666666667 t \N \N \N {} +5988 1821 2025-03-10 14:19:10.235543-07 2025-03-10 13:00:17.011132-07 test t L4 \N t \N \N \N {} +5972 1818 2025-03-10 14:20:22.971921-07 2025-03-10 13:57:46.364461-07 leaderboard t T4 0.006741959333333333 t \N \N \N {} +5973 1819 2025-03-10 14:09:36.845256-07 2025-03-10 12:47:23.183159-07 test f A100 \N t \N \N \N {} +5974 1819 2025-03-10 13:27:31.914549-07 2025-03-10 13:38:50.356656-07 benchmark f A100 \N t \N \N \N {} +5975 1819 2025-03-10 13:22:24.682951-07 2025-03-10 12:52:47.777634-07 leaderboard f A100 0.00066186475 t \N \N \N {} +5976 1820 2025-03-10 13:05:57.916285-07 2025-03-10 14:10:04.245483-07 test f H100 \N t \N \N \N {} +5977 1820 2025-03-10 13:06:16.389801-07 2025-03-10 13:56:50.012127-07 benchmark f H100 \N t \N \N \N {} +5980 1820 2025-03-10 14:27:30.646317-07 2025-03-10 14:22:26.85464-07 benchmark t H100 \N t \N \N \N {} +5981 1820 2025-03-10 13:34:45.928055-07 2025-03-10 14:10:03.141068-07 leaderboard t H100 0.00025382670689655173 t \N \N \N {} +5982 1821 2025-03-10 13:18:05.58891-07 2025-03-10 12:57:07.627811-07 test f L4 \N t \N \N \N {} +5983 1821 2025-03-10 13:10:21.68803-07 2025-03-10 12:59:33.456792-07 benchmark f L4 \N t \N \N \N {} +5984 1821 2025-03-10 14:27:34.743682-07 2025-03-10 13:17:42.837991-07 leaderboard f L4 0.0023442443333333337 t \N \N \N {} +5985 1819 2025-03-10 14:00:17.259921-07 2025-03-10 13:54:42.382815-07 test t A100 \N t \N \N \N {} +5986 1819 2025-03-10 13:40:20.642276-07 2025-03-10 13:23:25.385771-07 benchmark t A100 \N t \N \N \N {} +5991 1822 2025-03-10 13:01:51.312859-07 2025-03-10 13:23:25.247008-07 test f A100 \N t \N \N \N {} +5992 1822 2025-03-10 13:52:54.966043-07 2025-03-10 13:40:58.589221-07 benchmark f A100 \N t \N \N \N {} +5993 1822 2025-03-10 13:20:20.347858-07 2025-03-10 13:01:28.199599-07 leaderboard f A100 0.011864730666666667 t \N \N \N {} +5994 1822 2025-03-10 14:40:44.674945-07 2025-03-10 13:28:53.37983-07 test t A100 \N t \N \N \N {} +5995 1822 2025-03-10 14:21:29.232684-07 2025-03-10 13:52:16.353634-07 benchmark t A100 \N t \N \N \N {} +5996 1822 2025-03-10 13:03:56.315257-07 2025-03-10 13:23:43.023609-07 leaderboard t A100 0.011399361 t \N \N \N {} +6003 1824 2025-03-10 14:26:38.384984-07 2025-03-10 14:09:13.727909-07 test f L4 \N t \N \N \N {} +6004 1824 2025-03-10 13:12:15.653503-07 2025-03-10 14:52:55.190975-07 benchmark f L4 \N t \N \N \N {} +6005 1824 2025-03-10 13:13:37.254193-07 2025-03-10 13:18:36.888686-07 leaderboard f L4 0.07897275766666667 t \N \N \N {} +6011 1825 2025-03-10 13:09:55.890405-07 2025-03-10 13:48:11.961778-07 leaderboard t T4 0.07677698866666667 t \N \N \N {} +6013 1825 2025-03-10 14:28:08.622984-07 2025-03-10 14:46:31.402484-07 benchmark f T4 \N t \N \N \N {} +6014 1825 2025-03-10 14:21:16.246646-07 2025-03-10 13:28:59.68858-07 leaderboard f T4 0.07667353866666667 t \N \N \N {} +6095 1844 2025-03-10 22:07:21.414-07 2025-03-10 23:17:28.15254-07 benchmark t A100 \N t \N \N \N {} +6107 1846 2025-03-10 22:00:08.835784-07 2025-03-10 22:00:11.368406-07 benchmark f L4 \N t \N \N \N {} +6108 1846 2025-03-10 22:23:46.695176-07 2025-03-10 23:57:02.247288-07 leaderboard f L4 0.3110419582857143 t \N \N \N {} +6655 2057 2025-03-14 12:30:26.103998-07 2025-03-14 12:52:39.669941-07 benchmark f A100 \N t \N \N \N {} +6661 2058 2025-03-14 13:23:30.755804-07 2025-03-14 13:50:09.852519-07 leaderboard f A100 0.002474787 t \N \N \N {} +6672 2064 2025-03-14 15:30:52.385494-07 2025-03-14 14:26:29.449376-07 test t T4 \N f \N \N \N {} +6673 2064 2025-03-14 15:46:19.901898-07 2025-03-14 14:34:56.062065-07 test f T4 \N f \N \N \N {} +6693 2074 2025-03-14 16:21:36.880226-07 2025-03-14 17:05:48.053303-07 test f T4 \N f \N \N \N {} +6889 2142 2025-03-16 10:09:32.257151-07 2025-03-16 11:37:21.891364-07 test t H100 \N t \N \N \N {} +6694 2075 2025-03-14 16:49:43.41812-07 2025-03-14 16:35:30.091773-07 test f T4 \N f \N \N \N {} +6890 2142 2025-03-16 10:05:39.67375-07 2025-03-16 11:46:49.850371-07 benchmark t H100 \N f \N \N \N {} +6708 2080 2025-03-15 03:13:35.894439-07 2025-03-15 04:06:56.714414-07 benchmark f A100 \N t \N \N \N {} +6709 2080 2025-03-15 03:36:07.318078-07 2025-03-15 04:02:50.2109-07 leaderboard f A100 0.0025194633333333336 t \N \N \N {} +6711 2081 2025-03-15 03:52:50.095799-07 2025-03-15 03:57:17.540486-07 benchmark f A100 \N t \N \N \N {} +6721 2082 2025-03-15 02:47:16.092609-07 2025-03-15 02:57:12.920931-07 leaderboard f A100 0.0025854276666666667 t \N \N \N {} +6804 2105 2025-03-15 04:42:04.98069-07 2025-03-15 03:45:38.84867-07 benchmark f A100 \N t \N \N \N {} +6806 2107 2025-03-15 04:57:27.096967-07 2025-03-15 05:03:18.110884-07 benchmark f A100 \N t \N \N \N {} +6807 2108 2025-03-15 03:40:00.092892-07 2025-03-15 05:03:01.447944-07 benchmark f A100 \N t \N \N \N {} +6808 2109 2025-03-15 05:29:02.178375-07 2025-03-15 04:43:10.854188-07 benchmark f A100 \N t \N \N \N {} +6891 2142 2025-03-16 10:03:19.487989-07 2025-03-16 10:15:21.78931-07 test f H100 \N t \N \N \N {} +6816 2117 2025-03-15 09:06:01.891683-07 2025-03-15 09:51:02.921922-07 benchmark f A100 \N t \N \N \N {} +6893 2143 2025-03-16 12:26:21.451192-07 2025-03-16 11:55:44.286131-07 test f T4 \N f \N \N \N {} +6823 2120 2025-03-15 10:10:54.860558-07 2025-03-15 09:57:15.216128-07 benchmark t A100 \N t \N \N \N {} +6868 2128 2025-03-15 13:36:39.60935-07 2025-03-15 12:30:47.242903-07 test f T4 \N f \N \N \N {} +6869 2129 2025-03-15 12:55:04.568241-07 2025-03-15 13:27:08.036947-07 test t T4 \N f \N \N \N {} +6870 2129 2025-03-15 13:43:39.360608-07 2025-03-15 13:40:42.286936-07 test f T4 \N f \N \N \N {} +6871 2130 2025-03-15 12:56:13.688421-07 2025-03-15 13:31:59.222715-07 test t T4 \N f \N \N \N {} +6872 2130 2025-03-15 13:37:07.488274-07 2025-03-15 14:00:52.365959-07 test f T4 \N f \N \N \N {} +6874 2131 2025-03-15 14:10:20.038407-07 2025-03-15 13:06:30.274573-07 test t T4 \N f \N \N \N {} +6875 2132 2025-03-15 13:03:08.713745-07 2025-03-15 14:16:20.509728-07 test f T4 \N f \N \N \N {} +6876 2132 2025-03-15 12:46:33.250206-07 2025-03-15 14:16:03.803892-07 test t T4 \N f \N \N \N {} +6877 2133 2025-03-15 14:13:55.89837-07 2025-03-15 14:32:53.847522-07 test f T4 \N f \N \N \N {} +6878 2133 2025-03-15 13:49:17.874309-07 2025-03-15 13:42:01.322803-07 test t T4 \N f \N \N \N {} +6884 2137 2025-03-16 09:35:46.366813-07 2025-03-16 09:58:28.560263-07 test f H100 \N f \N \N \N {} +6941 2161 2025-03-16 15:54:46.742477-07 2025-03-16 16:55:32.003397-07 leaderboard t T4 0.00036617938461538463 t \N \N \N {} +6942 2161 2025-03-16 15:48:02.479891-07 2025-03-16 15:54:40.057512-07 test f T4 \N t \N \N \N {} +6943 2161 2025-03-16 16:14:50.39459-07 2025-03-16 16:50:14.655957-07 benchmark f T4 \N t \N \N \N {} +6945 2162 2025-03-16 15:42:59.335687-07 2025-03-16 16:32:27.860049-07 test t T4 \N t \N \N \N {} +6946 2162 2025-03-16 15:21:34.523769-07 2025-03-16 15:44:43.801402-07 benchmark t T4 \N t \N \N \N {} +6947 2162 2025-03-16 15:24:00.324625-07 2025-03-16 16:09:43.79608-07 leaderboard t T4 0.00028399790999999997 t \N \N \N {} +6948 2162 2025-03-16 15:07:29.788194-07 2025-03-16 16:45:42.575251-07 test f T4 \N t \N \N \N {} +6949 2162 2025-03-16 16:58:35.480846-07 2025-03-16 15:40:08.469048-07 benchmark f T4 \N t \N \N \N {} +2083 702 2025-02-25 13:37:08.890367-08 2025-02-25 14:31:19.469976-08 leaderboard f T4 0.019992128 t \N \N \N {} +2829 984 2025-02-26 23:44:55.909365-08 2025-02-26 23:59:03.36721-08 test f L4 \N t \N \N \N {} +2843 988 2025-02-27 00:16:23.988573-08 2025-02-26 23:57:40.2744-08 leaderboard f H100 0.00153285175 t \N \N \N {} +171 27 2025-02-23 10:45:11.479148-08 2025-02-23 11:31:42.340162-08 test t A100 \N t \N \N \N {} +172 27 2025-02-23 11:43:09.385926-08 2025-02-23 11:39:14.096637-08 benchmark t A100 \N t \N \N \N {} +220 37 2025-02-23 11:24:37.144954-08 2025-02-23 12:36:11.010727-08 benchmark t A100 \N t \N \N \N {} +173 27 2025-02-23 12:14:48.113644-08 2025-02-23 10:35:05.48636-08 leaderboard t A100 0.014219568666666666 t \N \N \N {} +174 27 2025-02-23 11:52:06.861806-08 2025-02-23 12:06:16.599311-08 test f A100 \N t \N \N \N {} +175 27 2025-02-23 11:14:31.938617-08 2025-02-23 10:50:54.199169-08 benchmark f A100 \N t \N \N \N {} +176 27 2025-02-23 10:55:35.824906-08 2025-02-23 11:40:46.262363-08 leaderboard f A100 0.014177354333333335 t \N \N \N {} +177 27 2025-02-23 10:54:29.927447-08 2025-02-23 11:20:37.982222-08 test f H100 \N t \N \N \N {} +178 27 2025-02-23 11:15:34.972209-08 2025-02-23 12:22:50.553085-08 benchmark f H100 \N t \N \N \N {} +179 27 2025-02-23 11:27:38.137008-08 2025-02-23 12:17:23.904583-08 leaderboard f H100 0.00628814 t \N \N \N {} +181 27 2025-02-23 12:09:44.028933-08 2025-02-23 11:26:46.80693-08 benchmark t T4 \N t \N \N \N {} +182 27 2025-02-23 11:56:25.478243-08 2025-02-23 10:36:01.655206-08 leaderboard t T4 0.07672247533333333 t \N \N \N {} +183 27 2025-02-23 11:51:05.559443-08 2025-02-23 10:39:36.577419-08 test f L4 \N t \N \N \N {} +184 27 2025-02-23 11:41:30.376423-08 2025-02-23 10:37:16.305101-08 benchmark f L4 \N t \N \N \N {} +185 27 2025-02-23 10:35:57.229029-08 2025-02-23 10:39:52.072211-08 leaderboard f L4 0.07915553666666666 t \N \N \N {} +186 27 2025-02-23 11:10:53.767744-08 2025-02-23 10:48:50.231998-08 test f T4 \N t \N \N \N {} +187 27 2025-02-23 12:02:00.098937-08 2025-02-23 11:08:01.038877-08 benchmark f T4 \N t \N \N \N {} +188 27 2025-02-23 11:11:25.926517-08 2025-02-23 10:30:09.448745-08 leaderboard f T4 0.076629145 t \N \N \N {} +189 27 2025-02-23 11:53:45.306343-08 2025-02-23 12:16:41.267247-08 test t L4 \N t \N \N \N {} +190 27 2025-02-23 11:28:09.310531-08 2025-02-23 10:48:48.140068-08 benchmark t L4 \N t \N \N \N {} +191 27 2025-02-23 12:08:23.202643-08 2025-02-23 11:17:51.689748-08 leaderboard t L4 0.07890172333333333 t \N \N \N {} +192 27 2025-02-23 10:44:11.726542-08 2025-02-23 11:02:53.940073-08 test t H100 \N t \N \N \N {} +193 27 2025-02-23 12:19:32.856515-08 2025-02-23 10:47:43.576992-08 benchmark t H100 \N t \N \N \N {} +194 27 2025-02-23 12:21:29.202301-08 2025-02-23 11:46:31.676892-08 leaderboard t H100 0.006306255 t \N \N \N {} +195 28 2025-02-23 11:13:19.67724-08 2025-02-23 10:39:48.64665-08 test f T4 \N f \N \N \N {} +196 29 2025-02-23 10:49:56.579251-08 2025-02-23 11:05:27.758434-08 test f T4 \N f \N \N \N {} +197 30 2025-02-23 12:27:41.798772-08 2025-02-23 12:02:37.751314-08 test f T4 \N f \N \N \N {} +198 31 2025-02-23 11:37:04.951166-08 2025-02-23 12:07:49.407209-08 test f T4 \N f \N \N \N {} +199 32 2025-02-23 11:58:09.474874-08 2025-02-23 10:43:38.089712-08 test f T4 \N t \N \N \N {} +200 33 2025-02-23 11:22:39.540982-08 2025-02-23 11:42:22.336345-08 benchmark f T4 \N t \N \N \N {} +201 34 2025-02-23 12:04:23.683096-08 2025-02-23 10:52:18.627224-08 test f T4 \N t \N \N \N {} +1622 539 2025-02-25 07:52:47.641192-08 2025-02-25 08:52:28.657841-08 test f H100 \N t \N \N \N {} +202 35 2025-02-23 12:24:22.817189-08 2025-02-23 10:54:19.203754-08 benchmark f T4 \N t \N \N \N {} +216 37 2025-02-23 11:14:38.746659-08 2025-02-23 11:03:54.206188-08 test t T4 \N t \N \N \N {} +243 50 2025-02-23 13:18:19.125258-08 2025-02-23 13:13:38.565546-08 test f T4 \N f \N \N \N {} +244 51 2025-02-23 13:01:35.786926-08 2025-02-23 12:24:04.575677-08 test f T4 \N f \N \N \N {} +245 52 2025-02-23 12:24:37.291631-08 2025-02-23 12:23:14.530709-08 test f T4 \N t \N \N \N {} +268 54 2025-02-23 12:25:21.411502-08 2025-02-23 12:05:19.559567-08 test t L4 \N t \N \N \N {} +247 53 2025-02-23 12:25:27.998153-08 2025-02-23 12:00:47.080967-08 benchmark f A100 \N t \N \N \N {} +248 53 2025-02-23 12:39:45.238062-08 2025-02-23 12:53:50.087404-08 benchmark f L4 \N t \N \N \N {} +249 53 2025-02-23 13:45:33.218295-08 2025-02-23 12:33:06.247895-08 benchmark f H100 \N t \N \N \N {} +250 54 2025-02-23 13:11:45.380271-08 2025-02-23 12:54:03.319507-08 test t A100 \N t \N \N \N {} +251 54 2025-02-23 12:41:48.180775-08 2025-02-23 12:18:18.458891-08 benchmark t A100 \N t \N \N \N {} +252 54 2025-02-23 13:47:01.02295-08 2025-02-23 13:12:03.617869-08 leaderboard t A100 0.00318012725 t \N \N \N {} +253 54 2025-02-23 12:36:35.151783-08 2025-02-23 12:27:40.807586-08 test f L4 \N t \N \N \N {} +3565 1211 2025-02-28 12:01:14.621003-08 2025-02-28 12:23:14.001408-08 test f H100 \N t \N \N \N {} +291 67 2025-02-23 13:52:22.554731-08 2025-02-23 15:04:28.302779-08 benchmark f H100 \N t \N \N \N {} +292 68 2025-02-23 14:13:32.52352-08 2025-02-23 13:39:23.211145-08 test f H100 \N t \N \N \N {} +294 70 2025-02-23 13:48:10.257644-08 2025-02-23 15:04:21.69425-08 test f H100 \N t \N \N \N {} +295 70 2025-02-23 13:45:01.008267-08 2025-02-23 15:19:04.053139-08 benchmark f H100 \N t \N \N \N {} +296 70 2025-02-23 14:08:32.500158-08 2025-02-23 13:34:17.476377-08 leaderboard f H100 0.0014198665 t \N \N \N {} +297 70 2025-02-23 14:22:14.947889-08 2025-02-23 14:43:43.399906-08 test t H100 \N t \N \N \N {} +395 104 2025-02-23 19:37:48.159657-08 2025-02-23 19:42:22.47971-08 leaderboard f L4 0.009219998333333333 t \N \N \N {} +396 104 2025-02-23 19:29:57.556657-08 2025-02-23 19:14:44.962181-08 test t T4 \N t \N \N \N {} +397 104 2025-02-23 19:06:51.535467-08 2025-02-23 19:09:13.584955-08 benchmark t T4 \N t \N \N \N {} +398 104 2025-02-23 19:08:03.666634-08 2025-02-23 18:31:14.289034-08 leaderboard t T4 0.009337150333333334 t \N \N \N {} +399 104 2025-02-23 18:20:58.004675-08 2025-02-23 18:12:39.887533-08 test t L4 \N t \N \N \N {} +400 104 2025-02-23 18:37:28.598432-08 2025-02-23 19:04:08.192125-08 benchmark t L4 \N t \N \N \N {} +401 104 2025-02-23 18:23:08.135641-08 2025-02-23 19:25:04.431404-08 leaderboard t L4 0.009286899666666666 t \N \N \N {} +440 137 2025-02-23 19:19:33.088698-08 2025-02-23 19:28:34.03025-08 test t A100 \N t \N \N \N {} +405 110 2025-02-23 19:36:40.687466-08 2025-02-23 19:24:36.001868-08 test f H100 \N f \N \N \N {} +406 111 2025-02-23 19:22:44.96918-08 2025-02-23 18:57:09.743491-08 test f L4 \N f \N \N \N {} +407 113 2025-02-23 18:49:58.235949-08 2025-02-23 19:26:22.885286-08 test f L4 \N f \N \N \N {} +408 112 2025-02-23 19:16:17.884334-08 2025-02-23 20:17:51.435513-08 test f H100 \N f \N \N \N {} +4354 1425 2025-03-01 20:13:46.966029-08 2025-03-01 18:52:04.589122-08 test t T4 \N t \N \N \N {} +465 144 2025-02-24 00:55:32.36629-08 2025-02-24 01:02:07.010747-08 leaderboard f A100 0.0031325836666666663 t \N \N \N {} +466 145 2025-02-24 00:32:22.702838-08 2025-02-24 01:20:22.072236-08 test f L4 \N t \N \N \N {} +467 145 2025-02-24 00:52:22.990735-08 2025-02-24 00:23:20.150431-08 benchmark f L4 \N t \N \N \N {} +468 145 2025-02-24 01:17:15.371174-08 2025-02-24 00:43:01.83229-08 leaderboard f L4 0.01690829933333333 t \N \N \N {} +472 145 2025-02-24 00:20:39.930346-08 2025-02-24 01:30:42.571148-08 test t L4 \N t \N \N \N {} +473 145 2025-02-24 01:15:01.028642-08 2025-02-24 01:45:10.923025-08 benchmark t L4 \N t \N \N \N {} +474 145 2025-02-24 01:13:01.276238-08 2025-02-24 02:03:18.528002-08 leaderboard t L4 0.016441613666666667 t \N \N \N {} +475 145 2025-02-24 00:23:20.873525-08 2025-02-24 00:29:22.877398-08 test t A100 \N t \N \N \N {} +476 145 2025-02-24 01:36:08.741349-08 2025-02-24 01:17:03.910006-08 benchmark t A100 \N t \N \N \N {} +477 145 2025-02-24 01:59:25.837709-08 2025-02-24 01:17:11.163833-08 leaderboard t A100 0.00310126 t \N \N \N {} +478 145 2025-02-24 02:01:13.502168-08 2025-02-24 01:09:26.58412-08 test t H100 \N t \N \N \N {} +298 70 2025-02-23 13:31:54.273224-08 2025-02-23 14:34:44.74164-08 benchmark t H100 \N t \N \N \N {} +299 70 2025-02-23 13:44:07.386708-08 2025-02-23 15:06:34.650489-08 leaderboard t H100 0.001028769 t \N \N \N {} +323 79 2025-02-23 15:26:33.721086-08 2025-02-23 15:20:07.296257-08 test f H100 \N t \N \N \N {} +486 145 2025-02-24 01:01:38.175295-08 2025-02-24 01:58:42.065024-08 test t T4 \N t \N \N \N {} +769 244 2025-02-24 11:02:11.786679-08 2025-02-24 09:19:00.797899-08 test f A100 \N t \N \N \N {} +479 145 2025-02-24 00:22:39.260481-08 2025-02-24 00:31:46.970049-08 benchmark t H100 \N t \N \N \N {} +480 145 2025-02-24 01:01:44.442094-08 2025-02-24 00:11:35.7708-08 leaderboard t H100 0.00142161375 t \N \N \N {} +481 145 2025-02-24 00:37:45.034628-08 2025-02-24 00:06:19.291315-08 test f H100 \N t \N \N \N {} +482 145 2025-02-24 01:23:58.17948-08 2025-02-24 00:43:49.157244-08 benchmark f H100 \N t \N \N \N {} +483 145 2025-02-24 00:09:29.066055-08 2025-02-24 00:27:00.430461-08 leaderboard f H100 0.00140814175 t \N \N \N {} +484 145 2025-02-24 02:01:37.341833-08 2025-02-24 01:22:53.107149-08 test f T4 \N t \N \N \N {} +541 157 2025-02-24 03:23:47.844322-08 2025-02-24 03:43:51.69252-08 test f A100 \N t \N \N \N {} +558 159 2025-02-24 03:52:44.512606-08 2025-02-24 05:07:17.070819-08 test f H100 \N t \N \N \N {} +542 157 2025-02-24 04:17:48.694412-08 2025-02-24 04:12:41.545476-08 benchmark f A100 \N t \N \N \N {} +549 157 2025-02-24 04:20:20.945717-08 2025-02-24 03:23:10.699715-08 leaderboard t T4 0.023143011666666668 t \N \N \N {} +1491 498 2025-02-25 07:26:44.389059-08 2025-02-25 06:14:45.396909-08 test f A100 \N f \N \N \N {} +550 157 2025-02-24 04:41:06.56919-08 2025-02-24 03:16:56.08599-08 test t A100 \N t \N \N \N {} +551 157 2025-02-24 04:37:58.696168-08 2025-02-24 03:37:13.752989-08 benchmark t A100 \N t \N \N \N {} +552 157 2025-02-24 03:31:21.654659-08 2025-02-24 04:02:58.352237-08 leaderboard t A100 0.006896261545454546 t \N \N \N {} +553 157 2025-02-24 05:03:09.664072-08 2025-02-24 05:13:14.483324-08 test t L4 \N f \N \N \N {} +554 157 2025-02-24 04:29:42.313161-08 2025-02-24 05:11:38.142222-08 test f L4 \N t \N \N \N {} +555 157 2025-02-24 04:36:10.687082-08 2025-02-24 03:31:24.036485-08 benchmark f L4 \N t \N \N \N {} +556 157 2025-02-24 03:38:18.277868-08 2025-02-24 03:25:50.851837-08 leaderboard f L4 0.018886963333333333 t \N \N \N {} +557 158 2025-02-24 04:36:41.191894-08 2025-02-24 04:18:45.993597-08 test f H100 \N t \N \N \N {} +1492 498 2025-02-25 06:34:51.735413-08 2025-02-25 05:41:39.626728-08 test t A100 \N f \N \N \N {} +559 160 2025-02-24 04:47:33.735469-08 2025-02-24 03:48:26.300529-08 test f H100 \N t \N \N \N {} +572 162 2025-02-24 05:00:12.670271-08 2025-02-24 05:05:35.448889-08 leaderboard f T4 0.018292196666666666 t \N \N \N {} +573 162 2025-02-24 03:26:01.433346-08 2025-02-24 03:56:51.262514-08 test f L4 \N f \N \N \N {} +574 162 2025-02-24 04:51:39.961542-08 2025-02-24 04:59:26.105146-08 test t L4 \N t \N \N \N {} +5259 1613 2025-03-06 06:50:43.258546-08 2025-03-06 08:19:11.165198-08 test f T4 \N t \N \N \N {} +600 172 2025-02-24 04:53:48.285554-08 2025-02-24 05:23:18.845114-08 test f A100 \N t \N \N \N {} +581 166 2025-02-24 03:35:47.72346-08 2025-02-24 03:38:46.586413-08 test f H100 \N f \N \N \N {} +606 172 2025-02-24 03:42:34.249272-08 2025-02-24 05:40:44.184189-08 test f L4 \N t \N \N \N {} +311 77 2025-02-23 14:12:30.101398-08 2025-02-23 15:24:57.946248-08 test f H100 \N t \N \N \N {} +312 77 2025-02-23 15:19:37.391023-08 2025-02-23 14:11:23.026317-08 benchmark f H100 \N t \N \N \N {} +313 77 2025-02-23 15:05:19.0137-08 2025-02-23 15:11:53.57026-08 leaderboard f H100 0.001450680857142857 t \N \N \N {} +315 77 2025-02-23 13:53:12.61876-08 2025-02-23 13:59:11.207858-08 benchmark t H100 \N t \N \N \N {} +316 77 2025-02-23 14:48:41.323897-08 2025-02-23 15:13:01.5905-08 leaderboard t H100 0.00145937425 t \N \N \N {} +317 78 2025-02-23 14:02:23.760311-08 2025-02-23 13:57:30.080076-08 test t H100 \N t \N \N \N {} +716 229 2025-02-24 08:35:17.736081-08 2025-02-24 07:54:05.005879-08 leaderboard f A100 0.004042775333333333 t \N \N \N {} +717 230 2025-02-24 09:22:18.875039-08 2025-02-24 08:00:26.274996-08 test f A100 \N t \N \N \N {} +1135 316 2025-02-24 20:02:44.708551-08 2025-02-24 18:21:09.94372-08 leaderboard t A100 0.011420428666666666 t \N \N \N {} +718 230 2025-02-24 08:33:59.269854-08 2025-02-24 07:51:14.292412-08 benchmark f A100 \N t \N \N \N {} +720 230 2025-02-24 09:24:41.117156-08 2025-02-24 08:14:51.47456-08 test t A100 \N t \N \N \N {} +721 230 2025-02-24 09:25:59.168682-08 2025-02-24 07:38:18.264898-08 benchmark t A100 \N t \N \N \N {} +722 230 2025-02-24 08:13:38.930592-08 2025-02-24 07:37:12.954266-08 leaderboard t A100 0.0032000705 t \N \N \N {} +723 231 2025-02-24 09:17:26.030333-08 2025-02-24 08:18:23.337179-08 test f H100 \N t \N \N \N {} +724 231 2025-02-24 09:02:18.510228-08 2025-02-24 08:27:34.118305-08 test f A100 \N t \N \N \N {} +1136 316 2025-02-24 18:52:30.044102-08 2025-02-24 19:36:46.191371-08 test f A100 \N t \N \N \N {} +726 231 2025-02-24 08:34:48.003809-08 2025-02-24 08:40:27.717239-08 test f L4 \N f \N \N \N {} +732 233 2025-02-24 09:29:37.727181-08 2025-02-24 08:02:29.392295-08 benchmark f A100 \N t \N \N \N {} +733 233 2025-02-24 08:20:40.863772-08 2025-02-24 07:58:20.853653-08 leaderboard f A100 0.0031231213333333336 t \N \N \N {} +734 233 2025-02-24 08:49:10.944512-08 2025-02-24 08:12:13.280323-08 test f H100 \N t \N \N \N {} +735 233 2025-02-24 08:30:07.553648-08 2025-02-24 08:58:56.261174-08 benchmark f H100 \N t \N \N \N {} +1354 451 2025-02-25 02:04:40.095144-08 2025-02-25 03:49:19.013675-08 benchmark f H100 \N t \N \N \N {} +736 233 2025-02-24 08:27:18.705589-08 2025-02-24 09:10:53.230123-08 leaderboard f H100 0.002061958 t \N \N \N {} +741 233 2025-02-24 08:13:06.765051-08 2025-02-24 07:57:46.536533-08 benchmark f T4 \N t \N \N \N {} +742 233 2025-02-24 09:28:14.411862-08 2025-02-24 07:55:13.882323-08 leaderboard f T4 0.004024178 t \N \N \N {} +743 233 2025-02-24 08:45:35.726194-08 2025-02-24 07:49:16.0139-08 test t H100 \N t \N \N \N {} +744 233 2025-02-24 08:30:17.598827-08 2025-02-24 08:45:38.138191-08 benchmark t H100 \N t \N \N \N {} +745 233 2025-02-24 08:29:13.376851-08 2025-02-24 08:41:38.50256-08 leaderboard t H100 0.0024627566666666667 t \N \N \N {} +318 78 2025-02-23 14:02:29.033294-08 2025-02-23 14:26:37.663496-08 benchmark t H100 \N t \N \N \N {} +319 78 2025-02-23 15:24:09.465856-08 2025-02-23 14:31:24.442508-08 leaderboard t H100 0.001455489 t \N \N \N {} +327 79 2025-02-23 15:44:13.226491-08 2025-02-23 13:56:26.731737-08 benchmark t H100 \N t \N \N \N {} +336 82 2025-02-23 17:25:08.638515-08 2025-02-23 17:57:21.345045-08 test f H100 \N f \N \N \N {} +337 83 2025-02-23 18:00:31.70052-08 2025-02-23 17:58:50.769159-08 test f H100 \N f \N \N \N {} +338 84 2025-02-23 18:06:57.258257-08 2025-02-23 16:51:18.239842-08 test f H100 \N f \N \N \N {} +339 85 2025-02-23 17:10:24.770217-08 2025-02-23 17:18:24.35571-08 test f H100 \N f \N \N \N {} +768 244 2025-02-24 10:02:14.922389-08 2025-02-24 10:56:52.559337-08 leaderboard t A100 0.0032697126666666665 t \N \N \N {} +340 86 2025-02-23 17:42:30.9851-08 2025-02-23 18:05:44.756059-08 test f H100 \N f \N \N \N {} +3566 1211 2025-02-28 12:19:44.674046-08 2025-02-28 11:20:06.595714-08 benchmark f H100 \N t \N \N \N {} +1356 453 2025-02-25 02:50:36.296171-08 2025-02-25 03:40:22.203999-08 benchmark f H100 \N t \N \N \N {} +777 250 2025-02-24 10:13:18.705553-08 2025-02-24 11:57:51.322708-08 test f A100 \N t \N \N \N {} +778 250 2025-02-24 11:03:04.113706-08 2025-02-24 11:50:05.562623-08 benchmark f A100 \N t \N \N \N {} +779 250 2025-02-24 11:49:40.71682-08 2025-02-24 10:20:18.789178-08 leaderboard f A100 0.0032043086666666666 t \N \N \N {} +780 250 2025-02-24 11:38:14.616988-08 2025-02-24 11:50:19.652707-08 test t A100 \N t \N \N \N {} +781 250 2025-02-24 11:06:57.166985-08 2025-02-24 11:37:32.323803-08 benchmark t A100 \N t \N \N \N {} +1358 456 2025-02-25 03:04:57.340562-08 2025-02-25 03:41:50.970915-08 benchmark f H100 \N t \N \N \N {} +782 250 2025-02-24 11:40:30.959556-08 2025-02-24 11:55:45.22426-08 leaderboard t A100 0.0031807153333333333 t \N \N \N {} +783 251 2025-02-24 10:28:34.15622-08 2025-02-24 10:26:21.513069-08 test t A100 \N t \N \N \N {} +784 251 2025-02-24 11:22:43.230478-08 2025-02-24 10:17:13.237357-08 benchmark t A100 \N t \N \N \N {} +785 251 2025-02-24 11:01:06.012176-08 2025-02-24 10:42:38.296715-08 leaderboard t A100 0.003201019 t \N \N \N {} +354 94 2025-02-23 18:07:11.918125-08 2025-02-23 18:49:09.9145-08 benchmark t H100 \N t \N \N \N {} +361 97 2025-02-23 19:03:55.153785-08 2025-02-23 19:25:28.656928-08 test t H100 \N t \N \N \N {} +367 98 2025-02-23 19:12:25.166912-08 2025-02-23 18:43:49.363313-08 benchmark f H100 \N t \N \N \N {} +368 99 2025-02-23 18:24:18.547946-08 2025-02-23 19:38:43.869117-08 test t H100 \N t \N \N \N {} +369 99 2025-02-23 19:55:02.527597-08 2025-02-23 19:45:12.210798-08 benchmark t H100 \N t \N \N \N {} +371 99 2025-02-23 19:05:59.158414-08 2025-02-23 18:08:37.209856-08 test f H100 \N t \N \N \N {} +377 102 2025-02-23 18:16:40.647606-08 2025-02-23 18:22:04.894771-08 benchmark f H100 \N t \N \N \N {} +378 104 2025-02-23 18:44:39.804161-08 2025-02-23 20:02:52.428781-08 test f H100 \N t \N \N \N {} +379 104 2025-02-23 18:34:28.483515-08 2025-02-23 19:39:00.450206-08 benchmark f H100 \N t \N \N \N {} +380 104 2025-02-23 19:35:24.292215-08 2025-02-23 20:07:53.402422-08 leaderboard f H100 0.0010334816666666667 t \N \N \N {} +385 104 2025-02-23 19:53:09.908295-08 2025-02-23 19:18:59.271408-08 benchmark f A100 \N t \N \N \N {} +394 104 2025-02-23 19:10:09.186446-08 2025-02-23 18:43:18.809569-08 benchmark f L4 \N t \N \N \N {} +403 108 2025-02-23 19:55:38.938073-08 2025-02-23 20:17:37.031218-08 test f H100 \N f \N \N \N {} +404 109 2025-02-23 19:08:16.246023-08 2025-02-23 19:56:30.430723-08 test f H100 \N f \N \N \N {} +410 114 2025-02-23 20:00:48.18605-08 2025-02-23 19:19:29.420657-08 test f H100 \N f \N \N \N {} +411 116 2025-02-23 19:34:58.202046-08 2025-02-23 20:01:11.73353-08 benchmark f T4 \N t \N \N \N {} +412 118 2025-02-23 20:37:38.267216-08 2025-02-23 19:02:18.39604-08 benchmark f T4 \N f \N \N \N {} +413 117 2025-02-23 19:24:46.341973-08 2025-02-23 18:57:45.646571-08 test f A100 \N f \N \N \N {} +414 117 2025-02-23 18:57:59.477452-08 2025-02-23 20:15:43.624804-08 test t A100 \N f \N \N \N {} +415 119 2025-02-23 18:57:34.156909-08 2025-02-23 18:54:25.73194-08 benchmark f T4 \N f \N \N \N {} +416 120 2025-02-23 20:05:08.814298-08 2025-02-23 18:59:37.20305-08 test f L4 \N t \N \N \N {} +418 122 2025-02-23 20:50:03.177865-08 2025-02-23 20:14:16.240173-08 test f L4 \N t \N \N \N {} +786 251 2025-02-24 10:26:17.480514-08 2025-02-24 10:27:14.231102-08 test t T4 \N t \N \N \N {} +792 251 2025-02-24 11:19:29.761457-08 2025-02-24 10:12:45.329053-08 test t H100 \N t \N \N \N {} +1444 484 2025-02-25 05:32:24.532424-08 2025-02-25 05:46:04.733398-08 test t A100 \N t \N \N \N {} +788 251 2025-02-24 10:59:47.100658-08 2025-02-24 11:38:00.698912-08 leaderboard t T4 0.017558684 t \N \N \N {} +789 251 2025-02-24 12:04:49.533693-08 2025-02-24 10:27:37.844537-08 test f A100 \N t \N \N \N {} +790 251 2025-02-24 12:04:40.357942-08 2025-02-24 10:51:07.619523-08 benchmark f A100 \N t \N \N \N {} +791 251 2025-02-24 11:22:21.198065-08 2025-02-24 11:52:00.418781-08 leaderboard f A100 0.003236336 t \N \N \N {} +811 253 2025-02-24 11:06:37.307391-08 2025-02-24 11:40:28.537535-08 test t A100 \N t \N \N \N {} +1447 484 2025-02-25 05:28:22.571964-08 2025-02-25 04:07:59.842135-08 test f A100 \N t \N \N \N {} +793 251 2025-02-24 11:52:18.452547-08 2025-02-24 11:37:31.847226-08 benchmark t H100 \N t \N \N \N {} +794 251 2025-02-24 10:55:56.967992-08 2025-02-24 12:02:37.363415-08 leaderboard t H100 0.001464676 t \N \N \N {} +795 251 2025-02-24 11:55:37.519662-08 2025-02-24 10:47:45.331086-08 test f H100 \N t \N \N \N {} +796 251 2025-02-24 10:51:49.810385-08 2025-02-24 10:25:51.454365-08 benchmark f H100 \N t \N \N \N {} +797 251 2025-02-24 11:15:30.778494-08 2025-02-24 11:00:25.052673-08 leaderboard f H100 0.0014206696 t \N \N \N {} +825 258 2025-02-24 13:00:16.497406-08 2025-02-24 13:34:23.466302-08 leaderboard t A100 0.0032076514 t \N \N \N {} +420 124 2025-02-23 20:02:27.111264-08 2025-02-23 20:04:53.203923-08 test f L4 \N t \N \N \N {} +422 126 2025-02-23 20:06:43.793931-08 2025-02-23 20:32:18.759967-08 test f L4 \N t \N \N \N {} +423 127 2025-02-23 20:24:10.490624-08 2025-02-23 20:17:20.346438-08 test f L4 \N f \N \N \N {} +425 129 2025-02-23 20:46:15.037395-08 2025-02-23 19:07:35.835051-08 test f L4 \N f \N \N \N {} +1450 485 2025-02-25 04:19:06.14904-08 2025-02-25 04:39:46.798027-08 test t A100 \N t \N \N \N {} +800 251 2025-02-24 10:59:21.599538-08 2025-02-24 10:25:36.051807-08 leaderboard f T4 0.01756872166666667 t \N \N \N {} +801 251 2025-02-24 10:42:44.855208-08 2025-02-24 10:38:35.745986-08 test f L4 \N t \N \N \N {} +802 251 2025-02-24 11:52:41.634738-08 2025-02-24 11:17:02.406538-08 benchmark f L4 \N t \N \N \N {} +826 258 2025-02-24 12:08:48.726753-08 2025-02-24 13:40:11.091506-08 test f A100 \N t \N \N \N {} +1453 485 2025-02-25 04:53:38.828316-08 2025-02-25 04:03:40.596895-08 test f A100 \N t \N \N \N {} +806 251 2025-02-24 10:12:06.628686-08 2025-02-24 10:32:34.248863-08 leaderboard t L4 0.017363646666666666 t \N \N \N {} +807 252 2025-02-24 10:34:25.534395-08 2025-02-24 12:10:21.1189-08 test f A100 \N f \N \N \N {} +808 252 2025-02-24 12:08:19.909711-08 2025-02-24 11:07:28.545277-08 test t A100 \N f \N \N \N {} +809 253 2025-02-24 10:56:51.671658-08 2025-02-24 12:19:25.430319-08 test f A100 \N t \N \N \N {} +810 253 2025-02-24 12:24:27.607242-08 2025-02-24 11:11:09.097006-08 benchmark f A100 \N f \N \N \N {} +821 256 2025-02-24 13:32:00.37212-08 2025-02-24 12:39:04.454955-08 test f A100 \N f \N \N \N {} +822 257 2025-02-24 11:59:03.654568-08 2025-02-24 12:25:11.023194-08 test f H100 \N t \N \N \N {} +823 258 2025-02-24 13:11:25.747263-08 2025-02-24 12:51:12.133715-08 test t A100 \N t \N \N \N {} +824 258 2025-02-24 12:23:35.99065-08 2025-02-24 12:54:48.794104-08 benchmark t A100 \N t \N \N \N {} +828 258 2025-02-24 12:28:03.710997-08 2025-02-24 12:02:38.320312-08 leaderboard f A100 0.00319396075 t \N \N \N {} +830 259 2025-02-24 13:38:53.547783-08 2025-02-24 12:05:16.740651-08 benchmark f H100 \N t \N \N \N {} +831 259 2025-02-24 12:19:41.162515-08 2025-02-24 13:02:34.045891-08 leaderboard f H100 0.0014907706363636365 t \N \N \N {} +832 259 2025-02-24 13:34:25.778466-08 2025-02-24 12:32:27.73762-08 test t H100 \N t \N \N \N {} +833 259 2025-02-24 11:57:09.771187-08 2025-02-24 13:17:24.239643-08 benchmark t H100 \N t \N \N \N {} +834 259 2025-02-24 12:53:22.94161-08 2025-02-24 13:02:51.615542-08 leaderboard t H100 0.0015141807894736843 t \N \N \N {} +837 261 2025-02-24 14:33:31.764087-08 2025-02-24 14:09:19.937583-08 benchmark f H100 \N t \N \N \N {} +838 261 2025-02-24 13:29:16.943379-08 2025-02-24 14:28:28.491199-08 benchmark f A100 \N t \N \N \N {} +965 286 2025-02-24 15:17:07.072445-08 2025-02-24 15:23:57.396796-08 test f A100 \N t \N \N \N {} +843 262 2025-02-24 14:18:36.348524-08 2025-02-24 14:09:28.031494-08 leaderboard f H100 0.00158875175 t \N \N \N {} +1456 486 2025-02-25 05:57:12.50863-08 2025-02-25 04:24:28.062012-08 test f A100 \N t \N \N \N {} +850 262 2025-02-24 13:53:27.308737-08 2025-02-24 14:16:34.938488-08 test f L4 \N t \N \N \N {} +851 262 2025-02-24 12:43:25.694004-08 2025-02-24 14:30:18.247948-08 benchmark f L4 \N t \N \N \N {} +852 262 2025-02-24 14:41:37.221242-08 2025-02-24 14:15:54.675901-08 leaderboard f L4 0.01720207333333333 t \N \N \N {} +853 262 2025-02-24 13:03:41.972489-08 2025-02-24 13:01:38.374088-08 test f A100 \N t \N \N \N {} +854 262 2025-02-24 14:31:55.855835-08 2025-02-24 13:01:31.443949-08 benchmark f A100 \N t \N \N \N {} +1359 457 2025-02-25 02:33:43.299256-08 2025-02-25 02:58:46.454768-08 benchmark f H100 \N t \N \N \N {} +860 262 2025-02-24 14:13:52.225617-08 2025-02-24 14:17:22.296848-08 test t H100 \N t \N \N \N {} +1360 458 2025-02-25 04:21:39.8674-08 2025-02-25 03:31:46.64471-08 benchmark f H100 \N t \N \N \N {} +866 264 2025-02-24 14:18:15.875273-08 2025-02-24 14:25:54.32018-08 test t A100 \N f \N \N \N {} +426 130 2025-02-23 19:07:19.532288-08 2025-02-23 19:27:05.035489-08 test f L4 \N f \N \N \N {} +427 131 2025-02-23 19:48:04.004883-08 2025-02-23 20:55:59.519835-08 test f L4 \N f \N \N \N {} +428 132 2025-02-23 19:43:42.154861-08 2025-02-23 19:58:51.977474-08 test f L4 \N t \N \N \N {} +430 134 2025-02-23 19:41:20.008317-08 2025-02-23 19:11:47.214628-08 test f L4 \N t \N \N \N {} +431 135 2025-02-23 20:41:38.742341-08 2025-02-23 20:10:24.488802-08 test t A100 \N t \N \N \N {} +867 264 2025-02-24 13:47:02.310423-08 2025-02-24 14:19:30.07954-08 test f A100 \N f \N \N \N {} +868 265 2025-02-24 13:25:41.120417-08 2025-02-24 13:19:04.569194-08 test f A100 \N f \N \N \N {} +869 265 2025-02-24 14:09:21.007191-08 2025-02-24 14:19:43.13391-08 test t A100 \N f \N \N \N {} +5676 1725 2025-03-09 09:53:25.9676-07 2025-03-09 10:54:50.998675-07 test t H100 \N t \N \N \N {} +1361 459 2025-02-25 02:30:56.124266-08 2025-02-25 02:51:08.772443-08 test t A100 \N t \N \N \N {} +871 266 2025-02-24 13:56:37.656623-08 2025-02-24 13:34:36.431618-08 test f H100 \N f \N \N \N {} +872 267 2025-02-24 13:27:30.522948-08 2025-02-24 14:25:22.159132-08 test f A100 \N t \N \N \N {} +873 267 2025-02-24 14:06:05.758615-08 2025-02-24 14:17:03.596666-08 benchmark f A100 \N t \N \N \N {} +874 267 2025-02-24 14:47:02.145718-08 2025-02-24 14:26:33.636732-08 leaderboard f A100 0.0032893633333333336 t \N \N \N {} +875 267 2025-02-24 14:02:32.569668-08 2025-02-24 13:04:10.687457-08 test t A100 \N t \N \N \N {} +876 267 2025-02-24 14:20:17.088913-08 2025-02-24 13:03:00.990794-08 benchmark t A100 \N t \N \N \N {} +877 267 2025-02-24 13:03:37.085609-08 2025-02-24 14:42:26.626312-08 leaderboard t A100 0.0032918306666666667 t \N \N \N {} +878 268 2025-02-24 13:32:17.727867-08 2025-02-24 14:42:15.893182-08 test f H100 \N t \N \N \N {} +879 268 2025-02-24 13:46:35.691848-08 2025-02-24 14:33:17.187051-08 benchmark f H100 \N t \N \N \N {} +880 268 2025-02-24 13:09:44.706773-08 2025-02-24 14:40:37.527146-08 leaderboard f H100 0.00148714025 t \N \N \N {} +881 268 2025-02-24 14:38:11.050645-08 2025-02-24 14:37:44.308056-08 test t H100 \N t \N \N \N {} +972 288 2025-02-24 16:59:51.753306-08 2025-02-24 16:32:43.265594-08 test t A100 \N t \N \N \N {} +889 270 2025-02-24 13:23:28.860312-08 2025-02-24 15:14:01.508532-08 benchmark f H100 \N t \N \N \N {} +890 270 2025-02-24 14:06:21.369663-08 2025-02-24 13:44:06.555384-08 leaderboard f H100 0.00633421 t \N \N \N {} +891 270 2025-02-24 15:11:19.3884-08 2025-02-24 14:22:05.663402-08 test t H100 \N t \N \N \N {} +892 270 2025-02-24 13:34:23.965761-08 2025-02-24 15:03:36.056155-08 benchmark t H100 \N t \N \N \N {} +897 271 2025-02-24 13:23:42.310773-08 2025-02-24 13:47:13.289788-08 benchmark f T4 \N t \N \N \N {} +432 135 2025-02-23 19:22:15.455814-08 2025-02-23 19:58:43.024263-08 benchmark t A100 \N t \N \N \N {} +433 135 2025-02-23 19:46:23.596065-08 2025-02-23 21:02:09.718637-08 leaderboard t A100 0.011416369 t \N \N \N {} +434 135 2025-02-23 20:11:32.421186-08 2025-02-23 20:54:46.274828-08 test f A100 \N t \N \N \N {} +435 135 2025-02-23 20:18:13.731355-08 2025-02-23 20:15:53.15599-08 benchmark f A100 \N t \N \N \N {} +436 135 2025-02-23 20:00:20.118277-08 2025-02-23 20:51:44.559411-08 leaderboard f A100 0.010131549619047619 t \N \N \N {} +437 137 2025-02-23 19:49:28.16914-08 2025-02-23 20:57:31.153582-08 test f A100 \N t \N \N \N {} +438 137 2025-02-23 20:09:57.38955-08 2025-02-23 20:26:38.729961-08 benchmark f A100 \N t \N \N \N {} +439 137 2025-02-23 19:51:53.206453-08 2025-02-23 20:31:39.322151-08 leaderboard f A100 0.010931205 t \N \N \N {} +441 137 2025-02-23 20:15:20.626379-08 2025-02-23 20:57:29.810715-08 benchmark t A100 \N t \N \N \N {} +442 137 2025-02-23 20:45:29.846555-08 2025-02-23 19:10:45.121007-08 leaderboard t A100 0.010102002266666667 t \N \N \N {} +576 162 2025-02-24 04:19:34.761817-08 2025-02-24 05:01:13.840464-08 leaderboard t L4 0.017179168666666668 t \N \N \N {} +577 163 2025-02-24 04:01:18.525271-08 2025-02-24 04:17:48.618121-08 test f T4 \N f \N \N \N {} +898 272 2025-02-24 14:03:42.781647-08 2025-02-24 13:55:34.397369-08 test t H100 \N t \N \N \N {} +899 272 2025-02-24 13:44:04.949972-08 2025-02-24 13:28:20.378369-08 benchmark t H100 \N t \N \N \N {} +900 272 2025-02-24 13:45:21.588357-08 2025-02-24 15:00:10.446354-08 leaderboard t H100 0.001380456 t \N \N \N {} +901 272 2025-02-24 14:59:05.351263-08 2025-02-24 15:02:57.541551-08 test f A100 \N t \N \N \N {} +3584 1228 2025-02-28 13:43:38.707851-08 2025-02-28 14:06:32.301701-08 test t A100 \N t \N \N \N {} +902 272 2025-02-24 13:41:50.302661-08 2025-02-24 14:48:59.498164-08 benchmark f A100 \N t \N \N \N {} +903 272 2025-02-24 13:24:49.213705-08 2025-02-24 13:38:04.997177-08 leaderboard f A100 0.0030942173333333334 t \N \N \N {} +904 272 2025-02-24 14:29:42.928168-08 2025-02-24 13:26:18.774967-08 test t A100 \N t \N \N \N {} +905 272 2025-02-24 14:12:54.933528-08 2025-02-24 14:48:10.033573-08 benchmark t A100 \N t \N \N \N {} +906 272 2025-02-24 14:06:49.466739-08 2025-02-24 14:26:14.363403-08 leaderboard t A100 0.0030895613333333334 t \N \N \N {} +907 272 2025-02-24 13:27:26.40339-08 2025-02-24 14:22:30.451832-08 test f T4 \N t \N \N \N {} +908 272 2025-02-24 13:50:22.416422-08 2025-02-24 14:23:23.865231-08 benchmark f T4 \N t \N \N \N {} +909 272 2025-02-24 15:02:51.400167-08 2025-02-24 14:45:31.55137-08 leaderboard f T4 0.016812591 t \N \N \N {} +910 272 2025-02-24 14:54:42.275644-08 2025-02-24 14:02:35.113798-08 test t T4 \N t \N \N \N {} +911 272 2025-02-24 14:14:55.576574-08 2025-02-24 13:55:39.639099-08 benchmark t T4 \N t \N \N \N {} +912 272 2025-02-24 14:30:30.596529-08 2025-02-24 15:11:06.343919-08 leaderboard t T4 0.016815438 t \N \N \N {} +913 272 2025-02-24 14:03:43.587782-08 2025-02-24 13:32:24.372553-08 test f H100 \N t \N \N \N {} +914 272 2025-02-24 13:58:45.530798-08 2025-02-24 14:37:34.053628-08 benchmark f H100 \N t \N \N \N {} +915 272 2025-02-24 13:44:39.285633-08 2025-02-24 14:28:08.305027-08 leaderboard f H100 0.0013929076666666668 t \N \N \N {} +916 273 2025-02-24 13:46:49.172996-08 2025-02-24 14:08:28.506582-08 test f H100 \N t \N \N \N {} +917 273 2025-02-24 13:44:56.476334-08 2025-02-24 14:40:36.885261-08 benchmark f H100 \N t \N \N \N {} +918 273 2025-02-24 14:57:37.29992-08 2025-02-24 14:13:39.653275-08 leaderboard f H100 0.00629756 t \N \N \N {} +919 272 2025-02-24 14:32:30.167492-08 2025-02-24 14:11:08.41029-08 test t L4 \N t \N \N \N {} +920 272 2025-02-24 13:47:24.679318-08 2025-02-24 13:20:10.828648-08 benchmark t L4 \N t \N \N \N {} +921 272 2025-02-24 15:14:24.435268-08 2025-02-24 15:12:42.536442-08 leaderboard t L4 0.016873888666666666 t \N \N \N {} +923 272 2025-02-24 14:50:59.333942-08 2025-02-24 14:12:16.270455-08 benchmark f L4 \N t \N \N \N {} +924 272 2025-02-24 14:47:47.523473-08 2025-02-24 13:44:27.360148-08 leaderboard f L4 0.016924354 t \N \N \N {} +925 273 2025-02-24 15:11:48.660708-08 2025-02-24 13:29:55.704446-08 test t H100 \N t \N \N \N {} +926 273 2025-02-24 15:15:14.766004-08 2025-02-24 13:19:47.409296-08 benchmark t H100 \N t \N \N \N {} +927 273 2025-02-24 14:10:49.544286-08 2025-02-24 14:57:42.082587-08 leaderboard t H100 0.004302405666666667 t \N \N \N {} +984 288 2025-02-24 15:26:18.349293-08 2025-02-24 15:44:04.650422-08 test f A100 \N t \N \N \N {} +928 274 2025-02-24 15:05:47.967562-08 2025-02-24 15:17:26.160717-08 test f H100 \N f \N \N \N {} +929 274 2025-02-24 15:04:47.728115-08 2025-02-24 14:57:41.957077-08 test t H100 \N f \N \N \N {} +3585 1228 2025-02-28 14:17:57.496054-08 2025-02-28 13:50:52.43144-08 benchmark t A100 \N t \N \N \N {} +935 278 2025-02-24 14:28:49.91519-08 2025-02-24 15:19:34.617051-08 benchmark t A100 \N t \N \N \N {} +936 278 2025-02-24 13:44:17.851045-08 2025-02-24 15:26:06.123862-08 leaderboard t A100 0.0031084026666666664 t \N \N \N {} +937 278 2025-02-24 15:01:10.720121-08 2025-02-24 14:26:25.903037-08 test f A100 \N t \N \N \N {} +990 288 2025-02-24 16:29:43.864283-08 2025-02-24 16:06:04.412307-08 test f L4 \N t \N \N \N {} +938 278 2025-02-24 15:22:13.9455-08 2025-02-24 15:34:30.906411-08 benchmark f A100 \N t \N \N \N {} +939 278 2025-02-24 15:34:14.43871-08 2025-02-24 14:09:55.104192-08 leaderboard f A100 0.003099416 t \N \N \N {} +449 138 2025-02-23 20:17:26.595429-08 2025-02-23 20:35:29.611944-08 benchmark f L4 \N t \N \N \N {} +451 139 2025-02-23 20:51:10.6817-08 2025-02-23 20:38:48.437704-08 test f A100 \N t \N \N \N {} +452 139 2025-02-23 19:49:38.747753-08 2025-02-23 19:40:42.834492-08 benchmark f A100 \N t \N \N \N {} +940 278 2025-02-24 15:09:41.101654-08 2025-02-24 13:40:05.003362-08 test f H100 \N t \N \N \N {} +941 278 2025-02-24 14:12:31.025675-08 2025-02-24 15:21:52.442215-08 benchmark f H100 \N t \N \N \N {} +942 278 2025-02-24 15:26:06.113726-08 2025-02-24 14:54:47.505262-08 leaderboard f H100 0.0013924253333333332 t \N \N \N {} +943 278 2025-02-24 13:41:17.222805-08 2025-02-24 14:40:55.819332-08 test t H100 \N t \N \N \N {} +944 278 2025-02-24 14:51:33.706683-08 2025-02-24 15:02:31.7023-08 benchmark t H100 \N t \N \N \N {} +945 278 2025-02-24 15:15:53.225485-08 2025-02-24 13:41:18.489963-08 leaderboard t H100 0.001391712 t \N \N \N {} +947 278 2025-02-24 13:47:07.517676-08 2025-02-24 14:35:18.503122-08 benchmark f L4 \N t \N \N \N {} +948 278 2025-02-24 15:32:37.786282-08 2025-02-24 14:47:49.400993-08 leaderboard f L4 0.016921183 t \N \N \N {} +950 278 2025-02-24 14:52:40.705932-08 2025-02-24 15:12:22.348896-08 benchmark t L4 \N t \N \N \N {} +951 278 2025-02-24 15:20:16.824802-08 2025-02-24 14:51:53.886502-08 leaderboard t L4 0.01689634233333333 t \N \N \N {} +952 278 2025-02-24 13:42:04.283901-08 2025-02-24 13:50:01.376078-08 test t T4 \N t \N \N \N {} +959 280 2025-02-24 14:37:57.453433-08 2025-02-24 13:55:35.067117-08 benchmark f A100 \N t \N \N \N {} +960 281 2025-02-24 15:29:16.351075-08 2025-02-24 15:15:34.238127-08 benchmark f A100 \N f \N \N \N {} +996 289 2025-02-24 17:05:53.711415-08 2025-02-24 16:12:17.469633-08 leaderboard f A100 0.0031874743333333336 t \N \N \N {} +961 282 2025-02-24 14:54:40.402838-08 2025-02-24 15:07:51.744951-08 benchmark f A100 \N t \N \N \N {} +1326 443 2025-02-25 01:31:19.548777-08 2025-02-25 02:21:57.556426-08 benchmark t A100 \N t \N \N \N {} +969 286 2025-02-24 14:27:36.98298-08 2025-02-24 14:24:18.873791-08 benchmark t A100 \N t \N \N \N {} +970 286 2025-02-24 15:45:02.969062-08 2025-02-24 15:20:58.131836-08 leaderboard t A100 0.003574085 t \N \N \N {} +971 287 2025-02-24 16:45:49.221907-08 2025-02-24 15:11:06.695278-08 benchmark f A100 \N t \N \N \N {} +1364 459 2025-02-25 03:07:07.507086-08 2025-02-25 03:28:33.611205-08 test f A100 \N t \N \N \N {} +973 288 2025-02-24 16:57:39.832425-08 2025-02-24 16:14:19.158825-08 benchmark t A100 \N t \N \N \N {} +974 288 2025-02-24 16:17:36.67277-08 2025-02-24 15:25:36.315446-08 leaderboard t A100 0.0035733863333333336 t \N \N \N {} +976 288 2025-02-24 16:12:37.835505-08 2025-02-24 16:32:25.940514-08 benchmark t H100 \N t \N \N \N {} +977 288 2025-02-24 16:02:16.632285-08 2025-02-24 15:30:08.973921-08 leaderboard t H100 0.0015792905 t \N \N \N {} +1367 460 2025-02-25 02:54:18.569745-08 2025-02-25 03:04:44.660402-08 test f H100 \N t \N \N \N {} +1462 487 2025-02-25 05:11:56.373844-08 2025-02-25 05:12:25.148355-08 test t H100 \N t \N \N \N {} +979 288 2025-02-24 16:39:20.43363-08 2025-02-24 16:31:03.690589-08 benchmark f H100 \N t \N \N \N {} +980 288 2025-02-24 15:39:09.650488-08 2025-02-24 15:08:31.848197-08 leaderboard f H100 0.001654801 t \N \N \N {} +981 288 2025-02-24 16:32:20.98845-08 2025-02-24 15:27:23.058332-08 test f T4 \N t \N \N \N {} +982 288 2025-02-24 15:11:31.029521-08 2025-02-24 15:54:36.042517-08 benchmark f T4 \N t \N \N \N {} +983 288 2025-02-24 15:57:27.675389-08 2025-02-24 16:16:35.300427-08 leaderboard f T4 0.01727558566666667 t \N \N \N {} +1370 460 2025-02-25 03:37:37.587036-08 2025-02-25 03:12:01.251999-08 test t H100 \N t \N \N \N {} +1465 487 2025-02-25 05:28:37.945849-08 2025-02-25 05:31:06.395877-08 test f H100 \N t \N \N \N {} +985 288 2025-02-24 16:00:46.319439-08 2025-02-24 16:11:47.492733-08 benchmark f A100 \N t \N \N \N {} +986 288 2025-02-24 16:16:37.007915-08 2025-02-24 16:47:35.551783-08 leaderboard f A100 0.0035907183333333333 t \N \N \N {} +987 288 2025-02-24 15:53:31.716837-08 2025-02-24 15:52:50.195056-08 test t T4 \N t \N \N \N {} +988 288 2025-02-24 15:24:51.892743-08 2025-02-24 15:37:05.023212-08 benchmark t T4 \N t \N \N \N {} +989 288 2025-02-24 15:27:07.594575-08 2025-02-24 16:56:10.768764-08 leaderboard t T4 0.017142831666666667 t \N \N \N {} +1373 461 2025-02-25 03:02:32.607966-08 2025-02-25 02:46:59.676745-08 test t H100 \N t \N \N \N {} +1469 489 2025-02-25 06:10:25.611874-08 2025-02-25 04:53:36.581953-08 test f A100 \N t \N \N \N {} +991 288 2025-02-24 16:15:35.403958-08 2025-02-24 15:42:05.66197-08 benchmark f L4 \N t \N \N \N {} +992 288 2025-02-24 15:44:25.515087-08 2025-02-24 16:58:38.135537-08 leaderboard f L4 0.017213298666666668 t \N \N \N {} +453 139 2025-02-23 20:25:09.33463-08 2025-02-23 21:00:42.630896-08 leaderboard f A100 2.3280243876666664 t \N \N \N {} +454 139 2025-02-23 19:54:51.244923-08 2025-02-23 20:51:32.367147-08 test t A100 \N t \N \N \N {} +455 139 2025-02-23 19:45:18.073547-08 2025-02-23 20:41:22.97686-08 benchmark t A100 \N t \N \N \N {} +456 139 2025-02-23 21:07:14.882985-08 2025-02-23 20:12:14.513014-08 leaderboard t A100 2.2617255652727275 t \N \N \N {} +458 142 2025-02-24 01:14:42.946921-08 2025-02-24 00:35:39.452004-08 test f A100 \N f \N \N \N {} +459 143 2025-02-24 01:11:00.689386-08 2025-02-24 00:41:21.530234-08 test f A100 \N t \N \N \N {} +460 144 2025-02-24 00:41:59.723288-08 2025-02-24 00:27:53.246315-08 test t A100 \N t \N \N \N {} +461 144 2025-02-24 00:21:52.786601-08 2025-02-24 01:33:07.398183-08 benchmark t A100 \N t \N \N \N {} +462 144 2025-02-24 01:54:02.801471-08 2025-02-24 01:19:41.256152-08 leaderboard t A100 0.003105596 t \N \N \N {} +463 144 2025-02-24 00:31:47.833871-08 2025-02-24 01:26:28.316452-08 test f A100 \N t \N \N \N {} +464 144 2025-02-24 01:15:21.059343-08 2025-02-24 01:40:04.362432-08 benchmark f A100 \N t \N \N \N {} +469 145 2025-02-24 00:21:16.048547-08 2025-02-24 01:12:36.574185-08 test f A100 \N t \N \N \N {} +470 145 2025-02-24 00:17:52.83432-08 2025-02-24 01:49:29.222442-08 benchmark f A100 \N t \N \N \N {} +471 145 2025-02-24 01:06:14.871957-08 2025-02-24 01:44:46.357832-08 leaderboard f A100 0.0030890706666666665 t \N \N \N {} +485 145 2025-02-24 01:20:54.849052-08 2025-02-24 01:08:55.25546-08 benchmark f T4 \N f \N \N \N {} +487 145 2025-02-24 00:17:54.793622-08 2025-02-24 00:31:22.4894-08 benchmark t T4 \N f \N \N \N {} +488 148 2025-02-24 03:09:35.205519-08 2025-02-24 03:27:23.971276-08 benchmark f A100 \N t \N \N \N {} +489 149 2025-02-24 03:30:55.449817-08 2025-02-24 03:37:56.567594-08 test f H100 \N t \N \N \N {} +490 150 2025-02-24 04:21:26.450468-08 2025-02-24 04:24:44.221828-08 test f H100 \N f \N \N \N {} +491 150 2025-02-24 04:05:31.466828-08 2025-02-24 03:04:56.25643-08 test t H100 \N t \N \N \N {} +993 288 2025-02-24 16:22:57.509895-08 2025-02-24 16:11:34.646111-08 test t L4 \N f \N \N \N {} +994 289 2025-02-24 16:42:29.124469-08 2025-02-24 16:15:50.980733-08 test f A100 \N t \N \N \N {} +995 289 2025-02-24 16:03:41.521804-08 2025-02-24 15:55:40.302003-08 benchmark f A100 \N t \N \N \N {} +997 289 2025-02-24 15:54:37.535241-08 2025-02-24 15:25:33.427509-08 test t A100 \N t \N \N \N {} +1327 443 2025-02-25 02:11:01.004727-08 2025-02-25 02:37:11.369093-08 leaderboard t A100 0.003228157 t \N \N \N {} +1328 443 2025-02-25 02:29:43.574743-08 2025-02-25 01:45:12.561001-08 test f A100 \N t \N \N \N {} +998 289 2025-02-24 15:30:32.784103-08 2025-02-24 15:14:53.659942-08 benchmark t A100 \N t \N \N \N {} +492 150 2025-02-24 03:09:36.145036-08 2025-02-24 03:37:47.928735-08 benchmark t H100 \N t \N \N \N {} +493 150 2025-02-24 03:50:14.789495-08 2025-02-24 04:18:31.651743-08 leaderboard t H100 0.00032365892307692306 t \N \N \N {} +494 151 2025-02-24 03:23:37.243425-08 2025-02-24 03:24:21.097123-08 test t H100 \N t \N \N \N {} +495 151 2025-02-24 04:50:10.450163-08 2025-02-24 03:28:44.586591-08 benchmark t H100 \N t \N \N \N {} +496 151 2025-02-24 04:56:39.642072-08 2025-02-24 04:24:35.925272-08 leaderboard t H100 0.00029347884745762714 t \N \N \N {} +497 151 2025-02-24 03:19:04.263395-08 2025-02-24 04:48:57.333637-08 test f H100 \N t \N \N \N {} +498 151 2025-02-24 04:44:55.465081-08 2025-02-24 04:38:58.27472-08 benchmark f H100 \N t \N \N \N {} +499 151 2025-02-24 04:22:14.523605-08 2025-02-24 03:36:00.958698-08 leaderboard f H100 0.0003125368620689655 t \N \N \N {} +5247 1613 2025-03-06 06:38:02.469655-08 2025-03-06 08:12:20.172654-08 test f H100 \N t \N \N \N {} +1322 442 2025-02-25 00:41:54.545147-08 2025-02-25 02:36:02.144792-08 test t T4 \N t \N \N \N {} +999 289 2025-02-24 15:25:33.457905-08 2025-02-24 16:27:05.872169-08 leaderboard t A100 0.0031989713333333337 t \N \N \N {} +1000 289 2025-02-24 16:48:53.415372-08 2025-02-24 16:21:48.670188-08 test t T4 \N t \N \N \N {} +1001 289 2025-02-24 17:09:32.511952-08 2025-02-24 15:33:31.453203-08 benchmark t T4 \N t \N \N \N {} +1376 461 2025-02-25 03:14:56.330738-08 2025-02-25 02:37:49.658574-08 test f H100 \N t \N \N \N {} +1471 491 2025-02-25 05:29:20.665807-08 2025-02-25 05:15:22.315477-08 test t A100 \N t \N \N \N {} +532 154 2025-02-24 04:48:16.131212-08 2025-02-24 04:44:31.007006-08 test f H100 \N t \N \N \N {} +3573 1218 2025-02-28 13:45:23.407437-08 2025-02-28 12:25:46.532879-08 test f H100 \N t \N \N \N {} +1118 304 2025-02-24 18:18:52.599968-08 2025-02-24 18:09:21.428872-08 leaderboard f L4 0.027837713666666666 t \N \N \N {} +2417 820 2025-02-26 12:21:06.882191-08 2025-02-26 13:15:22.606597-08 benchmark f H100 \N t \N \N \N {} +1123 306 2025-02-24 17:10:46.114629-08 2025-02-24 18:46:22.129619-08 benchmark f L4 \N t \N \N \N {} +1124 307 2025-02-24 17:18:47.489571-08 2025-02-24 17:33:10.036339-08 benchmark f H100 \N f \N \N \N {} +1129 312 2025-02-24 18:14:08.836383-08 2025-02-24 19:05:16.466962-08 test f T4 \N t \N \N \N {} +1130 313 2025-02-24 18:32:21.793683-08 2025-02-24 18:56:10.553395-08 test f T4 \N f \N \N \N {} +1131 314 2025-02-24 19:53:32.767284-08 2025-02-24 18:20:01.673708-08 test f A100 \N t \N \N \N {} +1132 315 2025-02-24 19:41:33.984587-08 2025-02-24 19:22:46.368176-08 test f A100 \N f \N \N \N {} +1133 316 2025-02-24 19:51:34.421459-08 2025-02-24 18:58:50.828608-08 test t A100 \N t \N \N \N {} +1134 316 2025-02-24 19:10:22.774186-08 2025-02-24 18:51:45.619893-08 benchmark t A100 \N t \N \N \N {} +1137 316 2025-02-24 19:55:11.126516-08 2025-02-24 18:17:41.336484-08 benchmark f A100 \N t \N \N \N {} +1138 316 2025-02-24 19:34:48.644475-08 2025-02-24 18:14:08.964849-08 leaderboard f A100 0.01013649895 t \N \N \N {} +1139 317 2025-02-24 19:01:55.707471-08 2025-02-24 19:26:25.81366-08 test f A100 \N f \N \N \N {} +1140 318 2025-02-24 18:34:52.552066-08 2025-02-24 19:48:38.716235-08 benchmark f A100 \N t \N \N \N {} +1141 319 2025-02-24 19:23:43.21952-08 2025-02-24 18:51:42.032684-08 test f A100 \N t \N \N \N {} +1142 320 2025-02-24 19:25:23.832847-08 2025-02-24 18:33:34.811732-08 benchmark f A100 \N t \N \N \N {} +1143 321 2025-02-24 20:03:32.364119-08 2025-02-24 18:41:04.100608-08 test f A100 \N t \N \N \N {} +1148 321 2025-02-24 19:35:02.480415-08 2025-02-24 19:46:33.415377-08 leaderboard t A100 0.008000332333333334 t \N \N \N {} +533 154 2025-02-24 04:30:12.737914-08 2025-02-24 03:41:45.719226-08 benchmark f H100 \N t \N \N \N {} +534 154 2025-02-24 04:26:39.743431-08 2025-02-24 03:49:38.668231-08 leaderboard f H100 0.00028956029310344826 t \N \N \N {} +1652 545 2025-02-25 09:28:59.147977-08 2025-02-25 08:22:34.616891-08 benchmark f A100 \N t \N \N \N {} +1653 545 2025-02-25 09:29:22.402709-08 2025-02-25 09:43:20.480827-08 leaderboard f A100 0.0031917851666666665 t \N \N \N {} +1654 545 2025-02-25 09:23:36.561531-08 2025-02-25 09:22:56.481202-08 test t A100 \N t \N \N \N {} +1655 545 2025-02-25 09:14:47.319988-08 2025-02-25 09:09:28.866828-08 benchmark t A100 \N t \N \N \N {} +1665 549 2025-02-25 09:27:34.175531-08 2025-02-25 08:31:10.590161-08 test t H100 \N t \N \N \N {} +2774 966 2025-02-26 20:38:42.929598-08 2025-02-26 19:33:54.323653-08 benchmark f A100 \N t \N \N \N {} +1671 550 2025-02-25 08:02:54.544859-08 2025-02-25 09:33:33.645968-08 test t H100 \N t \N \N \N {} +1672 550 2025-02-25 08:20:15.110251-08 2025-02-25 09:46:59.895856-08 benchmark t H100 \N t \N \N \N {} +1673 550 2025-02-25 08:16:05.976617-08 2025-02-25 08:56:29.376989-08 leaderboard t H100 0.0015150700833333332 t \N \N \N {} +1674 550 2025-02-25 09:23:16.872377-08 2025-02-25 09:42:17.075687-08 test f H100 \N t \N \N \N {} +1675 550 2025-02-25 08:00:39.040555-08 2025-02-25 08:14:08.930921-08 benchmark f H100 \N t \N \N \N {} +1677 551 2025-02-25 09:24:55.210117-08 2025-02-25 08:31:50.387183-08 benchmark f H100 \N t \N \N \N {} +4802 1487 2025-03-02 13:11:42.877171-08 2025-03-02 12:36:48.868582-08 test t A100 \N t \N \N \N {} +1704 565 2025-02-25 10:02:10.859525-08 2025-02-25 10:07:50.65938-08 test f A100 \N f \N \N \N {} +1705 566 2025-02-25 08:18:19.009138-08 2025-02-25 09:21:13.615277-08 test f A100 \N f \N \N \N {} +1706 566 2025-02-25 08:59:25.06759-08 2025-02-25 08:45:34.409254-08 test t A100 \N f \N \N \N {} +1708 570 2025-02-25 10:07:15.647675-08 2025-02-25 08:36:37.22994-08 test f T4 \N f \N \N \N {} +1709 570 2025-02-25 09:55:08.132128-08 2025-02-25 08:32:55.552216-08 test t T4 \N f \N \N \N {} +535 154 2025-02-24 04:07:21.29398-08 2025-02-24 04:13:20.791686-08 test t H100 \N t \N \N \N {} +536 154 2025-02-24 03:48:34.48216-08 2025-02-24 03:16:51.711753-08 benchmark t H100 \N t \N \N \N {} +537 154 2025-02-24 04:32:16.672746-08 2025-02-24 03:57:43.518406-08 leaderboard t H100 0.0002925063181818182 t \N \N \N {} +538 155 2025-02-24 04:35:32.646526-08 2025-02-24 05:04:46.599926-08 test f L4 \N f \N \N \N {} +539 155 2025-02-24 04:18:31.344638-08 2025-02-24 04:55:25.858103-08 test f T4 \N f \N \N \N {} +540 156 2025-02-24 03:43:29.839126-08 2025-02-24 03:21:47.390495-08 test f T4 \N t \N \N \N {} +543 157 2025-02-24 04:59:50.4166-08 2025-02-24 04:19:05.690038-08 leaderboard f A100 0.007824704333333333 t \N \N \N {} +544 157 2025-02-24 03:39:23.735651-08 2025-02-24 04:15:01.411276-08 test f T4 \N t \N \N \N {} +545 157 2025-02-24 05:04:10.421675-08 2025-02-24 04:14:54.241718-08 benchmark f T4 \N t \N \N \N {} +546 157 2025-02-24 04:57:33.857732-08 2025-02-24 04:22:04.003536-08 leaderboard f T4 0.022980689 t \N \N \N {} +547 157 2025-02-24 05:07:45.788613-08 2025-02-24 05:08:16.466658-08 test t T4 \N t \N \N \N {} +548 157 2025-02-24 03:17:36.228955-08 2025-02-24 05:13:27.73913-08 benchmark t T4 \N t \N \N \N {} +561 162 2025-02-24 05:06:05.251848-08 2025-02-24 03:48:29.825948-08 test f A100 \N t \N \N \N {} +562 162 2025-02-24 04:57:26.287277-08 2025-02-24 04:27:02.699784-08 benchmark f A100 \N t \N \N \N {} +563 162 2025-02-24 03:32:11.303899-08 2025-02-24 04:54:23.971009-08 leaderboard f A100 0.003460327 t \N \N \N {} +564 162 2025-02-24 04:54:00.853848-08 2025-02-24 04:59:22.66851-08 test t A100 \N t \N \N \N {} +565 162 2025-02-24 03:49:00.261076-08 2025-02-24 04:32:14.986087-08 benchmark t A100 \N t \N \N \N {} +566 162 2025-02-24 05:24:04.211942-08 2025-02-24 04:24:57.431418-08 leaderboard t A100 0.0034634703333333333 t \N \N \N {} +567 162 2025-02-24 04:05:26.770481-08 2025-02-24 03:41:41.45303-08 test t T4 \N t \N \N \N {} +1823 621 2025-02-25 09:37:22.310956-08 2025-02-25 10:04:30.945593-08 test f H100 \N t \N \N \N {} +1824 621 2025-02-25 09:48:47.475736-08 2025-02-25 10:36:25.122648-08 benchmark f H100 \N t \N \N \N {} +2416 818 2025-02-26 13:08:10.332573-08 2025-02-26 12:27:32.298697-08 benchmark f H100 \N f \N \N \N {} +2631 885 2025-02-26 14:51:45.736554-08 2025-02-26 14:47:20.105491-08 test f T4 \N t \N \N \N {} +1829 621 2025-02-25 09:36:01.751668-08 2025-02-25 10:06:26.482401-08 test t H100 \N t \N \N \N {} +1835 621 2025-02-25 10:55:06.644298-08 2025-02-25 10:16:25.436989-08 test t T4 \N t \N \N \N {} +1831 621 2025-02-25 09:36:43.309023-08 2025-02-25 09:39:20.008693-08 leaderboard t H100 0.00007977966666666668 t \N \N \N {} +1832 621 2025-02-25 09:37:12.560705-08 2025-02-25 11:23:33.885332-08 test f T4 \N t \N \N \N {} +1833 621 2025-02-25 10:54:16.770926-08 2025-02-25 10:13:23.913495-08 benchmark f T4 \N t \N \N \N {} +1834 621 2025-02-25 10:45:06.995356-08 2025-02-25 11:28:55.043321-08 leaderboard f T4 0.0003147605 t \N \N \N {} +1885 637 2025-02-25 11:14:01.886924-08 2025-02-25 10:33:34.875247-08 test t A100 \N t \N \N \N {} +3795 1280 2025-03-01 03:06:18.765637-08 2025-03-01 02:10:41.553886-08 test f A100 \N t \N \N \N {} +1836 621 2025-02-25 09:59:07.958405-08 2025-02-25 11:28:20.518166-08 benchmark t T4 \N t \N \N \N {} +1837 621 2025-02-25 10:14:38.896693-08 2025-02-25 10:18:05.012617-08 leaderboard t T4 0.00028145 t \N \N \N {} +568 162 2025-02-24 04:07:53.242047-08 2025-02-24 04:45:46.701262-08 benchmark t T4 \N t \N \N \N {} +569 162 2025-02-24 04:57:58.483951-08 2025-02-24 03:47:41.879786-08 leaderboard t T4 0.018000088 t \N \N \N {} +570 162 2025-02-24 04:07:02.728359-08 2025-02-24 03:30:28.466692-08 test f T4 \N t \N \N \N {} +571 162 2025-02-24 04:55:55.111211-08 2025-02-24 03:47:05.632536-08 benchmark f T4 \N t \N \N \N {} +578 164 2025-02-24 03:34:30.302743-08 2025-02-24 03:44:44.420625-08 test t H100 \N f \N \N \N {} +579 164 2025-02-24 04:40:03.835891-08 2025-02-24 05:14:49.678388-08 test f H100 \N f \N \N \N {} +580 165 2025-02-24 03:46:14.412089-08 2025-02-24 03:30:10.462639-08 test f T4 \N f \N \N \N {} +583 168 2025-02-24 03:40:59.436525-08 2025-02-24 04:13:33.24366-08 test f T4 \N f \N \N \N {} +584 169 2025-02-24 04:11:41.64701-08 2025-02-24 04:29:29.162765-08 test f T4 \N f \N \N \N {} +585 170 2025-02-24 03:50:54.355136-08 2025-02-24 04:32:48.353854-08 test f T4 \N f \N \N \N {} +609 172 2025-02-24 04:16:21.994773-08 2025-02-24 05:27:31.23999-08 test t L4 \N t \N \N \N {} +1839 621 2025-02-25 10:11:09.972425-08 2025-02-25 11:10:01.691566-08 benchmark f L4 \N t \N \N \N {} +1840 621 2025-02-25 10:32:35.014208-08 2025-02-25 10:14:06.170474-08 leaderboard f L4 0.00015338506896551724 t \N \N \N {} +1891 641 2025-02-25 12:30:16.797873-08 2025-02-25 12:50:15.869754-08 test t A100 \N t \N \N \N {} +3798 1280 2025-03-01 02:58:08.249313-08 2025-03-01 02:10:02.417766-08 test t A100 \N t \N \N \N {} +1844 622 2025-02-25 10:39:39.253611-08 2025-02-25 10:37:50.127615-08 benchmark f H100 \N t \N \N \N {} +1845 623 2025-02-25 11:18:57.219265-08 2025-02-25 10:30:49.939246-08 benchmark f H100 \N t \N \N \N {} +1851 626 2025-02-25 11:37:40.953324-08 2025-02-25 10:18:34.146973-08 test t H100 \N t \N \N \N {} +1911 658 2025-02-25 13:07:44.459666-08 2025-02-25 12:36:55.345563-08 test f L4 \N t \N \N \N {} +1854 627 2025-02-25 11:09:31.179796-08 2025-02-25 09:46:07.024596-08 test f H100 \N t \N \N \N {} +1855 627 2025-02-25 10:50:06.45193-08 2025-02-25 10:00:12.679191-08 benchmark f H100 \N t \N \N \N {} +1856 627 2025-02-25 11:18:03.678053-08 2025-02-25 09:50:41.832265-08 leaderboard f H100 0.001456563 t \N \N \N {} +1857 627 2025-02-25 10:15:45.419667-08 2025-02-25 09:46:39.110459-08 test t H100 \N t \N \N \N {} +1858 627 2025-02-25 11:37:33.070625-08 2025-02-25 10:23:48.04595-08 benchmark t H100 \N t \N \N \N {} +1859 627 2025-02-25 10:14:25.240727-08 2025-02-25 10:53:04.749149-08 leaderboard t H100 0.0014359026000000002 t \N \N \N {} +1860 628 2025-02-25 10:38:15.088855-08 2025-02-25 11:33:14.17158-08 benchmark f A100 \N t \N \N \N {} +1861 629 2025-02-25 11:24:33.838768-08 2025-02-25 11:45:59.052869-08 test f T4 \N t \N \N \N {} +1862 629 2025-02-25 10:18:02.87223-08 2025-02-25 10:01:30.670626-08 benchmark f T4 \N t \N \N \N {} +1863 629 2025-02-25 10:09:20.677026-08 2025-02-25 09:56:35.921709-08 leaderboard f T4 0.02022982983333333 t \N \N \N {} +2418 821 2025-02-26 12:32:06.906595-08 2025-02-26 12:30:33.821366-08 benchmark f H100 \N f \N \N \N {} +1872 635 2025-02-25 10:44:47.83768-08 2025-02-25 11:13:43.163043-08 leaderboard f A100 0.0031772633333333336 t \N \N \N {} +1873 635 2025-02-25 11:15:16.010215-08 2025-02-25 11:53:54.827853-08 test t A100 \N t \N \N \N {} +1875 635 2025-02-25 11:43:28.20069-08 2025-02-25 10:13:38.45603-08 leaderboard t A100 0.0032446378333333336 t \N \N \N {} +1877 636 2025-02-25 11:17:18.590101-08 2025-02-25 10:55:34.300914-08 benchmark f H100 \N t \N \N \N {} +1878 636 2025-02-25 11:59:53.786933-08 2025-02-25 11:34:35.925027-08 leaderboard f H100 0.0015205423333333333 t \N \N \N {} +588 172 2025-02-24 03:58:37.431798-08 2025-02-24 05:14:46.271612-08 test t A100 \N t \N \N \N {} +589 172 2025-02-24 05:31:54.505019-08 2025-02-24 04:23:39.720037-08 benchmark t A100 \N t \N \N \N {} +590 172 2025-02-24 03:42:59.187635-08 2025-02-24 04:26:15.997691-08 leaderboard t A100 0.003166644 t \N \N \N {} +591 172 2025-02-24 05:33:24.628818-08 2025-02-24 05:27:53.609663-08 test f H100 \N t \N \N \N {} +624 174 2025-02-24 04:00:03.344427-08 2025-02-24 04:30:10.212094-08 test f H100 \N t \N \N \N {} +592 172 2025-02-24 04:51:20.046359-08 2025-02-24 04:45:20.807066-08 benchmark f H100 \N t \N \N \N {} +1879 636 2025-02-25 10:21:35.338595-08 2025-02-25 10:41:36.288859-08 test t H100 \N t \N \N \N {} +1880 636 2025-02-25 11:47:02.581075-08 2025-02-25 11:37:54.178586-08 benchmark t H100 \N t \N \N \N {} +1881 636 2025-02-25 11:55:10.637717-08 2025-02-25 10:36:44.890626-08 leaderboard t H100 0.0014829484285714286 t \N \N \N {} +1886 637 2025-02-25 12:00:57.953939-08 2025-02-25 10:55:31.592722-08 benchmark t A100 \N t \N \N \N {} +1887 637 2025-02-25 11:56:19.406489-08 2025-02-25 11:35:39.598186-08 leaderboard t A100 0.003213315 t \N \N \N {} +1888 638 2025-02-25 12:00:43.449685-08 2025-02-25 12:07:45.096661-08 benchmark f A100 \N t \N \N \N {} +2420 823 2025-02-26 13:00:06.805549-08 2025-02-26 12:21:44.75151-08 benchmark f H100 \N t \N \N \N {} +1895 641 2025-02-25 12:54:01.264518-08 2025-02-25 11:46:30.101684-08 benchmark f A100 \N t \N \N \N {} +1896 641 2025-02-25 13:38:23.446635-08 2025-02-25 13:36:59.184489-08 leaderboard f A100 0.0030864853333333335 t \N \N \N {} +1897 642 2025-02-25 13:27:14.029692-08 2025-02-25 12:51:21.781335-08 test f T4 \N f \N \N \N {} +1912 658 2025-02-25 13:03:54.537257-08 2025-02-25 13:47:58.322026-08 benchmark f L4 \N t \N \N \N {} +1898 643 2025-02-25 13:22:18.087617-08 2025-02-25 11:54:21.931015-08 benchmark f A100 \N t \N \N \N {} +2421 825 2025-02-26 13:18:04.197511-08 2025-02-26 13:05:21.334106-08 benchmark f H100 \N t \N \N \N {} +1904 650 2025-02-25 12:31:28.402471-08 2025-02-25 13:39:04.170868-08 benchmark f A100 \N t \N \N \N {} +1905 651 2025-02-25 13:24:04.41766-08 2025-02-25 13:44:49.042952-08 benchmark f A100 \N t \N \N \N {} +1914 658 2025-02-25 13:39:12.203771-08 2025-02-25 12:35:51.134173-08 test t L4 \N t \N \N \N {} +1906 652 2025-02-25 13:04:03.79534-08 2025-02-25 12:26:24.162075-08 benchmark f A100 \N t \N \N \N {} +1907 654 2025-02-25 12:31:53.004451-08 2025-02-25 12:56:24.053648-08 test f T4 \N f \N \N \N {} +1908 653 2025-02-25 12:56:54.374943-08 2025-02-25 12:02:39.31621-08 benchmark f A100 \N t \N \N \N {} +1909 656 2025-02-25 13:47:06.574062-08 2025-02-25 13:06:17.287316-08 benchmark f A100 \N t \N \N \N {} +1910 657 2025-02-25 12:05:55.108287-08 2025-02-25 12:31:57.728641-08 benchmark f A100 \N t \N \N \N {} +3804 1281 2025-03-01 03:15:18.618566-08 2025-03-01 03:09:21.435376-08 test t A100 \N t \N \N \N {} +1915 658 2025-02-25 12:52:49.663863-08 2025-02-25 12:15:36.754739-08 benchmark t L4 \N t \N \N \N {} +1916 658 2025-02-25 13:46:15.680818-08 2025-02-25 12:14:55.778199-08 leaderboard t L4 0.017479398 t \N \N \N {} +593 172 2025-02-24 04:00:11.157726-08 2025-02-24 04:23:52.92541-08 leaderboard f H100 0.001457972 t \N \N \N {} +594 172 2025-02-24 04:24:57.48744-08 2025-02-24 05:39:56.243248-08 test t H100 \N t \N \N \N {} +595 172 2025-02-24 04:05:36.140858-08 2025-02-24 04:45:49.663541-08 benchmark t H100 \N t \N \N \N {} +596 172 2025-02-24 04:03:24.134887-08 2025-02-24 04:07:50.692444-08 leaderboard t H100 0.0014493626666666668 t \N \N \N {} +597 172 2025-02-24 04:22:04.734823-08 2025-02-24 05:20:24.419522-08 test t T4 \N t \N \N \N {} +598 172 2025-02-24 04:54:26.09647-08 2025-02-24 04:37:32.588527-08 benchmark t T4 \N t \N \N \N {} +599 172 2025-02-24 05:20:32.672091-08 2025-02-24 04:22:02.580439-08 leaderboard t T4 0.01753550875 t \N \N \N {} +601 172 2025-02-24 04:01:40.262757-08 2025-02-24 04:31:18.294476-08 benchmark f A100 \N t \N \N \N {} +602 172 2025-02-24 04:53:49.361583-08 2025-02-24 04:48:49.182764-08 leaderboard f A100 0.0031660506666666664 t \N \N \N {} +603 172 2025-02-24 03:52:06.072469-08 2025-02-24 03:59:33.848814-08 test f T4 \N t \N \N \N {} +604 172 2025-02-24 03:42:48.462511-08 2025-02-24 05:26:31.44727-08 benchmark f T4 \N t \N \N \N {} +610 172 2025-02-24 05:04:05.242805-08 2025-02-24 03:44:40.846459-08 benchmark t L4 \N t \N \N \N {} +611 172 2025-02-24 04:35:15.919214-08 2025-02-24 05:11:46.93162-08 leaderboard t L4 0.017569029 t \N \N \N {} +612 173 2025-02-24 05:24:50.95809-08 2025-02-24 04:11:33.537819-08 test f H100 \N f \N \N \N {} +613 173 2025-02-24 03:51:03.148107-08 2025-02-24 05:38:19.375618-08 test t H100 \N f \N \N \N {} +614 175 2025-02-24 04:40:13.642706-08 2025-02-24 05:40:34.3719-08 test f T4 \N f \N \N \N {} +615 174 2025-02-24 05:40:44.406358-08 2025-02-24 03:57:34.265608-08 test f A100 \N t \N \N \N {} +625 174 2025-02-24 05:28:15.27384-08 2025-02-24 04:58:32.636743-08 benchmark f H100 \N t \N \N \N {} +626 174 2025-02-24 04:27:19.875646-08 2025-02-24 05:38:11.378119-08 leaderboard f H100 0.00146166825 t \N \N \N {} +627 174 2025-02-24 04:57:35.414356-08 2025-02-24 05:31:36.9668-08 test t T4 \N t \N \N \N {} +628 174 2025-02-24 04:45:34.255845-08 2025-02-24 04:05:43.860604-08 benchmark t T4 \N t \N \N \N {} +629 174 2025-02-24 05:27:09.661991-08 2025-02-24 04:50:39.24896-08 leaderboard t T4 0.0208113558 t \N \N \N {} +1499 502 2025-02-25 06:47:02.590704-08 2025-02-25 06:27:19.767132-08 test f T4 \N t \N \N \N {} +633 174 2025-02-24 04:12:12.277241-08 2025-02-24 05:10:27.381931-08 test t L4 \N t \N \N \N {} +634 174 2025-02-24 05:00:36.484962-08 2025-02-24 04:04:08.518337-08 benchmark t L4 \N t \N \N \N {} +635 174 2025-02-24 04:34:32.791262-08 2025-02-24 04:57:05.719322-08 leaderboard t L4 0.017792222333333333 t \N \N \N {} +636 174 2025-02-24 04:08:01.422936-08 2025-02-24 05:26:59.018927-08 test f L4 \N t \N \N \N {} +637 174 2025-02-24 05:17:03.979995-08 2025-02-24 04:45:28.587227-08 benchmark f L4 \N t \N \N \N {} +638 174 2025-02-24 04:27:22.118036-08 2025-02-24 03:57:14.285261-08 leaderboard f L4 0.017829622 t \N \N \N {} +3276 1144 2025-02-28 06:55:15.001553-08 2025-02-28 05:46:00.750654-08 benchmark f A100 \N t \N \N \N {} +1961 670 2025-02-25 12:57:56.587732-08 2025-02-25 13:49:48.593819-08 benchmark f A100 \N t \N \N \N {} +642 177 2025-02-24 05:07:49.574927-08 2025-02-24 04:45:31.62306-08 test f T4 \N f \N \N \N {} +1962 671 2025-02-25 13:46:47.86222-08 2025-02-25 14:02:39.534938-08 benchmark f A100 \N t \N \N \N {} +1963 672 2025-02-25 14:00:04.67254-08 2025-02-25 13:25:13.842123-08 test t H100 \N t \N \N \N {} +1964 672 2025-02-25 12:36:59.057138-08 2025-02-25 14:02:22.120333-08 benchmark t H100 \N t \N \N \N {} +1965 672 2025-02-25 12:29:52.01167-08 2025-02-25 14:05:06.656703-08 leaderboard t H100 0.003437508 t \N \N \N {} +1966 672 2025-02-25 12:39:24.055726-08 2025-02-25 13:00:24.611271-08 test f H100 \N t \N \N \N {} +1967 672 2025-02-25 12:58:49.474139-08 2025-02-25 13:26:41.827259-08 benchmark f H100 \N t \N \N \N {} +1968 672 2025-02-25 13:26:33.936094-08 2025-02-25 12:40:21.065589-08 leaderboard f H100 0.003485398 t \N \N \N {} +2215 724 2025-02-25 14:37:54.567252-08 2025-02-25 14:43:57.444662-08 leaderboard t A100 0.010253208460000001 t \N \N \N {} +2216 724 2025-02-25 15:33:44.119406-08 2025-02-25 15:13:09.534917-08 test t H100 \N t \N \N \N {} +2253 752 2025-02-26 00:42:28.558196-08 2025-02-26 02:01:39.061078-08 benchmark f H100 \N f \N \N \N {} +2218 724 2025-02-25 15:32:48.937419-08 2025-02-25 15:25:26.740188-08 leaderboard t H100 0.00353085645 t \N \N \N {} +2219 724 2025-02-25 15:11:42.946931-08 2025-02-25 14:44:35.593924-08 test f A100 \N t \N \N \N {} +2220 724 2025-02-25 14:34:20.966564-08 2025-02-25 15:36:27.340882-08 benchmark f A100 \N t \N \N \N {} +2221 724 2025-02-25 15:58:30.291254-08 2025-02-25 14:42:07.237481-08 leaderboard f A100 0.0107430945 t \N \N \N {} +691 217 2025-02-24 07:04:53.233308-08 2025-02-24 08:10:24.296185-08 test t A100 \N f \N \N \N {} +5663 1723 2025-03-09 10:05:43.141436-07 2025-03-09 10:57:02.505632-07 leaderboard t T4 0.016583018 t \N \N \N {} +2313 778 2025-02-26 03:54:39.554659-08 2025-02-26 04:48:44.686597-08 benchmark f A100 \N t \N \N \N {} +2314 778 2025-02-26 03:35:24.258305-08 2025-02-26 04:57:01.237295-08 leaderboard f A100 0.0030904886666666665 t \N \N \N {} +2315 781 2025-02-26 08:10:42.313296-08 2025-02-26 08:01:03.24323-08 test t A100 \N t \N \N \N {} +2330 784 2025-02-26 08:48:24.701263-08 2025-02-26 06:56:52.366196-08 test t A100 \N f \N \N \N {} +2318 781 2025-02-26 08:17:57.941797-08 2025-02-26 07:48:29.179932-08 test f A100 \N t \N \N \N {} +2319 781 2025-02-26 08:04:49.559876-08 2025-02-26 07:59:49.9534-08 benchmark f A100 \N t \N \N \N {} +2320 781 2025-02-26 07:21:32.163845-08 2025-02-26 08:26:16.891069-08 leaderboard f A100 0.0031749516666666664 t \N \N \N {} +2321 782 2025-02-26 07:07:41.841142-08 2025-02-26 08:08:18.275768-08 test f T4 \N f \N \N \N {} +2322 782 2025-02-26 08:40:25.972436-08 2025-02-26 07:46:04.302799-08 test t T4 \N f \N \N \N {} +2323 783 2025-02-26 08:25:20.355357-08 2025-02-26 07:28:44.30517-08 test t A100 \N t \N \N \N {} +2334 785 2025-02-26 08:50:29.968504-08 2025-02-26 08:16:16.86245-08 test t A100 \N t \N \N \N {} +3266 1142 2025-02-28 05:40:58.845992-08 2025-02-28 06:20:23.84623-08 test t A100 \N t \N \N \N {} +692 218 2025-02-24 07:35:12.909281-08 2025-02-24 08:32:29.712533-08 test f T4 \N f \N \N \N {} +3856 1291 2025-03-01 03:58:25.966221-08 2025-03-01 02:58:47.903585-08 test f A100 \N t \N \N \N {} +2500 871 2025-02-26 14:21:21.727765-08 2025-02-26 14:48:13.334885-08 test f T4 \N t \N \N \N {} +2501 872 2025-02-26 14:53:44.720161-08 2025-02-26 14:09:15.911305-08 test t A100 \N t \N \N \N {} +2502 872 2025-02-26 14:29:17.86941-08 2025-02-26 14:42:23.807854-08 benchmark t A100 \N t \N \N \N {} +2504 872 2025-02-26 14:21:53.085089-08 2025-02-26 13:33:28.796563-08 test t H100 \N t \N \N \N {} +3396 1171 2025-02-28 08:52:34.344836-08 2025-02-28 10:12:06.257868-08 test t L4 \N f \N \N \N {} +663 194 2025-02-24 05:41:36.621215-08 2025-02-24 06:09:54.974777-08 test f T4 \N t \N \N \N {} +664 195 2025-02-24 06:08:45.709175-08 2025-02-24 05:08:22.138404-08 benchmark f H100 \N f \N \N \N {} +666 197 2025-02-24 06:56:56.061652-08 2025-02-24 06:22:23.208702-08 benchmark f H100 \N f \N \N \N {} +667 198 2025-02-24 06:34:23.314709-08 2025-02-24 05:20:01.543454-08 benchmark f H100 \N f \N \N \N {} +668 199 2025-02-24 04:59:42.957047-08 2025-02-24 05:30:49.743808-08 benchmark f H100 \N f \N \N \N {} +2598 885 2025-02-26 14:21:31.605809-08 2025-02-26 13:26:53.536131-08 test t L4 \N t \N \N \N {} +2599 885 2025-02-26 14:56:23.588162-08 2025-02-26 13:44:20.657543-08 benchmark t L4 \N t \N \N \N {} +2600 885 2025-02-26 14:30:27.342225-08 2025-02-26 14:48:35.398787-08 leaderboard t L4 0.017775642666666668 t \N \N \N {} +2601 884 2025-02-26 14:45:15.947305-08 2025-02-26 14:55:41.866073-08 test t H100 \N t \N \N \N {} +2613 884 2025-02-26 13:25:03.130497-08 2025-02-26 13:21:04.408924-08 test f T4 \N t \N \N \N {} +2604 884 2025-02-26 15:11:47.082796-08 2025-02-26 14:52:15.530642-08 test t A100 \N t \N \N \N {} +2605 884 2025-02-26 13:27:46.343561-08 2025-02-26 14:55:24.020372-08 benchmark t A100 \N t \N \N \N {} +2606 884 2025-02-26 14:44:05.568836-08 2025-02-26 13:52:58.061404-08 leaderboard t A100 0.0032559906666666666 t \N \N \N {} +2621 885 2025-02-26 14:23:14.309831-08 2025-02-26 13:26:20.529447-08 leaderboard t A100 0.00319686775 t \N \N \N {} +3421 1179 2025-02-28 10:14:03.171361-08 2025-02-28 10:16:52.20393-08 test t L4 \N f \N \N \N {} +2610 884 2025-02-26 15:13:57.604086-08 2025-02-26 13:38:33.967499-08 test f H100 \N t \N \N \N {} +2611 884 2025-02-26 14:21:20.575521-08 2025-02-26 14:28:32.061414-08 benchmark f H100 \N t \N \N \N {} +2612 884 2025-02-26 14:43:21.793286-08 2025-02-26 15:00:54.664103-08 leaderboard f H100 0.0014509983333333332 t \N \N \N {} +2622 885 2025-02-26 14:42:43.496715-08 2025-02-26 14:25:56.942011-08 test f L4 \N t \N \N \N {} +3428 1183 2025-02-28 10:08:45.167126-08 2025-02-28 11:18:19.555595-08 test t L4 \N f \N \N \N {} +2614 884 2025-02-26 15:08:00.153692-08 2025-02-26 14:14:23.980237-08 benchmark f T4 \N t \N \N \N {} +2615 884 2025-02-26 14:02:53.674937-08 2025-02-26 14:12:29.617332-08 leaderboard f T4 0.01843677733333333 t \N \N \N {} +2616 885 2025-02-26 13:36:00.637075-08 2025-02-26 13:52:46.766673-08 test t H100 \N t \N \N \N {} +2617 885 2025-02-26 14:50:02.881007-08 2025-02-26 15:06:57.135832-08 benchmark t H100 \N t \N \N \N {} +2618 885 2025-02-26 13:35:29.138108-08 2025-02-26 15:14:08.351625-08 leaderboard t H100 0.0014837716 t \N \N \N {} +2619 885 2025-02-26 13:51:02.414525-08 2025-02-26 14:11:16.314329-08 test t A100 \N t \N \N \N {} +2620 885 2025-02-26 14:21:00.02625-08 2025-02-26 13:34:27.254132-08 benchmark t A100 \N t \N \N \N {} +2625 885 2025-02-26 13:29:27.081756-08 2025-02-26 14:06:55.891402-08 test f A100 \N t \N \N \N {} +2626 885 2025-02-26 14:36:19.92372-08 2025-02-26 14:31:38.799097-08 benchmark f A100 \N t \N \N \N {} +2627 885 2025-02-26 13:30:35.621947-08 2025-02-26 14:27:23.062497-08 leaderboard f A100 0.003209451 t \N \N \N {} +2628 885 2025-02-26 14:10:08.722188-08 2025-02-26 15:18:05.698046-08 test t T4 \N t \N \N \N {} +2629 885 2025-02-26 14:59:49.529515-08 2025-02-26 13:34:26.486834-08 benchmark t T4 \N t \N \N \N {} +2630 885 2025-02-26 14:58:52.752015-08 2025-02-26 14:53:03.340521-08 leaderboard t T4 0.017220341666666666 t \N \N \N {} +2634 886 2025-02-26 14:08:14.352049-08 2025-02-26 13:46:49.236868-08 benchmark f H100 \N t \N \N \N {} +670 201 2025-02-24 05:45:11.842551-08 2025-02-24 06:28:30.253378-08 benchmark f H100 \N f \N \N \N {} +704 225 2025-02-24 08:56:12.740421-08 2025-02-24 08:34:20.349342-08 leaderboard f A100 0.011330630333333334 t \N \N \N {} +1439 480 2025-02-25 04:48:27.37121-08 2025-02-25 05:01:05.017307-08 test t A100 \N f \N \N \N {} +671 203 2025-02-24 06:24:20.549783-08 2025-02-24 05:24:34.368331-08 test f H100 \N f \N \N \N {} +673 205 2025-02-24 07:04:18.018562-08 2025-02-24 05:35:41.546773-08 test f T4 \N t \N \N \N {} +674 206 2025-02-24 06:06:45.212354-08 2025-02-24 07:06:51.567687-08 benchmark f T4 \N t \N \N \N {} +676 208 2025-02-24 06:06:40.542676-08 2025-02-24 06:41:12.566535-08 test f H100 \N t \N \N \N {} +705 225 2025-02-24 08:55:10.930728-08 2025-02-24 07:46:29.223577-08 test t A100 \N t \N \N \N {} +1502 502 2025-02-25 07:17:02.787-08 2025-02-25 07:28:38.183565-08 test t T4 \N t \N \N \N {} +677 209 2025-02-24 05:52:20.627547-08 2025-02-24 06:50:43.221253-08 benchmark f H100 \N t \N \N \N {} +678 210 2025-02-24 05:28:59.266404-08 2025-02-24 06:40:36.860883-08 benchmark f H100 \N t \N \N \N {} +679 211 2025-02-24 05:59:18.290632-08 2025-02-24 05:31:04.882898-08 test t H100 \N t \N \N \N {} +6187 1904 2025-03-11 16:30:03.874833-07 2025-03-11 16:23:04.032961-07 test f T4 \N f \N \N \N {} +2635 887 2025-02-26 14:23:20.516781-08 2025-02-26 13:30:42.901197-08 benchmark f H100 \N t \N \N \N {} +2636 888 2025-02-26 15:08:09.895226-08 2025-02-26 15:02:44.771912-08 benchmark f H100 \N t \N \N \N {} +2637 889 2025-02-26 14:20:26.49628-08 2025-02-26 14:31:54.196513-08 benchmark f H100 \N t \N \N \N {} +3429 1183 2025-02-28 10:30:24.045825-08 2025-02-28 09:58:43.334761-08 test f L4 \N f \N \N \N {} +2658 917 2025-02-26 16:04:22.640816-08 2025-02-26 15:50:33.550145-08 test t L4 \N t \N \N \N {} +2642 894 2025-02-26 15:22:35.57726-08 2025-02-26 15:19:16.529445-08 benchmark f H100 \N t \N \N \N {} +2643 895 2025-02-26 14:19:06.387984-08 2025-02-26 13:54:35.774098-08 benchmark f H100 \N t \N \N \N {} +2644 896 2025-02-26 14:32:56.05166-08 2025-02-26 15:21:02.107636-08 benchmark f H100 \N t \N \N \N {} +2645 897 2025-02-26 14:43:32.97254-08 2025-02-26 15:35:10.004299-08 benchmark f H100 \N t \N \N \N {} +3642 1244 2025-02-28 23:55:30.080534-08 2025-03-01 00:02:11.688916-08 test f A100 \N t \N \N \N {} +2648 900 2025-02-26 14:55:59.543208-08 2025-02-26 15:16:42.738329-08 benchmark f H100 \N f \N \N \N {} +2649 902 2025-02-26 15:19:37.018566-08 2025-02-26 14:33:09.313223-08 benchmark f H100 \N t \N \N \N {} +2650 905 2025-02-26 15:06:58.156118-08 2025-02-26 15:39:57.941001-08 benchmark f H100 \N t \N \N \N {} +3868 1293 2025-03-01 03:27:42.833831-08 2025-03-01 02:55:20.805453-08 test t A100 \N t \N \N \N {} +2653 910 2025-02-26 15:22:55.54094-08 2025-02-26 15:29:37.757077-08 benchmark f H100 \N f \N \N \N {} +2654 912 2025-02-26 15:40:15.529449-08 2025-02-26 14:30:04.45723-08 test f H100 \N t \N \N \N {} +2655 913 2025-02-26 15:41:19.632645-08 2025-02-26 15:25:04.114543-08 benchmark f H100 \N t \N \N \N {} +2656 914 2025-02-26 15:28:36.303476-08 2025-02-26 14:41:59.161801-08 benchmark f A100 \N t \N \N \N {} +686 213 2025-02-24 06:35:37.280408-08 2025-02-24 07:07:31.745156-08 benchmark f H100 \N t \N \N \N {} +687 214 2025-02-24 06:45:56.939468-08 2025-02-24 06:25:32.523917-08 benchmark f H100 \N t \N \N \N {} +693 219 2025-02-24 09:02:17.179137-08 2025-02-24 08:31:55.588104-08 test f T4 \N f \N \N \N {} +694 220 2025-02-24 07:57:20.923442-08 2025-02-24 08:06:53.334965-08 test f T4 \N f \N \N \N {} +6861 2127 2025-03-15 09:30:21.684959-07 2025-03-15 10:10:14.889721-07 test f A100 \N t \N \N \N {} +2657 915 2025-02-26 15:22:50.97253-08 2025-02-26 14:13:32.883512-08 benchmark f H100 \N f \N \N \N {} +2659 917 2025-02-26 15:59:10.277444-08 2025-02-26 16:06:48.272166-08 benchmark t L4 \N t \N \N \N {} +2660 917 2025-02-26 15:22:27.480576-08 2025-02-26 15:49:28.223669-08 leaderboard t L4 0.017139778 t \N \N \N {} +2661 917 2025-02-26 14:50:12.413294-08 2025-02-26 14:36:02.481211-08 test t A100 \N t \N \N \N {} +2662 917 2025-02-26 14:49:43.883464-08 2025-02-26 15:40:03.528298-08 benchmark t A100 \N t \N \N \N {} +695 221 2025-02-24 08:04:03.997077-08 2025-02-24 09:06:50.538681-08 test f T4 \N f \N \N \N {} +696 222 2025-02-24 08:59:10.271492-08 2025-02-24 07:29:33.102147-08 benchmark f A100 \N f \N \N \N {} +697 222 2025-02-24 08:07:36.901531-08 2025-02-24 07:47:31.363673-08 benchmark f T4 \N f \N \N \N {} +1440 480 2025-02-25 04:05:00.096466-08 2025-02-25 05:05:26.708798-08 test f A100 \N f \N \N \N {} +698 222 2025-02-24 08:43:36.056535-08 2025-02-24 08:16:52.237368-08 benchmark f L4 \N f \N \N \N {} +699 222 2025-02-24 08:22:16.816685-08 2025-02-24 08:37:10.387978-08 benchmark f H100 \N f \N \N \N {} +700 223 2025-02-24 09:06:11.706145-08 2025-02-24 09:03:29.685017-08 benchmark f A100 \N f \N \N \N {} +701 224 2025-02-24 08:10:04.943823-08 2025-02-24 08:28:43.870189-08 test f T4 \N f \N \N \N {} +702 225 2025-02-24 07:56:55.117396-08 2025-02-24 08:48:49.945346-08 test f A100 \N t \N \N \N {} +703 225 2025-02-24 07:41:19.688526-08 2025-02-24 07:41:37.470123-08 benchmark f A100 \N t \N \N \N {} +706 225 2025-02-24 08:37:11.189015-08 2025-02-24 09:05:40.321522-08 benchmark t A100 \N t \N \N \N {} +708 226 2025-02-24 09:25:07.198881-08 2025-02-24 07:35:03.936398-08 benchmark f A100 \N f \N \N \N {} +709 227 2025-02-24 08:24:29.897497-08 2025-02-24 09:20:50.161506-08 benchmark f A100 \N t \N \N \N {} +710 228 2025-02-24 08:50:24.79349-08 2025-02-24 08:55:39.391024-08 test f T4 \N t \N \N \N {} +711 229 2025-02-24 08:11:00.669556-08 2025-02-24 09:13:50.969935-08 test t A100 \N t \N \N \N {} +1111 304 2025-02-24 17:03:32.591659-08 2025-02-24 17:41:47.088082-08 benchmark f H100 \N t \N \N \N {} +712 229 2025-02-24 08:28:09.285744-08 2025-02-24 09:27:17.171225-08 benchmark t A100 \N t \N \N \N {} +2663 917 2025-02-26 15:02:41.87638-08 2025-02-26 15:04:14.826114-08 leaderboard t A100 0.002618883 t \N \N \N {} +3474 1194 2025-02-28 10:51:23.270197-08 2025-02-28 10:28:38.739112-08 benchmark f A100 \N f \N \N \N {} +714 229 2025-02-24 09:20:32.013179-08 2025-02-24 09:10:19.888175-08 test f A100 \N t \N \N \N {} +719 230 2025-02-24 07:43:26.148361-08 2025-02-24 09:27:29.034748-08 leaderboard f A100 0.003160102 t \N \N \N {} +728 233 2025-02-24 07:51:31.813355-08 2025-02-24 07:55:17.330164-08 test t A100 \N t \N \N \N {} +729 233 2025-02-24 07:58:04.52096-08 2025-02-24 09:14:58.005432-08 benchmark t A100 \N t \N \N \N {} +730 233 2025-02-24 09:01:40.583243-08 2025-02-24 08:17:02.267771-08 leaderboard t A100 0.0031330916666666665 t \N \N \N {} +1443 483 2025-02-25 04:08:48.170684-08 2025-02-25 04:18:24.374905-08 test f A100 \N f \N \N \N {} +731 233 2025-02-24 08:17:49.25495-08 2025-02-24 09:15:06.490991-08 test f A100 \N t \N \N \N {} +5675 1724 2025-03-09 09:09:34.316424-07 2025-03-09 09:56:58.297658-07 leaderboard t A100 0.002777401 t \N \N \N {} +2664 917 2025-02-26 15:33:18.523093-08 2025-02-26 14:23:26.27638-08 test f T4 \N t \N \N \N {} +2665 917 2025-02-26 14:41:08.119956-08 2025-02-26 14:40:45.367615-08 benchmark f T4 \N t \N \N \N {} +2666 917 2025-02-26 14:19:24.006278-08 2025-02-26 15:07:56.17656-08 leaderboard f T4 0.017432490666666668 t \N \N \N {} +2667 917 2025-02-26 14:50:03.208616-08 2025-02-26 14:15:31.2517-08 test f H100 \N t \N \N \N {} +2668 917 2025-02-26 15:41:58.425053-08 2025-02-26 14:23:19.930763-08 benchmark f H100 \N t \N \N \N {} +3184 1121 2025-02-28 04:45:28.941252-08 2025-02-28 03:32:18.191207-08 test f A100 \N t \N \N \N {} +2669 917 2025-02-26 15:11:08.928473-08 2025-02-26 15:58:37.434081-08 leaderboard f H100 0.0014553213333333332 t \N \N \N {} +2670 917 2025-02-26 15:28:35.839236-08 2025-02-26 15:52:24.038032-08 test f A100 \N t \N \N \N {} +2671 917 2025-02-26 14:52:23.197769-08 2025-02-26 15:34:23.428279-08 benchmark f A100 \N t \N \N \N {} +2672 917 2025-02-26 14:12:48.444376-08 2025-02-26 14:28:23.028924-08 leaderboard f A100 0.003219043 t \N \N \N {} +2673 917 2025-02-26 14:12:35.128126-08 2025-02-26 14:43:26.451849-08 test t T4 \N t \N \N \N {} +737 233 2025-02-24 09:16:03.275201-08 2025-02-24 09:11:08.635071-08 test t T4 \N t \N \N \N {} +738 233 2025-02-24 08:28:01.859065-08 2025-02-24 09:22:10.211632-08 benchmark t T4 \N t \N \N \N {} +739 233 2025-02-24 09:08:18.555299-08 2025-02-24 09:02:46.143397-08 leaderboard t T4 0.0036530893333333336 t \N \N \N {} +740 233 2025-02-24 08:49:10.05294-08 2025-02-24 08:07:07.637237-08 test f T4 \N t \N \N \N {} +776 249 2025-02-24 10:24:50.908208-08 2025-02-24 11:51:41.650348-08 test f A100 \N t \N \N \N {} +3898 1300 2025-03-01 04:41:21.824886-08 2025-03-01 04:35:22.271324-08 benchmark f A100 \N t \N \N \N {} +747 235 2025-02-24 09:17:44.247963-08 2025-02-24 09:28:51.71791-08 test f A100 \N f \N \N \N {} +748 235 2025-02-24 09:36:38.154313-08 2025-02-24 09:52:27.287797-08 test t A100 \N f \N \N \N {} +749 236 2025-02-24 09:03:52.762503-08 2025-02-24 09:18:03.476551-08 test f A100 \N t \N \N \N {} +750 237 2025-02-24 09:27:00.648875-08 2025-02-24 09:17:20.060425-08 test t A100 \N t \N \N \N {} +758 241 2025-02-24 10:35:45.406451-08 2025-02-24 10:49:56.940266-08 test f A100 \N f \N \N \N {} +2679 917 2025-02-26 15:58:31.502323-08 2025-02-26 15:05:12.236739-08 test f L4 \N t \N \N \N {} +3475 1196 2025-02-28 10:58:00.781332-08 2025-02-28 11:31:50.239552-08 test t A100 \N t \N \N \N {} +2675 917 2025-02-26 15:10:31.598044-08 2025-02-26 15:45:21.966872-08 leaderboard t T4 0.017423966 t \N \N \N {} +2676 917 2025-02-26 15:37:23.354452-08 2025-02-26 14:45:19.799848-08 test t H100 \N t \N \N \N {} +2677 917 2025-02-26 15:32:46.804737-08 2025-02-26 14:42:52.925066-08 benchmark t H100 \N t \N \N \N {} +2678 917 2025-02-26 15:26:19.888166-08 2025-02-26 15:42:16.647691-08 leaderboard t H100 0.0014749786666666668 t \N \N \N {} +3478 1196 2025-02-28 11:49:03.159248-08 2025-02-28 11:21:38.865185-08 test f A100 \N t \N \N \N {} +3871 1294 2025-03-01 03:25:39.575831-08 2025-03-01 03:47:44.417542-08 test f A100 \N t \N \N \N {} +2680 917 2025-02-26 14:12:15.287164-08 2025-02-26 14:56:49.444837-08 benchmark f L4 \N t \N \N \N {} +2681 917 2025-02-26 14:13:57.106393-08 2025-02-26 14:37:54.475639-08 leaderboard f L4 0.017202002666666667 t \N \N \N {} +2682 919 2025-02-26 15:07:06.306277-08 2025-02-26 14:43:54.993315-08 test f T4 \N t \N \N \N {} +2683 918 2025-02-26 14:37:18.145006-08 2025-02-26 14:58:27.554341-08 test f H100 \N f \N \N \N {} +2684 920 2025-02-26 15:32:10.569826-08 2025-02-26 15:21:11.603029-08 benchmark f H100 \N f \N \N \N {} +2690 925 2025-02-26 15:23:42.15559-08 2025-02-26 16:09:05.490772-08 leaderboard t A100 0.003207825 t \N \N \N {} +2685 921 2025-02-26 15:18:14.224991-08 2025-02-26 15:44:27.451168-08 benchmark f H100 \N f \N \N \N {} +2687 923 2025-02-26 15:03:01.412592-08 2025-02-26 15:06:11.143738-08 benchmark f H100 \N t \N \N \N {} +2688 925 2025-02-26 16:08:56.665897-08 2025-02-26 15:00:21.657629-08 test t A100 \N t \N \N \N {} +2689 925 2025-02-26 16:07:17.241061-08 2025-02-26 14:31:28.824618-08 benchmark t A100 \N t \N \N \N {} +751 237 2025-02-24 10:32:35.667519-08 2025-02-24 09:04:14.834137-08 benchmark t A100 \N t \N \N \N {} +752 237 2025-02-24 10:42:58.851055-08 2025-02-24 09:40:55.016006-08 leaderboard t A100 0.0033126033333333335 t \N \N \N {} +753 237 2025-02-24 10:22:09.7399-08 2025-02-24 10:48:42.661894-08 test f A100 \N t \N \N \N {} +754 237 2025-02-24 09:37:37.615117-08 2025-02-24 10:02:09.097882-08 benchmark f A100 \N t \N \N \N {} +755 237 2025-02-24 10:36:07.704848-08 2025-02-24 10:23:31.161901-08 leaderboard f A100 0.003277688 t \N \N \N {} +756 238 2025-02-24 09:28:50.167162-08 2025-02-24 09:49:32.625124-08 test f A100 \N f \N \N \N {} +759 242 2025-02-24 10:36:29.62006-08 2025-02-24 10:53:18.167093-08 test f A100 \N t \N \N \N {} +3485 1197 2025-02-28 11:18:37.224946-08 2025-02-28 11:49:48.964789-08 test f A100 \N t \N \N \N {} +3874 1294 2025-03-01 03:31:01.665765-08 2025-03-01 04:31:31.171928-08 test t A100 \N t \N \N \N {} +760 242 2025-02-24 09:47:29.285286-08 2025-02-24 10:30:48.947657-08 benchmark f A100 \N t \N \N \N {} +761 242 2025-02-24 10:47:46.257274-08 2025-02-24 10:32:35.015062-08 leaderboard f A100 0.0032635085 t \N \N \N {} +762 242 2025-02-24 10:33:54.353655-08 2025-02-24 09:20:51.991089-08 test t A100 \N t \N \N \N {} +763 242 2025-02-24 10:41:28.131991-08 2025-02-24 10:23:01.349186-08 benchmark t A100 \N t \N \N \N {} +764 242 2025-02-24 10:31:24.13368-08 2025-02-24 11:10:39.141474-08 leaderboard t A100 0.0032074475454545452 t \N \N \N {} +766 244 2025-02-24 11:12:28.205288-08 2025-02-24 10:55:23.216173-08 test t A100 \N t \N \N \N {} +767 244 2025-02-24 10:50:20.125343-08 2025-02-24 10:53:26.469742-08 benchmark t A100 \N t \N \N \N {} +2691 925 2025-02-26 15:03:38.341726-08 2025-02-26 14:39:04.512785-08 test f H100 \N t \N \N \N {} +770 244 2025-02-24 09:48:07.049405-08 2025-02-24 11:16:17.375008-08 benchmark f A100 \N t \N \N \N {} +771 244 2025-02-24 10:16:24.565263-08 2025-02-24 10:42:42.951293-08 leaderboard f A100 0.003206431 t \N \N \N {} +774 247 2025-02-24 11:24:50.833311-08 2025-02-24 11:11:00.480374-08 test f A100 \N t \N \N \N {} +775 248 2025-02-24 11:18:01.396675-08 2025-02-24 09:46:03.356509-08 benchmark f A100 \N t \N \N \N {} +1357 454 2025-02-25 03:22:20.667093-08 2025-02-25 02:18:17.785956-08 benchmark f H100 \N f \N \N \N {} +2692 925 2025-02-26 15:31:22.064733-08 2025-02-26 15:24:18.486092-08 benchmark f H100 \N t \N \N \N {} +2693 925 2025-02-26 15:40:09.437246-08 2025-02-26 14:33:37.515202-08 leaderboard f H100 0.0014418213333333332 t \N \N \N {} +2694 925 2025-02-26 14:21:30.999182-08 2025-02-26 14:59:14.162816-08 test f A100 \N t \N \N \N {} +2695 925 2025-02-26 14:49:05.851761-08 2025-02-26 16:02:19.46454-08 benchmark f A100 \N t \N \N \N {} +3187 1121 2025-02-28 04:13:45.106756-08 2025-02-28 05:15:10.243175-08 test t A100 \N t \N \N \N {} +2697 925 2025-02-26 15:49:51.58385-08 2025-02-26 15:15:43.400087-08 test t H100 \N t \N \N \N {} +2698 925 2025-02-26 15:20:13.529064-08 2025-02-26 14:54:28.716289-08 benchmark t H100 \N t \N \N \N {} +2699 925 2025-02-26 15:12:28.653251-08 2025-02-26 15:26:50.456366-08 leaderboard t H100 0.0014572976666666667 t \N \N \N {} +2700 925 2025-02-26 14:23:46.440832-08 2025-02-26 15:33:30.662407-08 test f T4 \N t \N \N \N {} +2706 925 2025-02-26 15:56:56.719925-08 2025-02-26 15:31:05.149568-08 test f L4 \N t \N \N \N {} +2701 925 2025-02-26 14:27:02.624231-08 2025-02-26 14:29:33.448522-08 benchmark f T4 \N t \N \N \N {} +2702 925 2025-02-26 15:37:17.423193-08 2025-02-26 14:15:41.379785-08 leaderboard f T4 0.01748257666666667 t \N \N \N {} +2703 925 2025-02-26 15:02:07.78409-08 2025-02-26 14:30:59.074601-08 test t T4 \N t \N \N \N {} +2704 925 2025-02-26 15:25:00.161914-08 2025-02-26 14:41:31.01698-08 benchmark t T4 \N t \N \N \N {} +2705 925 2025-02-26 15:45:54.49678-08 2025-02-26 15:14:52.211382-08 leaderboard t T4 0.01740308366666667 t \N \N \N {} +2712 926 2025-02-26 14:49:00.905432-08 2025-02-26 15:15:12.066984-08 test f H100 \N f \N \N \N {} +2707 925 2025-02-26 15:14:33.474068-08 2025-02-26 16:13:05.73025-08 benchmark f L4 \N t \N \N \N {} +2708 925 2025-02-26 14:20:21.316161-08 2025-02-26 14:20:47.541084-08 leaderboard f L4 0.017136907333333333 t \N \N \N {} +2709 925 2025-02-26 14:35:12.137581-08 2025-02-26 15:27:12.663189-08 test t L4 \N t \N \N \N {} +2710 925 2025-02-26 14:22:20.587234-08 2025-02-26 15:41:26.009938-08 benchmark t L4 \N t \N \N \N {} +2711 925 2025-02-26 14:28:34.108955-08 2025-02-26 15:21:09.620226-08 leaderboard t L4 0.017147484 t \N \N \N {} +2757 955 2025-02-26 19:44:57.978305-08 2025-02-26 20:19:00.046496-08 test f A100 \N f \N \N \N {} +3488 1197 2025-02-28 12:13:22.706712-08 2025-02-28 11:38:53.381675-08 test t A100 \N t \N \N \N {} +2715 927 2025-02-26 15:38:50.899973-08 2025-02-26 15:07:34.985733-08 leaderboard f H100 0.0008018275555555555 t \N \N \N {} +787 251 2025-02-24 11:51:27.707677-08 2025-02-24 11:37:57.611418-08 benchmark t T4 \N t \N \N \N {} +798 251 2025-02-24 10:21:21.926398-08 2025-02-24 11:59:58.050957-08 test f T4 \N t \N \N \N {} +799 251 2025-02-24 10:39:23.728982-08 2025-02-24 12:03:26.185356-08 benchmark f T4 \N t \N \N \N {} +803 251 2025-02-24 11:17:20.227202-08 2025-02-24 12:07:21.562687-08 leaderboard f L4 0.017377531 t \N \N \N {} +804 251 2025-02-24 10:36:19.381723-08 2025-02-24 11:38:04.632489-08 test t L4 \N t \N \N \N {} +805 251 2025-02-24 11:30:07.481337-08 2025-02-24 11:39:59.955081-08 benchmark t L4 \N t \N \N \N {} +812 253 2025-02-24 12:20:58.985139-08 2025-02-24 11:40:09.015087-08 benchmark t A100 \N f \N \N \N {} +814 255 2025-02-24 12:02:11.606734-08 2025-02-24 13:15:58.848433-08 test f H100 \N t \N \N \N {} +815 255 2025-02-24 12:02:01.586263-08 2025-02-24 12:14:04.067053-08 benchmark f H100 \N t \N \N \N {} +816 255 2025-02-24 12:48:59.000761-08 2025-02-24 12:42:09.155319-08 leaderboard f H100 0.006132886 t \N \N \N {} +817 255 2025-02-24 12:15:55.811254-08 2025-02-24 12:36:09.13928-08 test t H100 \N t \N \N \N {} +844 262 2025-02-24 13:35:47.457777-08 2025-02-24 13:57:51.505602-08 test t A100 \N t \N \N \N {} +2716 927 2025-02-26 15:52:42.380063-08 2025-02-26 14:28:51.260977-08 test t H100 \N t \N \N \N {} +2717 927 2025-02-26 16:02:37.652274-08 2025-02-26 16:05:08.637406-08 benchmark t H100 \N t \N \N \N {} +2780 968 2025-02-26 20:29:07.654188-08 2025-02-26 19:21:21.673508-08 test f A100 \N t \N \N \N {} +3491 1198 2025-02-28 12:21:58.688283-08 2025-02-28 12:05:53.605746-08 test f A100 \N t \N \N \N {} +2719 928 2025-02-26 16:32:16.602958-08 2025-02-26 16:19:31.629908-08 test f H100 \N f \N \N \N {} +2720 930 2025-02-26 15:23:05.898632-08 2025-02-26 15:07:22.998261-08 test f H100 \N f \N \N \N {} +2721 934 2025-02-26 17:22:57.87553-08 2025-02-26 15:53:13.995243-08 test f T4 \N t \N \N \N {} +2722 935 2025-02-26 16:04:13.430604-08 2025-02-26 15:30:59.454902-08 benchmark f H100 \N t \N \N \N {} +2723 936 2025-02-26 15:35:49.505504-08 2025-02-26 17:20:48.238503-08 benchmark f H100 \N t \N \N \N {} +2782 970 2025-02-26 21:07:51.669113-08 2025-02-26 20:49:24.139845-08 test f A100 \N f \N \N \N {} +2729 937 2025-02-26 17:32:52.663266-08 2025-02-26 16:47:18.507852-08 leaderboard t H100 0.0009355417142857144 t \N \N \N {} +2730 938 2025-02-26 16:48:14.702847-08 2025-02-26 16:57:10.702808-08 benchmark f H100 \N t \N \N \N {} +2731 939 2025-02-26 17:31:10.651035-08 2025-02-26 17:03:20.742136-08 test f H100 \N t \N \N \N {} +2732 939 2025-02-26 16:04:46.223011-08 2025-02-26 17:44:01.018405-08 benchmark f H100 \N t \N \N \N {} +2733 939 2025-02-26 16:09:00.549881-08 2025-02-26 16:26:03.875919-08 leaderboard f H100 0.00047589718181818185 t \N \N \N {} +2751 952 2025-02-26 18:36:20.359926-08 2025-02-26 18:56:33.112066-08 leaderboard t H100 0.00004619103 t \N \N \N {} +2752 952 2025-02-26 17:58:47.604278-08 2025-02-26 18:46:38.011565-08 test f H100 \N t \N \N \N {} +2759 957 2025-02-26 19:37:58.190081-08 2025-02-26 19:32:35.710825-08 test f A100 \N f \N \N \N {} +2760 958 2025-02-26 19:20:20.101499-08 2025-02-26 18:54:34.381861-08 test f A100 \N t \N \N \N {} +818 255 2025-02-24 12:36:47.623295-08 2025-02-24 11:44:27.318735-08 benchmark t H100 \N t \N \N \N {} +819 255 2025-02-24 12:45:56.514079-08 2025-02-24 13:03:45.699083-08 leaderboard t H100 0.006123367 t \N \N \N {} +820 256 2025-02-24 12:00:59.412291-08 2025-02-24 11:48:22.822008-08 test t A100 \N f \N \N \N {} +827 258 2025-02-24 12:14:03.313645-08 2025-02-24 13:12:29.40344-08 benchmark f A100 \N t \N \N \N {} +829 259 2025-02-24 13:27:44.987486-08 2025-02-24 12:15:45.854513-08 test f H100 \N t \N \N \N {} +835 260 2025-02-24 12:44:46.406235-08 2025-02-24 12:32:35.981113-08 test f H100 \N f \N \N \N {} +836 260 2025-02-24 11:57:43.314454-08 2025-02-24 13:37:10.081022-08 test t H100 \N f \N \N \N {} +839 261 2025-02-24 13:24:41.686947-08 2025-02-24 14:04:01.45224-08 benchmark f L4 \N t \N \N \N {} +840 261 2025-02-24 12:44:55.198063-08 2025-02-24 13:15:16.477866-08 benchmark f T4 \N t \N \N \N {} +841 262 2025-02-24 14:40:33.295376-08 2025-02-24 14:37:39.287063-08 test f H100 \N t \N \N \N {} +842 262 2025-02-24 13:32:24.520488-08 2025-02-24 12:51:42.430233-08 benchmark f H100 \N t \N \N \N {} +845 262 2025-02-24 13:16:57.772484-08 2025-02-24 13:43:01.738354-08 benchmark t A100 \N t \N \N \N {} +846 262 2025-02-24 14:40:12.521043-08 2025-02-24 12:41:50.695193-08 leaderboard t A100 0.0035759156666666666 t \N \N \N {} +847 262 2025-02-24 13:06:53.677846-08 2025-02-24 13:04:43.106693-08 test t T4 \N t \N \N \N {} +848 262 2025-02-24 13:21:20.33413-08 2025-02-24 14:07:43.524626-08 benchmark t T4 \N t \N \N \N {} +849 262 2025-02-24 13:08:55.082034-08 2025-02-24 14:11:39.783402-08 leaderboard t T4 0.017268438333333334 t \N \N \N {} +855 262 2025-02-24 13:00:14.369727-08 2025-02-24 12:58:55.821216-08 leaderboard f A100 0.0035754176666666663 t \N \N \N {} +856 263 2025-02-24 14:12:04.211732-08 2025-02-24 14:32:29.922479-08 benchmark f A100 \N f \N \N \N {} +857 262 2025-02-24 13:01:52.259649-08 2025-02-24 13:08:26.610715-08 test f T4 \N t \N \N \N {} +858 262 2025-02-24 13:09:26.79495-08 2025-02-24 14:17:18.323779-08 benchmark f T4 \N t \N \N \N {} +859 262 2025-02-24 14:15:52.323889-08 2025-02-24 14:32:11.723833-08 leaderboard f T4 0.017249578 t \N \N \N {} +1459 486 2025-02-25 04:17:57.329398-08 2025-02-25 04:19:00.137084-08 test t A100 \N t \N \N \N {} +861 262 2025-02-24 13:55:33.663644-08 2025-02-24 14:37:09.657969-08 benchmark t H100 \N t \N \N \N {} +862 262 2025-02-24 14:11:40.394727-08 2025-02-24 14:04:47.293746-08 leaderboard t H100 0.00157941125 t \N \N \N {} +863 262 2025-02-24 14:14:35.256564-08 2025-02-24 13:32:29.562338-08 test t L4 \N t \N \N \N {} +864 262 2025-02-24 12:48:02.755552-08 2025-02-24 14:13:00.945425-08 benchmark t L4 \N t \N \N \N {} +865 262 2025-02-24 12:46:48.53663-08 2025-02-24 14:29:57.105212-08 leaderboard t L4 0.017202579333333332 t \N \N \N {} +870 266 2025-02-24 14:10:29.80768-08 2025-02-24 13:31:05.267924-08 test t H100 \N f \N \N \N {} +882 268 2025-02-24 13:48:16.246206-08 2025-02-24 13:16:22.804481-08 benchmark t H100 \N t \N \N \N {} +883 268 2025-02-24 13:47:51.158916-08 2025-02-24 14:12:31.13551-08 leaderboard t H100 0.001499999 t \N \N \N {} +884 269 2025-02-24 13:36:44.335857-08 2025-02-24 13:23:48.204042-08 test f L4 \N t \N \N \N {} +885 269 2025-02-24 13:40:59.689967-08 2025-02-24 14:59:46.238789-08 test f A100 \N t \N \N \N {} +886 269 2025-02-24 14:18:48.147333-08 2025-02-24 14:54:09.931319-08 test f T4 \N t \N \N \N {} +887 269 2025-02-24 13:58:15.398454-08 2025-02-24 14:08:43.206835-08 test f H100 \N t \N \N \N {} +2761 959 2025-02-26 20:04:15.692556-08 2025-02-26 20:10:38.466984-08 test f A100 \N t \N \N \N {} +3506 1200 2025-02-28 12:29:44.776123-08 2025-02-28 11:41:55.023296-08 test t A100 \N t \N \N \N {} +2762 960 2025-02-26 19:50:11.129181-08 2025-02-26 19:57:28.353335-08 test f A100 \N t \N \N \N {} +888 270 2025-02-24 14:36:02.624025-08 2025-02-24 15:09:25.11226-08 test f H100 \N t \N \N \N {} +893 270 2025-02-24 13:25:28.447878-08 2025-02-24 14:26:32.820494-08 leaderboard t H100 0.006313673333333333 t \N \N \N {} +978 288 2025-02-24 16:16:39.684041-08 2025-02-24 16:53:33.866555-08 test f H100 \N t \N \N \N {} +894 271 2025-02-24 13:37:51.574913-08 2025-02-24 13:30:44.90995-08 benchmark f A100 \N t \N \N \N {} +895 271 2025-02-24 14:42:33.925165-08 2025-02-24 13:50:25.827947-08 benchmark f L4 \N t \N \N \N {} +896 271 2025-02-24 15:14:26.679127-08 2025-02-24 13:24:56.860101-08 benchmark f H100 \N t \N \N \N {} +2763 961 2025-02-26 20:33:39.926563-08 2025-02-26 18:53:59.629244-08 test f A100 \N f \N \N \N {} +3591 1229 2025-02-28 14:00:43.377485-08 2025-02-28 13:02:18.799382-08 benchmark t H100 \N t \N \N \N {} +922 272 2025-02-24 14:21:16.338815-08 2025-02-24 13:57:53.832712-08 test f L4 \N t \N \N \N {} +930 275 2025-02-24 14:06:04.090352-08 2025-02-24 15:32:28.313994-08 test t H100 \N f \N \N \N {} +931 275 2025-02-24 15:08:25.725574-08 2025-02-24 14:48:09.110921-08 test f H100 \N f \N \N \N {} +932 276 2025-02-24 15:08:07.755574-08 2025-02-24 14:00:19.773242-08 benchmark f H100 \N t \N \N \N {} +933 277 2025-02-24 15:11:54.355362-08 2025-02-24 14:30:44.412152-08 benchmark f H100 \N t \N \N \N {} +934 278 2025-02-24 15:11:08.217229-08 2025-02-24 13:56:46.532795-08 test t A100 \N t \N \N \N {} +2765 963 2025-02-26 21:05:46.567488-08 2025-02-26 21:01:24.814437-08 test f A100 \N t \N \N \N {} +949 278 2025-02-24 14:01:41.92456-08 2025-02-24 13:43:58.586813-08 test t L4 \N t \N \N \N {} +953 278 2025-02-24 15:30:55.085434-08 2025-02-24 15:03:27.528219-08 benchmark t T4 \N t \N \N \N {} +954 278 2025-02-24 14:13:18.813125-08 2025-02-24 14:25:32.061848-08 leaderboard t T4 0.01679078633333333 t \N \N \N {} +955 278 2025-02-24 13:55:20.752079-08 2025-02-24 14:29:46.038209-08 test f T4 \N t \N \N \N {} +956 278 2025-02-24 14:26:05.784131-08 2025-02-24 14:04:02.728948-08 benchmark f T4 \N t \N \N \N {} +957 278 2025-02-24 14:40:08.470773-08 2025-02-24 14:15:08.834032-08 leaderboard f T4 0.01680730033333333 t \N \N \N {} +958 279 2025-02-24 13:58:02.12755-08 2025-02-24 14:09:39.155243-08 test f A100 \N t \N \N \N {} +962 283 2025-02-24 15:58:39.196492-08 2025-02-24 14:11:06.156964-08 benchmark f A100 \N t \N \N \N {} +963 284 2025-02-24 14:28:10.002662-08 2025-02-24 14:33:20.990272-08 benchmark f A100 \N t \N \N \N {} +964 285 2025-02-24 14:50:05.118746-08 2025-02-24 16:06:00.21924-08 benchmark f A100 \N t \N \N \N {} +966 286 2025-02-24 15:11:06.589828-08 2025-02-24 15:54:57.512172-08 benchmark f A100 \N t \N \N \N {} +2766 964 2025-02-26 19:08:30.791267-08 2025-02-26 20:25:23.844568-08 test f A100 \N t \N \N \N {} +967 286 2025-02-24 15:50:38.06952-08 2025-02-24 14:35:41.812052-08 leaderboard f A100 0.003564924125 t \N \N \N {} +968 286 2025-02-24 15:34:26.225087-08 2025-02-24 14:31:55.790602-08 test t A100 \N t \N \N \N {} +975 288 2025-02-24 16:06:09.241772-08 2025-02-24 15:20:44.834631-08 test t H100 \N t \N \N \N {} +1003 289 2025-02-24 16:59:20.310533-08 2025-02-24 16:33:52.660533-08 test f H100 \N t \N \N \N {} +1007 289 2025-02-24 15:48:45.290987-08 2025-02-24 16:12:36.369625-08 benchmark f T4 \N t \N \N \N {} +1008 289 2025-02-24 16:43:13.03202-08 2025-02-24 16:09:32.189032-08 leaderboard f T4 0.017463506 t \N \N \N {} +1009 289 2025-02-24 15:26:10.010531-08 2025-02-24 15:31:30.134666-08 test f L4 \N t \N \N \N {} +1010 289 2025-02-24 15:34:17.620631-08 2025-02-24 15:20:56.610577-08 benchmark f L4 \N t \N \N \N {} +1011 289 2025-02-24 15:17:08.27862-08 2025-02-24 16:53:24.408023-08 leaderboard f L4 0.017437469 t \N \N \N {} +1013 289 2025-02-24 17:10:06.760302-08 2025-02-24 16:01:34.694517-08 benchmark t L4 \N t \N \N \N {} +1014 289 2025-02-24 17:02:49.759681-08 2025-02-24 16:46:54.792055-08 leaderboard t L4 0.017468976333333334 t \N \N \N {} +1015 289 2025-02-24 16:23:10.660698-08 2025-02-24 16:34:48.55816-08 test t H100 \N t \N \N \N {} +1016 289 2025-02-24 17:08:58.086549-08 2025-02-24 17:12:19.202409-08 benchmark t H100 \N t \N \N \N {} +1017 289 2025-02-24 15:29:25.320543-08 2025-02-24 16:41:10.791533-08 leaderboard t H100 0.001555808 t \N \N \N {} +1021 290 2025-02-24 17:27:27.359016-08 2025-02-24 17:10:34.608977-08 test f T4 \N f \N \N \N {} +1022 290 2025-02-24 16:04:38.081723-08 2025-02-24 16:01:10.893306-08 test t T4 \N f \N \N \N {} +1023 290 2025-02-24 17:30:53.275597-08 2025-02-24 16:15:31.909539-08 test f H100 \N f \N \N \N {} +1024 290 2025-02-24 16:19:28.643489-08 2025-02-24 17:41:16.119971-08 test t L4 \N f \N \N \N {} +2767 965 2025-02-26 20:43:06.138197-08 2025-02-26 20:38:46.941894-08 test f A100 \N t \N \N \N {} +1025 290 2025-02-24 15:50:33.00311-08 2025-02-24 17:22:52.823354-08 test f L4 \N f \N \N \N {} +1026 291 2025-02-24 16:00:27.990747-08 2025-02-24 17:05:43.520942-08 test t A100 \N t \N \N \N {} +1027 291 2025-02-24 17:29:02.565531-08 2025-02-24 17:17:14.052814-08 benchmark t A100 \N t \N \N \N {} +1028 291 2025-02-24 16:02:23.141852-08 2025-02-24 16:33:31.463874-08 leaderboard t A100 0.003320683 t \N \N \N {} +1029 291 2025-02-24 16:21:24.320655-08 2025-02-24 16:33:35.189391-08 test f A100 \N t \N \N \N {} +1030 291 2025-02-24 16:54:28.950428-08 2025-02-24 16:34:49.566872-08 benchmark f A100 \N t \N \N \N {} +1041 291 2025-02-24 16:51:50.87004-08 2025-02-24 16:52:44.22599-08 test t T4 \N t \N \N \N {} +1031 291 2025-02-24 16:57:17.908772-08 2025-02-24 17:21:40.225798-08 leaderboard f A100 0.0033077086666666667 t \N \N \N {} +1061 292 2025-02-24 16:14:47.786921-08 2025-02-24 16:02:43.643719-08 leaderboard f H100 0.0014229046666666668 t \N \N \N {} +1062 292 2025-02-24 16:05:24.343352-08 2025-02-24 16:26:39.742121-08 test t T4 \N t \N \N \N {} +1068 292 2025-02-24 16:14:27.218676-08 2025-02-24 16:12:18.471021-08 test f L4 \N t \N \N \N {} +1071 292 2025-02-24 16:21:46.18687-08 2025-02-24 17:29:33.25851-08 test t H100 \N t \N \N \N {} +1077 295 2025-02-24 17:38:05.121185-08 2025-02-24 17:38:57.063894-08 benchmark f H100 \N t \N \N \N {} +1078 295 2025-02-24 17:58:44.68097-08 2025-02-24 17:14:34.343302-08 leaderboard f H100 0.0010347983333333333 t \N \N \N {} +1079 295 2025-02-24 17:04:05.158398-08 2025-02-24 17:25:58.879946-08 test t H100 \N t \N \N \N {} +1080 295 2025-02-24 16:58:08.102388-08 2025-02-24 17:56:34.485958-08 benchmark t H100 \N t \N \N \N {} +1081 295 2025-02-24 16:55:29.797081-08 2025-02-24 17:47:07.608762-08 leaderboard t H100 0.001039122 t \N \N \N {} +1096 304 2025-02-24 18:56:04.543979-08 2025-02-24 17:04:43.557942-08 benchmark f A100 \N t \N \N \N {} +1082 296 2025-02-24 17:31:42.820566-08 2025-02-24 16:33:59.876329-08 benchmark f H100 \N f \N \N \N {} +1084 299 2025-02-24 17:39:48.424492-08 2025-02-24 17:07:52.030306-08 benchmark f A100 \N t \N \N \N {} +1085 298 2025-02-24 16:35:28.403851-08 2025-02-24 17:02:30.160259-08 benchmark f H100 \N f \N \N \N {} +1086 300 2025-02-24 17:14:06.318787-08 2025-02-24 16:50:30.934776-08 test t A100 \N t \N \N \N {} +1091 300 2025-02-24 17:29:22.731228-08 2025-02-24 17:12:13.670748-08 leaderboard f A100 0.014157763333333333 t \N \N \N {} +1092 301 2025-02-24 17:59:41.646434-08 2025-02-24 16:56:21.661999-08 benchmark f H100 \N f \N \N \N {} +1093 302 2025-02-24 18:21:23.246489-08 2025-02-24 17:07:12.364112-08 benchmark f A100 \N f \N \N \N {} +1094 303 2025-02-24 17:50:55.598954-08 2025-02-24 17:13:45.078131-08 benchmark f H100 \N t \N \N \N {} +1095 304 2025-02-24 17:38:46.646025-08 2025-02-24 17:11:44.810342-08 test f A100 \N t \N \N \N {} +1486 495 2025-02-25 05:26:36.575717-08 2025-02-25 05:35:53.585466-08 test f A100 \N f \N \N \N {} +1097 304 2025-02-24 17:03:14.22191-08 2025-02-24 17:48:13.256134-08 leaderboard f A100 0.006075353 t \N \N \N {} +3576 1223 2025-02-28 12:52:29.871321-08 2025-02-28 13:12:16.401319-08 test f H100 \N t \N \N \N {} +1098 304 2025-02-24 18:52:41.173136-08 2025-02-24 18:10:45.04049-08 test t A100 \N t \N \N \N {} +1099 304 2025-02-24 17:19:09.825043-08 2025-02-24 17:28:38.91665-08 benchmark t A100 \N t \N \N \N {} +1100 304 2025-02-24 18:23:50.783076-08 2025-02-24 18:13:43.170325-08 leaderboard t A100 0.006081086333333333 t \N \N \N {} +1105 304 2025-02-24 17:34:55.399719-08 2025-02-24 17:27:11.226139-08 benchmark f T4 \N t \N \N \N {} +1106 304 2025-02-24 18:28:11.076985-08 2025-02-24 18:13:11.8461-08 leaderboard f T4 0.028245293666666667 t \N \N \N {} +1107 304 2025-02-24 18:33:57.535639-08 2025-02-24 17:46:08.937512-08 test t T4 \N t \N \N \N {} +1113 304 2025-02-24 18:34:00.598847-08 2025-02-24 18:51:13.208664-08 test t L4 \N t \N \N \N {} +1114 304 2025-02-24 18:06:21.101459-08 2025-02-24 17:31:57.238174-08 benchmark t L4 \N t \N \N \N {} +1119 305 2025-02-24 17:42:57.925612-08 2025-02-24 17:15:14.683808-08 benchmark f H100 \N f \N \N \N {} +1120 306 2025-02-24 17:21:15.587258-08 2025-02-24 18:44:01.440783-08 benchmark f A100 \N t \N \N \N {} +1121 306 2025-02-24 18:36:34.865426-08 2025-02-24 17:49:17.471548-08 benchmark f H100 \N t \N \N \N {} +1122 306 2025-02-24 17:26:55.287166-08 2025-02-24 18:35:50.569086-08 benchmark f T4 \N t \N \N \N {} +1125 308 2025-02-24 18:34:58.15099-08 2025-02-24 17:56:55.545892-08 benchmark f H100 \N t \N \N \N {} +1126 309 2025-02-24 18:10:41.370592-08 2025-02-24 19:01:32.847335-08 test f T4 \N t \N \N \N {} +1127 310 2025-02-24 18:21:38.286944-08 2025-02-24 19:30:50.197993-08 test f T4 \N f \N \N \N {} +1128 311 2025-02-24 19:40:10.137721-08 2025-02-24 19:12:06.15593-08 test f T4 \N f \N \N \N {} +1144 321 2025-02-24 19:09:00.011949-08 2025-02-24 19:27:42.347167-08 benchmark f A100 \N t \N \N \N {} +1145 321 2025-02-24 18:28:50.942544-08 2025-02-24 20:07:48.608885-08 leaderboard f A100 0.008048783333333333 t \N \N \N {} +1146 321 2025-02-24 19:54:14.030406-08 2025-02-24 19:26:42.983807-08 test t A100 \N t \N \N \N {} +1147 321 2025-02-24 19:37:58.992398-08 2025-02-24 18:38:17.162254-08 benchmark t A100 \N t \N \N \N {} +1155 328 2025-02-24 18:59:30.528684-08 2025-02-24 18:51:38.131222-08 benchmark f A100 \N t \N \N \N {} +1160 329 2025-02-24 19:38:57.967787-08 2025-02-24 18:57:02.949354-08 benchmark t H100 \N t \N \N \N {} +1161 329 2025-02-24 19:14:33.290908-08 2025-02-24 20:08:23.310109-08 leaderboard t H100 0.0015865033333333333 t \N \N \N {} +1166 334 2025-02-24 19:50:26.32731-08 2025-02-24 20:31:43.298423-08 test f A100 \N f \N \N \N {} +1167 335 2025-02-24 19:52:47.832137-08 2025-02-24 18:40:52.747222-08 test f A100 \N f \N \N \N {} +1168 336 2025-02-24 18:58:04.317224-08 2025-02-24 20:40:01.428171-08 test f A100 \N t \N \N \N {} +1169 337 2025-02-24 19:33:06.587039-08 2025-02-24 20:20:18.024082-08 benchmark f A100 \N t \N \N \N {} +1171 339 2025-02-24 20:25:27.128083-08 2025-02-24 18:45:26.166042-08 benchmark f A100 \N t \N \N \N {} +1175 343 2025-02-24 20:01:36.447161-08 2025-02-24 20:17:57.794145-08 benchmark f A100 \N t \N \N \N {} +1176 344 2025-02-24 18:57:03.668266-08 2025-02-24 20:32:08.204872-08 benchmark f A100 \N t \N \N \N {} +1177 345 2025-02-24 19:19:48.575647-08 2025-02-24 20:35:58.402184-08 benchmark f A100 \N f \N \N \N {} +1180 348 2025-02-24 20:38:35.466753-08 2025-02-24 20:27:20.582819-08 benchmark f A100 \N t \N \N \N {} +1181 349 2025-02-24 19:50:45.13357-08 2025-02-24 19:35:45.516463-08 test f A100 \N f \N \N \N {} +1186 356 2025-02-24 20:44:38.220701-08 2025-02-24 20:35:27.553404-08 benchmark f A100 \N f \N \N \N {} +1187 357 2025-02-24 20:31:41.284738-08 2025-02-24 20:51:31.622096-08 benchmark f A100 \N t \N \N \N {} +1191 364 2025-02-24 19:38:43.168308-08 2025-02-24 20:03:40.713134-08 benchmark f A100 \N t \N \N \N {} +1192 365 2025-02-24 20:20:30.975678-08 2025-02-24 20:47:45.806082-08 test f A100 \N t \N \N \N {} +1198 366 2025-02-24 20:50:37.04848-08 2025-02-24 20:03:24.288635-08 benchmark f A100 \N f \N \N \N {} +1199 367 2025-02-24 20:01:53.464636-08 2025-02-24 20:50:55.06292-08 benchmark f A100 \N f \N \N \N {} +1200 368 2025-02-24 20:48:53.017372-08 2025-02-24 21:07:09.746977-08 benchmark f A100 \N f \N \N \N {} +1203 371 2025-02-24 21:03:40.316811-08 2025-02-24 20:06:59.770551-08 benchmark f A100 \N f \N \N \N {} +1204 372 2025-02-24 21:16:31.917525-08 2025-02-24 20:19:23.12487-08 benchmark f A100 \N f \N \N \N {} +1212 380 2025-02-24 21:30:39.643844-08 2025-02-24 21:22:48.195881-08 benchmark f A100 \N f \N \N \N {} +1213 381 2025-02-24 21:27:50.804873-08 2025-02-24 20:30:39.877648-08 benchmark f A100 \N f \N \N \N {} +1214 382 2025-02-24 21:26:22.567911-08 2025-02-24 20:42:27.609326-08 benchmark f A100 \N t \N \N \N {} +1215 383 2025-02-24 21:43:35.675505-08 2025-02-24 21:41:12.864918-08 benchmark f A100 \N f \N \N \N {} +1217 385 2025-02-24 20:39:07.414152-08 2025-02-24 21:16:56.728524-08 benchmark f A100 \N t \N \N \N {} +1218 386 2025-02-24 21:24:25.618173-08 2025-02-24 20:59:42.5791-08 benchmark f A100 \N f \N \N \N {} +1219 387 2025-02-24 20:37:38.559826-08 2025-02-24 20:50:09.030352-08 benchmark f A100 \N t \N \N \N {} +1220 388 2025-02-24 21:12:35.63089-08 2025-02-24 21:07:52.286646-08 test f H100 \N f \N \N \N {} +1221 390 2025-02-24 20:41:29.07709-08 2025-02-24 21:50:07.217387-08 test f A100 \N f \N \N \N {} +1222 391 2025-02-24 20:36:54.532658-08 2025-02-24 20:27:33.575008-08 test f A100 \N f \N \N \N {} +1223 391 2025-02-24 22:09:52.859803-08 2025-02-24 20:25:04.338196-08 test f L4 \N f \N \N \N {} +1224 391 2025-02-24 20:57:57.971901-08 2025-02-24 22:07:50.668858-08 test f T4 \N f \N \N \N {} +1225 391 2025-02-24 21:26:12.689911-08 2025-02-24 20:21:51.434441-08 test f H100 \N f \N \N \N {} +1226 392 2025-02-24 21:22:04.179078-08 2025-02-24 21:02:36.047543-08 test f A100 \N f \N \N \N {} +1227 393 2025-02-24 21:27:17.29266-08 2025-02-24 21:05:23.934691-08 test t A100 \N t \N \N \N {} +1228 393 2025-02-24 21:57:42.36025-08 2025-02-24 20:56:27.385816-08 benchmark t A100 \N t \N \N \N {} +1232 393 2025-02-24 21:19:55.551644-08 2025-02-24 20:43:59.005894-08 leaderboard f A100 0.0033388506666666667 t \N \N \N {} +1265 416 2025-02-24 21:37:21.368251-08 2025-02-24 21:44:53.806727-08 leaderboard t H100 0.0021056229500000002 t \N \N \N {} +1233 394 2025-02-24 20:48:53.683982-08 2025-02-24 21:10:47.005539-08 test f A100 \N f \N \N \N {} +1234 395 2025-02-24 21:42:25.587766-08 2025-02-24 21:13:54.206602-08 test f A100 \N f \N \N \N {} +1235 396 2025-02-24 21:31:47.507986-08 2025-02-24 21:41:14.621874-08 test f A100 \N f \N \N \N {} +1236 397 2025-02-24 20:53:51.866306-08 2025-02-24 21:41:52.555281-08 test f A100 \N f \N \N \N {} +1237 398 2025-02-24 22:13:49.271446-08 2025-02-24 21:54:00.876085-08 test f A100 \N f \N \N \N {} +1273 424 2025-02-24 23:15:56.010969-08 2025-02-24 22:22:18.360257-08 test f H100 \N f \N \N \N {} +1238 399 2025-02-24 21:20:05.240709-08 2025-02-24 22:10:17.303543-08 test f A100 \N f \N \N \N {} +1239 400 2025-02-24 21:02:33.153711-08 2025-02-24 22:14:52.737511-08 test f A100 \N f \N \N \N {} +1242 403 2025-02-24 21:44:20.600901-08 2025-02-24 22:19:00.048671-08 test f A100 \N t \N \N \N {} +1274 425 2025-02-24 22:32:28.342997-08 2025-02-24 22:46:18.153042-08 test f H100 \N f \N \N \N {} +1244 405 2025-02-24 21:15:42.934708-08 2025-02-24 22:00:36.897134-08 benchmark f A100 \N t \N \N \N {} +1245 406 2025-02-24 22:41:21.855098-08 2025-02-24 21:22:41.861309-08 test f A100 \N f \N \N \N {} +1246 407 2025-02-24 22:14:38.935217-08 2025-02-24 21:17:28.999889-08 test f A100 \N f \N \N \N {} +1247 408 2025-02-24 21:15:17.037056-08 2025-02-24 22:47:31.85541-08 test f A100 \N f \N \N \N {} +1255 413 2025-02-24 22:41:32.544432-08 2025-02-24 21:50:59.410312-08 test t A100 \N t \N \N \N {} +1275 426 2025-02-24 22:50:00.313014-08 2025-02-24 22:01:51.946596-08 test f H100 \N f \N \N \N {} +1256 413 2025-02-24 21:28:53.296618-08 2025-02-24 21:56:09.683656-08 benchmark t A100 \N t \N \N \N {} +1257 413 2025-02-24 22:52:41.725644-08 2025-02-24 21:02:44.177771-08 leaderboard t A100 0.00082829875 t \N \N \N {} +1258 414 2025-02-24 22:43:01.622934-08 2025-02-24 21:44:54.450946-08 test f H100 \N t \N \N \N {} +1259 415 2025-02-24 21:05:33.640351-08 2025-02-24 22:22:12.322858-08 benchmark f H100 \N t \N \N \N {} +1260 416 2025-02-24 22:50:18.115204-08 2025-02-24 22:10:37.88296-08 test f H100 \N t \N \N \N {} +1263 416 2025-02-24 22:41:58.320403-08 2025-02-24 22:46:15.121319-08 test t H100 \N t \N \N \N {} +1264 416 2025-02-24 21:33:11.644917-08 2025-02-24 22:43:20.018249-08 benchmark t H100 \N t \N \N \N {} +1266 417 2025-02-24 23:17:42.124344-08 2025-02-24 22:14:36.706679-08 test f L4 \N f \N \N \N {} +1271 422 2025-02-24 23:24:55.93824-08 2025-02-24 23:09:05.952447-08 test f H100 \N f \N \N \N {} +1272 423 2025-02-24 23:33:25.559896-08 2025-02-24 23:04:20.171899-08 test f H100 \N f \N \N \N {} +1277 427 2025-02-25 00:21:05.704041-08 2025-02-24 23:17:15.254253-08 benchmark t H100 \N t \N \N \N {} +1278 427 2025-02-24 23:05:44.473555-08 2025-02-24 22:36:18.623353-08 leaderboard t H100 0.00151173175 t \N \N \N {} +1279 427 2025-02-24 23:22:06.892269-08 2025-02-25 00:08:22.994675-08 test f H100 \N t \N \N \N {} +1280 427 2025-02-25 00:06:49.982745-08 2025-02-25 00:02:16.454215-08 benchmark f H100 \N t \N \N \N {} +1281 427 2025-02-24 23:30:10.509612-08 2025-02-24 22:24:37.733967-08 leaderboard f H100 0.0014917988095238094 t \N \N \N {} +1282 428 2025-02-25 00:29:11.621733-08 2025-02-25 00:25:31.895544-08 test f L4 \N t \N \N \N {} +1283 429 2025-02-25 00:23:40.437427-08 2025-02-25 00:28:38.668036-08 benchmark f L4 \N t \N \N \N {} +1420 472 2025-02-25 05:09:52.78126-08 2025-02-25 03:33:17.606456-08 test f A100 \N t \N \N \N {} +1284 430 2025-02-24 22:56:08.41611-08 2025-02-24 23:53:11.011713-08 benchmark f L4 \N t \N \N \N {} +1291 434 2025-02-25 00:38:27.016325-08 2025-02-25 01:33:09.270327-08 test t A100 \N f \N \N \N {} +1292 435 2025-02-25 01:13:37.578908-08 2025-02-25 02:04:12.790967-08 test t A100 \N f \N \N \N {} +1293 435 2025-02-25 00:34:17.674028-08 2025-02-25 01:20:00.978456-08 test f A100 \N f \N \N \N {} +1294 436 2025-02-25 00:52:16.868772-08 2025-02-25 01:46:34.105184-08 test t A100 \N t \N \N \N {} +1295 436 2025-02-25 00:34:18.664042-08 2025-02-25 01:29:38.828363-08 benchmark t A100 \N t \N \N \N {} +2768 965 2025-02-26 19:49:02.478379-08 2025-02-26 19:27:59.359226-08 benchmark f A100 \N t \N \N \N {} +2769 965 2025-02-26 20:58:22.764397-08 2025-02-26 20:38:26.615803-08 leaderboard f A100 0.014282038666666667 t \N \N \N {} +2770 966 2025-02-26 20:24:44.026742-08 2025-02-26 19:35:29.731923-08 test t A100 \N t \N \N \N {} +2771 966 2025-02-26 19:37:41.565742-08 2025-02-26 20:03:41.368376-08 benchmark t A100 \N t \N \N \N {} +2790 974 2025-02-26 20:10:51.878483-08 2025-02-26 21:28:32.546366-08 benchmark t A100 \N t \N \N \N {} +1296 436 2025-02-25 00:18:28.614962-08 2025-02-25 00:32:08.809058-08 leaderboard t A100 0.01866328503614458 t \N \N \N {} +1297 436 2025-02-25 00:51:06.440802-08 2025-02-25 00:45:31.265356-08 test f A100 \N t \N \N \N {} +1423 472 2025-02-25 04:26:56.092902-08 2025-02-25 04:03:45.687235-08 test t A100 \N t \N \N \N {} +1298 436 2025-02-25 00:31:34.02733-08 2025-02-25 01:09:08.022812-08 benchmark f A100 \N t \N \N \N {} +1299 436 2025-02-25 01:34:03.732205-08 2025-02-25 01:19:15.994949-08 leaderboard f A100 0.01933390226 t \N \N \N {} +1302 438 2025-02-25 02:13:44.700479-08 2025-02-25 01:53:56.844408-08 benchmark f A100 \N t \N \N \N {} +1303 438 2025-02-25 01:52:21.421101-08 2025-02-25 01:13:41.748765-08 leaderboard f A100 0.00318387075 t \N \N \N {} +1304 438 2025-02-25 00:58:34.99604-08 2025-02-25 00:26:07.558315-08 test t A100 \N t \N \N \N {} +1426 473 2025-02-25 03:33:23.323189-08 2025-02-25 04:45:59.064294-08 test t A100 \N t \N \N \N {} +1522 506 2025-02-25 06:29:30.73263-08 2025-02-25 06:29:47.306703-08 test f H100 \N t \N \N \N {} +1305 438 2025-02-25 01:45:16.792963-08 2025-02-25 00:41:59.854224-08 benchmark t A100 \N t \N \N \N {} +1308 439 2025-02-25 01:05:46.577398-08 2025-02-25 01:11:50.101033-08 benchmark t A100 \N t \N \N \N {} +1309 439 2025-02-25 00:31:25.37309-08 2025-02-25 01:33:59.391129-08 leaderboard t A100 0.01829094864864865 t \N \N \N {} +1429 473 2025-02-25 04:12:04.123883-08 2025-02-25 04:05:49.033486-08 test f A100 \N t \N \N \N {} +1310 439 2025-02-25 00:48:25.781713-08 2025-02-25 00:53:44.022446-08 test f A100 \N t \N \N \N {} +1311 439 2025-02-25 00:44:11.592056-08 2025-02-25 01:42:04.821849-08 benchmark f A100 \N t \N \N \N {} +3577 1224 2025-02-28 13:39:20.462449-08 2025-02-28 14:41:42.556499-08 benchmark f H100 \N t \N \N \N {} +2781 969 2025-02-26 19:55:03.2042-08 2025-02-26 20:10:38.143515-08 test f A100 \N f \N \N \N {} +2792 975 2025-02-26 20:50:18.58077-08 2025-02-26 21:14:29.36909-08 test t A100 \N t \N \N \N {} +2785 973 2025-02-26 19:58:45.213429-08 2025-02-26 20:46:29.95438-08 test f A100 \N t \N \N \N {} +2786 974 2025-02-26 21:50:24.917282-08 2025-02-26 21:26:01.992988-08 test f A100 \N t \N \N \N {} +1317 441 2025-02-25 01:27:39.741757-08 2025-02-25 01:07:56.270804-08 test f A100 \N t \N \N \N {} +1318 441 2025-02-25 00:45:28.035248-08 2025-02-25 01:01:14.020655-08 benchmark f A100 \N f \N \N \N {} +1319 442 2025-02-25 01:17:35.027985-08 2025-02-25 01:08:09.855491-08 test f T4 \N t \N \N \N {} +1320 442 2025-02-25 02:23:45.784619-08 2025-02-25 01:07:58.424072-08 benchmark f T4 \N t \N \N \N {} +1321 442 2025-02-25 02:07:32.64996-08 2025-02-25 01:07:33.995652-08 leaderboard f T4 0.017366781666666668 t \N \N \N {} +1323 442 2025-02-25 02:26:58.697335-08 2025-02-25 02:40:12.565833-08 benchmark t T4 \N t \N \N \N {} +1324 442 2025-02-25 02:12:07.116909-08 2025-02-25 00:49:02.771059-08 leaderboard t T4 0.017343184 t \N \N \N {} +1325 443 2025-02-25 02:37:32.638106-08 2025-02-25 02:42:15.65973-08 test t A100 \N t \N \N \N {} +1331 444 2025-02-25 01:34:34.727263-08 2025-02-25 01:39:09.005069-08 test f A100 \N f \N \N \N {} +1332 444 2025-02-25 02:59:13.676931-08 2025-02-25 02:28:54.180908-08 test t A100 \N f \N \N \N {} +1333 445 2025-02-25 01:51:47.936405-08 2025-02-25 03:05:33.196327-08 test f A100 \N t \N \N \N {} +1334 445 2025-02-25 02:55:01.288465-08 2025-02-25 02:42:58.024491-08 benchmark f A100 \N t \N \N \N {} +1337 445 2025-02-25 02:37:39.036581-08 2025-02-25 03:06:46.109288-08 benchmark t A100 \N t \N \N \N {} +1338 445 2025-02-25 02:27:46.062726-08 2025-02-25 02:38:14.514333-08 leaderboard t A100 0.0030933733333333335 t \N \N \N {} +2902 1013 2025-02-27 02:13:19.283137-08 2025-02-27 00:49:55.016565-08 benchmark f L4 \N t \N \N \N {} +2972 1054 2025-02-27 11:12:54.54238-08 2025-02-27 09:51:41.285788-08 benchmark f A100 \N t \N \N \N {} +3032 1085 2025-02-27 16:19:10.737007-08 2025-02-27 15:35:26.72855-08 benchmark t H100 \N t \N \N \N {} +3033 1085 2025-02-27 15:30:06.20078-08 2025-02-27 14:54:00.220725-08 leaderboard t H100 0.0025986981400000003 t \N \N \N {} +3190 1122 2025-02-28 05:22:46.19105-08 2025-02-28 05:19:12.334732-08 test f A100 \N t \N \N \N {} +2787 974 2025-02-26 21:02:26.857575-08 2025-02-26 20:41:35.440073-08 benchmark f A100 \N t \N \N \N {} +2788 974 2025-02-26 21:28:18.31903-08 2025-02-26 20:11:08.511332-08 leaderboard f A100 0.003155315 t \N \N \N {} +3512 1201 2025-02-28 12:09:29.633702-08 2025-02-28 11:30:22.549861-08 test t A100 \N t \N \N \N {} +2794 975 2025-02-26 21:18:52.089772-08 2025-02-26 20:24:31.866857-08 leaderboard t A100 0.003347336 t \N \N \N {} +2796 975 2025-02-26 21:50:59.541074-08 2025-02-26 21:19:54.451136-08 benchmark f A100 \N t \N \N \N {} +2797 975 2025-02-26 21:39:11.70514-08 2025-02-26 20:18:16.784568-08 leaderboard f A100 0.0033508456666666666 t \N \N \N {} +2798 976 2025-02-26 20:38:37.194925-08 2025-02-26 20:58:59.28349-08 test f A100 \N t \N \N \N {} +2799 976 2025-02-26 21:00:51.351138-08 2025-02-26 21:11:09.227246-08 benchmark f A100 \N t \N \N \N {} +2814 982 2025-02-26 23:11:35.068266-08 2025-02-26 22:05:12.788499-08 test t A100 \N t \N \N \N {} +2815 982 2025-02-26 22:41:38.849192-08 2025-02-26 22:55:52.272396-08 benchmark t A100 \N t \N \N \N {} +2816 982 2025-02-26 22:55:13.597102-08 2025-02-26 22:18:52.107493-08 leaderboard t A100 0.0031956527999999996 t \N \N \N {} +2817 982 2025-02-26 22:04:16.387504-08 2025-02-26 22:18:31.80599-08 test f A100 \N t \N \N \N {} +2818 982 2025-02-26 23:45:04.257267-08 2025-02-26 22:14:37.906328-08 benchmark f A100 \N t \N \N \N {} +2819 982 2025-02-26 22:55:44.283823-08 2025-02-26 23:06:19.863356-08 leaderboard f A100 0.00318407825 t \N \N \N {} +1362 459 2025-02-25 02:55:11.371716-08 2025-02-25 03:57:41.197189-08 benchmark t A100 \N t \N \N \N {} +1363 459 2025-02-25 04:23:27.373732-08 2025-02-25 03:20:58.157958-08 leaderboard t A100 0.0030870156666666666 t \N \N \N {} +2903 1014 2025-02-27 01:15:06.478798-08 2025-02-27 01:26:08.615896-08 benchmark f L4 \N t \N \N \N {} +3034 1086 2025-02-27 16:07:05.961453-08 2025-02-27 15:24:55.001093-08 test t A100 \N t \N \N \N {} +3035 1086 2025-02-27 14:56:09.755895-08 2025-02-27 14:44:03.736702-08 benchmark t A100 \N t \N \N \N {} +3036 1086 2025-02-27 15:11:18.717508-08 2025-02-27 16:25:37.497879-08 leaderboard t A100 0.00219666254 t \N \N \N {} +2822 983 2025-02-26 23:58:17.638886-08 2025-02-26 23:08:43.554386-08 leaderboard f T4 0.017258812666666668 t \N \N \N {} +2823 983 2025-02-26 23:08:02.482508-08 2025-02-26 22:46:14.565363-08 test t T4 \N t \N \N \N {} +2824 983 2025-02-26 22:41:21.612063-08 2025-02-26 23:37:03.076526-08 benchmark t T4 \N t \N \N \N {} +2825 983 2025-02-26 22:11:02.562982-08 2025-02-26 23:08:38.480756-08 leaderboard t T4 0.017232594333333334 t \N \N \N {} +2826 984 2025-02-26 23:55:23.356201-08 2025-02-26 22:34:06.41761-08 test t L4 \N t \N \N \N {} +2827 984 2025-02-26 23:39:16.573547-08 2025-02-26 23:45:06.294967-08 benchmark t L4 \N t \N \N \N {} +1365 459 2025-02-25 04:10:18.717828-08 2025-02-25 03:53:14.773432-08 benchmark f A100 \N t \N \N \N {} +1366 459 2025-02-25 02:36:43.695926-08 2025-02-25 03:37:20.520163-08 leaderboard f A100 0.003083136 t \N \N \N {} +2904 1015 2025-02-27 01:39:23.749307-08 2025-02-27 00:47:34.63079-08 benchmark f L4 \N t \N \N \N {} +3037 1086 2025-02-27 15:04:43.578785-08 2025-02-27 16:05:13.802056-08 test f A100 \N t \N \N \N {} +3038 1086 2025-02-27 15:52:40.863821-08 2025-02-27 15:55:36.239122-08 benchmark f A100 \N t \N \N \N {} +3039 1086 2025-02-27 14:57:57.258869-08 2025-02-27 16:22:35.716253-08 leaderboard f A100 0.006317708599999999 t \N \N \N {} +2828 984 2025-02-26 22:43:53.565232-08 2025-02-26 22:20:13.280092-08 leaderboard t L4 0.01789583566666667 t \N \N \N {} +2835 986 2025-02-26 23:41:34.087432-08 2025-02-26 22:22:07.716492-08 test f H100 \N f \N \N \N {} +2839 988 2025-02-26 23:14:11.180538-08 2025-02-26 22:55:38.097536-08 benchmark t H100 \N t \N \N \N {} +2840 988 2025-02-26 23:51:51.947336-08 2025-02-27 00:11:57.409637-08 leaderboard t H100 0.0014754095555555556 t \N \N \N {} +2841 988 2025-02-26 23:43:56.311146-08 2025-02-26 22:51:36.110909-08 test f H100 \N t \N \N \N {} +2842 988 2025-02-27 00:31:41.8135-08 2025-02-26 23:01:52.346478-08 benchmark f H100 \N t \N \N \N {} +1368 460 2025-02-25 03:56:45.320682-08 2025-02-25 04:12:23.36629-08 benchmark f H100 \N t \N \N \N {} +1369 460 2025-02-25 03:15:26.015945-08 2025-02-25 03:02:05.905441-08 leaderboard f H100 0.0014066026666666668 t \N \N \N {} +2905 1016 2025-02-27 01:18:12.382929-08 2025-02-27 01:17:06.286041-08 test t L4 \N t \N \N \N {} +2906 1016 2025-02-27 01:26:20.055734-08 2025-02-27 00:56:16.777841-08 benchmark t L4 \N t \N \N \N {} +2907 1016 2025-02-27 02:18:18.942716-08 2025-02-27 02:21:22.058687-08 leaderboard t L4 0.017129494 t \N \N \N {} +3040 1087 2025-02-27 16:44:40.535196-08 2025-02-27 15:03:57.635039-08 test f A100 \N t \N \N \N {} +3193 1122 2025-02-28 03:38:44.016859-08 2025-02-28 03:42:55.449712-08 test t A100 \N t \N \N \N {} +2847 989 2025-02-26 23:11:09.581049-08 2025-02-26 23:57:11.977062-08 test f H100 \N t \N \N \N {} +2848 989 2025-02-26 23:28:17.877389-08 2025-02-26 22:53:38.145262-08 benchmark f H100 \N t \N \N \N {} +1371 460 2025-02-25 03:58:04.611454-08 2025-02-25 02:31:49.105023-08 benchmark t H100 \N t \N \N \N {} +1372 460 2025-02-25 04:03:19.955243-08 2025-02-25 04:08:28.45914-08 leaderboard t H100 0.001423033 t \N \N \N {} +2908 1016 2025-02-27 01:21:48.281072-08 2025-02-27 00:41:29.117391-08 test f L4 \N t \N \N \N {} +1374 461 2025-02-25 04:09:34.446371-08 2025-02-25 02:35:46.24839-08 benchmark t H100 \N t \N \N \N {} +1375 461 2025-02-25 03:23:18.745022-08 2025-02-25 04:16:13.049744-08 leaderboard t H100 0.0014008253333333332 t \N \N \N {} +2911 1017 2025-02-27 03:00:45.796745-08 2025-02-27 01:43:35.233574-08 test f H100 \N f \N \N \N {} +3042 1089 2025-02-27 16:38:00.448626-08 2025-02-27 15:58:00.42158-08 benchmark f A100 \N t \N \N \N {} +3074 1102 2025-02-28 02:56:01.659272-08 2025-02-28 01:40:10.731732-08 benchmark t A100 \N t \N \N \N {} +3075 1102 2025-02-28 02:21:16.939778-08 2025-02-28 02:01:28.710259-08 leaderboard t A100 0.0030923946666666664 t \N \N \N {} +2849 989 2025-02-27 00:12:17.835592-08 2025-02-26 22:40:43.431694-08 leaderboard f H100 0.001638096 t \N \N \N {} +2850 990 2025-02-26 22:57:30.808633-08 2025-02-27 00:18:26.83744-08 test t H100 \N t \N \N \N {} +2851 990 2025-02-26 23:06:02.704806-08 2025-02-26 23:22:40.730823-08 benchmark t H100 \N t \N \N \N {} +2852 990 2025-02-26 23:16:39.034056-08 2025-02-27 00:31:18.681964-08 leaderboard t H100 0.001507587888888889 t \N \N \N {} +2855 990 2025-02-26 22:47:36.052869-08 2025-02-27 00:05:47.184279-08 leaderboard f H100 0.0015178355 t \N \N \N {} +2856 991 2025-02-26 23:22:25.154438-08 2025-02-27 00:25:57.062615-08 test f H100 \N f \N \N \N {} +1377 461 2025-02-25 03:11:23.466982-08 2025-02-25 03:14:58.304434-08 benchmark f H100 \N t \N \N \N {} +1378 461 2025-02-25 03:19:43.776884-08 2025-02-25 03:47:15.782517-08 leaderboard f H100 0.0014126543333333333 t \N \N \N {} +2912 1018 2025-02-27 02:15:28.857362-08 2025-02-27 03:05:57.617691-08 test f H100 \N t \N \N \N {} +3043 1090 2025-02-27 16:23:02.016071-08 2025-02-27 15:37:11.121549-08 benchmark f A100 \N t \N \N \N {} +3076 1103 2025-02-28 01:45:24.142839-08 2025-02-28 01:57:12.178482-08 test f A100 \N t \N \N \N {} +3119 1110 2025-02-28 03:01:10.825974-08 2025-02-28 02:12:58.251641-08 benchmark t A100 \N t \N \N \N {} +2857 991 2025-02-27 00:12:21.904324-08 2025-02-26 23:35:25.811072-08 test t H100 \N f \N \N \N {} +2858 992 2025-02-27 00:04:38.346043-08 2025-02-26 23:29:33.532762-08 test t H100 \N t \N \N \N {} +2859 992 2025-02-27 00:22:03.205134-08 2025-02-26 23:23:33.781116-08 benchmark t H100 \N t \N \N \N {} +2864 993 2025-02-26 23:55:03.486115-08 2025-02-26 23:18:13.205693-08 test f H100 \N t \N \N \N {} +2865 993 2025-02-27 00:45:02.104993-08 2025-02-26 23:57:45.704039-08 benchmark f H100 \N t \N \N \N {} +2866 993 2025-02-26 23:20:23.784092-08 2025-02-26 23:18:10.376928-08 leaderboard f H100 0.0015042338999999999 t \N \N \N {} +1382 464 2025-02-25 04:24:56.79314-08 2025-02-25 04:46:29.308114-08 benchmark f H100 \N t \N \N \N {} +1383 464 2025-02-25 03:22:15.683757-08 2025-02-25 03:47:50.3734-08 leaderboard f H100 0.0014120043333333333 t \N \N \N {} +2913 1019 2025-02-27 02:15:25.259011-08 2025-02-27 02:01:14.038287-08 test f H100 \N f \N \N \N {} +3044 1091 2025-02-27 16:34:45.373039-08 2025-02-27 17:04:56.053201-08 benchmark f A100 \N t \N \N \N {} +3077 1103 2025-02-28 02:26:24.054371-08 2025-02-28 02:57:27.049866-08 benchmark f A100 \N t \N \N \N {} +2867 993 2025-02-27 00:20:03.256964-08 2025-02-26 23:38:08.774778-08 test t H100 \N t \N \N \N {} +2868 993 2025-02-27 00:17:21.143055-08 2025-02-26 23:43:44.61358-08 benchmark t H100 \N t \N \N \N {} +2869 993 2025-02-27 00:41:17.765937-08 2025-02-27 00:38:24.740261-08 leaderboard t H100 0.0014829258999999998 t \N \N \N {} +2873 995 2025-02-26 23:14:08.542885-08 2025-02-27 00:24:07.258227-08 benchmark t H100 \N t \N \N \N {} +2874 995 2025-02-26 22:57:32.493875-08 2025-02-26 23:39:16.1763-08 leaderboard t H100 0.0014810648 t \N \N \N {} +2875 995 2025-02-26 23:55:02.996586-08 2025-02-27 00:01:45.681815-08 test f H100 \N t \N \N \N {} +2889 1000 2025-02-27 00:47:29.323693-08 2025-02-27 01:19:30.043071-08 benchmark f L4 \N t \N \N \N {} +2876 995 2025-02-27 00:48:27.410452-08 2025-02-26 23:02:52.737338-08 benchmark f H100 \N t \N \N \N {} +2877 995 2025-02-27 00:46:15.879218-08 2025-02-26 22:53:09.26022-08 leaderboard f H100 0.00151809525 t \N \N \N {} +2878 996 2025-02-26 23:32:33.325133-08 2025-02-27 00:20:52.668636-08 test t H100 \N f \N \N \N {} +2879 996 2025-02-26 23:32:09.509312-08 2025-02-27 00:16:13.600353-08 test f H100 \N f \N \N \N {} +2880 997 2025-02-26 23:10:26.947465-08 2025-02-27 00:59:43.704157-08 test f H100 \N f \N \N \N {} +2895 1006 2025-02-27 00:30:50.677826-08 2025-02-27 02:02:45.191772-08 benchmark f T4 \N t \N \N \N {} +2881 997 2025-02-26 23:16:25.148851-08 2025-02-26 23:15:22.787437-08 test t H100 \N f \N \N \N {} +1385 464 2025-02-25 03:54:35.416434-08 2025-02-25 03:14:13.277902-08 benchmark t H100 \N t \N \N \N {} +1386 464 2025-02-25 03:44:28.181743-08 2025-02-25 04:52:22.685863-08 leaderboard t H100 0.0013942456666666667 t \N \N \N {} +2914 1021 2025-02-27 04:12:21.245081-08 2025-02-27 02:35:09.897785-08 test f H100 \N t \N \N \N {} +3045 1092 2025-02-27 15:48:36.342402-08 2025-02-27 16:50:15.479403-08 benchmark f A100 \N t \N \N \N {} +2882 998 2025-02-26 23:31:45.732141-08 2025-02-26 23:28:03.798982-08 test f H100 \N t \N \N \N {} +2883 998 2025-02-27 00:46:38.657542-08 2025-02-27 00:13:36.156442-08 benchmark f H100 \N t \N \N \N {} +2884 998 2025-02-26 23:36:22.797455-08 2025-02-27 00:38:35.235535-08 leaderboard f H100 0.0014596549 t \N \N \N {} +2885 998 2025-02-27 00:25:48.975564-08 2025-02-27 00:17:37.217535-08 test t H100 \N t \N \N \N {} +2886 998 2025-02-27 01:13:36.817864-08 2025-02-27 00:29:21.912198-08 benchmark t H100 \N t \N \N \N {} +2887 998 2025-02-27 00:25:46.035637-08 2025-02-27 00:48:51.353021-08 leaderboard t H100 0.00146596 t \N \N \N {} +2888 999 2025-02-26 23:44:36.851934-08 2025-02-27 00:13:44.746988-08 test f H100 \N f \N \N \N {} +2891 1002 2025-02-27 00:08:35.698636-08 2025-02-27 00:37:38.529546-08 benchmark f L4 \N t \N \N \N {} +2892 1003 2025-02-27 01:04:08.050505-08 2025-02-27 01:56:08.100836-08 benchmark f T4 \N f \N \N \N {} +2893 1004 2025-02-27 01:20:48.626974-08 2025-02-27 02:00:18.32054-08 benchmark f T4 \N t \N \N \N {} +2894 1005 2025-02-27 00:48:26.399604-08 2025-02-27 01:32:13.188985-08 benchmark f T4 \N t \N \N \N {} +3515 1202 2025-02-28 10:44:20.111423-08 2025-02-28 10:41:52.613484-08 test f A100 \N t \N \N \N {} +3883 1296 2025-03-01 03:31:39.319577-08 2025-03-01 04:35:51.070787-08 test t A100 \N t \N \N \N {} +2896 1007 2025-02-27 00:30:01.989297-08 2025-02-27 01:26:11.483563-08 benchmark f L4 \N t \N \N \N {} +2897 1008 2025-02-27 00:19:27.820406-08 2025-02-27 01:13:08.554592-08 benchmark f L4 \N t \N \N \N {} +2898 1009 2025-02-27 00:14:37.058625-08 2025-02-27 00:33:25.198225-08 benchmark f T4 \N t \N \N \N {} +2899 1010 2025-02-27 01:32:50.152071-08 2025-02-27 01:26:35.416449-08 benchmark f L4 \N f \N \N \N {} +2900 1011 2025-02-27 00:40:17.675693-08 2025-02-27 00:34:54.012193-08 benchmark f L4 \N t \N \N \N {} +2901 1012 2025-02-27 00:57:52.617774-08 2025-02-27 01:00:23.764973-08 benchmark f L4 \N t \N \N \N {} +3031 1085 2025-02-27 15:24:29.601416-08 2025-02-27 14:51:34.912701-08 test t H100 \N t \N \N \N {} +6462 2006 2025-03-14 07:51:45.870673-07 2025-03-14 07:40:39.999265-07 benchmark f A100 \N t \N \N \N {} +3102 1107 2025-02-28 01:54:23.349256-08 2025-02-28 01:56:27.939879-08 leaderboard f A100 0.003092532 t \N \N \N {} +6142 1883 2025-03-11 11:28:02.967532-07 2025-03-11 11:35:29.392865-07 test t T4 \N f \N \N \N {} +6314 1954 2025-03-12 20:17:28.206705-07 2025-03-12 20:22:28.033335-07 test f A100 \N t \N \N \N {} +6315 1954 2025-03-12 19:44:04.442778-07 2025-03-12 20:05:34.313377-07 benchmark f A100 \N t \N \N \N {} +6894 2143 2025-03-16 12:07:38.644838-07 2025-03-16 12:03:19.501535-07 test f L4 \N f \N \N \N {} +1388 465 2025-02-25 03:44:13.489138-08 2025-02-25 04:58:52.178206-08 benchmark t H100 \N t \N \N \N {} +1389 465 2025-02-25 04:48:22.345502-08 2025-02-25 03:34:29.544054-08 leaderboard t H100 0.000999849 t \N \N \N {} +2915 1022 2025-02-27 03:08:40.368035-08 2025-02-27 03:55:02.355278-08 benchmark f A100 \N t \N \N \N {} +3046 1093 2025-02-27 16:57:16.382054-08 2025-02-27 16:07:28.917105-08 test t A100 \N t \N \N \N {} +6895 2144 2025-03-16 12:40:59.707177-07 2025-03-16 12:04:30.038571-07 benchmark f T4 \N f \N \N \N {} +3108 1108 2025-02-28 02:25:29.231129-08 2025-02-28 01:30:03.455293-08 leaderboard t A100 0.0030845013333333335 t \N \N \N {} +6144 1885 2025-03-11 11:27:23.29413-07 2025-03-11 12:06:00.249613-07 test f T4 \N f \N \N \N {} +6317 1954 2025-03-12 20:05:57.526603-07 2025-03-12 19:36:21.76166-07 test t A100 \N t \N \N \N {} +6318 1954 2025-03-12 20:14:29.660956-07 2025-03-12 20:07:14.51853-07 benchmark t A100 \N t \N \N \N {} +6319 1954 2025-03-12 20:17:32.676671-07 2025-03-12 19:52:30.024723-07 leaderboard t A100 0.0013705575900000001 t \N \N \N {} +6465 2008 2025-03-14 06:25:10.510564-07 2025-03-14 06:10:34.856104-07 benchmark f A100 \N t \N \N \N {} +6467 2008 2025-03-14 07:05:50.283776-07 2025-03-14 06:54:47.878347-07 test t A100 \N t \N \N \N {} +6468 2008 2025-03-14 07:57:23.655081-07 2025-03-14 07:57:46.860585-07 benchmark t A100 \N t \N \N \N {} +6321 1956 2025-03-13 09:39:59.949032-07 2025-03-13 10:36:07.030735-07 test t T4 \N f \N \N \N {} +6470 2009 2025-03-14 07:54:17.367584-07 2025-03-14 07:52:14.667761-07 test t A100 \N t \N \N \N {} +6471 2009 2025-03-14 08:25:39.938379-07 2025-03-14 06:36:09.564554-07 benchmark t A100 \N t \N \N \N {} +3122 1110 2025-02-28 03:18:32.981187-08 2025-02-28 02:42:29.642779-08 benchmark f A100 \N t \N \N \N {} +3123 1110 2025-02-28 02:50:51.08401-08 2025-02-28 01:46:09.197929-08 leaderboard f A100 0.003097124 t \N \N \N {} +6322 1956 2025-03-13 08:55:36.22372-07 2025-03-13 10:04:13.347907-07 test f T4 \N f \N \N \N {} +6473 2009 2025-03-14 07:46:08.399013-07 2025-03-14 08:25:12.694431-07 test f A100 \N t \N \N \N {} +3578 1225 2025-02-28 14:25:25.372721-08 2025-02-28 13:59:09.123935-08 benchmark f A100 \N t \N \N \N {} +3128 1111 2025-02-28 02:02:43.50836-08 2025-02-28 01:29:59.933956-08 benchmark t A100 \N t \N \N \N {} +3129 1111 2025-02-28 02:09:35.864061-08 2025-02-28 02:11:31.900287-08 leaderboard t A100 0.00320221 t \N \N \N {} +6150 1888 2025-03-11 12:00:16.100477-07 2025-03-11 12:59:35.590119-07 test f T4 \N f \N \N \N {} +6324 1957 2025-03-13 10:47:36.919965-07 2025-03-13 09:33:50.353862-07 test f T4 \N f \N \N \N {} +3132 1112 2025-02-28 02:00:15.175704-08 2025-02-28 01:42:45.822482-08 leaderboard f A100 0.0030739046666666664 t \N \N \N {} +6151 1888 2025-03-11 11:24:19.34183-07 2025-03-11 11:15:09.363246-07 test t T4 \N f \N \N \N {} +6325 1958 2025-03-13 10:54:39.858929-07 2025-03-13 10:57:53.21954-07 test t T4 \N f \N \N \N {} +6481 2010 2025-03-14 07:16:40.068566-07 2025-03-14 08:11:25.193743-07 leaderboard f A100 0.0026579623333333334 t \N \N \N {} +3134 1112 2025-02-28 02:21:04.795319-08 2025-02-28 03:19:20.667414-08 benchmark t A100 \N t \N \N \N {} +3135 1112 2025-02-28 02:49:56.527812-08 2025-02-28 02:10:59.95073-08 leaderboard t A100 0.0030922646666666667 t \N \N \N {} +6153 1889 2025-03-11 11:42:50.98288-07 2025-03-11 13:36:59.142943-07 benchmark t T4 \N f \N \N \N {} +1391 465 2025-02-25 04:55:41.868356-08 2025-02-25 03:36:33.891601-08 benchmark f H100 \N t \N \N \N {} +1392 465 2025-02-25 04:37:36.417233-08 2025-02-25 03:28:58.50135-08 leaderboard f H100 0.00140509 t \N \N \N {} +1393 466 2025-02-25 03:10:36.309883-08 2025-02-25 03:21:53.515918-08 test f H100 \N f \N \N \N {} +1394 466 2025-02-25 03:11:29.000771-08 2025-02-25 03:41:58.917775-08 test t H100 \N f \N \N \N {} +2916 1023 2025-02-27 02:46:36.620437-08 2025-02-27 02:58:33.660277-08 test f A100 \N t \N \N \N {} +3078 1103 2025-02-28 01:22:48.676241-08 2025-02-28 02:52:22.813395-08 leaderboard f A100 0.0031237426666666664 t \N \N \N {} +6155 1889 2025-03-11 12:10:23.846696-07 2025-03-11 12:13:59.236186-07 benchmark f T4 \N f \N \N \N {} +1397 468 2025-02-25 03:38:51.530876-08 2025-02-25 04:52:35.406014-08 benchmark f A100 \N t \N \N \N {} +1398 468 2025-02-25 04:38:38.385416-08 2025-02-25 03:58:06.257632-08 leaderboard f A100 0.0033990103333333336 t \N \N \N {} +2917 1023 2025-02-27 03:14:37.23347-08 2025-02-27 03:11:45.911539-08 benchmark f A100 \N t \N \N \N {} +2918 1023 2025-02-27 03:03:16.65023-08 2025-02-27 04:18:18.937857-08 leaderboard f A100 0.0001010261724137931 t \N \N \N {} +3079 1103 2025-02-28 01:31:17.940598-08 2025-02-28 02:41:55.766318-08 test t A100 \N t \N \N \N {} +6897 2146 2025-03-16 12:39:46.739273-07 2025-03-16 13:06:56.642031-07 benchmark f T4 \N f \N \N \N {} +3141 1113 2025-02-28 01:34:25.294505-08 2025-02-28 02:09:38.976633-08 leaderboard t A100 0.0030885226666666665 t \N \N \N {} +3579 1226 2025-02-28 13:23:19.822733-08 2025-02-28 13:38:01.202424-08 benchmark f H100 \N t \N \N \N {} +3144 1114 2025-02-28 03:32:16.197256-08 2025-02-28 02:47:43.563886-08 leaderboard t A100 0.003070266 t \N \N \N {} +6158 1890 2025-03-11 13:28:57.157083-07 2025-03-11 12:19:09.927667-07 test f T4 \N t \N \N \N {} +3147 1114 2025-02-28 03:28:26.003711-08 2025-02-28 01:44:07.837604-08 leaderboard f A100 0.0030685503333333333 t \N \N \N {} +6160 1891 2025-03-11 13:43:23.540375-07 2025-03-11 13:07:39.214045-07 test f T4 \N t \N \N \N {} +6484 2011 2025-03-14 07:02:10.29025-07 2025-03-14 08:11:39.923824-07 leaderboard f A100 0.0025160156666666667 t \N \N \N {} +3149 1115 2025-02-28 02:35:43.630546-08 2025-02-28 02:15:39.443589-08 benchmark f A100 \N t \N \N \N {} +3150 1115 2025-02-28 02:15:43.119849-08 2025-02-28 01:43:19.893985-08 leaderboard f A100 0.0030932896666666667 t \N \N \N {} +6161 1892 2025-03-11 11:57:49.455042-07 2025-03-11 12:21:21.948804-07 benchmark f T4 \N f \N \N \N {} +3580 1227 2025-02-28 13:22:39.155063-08 2025-02-28 13:51:24.355953-08 benchmark f H100 \N t \N \N \N {} +3152 1115 2025-02-28 01:56:27.157682-08 2025-02-28 01:41:45.62036-08 benchmark t A100 \N t \N \N \N {} +3153 1115 2025-02-28 03:24:29.539728-08 2025-02-28 03:03:11.575433-08 leaderboard t A100 0.0030942826666666666 t \N \N \N {} +6162 1893 2025-03-11 12:05:53.460089-07 2025-03-11 13:52:23.016257-07 benchmark f T4 \N f \N \N \N {} +3155 1116 2025-02-28 01:56:03.204295-08 2025-02-28 02:29:07.453242-08 benchmark f A100 \N t \N \N \N {} +3156 1116 2025-02-28 03:41:05.90631-08 2025-02-28 03:37:39.667047-08 leaderboard f A100 0.0030983496666666666 t \N \N \N {} +3581 1228 2025-02-28 14:07:30.971092-08 2025-02-28 14:44:25.55772-08 test f A100 \N t \N \N \N {} +6487 2011 2025-03-14 07:33:24.422282-07 2025-03-14 07:03:24.285193-07 leaderboard t A100 0.0024820293333333333 t \N \N \N {} +6674 2065 2025-03-14 15:48:12.867984-07 2025-03-14 15:10:31.440491-07 test t T4 \N f \N \N \N {} +1406 469 2025-02-25 04:38:40.125755-08 2025-02-25 04:37:21.18501-08 benchmark t A100 \N t \N \N \N {} +1407 469 2025-02-25 04:41:04.15737-08 2025-02-25 04:10:44.089578-08 leaderboard t A100 0.0030951176666666664 t \N \N \N {} +2925 1024 2025-02-27 04:23:16.250435-08 2025-02-27 03:24:58.288515-08 test t T4 \N t \N \N \N {} +1412 470 2025-02-25 03:40:12.003397-08 2025-02-25 04:12:17.525955-08 benchmark f A100 \N t \N \N \N {} +1413 470 2025-02-25 04:37:09.951863-08 2025-02-25 03:30:09.91909-08 leaderboard f A100 0.003099017 t \N \N \N {} +2931 1025 2025-02-27 03:01:05.861477-08 2025-02-27 04:00:58.846217-08 test t L4 \N t \N \N \N {} +2932 1025 2025-02-27 04:33:16.006317-08 2025-02-27 04:33:30.552743-08 benchmark t L4 \N t \N \N \N {} +2933 1025 2025-02-27 03:00:47.162532-08 2025-02-27 03:18:26.934968-08 leaderboard t L4 0.00011731689361702128 t \N \N \N {} +3127 1111 2025-02-28 03:03:45.476308-08 2025-02-28 01:34:30.667207-08 test t A100 \N t \N \N \N {} +1415 471 2025-02-25 05:07:03.031352-08 2025-02-25 04:44:07.11511-08 benchmark f A100 \N t \N \N \N {} +1416 471 2025-02-25 03:49:37.382368-08 2025-02-25 03:16:26.151128-08 leaderboard f A100 0.003083305 t \N \N \N {} +2934 1026 2025-02-27 03:29:31.212038-08 2025-02-27 03:36:48.365495-08 benchmark f A100 \N t \N \N \N {} +3049 1093 2025-02-27 15:47:45.331679-08 2025-02-27 16:54:04.404866-08 test f A100 \N t \N \N \N {} +3130 1112 2025-02-28 02:53:32.669829-08 2025-02-28 01:59:58.104291-08 test f A100 \N t \N \N \N {} +3158 1116 2025-02-28 03:00:27.935274-08 2025-02-28 03:06:58.848252-08 benchmark t A100 \N t \N \N \N {} +3582 1228 2025-02-28 14:47:56.576495-08 2025-02-28 13:38:26.115181-08 benchmark f A100 \N t \N \N \N {} +6334 1962 2025-03-13 12:17:44.907621-07 2025-03-13 10:47:39.798292-07 test f T4 \N f \N \N \N {} +6488 2012 2025-03-14 07:37:48.319936-07 2025-03-14 06:40:10.451158-07 test t A100 \N t \N \N \N {} +6905 2149 2025-03-16 13:54:55.728222-07 2025-03-16 12:29:31.998021-07 test f A100 \N f \N \N \N {} +3161 1117 2025-02-28 03:20:59.962421-08 2025-02-28 03:07:10.955256-08 benchmark t A100 \N t \N \N \N {} +6489 2012 2025-03-14 07:07:28.772602-07 2025-03-14 06:43:54.465366-07 benchmark t A100 \N t \N \N \N {} +6490 2012 2025-03-14 07:43:28.617109-07 2025-03-14 07:02:38.240011-07 leaderboard t A100 0.002517398 t \N \N \N {} +6912 2154 2025-03-16 16:29:02.680063-07 2025-03-16 16:07:12.630657-07 test t T4 \N f \N \N \N {} +3164 1117 2025-02-28 01:55:34.743977-08 2025-02-28 03:46:05.60909-08 benchmark f A100 \N t \N \N \N {} +3165 1117 2025-02-28 03:22:15.291884-08 2025-02-28 03:01:38.071188-08 leaderboard f A100 0.0031003043333333335 t \N \N \N {} +3166 1118 2025-02-28 02:36:20.544219-08 2025-02-28 02:03:38.914711-08 test t A100 \N t \N \N \N {} +3167 1118 2025-02-28 02:27:47.915164-08 2025-02-28 02:31:18.69465-08 benchmark t A100 \N t \N \N \N {} +3168 1118 2025-02-28 03:38:38.742518-08 2025-02-28 02:42:05.29698-08 leaderboard t A100 0.0031557713333333335 t \N \N \N {} +3467 1192 2025-02-28 11:34:31.331723-08 2025-02-28 10:29:39.69554-08 test f A100 \N f \N \N \N {} +1418 471 2025-02-25 04:36:59.718452-08 2025-02-25 03:23:10.279404-08 benchmark t A100 \N t \N \N \N {} +1419 471 2025-02-25 03:23:16.170077-08 2025-02-25 04:14:16.278504-08 leaderboard t A100 0.0030839833333333334 t \N \N \N {} +2935 1027 2025-02-27 04:13:09.20703-08 2025-02-27 03:53:36.446829-08 benchmark f A100 \N t \N \N \N {} +3133 1112 2025-02-28 01:44:54.216121-08 2025-02-28 02:38:03.906338-08 test t A100 \N t \N \N \N {} +3518 1202 2025-02-28 11:33:17.396025-08 2025-02-28 12:16:13.271104-08 test t A100 \N t \N \N \N {} +6913 2154 2025-03-16 16:38:39.962668-07 2025-03-16 14:53:33.732977-07 test f T4 \N f \N \N \N {} +3568 1211 2025-02-28 11:12:30.656088-08 2025-02-28 12:38:32.554868-08 test t H100 \N f \N \N \N {} +3171 1118 2025-02-28 03:57:16.999936-08 2025-02-28 02:52:16.718421-08 leaderboard f A100 0.003146471 t \N \N \N {} +3172 1119 2025-02-28 03:47:11.316659-08 2025-02-28 03:09:45.561459-08 test f A100 \N t \N \N \N {} +3173 1119 2025-02-28 02:39:13.039039-08 2025-02-28 02:10:32.463474-08 benchmark f A100 \N t \N \N \N {} +6914 2155 2025-03-16 15:06:19.793513-07 2025-03-16 16:22:51.137738-07 test t T4 \N f \N \N \N {} +3177 1119 2025-02-28 03:56:27.839518-08 2025-02-28 03:53:00.543375-08 leaderboard t A100 0.003148472 t \N \N \N {} +6166 1897 2025-03-11 12:32:48.919729-07 2025-03-11 13:04:35.751745-07 benchmark f T4 \N t \N \N \N {} +6336 1963 2025-03-13 11:44:12.666263-07 2025-03-13 11:35:54.4531-07 test t T4 \N f \N \N \N {} +6491 2012 2025-03-14 06:56:50.114221-07 2025-03-14 07:03:33.962589-07 test f A100 \N t \N \N \N {} +6915 2155 2025-03-16 16:05:45.981468-07 2025-03-16 16:53:11.056967-07 test f T4 \N f \N \N \N {} +3886 1296 2025-03-01 04:03:42.524707-08 2025-03-01 04:26:34.298074-08 test f A100 \N t \N \N \N {} +6167 1898 2025-03-11 14:02:34.088566-07 2025-03-11 13:21:01.318443-07 test f T4 \N t \N \N \N {} +6168 1898 2025-03-11 13:56:37.141151-07 2025-03-11 12:54:02.739765-07 benchmark f T4 \N t \N \N \N {} +6169 1898 2025-03-11 13:26:27.542536-07 2025-03-11 12:35:42.421543-07 leaderboard f T4 3.1035724833333336 t \N \N \N {} +6337 1964 2025-03-13 12:31:57.776048-07 2025-03-13 12:44:18.662819-07 test t T4 \N f \N \N \N {} +6675 2065 2025-03-14 15:10:20.43581-07 2025-03-14 15:25:35.47986-07 test f T4 \N f \N \N \N {} +6170 1898 2025-03-11 14:05:54.41326-07 2025-03-11 13:31:34.094196-07 test t T4 \N t \N \N \N {} +6171 1898 2025-03-11 13:56:20.919926-07 2025-03-11 12:56:21.007587-07 benchmark t T4 \N t \N \N \N {} +6172 1898 2025-03-11 13:32:59.303232-07 2025-03-11 14:04:58.378204-07 leaderboard t T4 3.1852416556666667 t \N \N \N {} +6338 1964 2025-03-13 11:48:09.106344-07 2025-03-13 11:48:18.838973-07 test f T4 \N f \N \N \N {} +6676 2066 2025-03-14 15:18:53.997082-07 2025-03-14 14:33:10.917221-07 test f T4 \N f \N \N \N {} +3185 1121 2025-02-28 04:01:47.89985-08 2025-02-28 04:25:50.220487-08 benchmark f A100 \N t \N \N \N {} +3186 1121 2025-02-28 04:48:14.978807-08 2025-02-28 04:05:51.546019-08 leaderboard f A100 0.0031212863333333336 t \N \N \N {} +6173 1899 2025-03-11 13:25:54.795056-07 2025-03-11 13:19:39.098623-07 test t T4 \N f \N \N \N {} +6339 1965 2025-03-13 11:39:17.316162-07 2025-03-13 11:28:19.690857-07 test t T4 \N f \N \N \N {} +3468 1193 2025-02-28 12:15:00.613441-08 2025-02-28 10:38:48.030816-08 test f A100 \N t \N \N \N {} +1421 472 2025-02-25 04:29:57.622938-08 2025-02-25 04:49:21.634274-08 benchmark f A100 \N t \N \N \N {} +1422 472 2025-02-25 03:37:41.571503-08 2025-02-25 03:49:33.02462-08 leaderboard f A100 0.0030871423333333333 t \N \N \N {} +2936 1028 2025-02-27 03:16:50.844905-08 2025-02-27 03:44:10.314673-08 benchmark f A100 \N t \N \N \N {} +3136 1113 2025-02-28 02:58:56.415099-08 2025-02-28 02:44:36.404391-08 test f A100 \N t \N \N \N {} +6492 2012 2025-03-14 08:00:25.963285-07 2025-03-14 06:46:07.163228-07 benchmark f A100 \N t \N \N \N {} +6493 2012 2025-03-14 07:09:08.72814-07 2025-03-14 07:18:27.447251-07 leaderboard f A100 0.002505465 t \N \N \N {} +6599 2045 2025-03-14 12:56:47.350056-07 2025-03-14 13:11:04.653905-07 benchmark f A100 \N t \N \N \N {} +6600 2045 2025-03-14 13:04:51.216706-07 2025-03-14 13:43:13.503645-07 leaderboard f A100 0.0025198196666666667 t \N \N \N {} +6645 2053 2025-03-14 13:47:46.801513-07 2025-03-14 12:15:48.666184-07 benchmark f A100 \N t \N \N \N {} +3192 1122 2025-02-28 03:46:09.723947-08 2025-02-28 04:28:57.570091-08 leaderboard f A100 0.0031000573333333334 t \N \N \N {} +6341 1966 2025-03-13 12:45:43.296749-07 2025-03-13 12:17:33.727157-07 test f T4 \N t \N \N \N {} +6342 1966 2025-03-13 12:13:23.993863-07 2025-03-13 12:35:05.634615-07 benchmark f T4 \N t \N \N \N {} +6343 1966 2025-03-13 13:00:24.511463-07 2025-03-13 12:19:23.080746-07 leaderboard f T4 3.5582660766666665 t \N \N \N {} +6495 2013 2025-03-14 12:06:59.03304-07 2025-03-14 11:21:26.358885-07 test f H100 \N f \N \N \N {} +3198 1123 2025-02-28 04:42:25.083061-08 2025-02-28 04:52:17.118702-08 leaderboard f A100 0.0031039456666666666 t \N \N \N {} +3521 1203 2025-02-28 12:13:18.73249-08 2025-02-28 11:42:59.789557-08 test t A100 \N f \N \N \N {} +3200 1123 2025-02-28 04:09:22.67407-08 2025-02-28 05:42:51.81569-08 benchmark t A100 \N t \N \N \N {} +3201 1123 2025-02-28 04:49:03.536471-08 2025-02-28 05:30:26.750128-08 leaderboard t A100 0.003107339 t \N \N \N {} +6176 1900 2025-03-11 14:27:12.915155-07 2025-03-11 13:27:30.861214-07 test f T4 \N f \N \N \N {} +6344 1966 2025-03-13 12:39:39.896955-07 2025-03-13 11:42:10.53502-07 test t T4 \N t \N \N \N {} +6345 1966 2025-03-13 11:46:33.159161-07 2025-03-13 11:42:35.832436-07 benchmark t T4 \N t \N \N \N {} +6346 1966 2025-03-13 12:49:02.202475-07 2025-03-13 11:43:42.365767-07 leaderboard t T4 3.6324577356666663 t \N \N \N {} +3202 1124 2025-02-28 05:47:04.982464-08 2025-02-28 04:47:56.713443-08 test f A100 \N t \N \N \N {} +3203 1124 2025-02-28 04:08:24.397878-08 2025-02-28 04:24:14.653298-08 benchmark f A100 \N t \N \N \N {} +3204 1124 2025-02-28 05:40:56.722412-08 2025-02-28 05:44:46.320859-08 leaderboard f A100 0.0030972656666666664 t \N \N \N {} +3205 1124 2025-02-28 04:40:01.037489-08 2025-02-28 03:58:41.704134-08 test t A100 \N t \N \N \N {} +3206 1124 2025-02-28 05:42:55.343085-08 2025-02-28 04:23:54.198083-08 benchmark t A100 \N t \N \N \N {} +3207 1124 2025-02-28 05:15:33.617644-08 2025-02-28 05:36:54.360407-08 leaderboard t A100 0.0031034153333333336 t \N \N \N {} +3212 1125 2025-02-28 05:41:25.119279-08 2025-02-28 06:47:46.688795-08 benchmark f A100 \N t \N \N \N {} +3218 1126 2025-02-28 06:15:42.624802-08 2025-02-28 05:18:36.139802-08 benchmark f A100 \N t \N \N \N {} +3224 1127 2025-02-28 05:23:45.306771-08 2025-02-28 06:39:04.576528-08 benchmark f A100 \N t \N \N \N {} +3230 1130 2025-02-28 06:11:34.161879-08 2025-02-28 07:19:05.529092-08 benchmark t A100 \N t \N \N \N {} +3522 1203 2025-02-28 11:17:41.164832-08 2025-02-28 10:45:35.314206-08 test f A100 \N f \N \N \N {} +3238 1133 2025-02-28 05:58:19.469647-08 2025-02-28 05:27:59.168429-08 leaderboard t A100 0.0030885113333333335 t \N \N \N {} +3471 1193 2025-02-28 12:08:38.072989-08 2025-02-28 11:36:29.661671-08 test t A100 \N t \N \N \N {} +1424 472 2025-02-25 05:07:18.96443-08 2025-02-25 04:27:31.619653-08 benchmark t A100 \N t \N \N \N {} +1425 472 2025-02-25 04:26:35.663772-08 2025-02-25 03:52:24.217898-08 leaderboard t A100 0.0031005363333333337 t \N \N \N {} +2937 1029 2025-02-27 04:45:23.397168-08 2025-02-27 04:26:08.472553-08 benchmark f A100 \N t \N \N \N {} +3050 1093 2025-02-27 15:36:27.175994-08 2025-02-27 15:34:55.376904-08 benchmark f A100 \N t \N \N \N {} +3239 1134 2025-02-28 06:50:37.72243-08 2025-02-28 06:25:11.379874-08 benchmark f A100 \N t \N \N \N {} +3240 1133 2025-02-28 05:32:42.89057-08 2025-02-28 06:35:04.012708-08 test f A100 \N t \N \N \N {} +3241 1133 2025-02-28 05:55:48.275123-08 2025-02-28 06:28:43.235753-08 benchmark f A100 \N t \N \N \N {} +3242 1133 2025-02-28 06:28:41.176944-08 2025-02-28 06:11:12.832836-08 leaderboard f A100 0.003086809 t \N \N \N {} +3243 1136 2025-02-28 05:47:39.958836-08 2025-02-28 05:44:16.957515-08 benchmark f A100 \N t \N \N \N {} +3244 1135 2025-02-28 06:38:46.857624-08 2025-02-28 07:16:05.450915-08 test f A100 \N t \N \N \N {} +3245 1135 2025-02-28 06:04:38.048901-08 2025-02-28 07:07:00.823655-08 benchmark f A100 \N t \N \N \N {} +3246 1135 2025-02-28 05:37:48.064623-08 2025-02-28 05:38:45.603311-08 leaderboard f A100 0.0030916216666666664 t \N \N \N {} +3247 1135 2025-02-28 05:52:38.194905-08 2025-02-28 05:43:19.652095-08 test t A100 \N t \N \N \N {} +3523 1204 2025-02-28 11:24:15.172488-08 2025-02-28 11:35:08.725248-08 test f A100 \N t \N \N \N {} +3252 1137 2025-02-28 07:21:26.0063-08 2025-02-28 07:16:56.022377-08 leaderboard f A100 0.0030857416666666667 t \N \N \N {} +3253 1137 2025-02-28 06:00:28.572485-08 2025-02-28 05:34:19.879496-08 test t A100 \N t \N \N \N {} +3254 1137 2025-02-28 06:10:53.662809-08 2025-02-28 06:32:30.162306-08 benchmark t A100 \N t \N \N \N {} +3255 1137 2025-02-28 07:12:35.431057-08 2025-02-28 06:05:27.261706-08 leaderboard t A100 0.0030894023333333334 t \N \N \N {} +3256 1138 2025-02-28 05:44:23.296315-08 2025-02-28 05:54:30.649398-08 benchmark f A100 \N t \N \N \N {} +3258 1139 2025-02-28 06:04:00.85979-08 2025-02-28 06:27:21.740985-08 benchmark f A100 \N f \N \N \N {} +3139 1113 2025-02-28 02:22:31.319401-08 2025-02-28 02:55:45.560083-08 test t A100 \N t \N \N \N {} +1427 473 2025-02-25 04:47:05.317584-08 2025-02-25 03:32:09.61539-08 benchmark t A100 \N t \N \N \N {} +1428 473 2025-02-25 04:44:32.069859-08 2025-02-25 03:55:20.86169-08 leaderboard t A100 0.003175767 t \N \N \N {} +2938 1030 2025-02-27 04:38:20.668328-08 2025-02-27 04:24:06.448431-08 test f A100 \N t \N \N \N {} +2939 1030 2025-02-27 03:05:03.419869-08 2025-02-27 04:22:37.87157-08 benchmark f A100 \N t \N \N \N {} +2940 1030 2025-02-27 04:07:07.030129-08 2025-02-27 04:03:57.310379-08 leaderboard f A100 0.00007918067999999999 t \N \N \N {} +3142 1114 2025-02-28 01:49:44.822785-08 2025-02-28 02:25:24.680714-08 test t A100 \N t \N \N \N {} +3259 1139 2025-02-28 06:24:17.521761-08 2025-02-28 07:13:37.812929-08 test t A100 \N t \N \N \N {} +3562 1210 2025-02-28 11:57:02.979824-08 2025-02-28 10:51:28.47295-08 test t A100 \N t \N \N \N {} +1430 473 2025-02-25 04:40:01.662393-08 2025-02-25 04:30:55.139642-08 benchmark f A100 \N t \N \N \N {} +1431 473 2025-02-25 03:31:35.707171-08 2025-02-25 03:31:53.832049-08 leaderboard f A100 0.00317240225 t \N \N \N {} +1432 474 2025-02-25 04:06:52.039054-08 2025-02-25 04:58:28.878029-08 test f A100 \N f \N \N \N {} +1438 479 2025-02-25 05:28:40.021595-08 2025-02-25 04:21:12.668713-08 test f A100 \N f \N \N \N {} +1445 484 2025-02-25 04:56:56.648978-08 2025-02-25 04:48:16.147785-08 benchmark t A100 \N t \N \N \N {} +1446 484 2025-02-25 05:51:53.636258-08 2025-02-25 04:20:30.28663-08 leaderboard t A100 0.0030884286666666666 t \N \N \N {} +2942 1030 2025-02-27 04:11:40.863984-08 2025-02-27 03:12:21.326746-08 benchmark t A100 \N t \N \N \N {} +2943 1030 2025-02-27 04:47:50.715647-08 2025-02-27 04:01:18.005466-08 leaderboard t A100 0.0000793426 t \N \N \N {} +3260 1139 2025-02-28 07:19:43.310162-08 2025-02-28 06:04:41.287437-08 benchmark t A100 \N f \N \N \N {} +3526 1204 2025-02-28 12:03:21.457321-08 2025-02-28 12:30:39.578159-08 test t A100 \N t \N \N \N {} +6647 2054 2025-03-14 13:43:53.190099-07 2025-03-14 12:39:40.431314-07 test f A100 \N t \N \N \N {} +6500 2015 2025-03-14 12:17:09.225085-07 2025-03-14 10:53:14.727282-07 leaderboard f H100 0.006131182333333333 t \N \N \N {} +6607 2046 2025-03-14 13:24:07.723525-07 2025-03-14 12:31:15.468896-07 test f A100 \N t \N \N \N {} +6504 2016 2025-03-14 12:12:39.778966-07 2025-03-14 12:30:56.454439-07 benchmark f A100 \N t \N \N \N {} +3274 1143 2025-02-28 07:21:32.682632-08 2025-02-28 07:30:37.256411-08 leaderboard f A100 0.003102605 t \N \N \N {} +6184 1903 2025-03-11 17:13:03.62363-07 2025-03-11 16:22:00.434413-07 test f T4 \N t \N \N \N {} +6185 1903 2025-03-11 16:06:37.941692-07 2025-03-11 16:41:23.331167-07 benchmark f T4 \N t \N \N \N {} +6186 1903 2025-03-11 16:13:32.336442-07 2025-03-11 16:38:58.569653-07 leaderboard f T4 3.2504637173333335 t \N \N \N {} +6352 1969 2025-03-13 12:36:05.935244-07 2025-03-13 13:06:47.416038-07 test f T4 \N f \N \N \N {} +6505 2017 2025-03-14 12:15:29.300377-07 2025-03-14 12:34:01.640423-07 test t A100 \N t \N \N \N {} +6506 2017 2025-03-14 11:18:20.450795-07 2025-03-14 12:30:54.661761-07 benchmark t A100 \N t \N \N \N {} +6608 2046 2025-03-14 13:48:29.521438-07 2025-03-14 13:48:51.889234-07 benchmark f A100 \N t \N \N \N {} +6609 2046 2025-03-14 12:28:36.914817-07 2025-03-14 12:50:31.439118-07 leaderboard f A100 0.002515373 t \N \N \N {} +3279 1144 2025-02-28 06:01:10.470211-08 2025-02-28 07:19:57.064988-08 benchmark t A100 \N t \N \N \N {} +3280 1144 2025-02-28 05:49:45.171368-08 2025-02-28 05:55:10.670943-08 leaderboard t A100 0.0031060543333333336 t \N \N \N {} +3624 1239 2025-02-28 16:58:22.066529-08 2025-02-28 16:33:30.877062-08 benchmark t A100 \N t \N \N \N {} +3281 1145 2025-02-28 05:37:52.569256-08 2025-02-28 07:15:46.585683-08 benchmark f A100 \N t \N \N \N {} +3282 1146 2025-02-28 05:57:19.501056-08 2025-02-28 05:49:46.099644-08 benchmark f A100 \N t \N \N \N {} +3287 1148 2025-02-28 07:03:20.664723-08 2025-02-28 07:36:21.224833-08 test f A100 \N t \N \N \N {} +3288 1148 2025-02-28 06:40:03.960027-08 2025-02-28 07:14:31.066145-08 benchmark f A100 \N t \N \N \N {} +3289 1148 2025-02-28 06:38:52.008227-08 2025-02-28 06:21:28.630464-08 leaderboard f A100 0.0037143906666666665 t \N \N \N {} +3290 1148 2025-02-28 07:14:12.948729-08 2025-02-28 06:08:46.103883-08 test t A100 \N t \N \N \N {} +3291 1148 2025-02-28 07:54:28.192996-08 2025-02-28 06:24:00.652714-08 benchmark t A100 \N t \N \N \N {} +3292 1148 2025-02-28 06:29:06.723096-08 2025-02-28 06:39:10.572344-08 leaderboard t A100 0.0036469966666666663 t \N \N \N {} +3293 1148 2025-02-28 06:40:23.470841-08 2025-02-28 07:24:25.091246-08 test f H100 \N t \N \N \N {} +3294 1148 2025-02-28 07:40:06.006249-08 2025-02-28 07:18:53.588443-08 benchmark f H100 \N t \N \N \N {} +3148 1115 2025-02-28 02:02:27.848298-08 2025-02-28 02:01:06.813683-08 test f A100 \N t \N \N \N {} +1448 484 2025-02-25 05:29:00.477152-08 2025-02-25 05:41:06.371795-08 benchmark f A100 \N t \N \N \N {} +1449 484 2025-02-25 04:52:55.95894-08 2025-02-25 04:18:57.804532-08 leaderboard f A100 0.003093351 t \N \N \N {} +2944 1031 2025-02-27 07:43:30.027296-08 2025-02-27 09:06:35.166274-08 benchmark f A100 \N t \N \N \N {} +3295 1148 2025-02-28 07:32:20.445776-08 2025-02-28 07:54:04.00939-08 leaderboard f H100 0.0018497506666666668 t \N \N \N {} +3296 1148 2025-02-28 07:09:18.873569-08 2025-02-28 07:00:06.853282-08 test t T4 \N t \N \N \N {} +3298 1148 2025-02-28 06:42:57.127187-08 2025-02-28 07:48:36.011787-08 leaderboard t T4 0.010518033333333334 t \N \N \N {} +3299 1148 2025-02-28 07:34:33.292144-08 2025-02-28 07:45:13.263944-08 test t H100 \N t \N \N \N {} +3305 1148 2025-02-28 06:56:08.877192-08 2025-02-28 07:30:09.470963-08 test f L4 \N t \N \N \N {} +3306 1148 2025-02-28 06:49:29.091858-08 2025-02-28 06:59:56.481035-08 benchmark f L4 \N t \N \N \N {} +3316 1151 2025-02-28 07:32:43.276426-08 2025-02-28 06:52:43.01807-08 test t H100 \N f \N \N \N {} +3317 1151 2025-02-28 06:04:40.63296-08 2025-02-28 06:57:06.096217-08 test t T4 \N f \N \N \N {} +3318 1151 2025-02-28 06:13:37.060218-08 2025-02-28 06:40:07.898882-08 test f L4 \N f \N \N \N {} +3529 1205 2025-02-28 11:00:20.479694-08 2025-02-28 11:27:21.065676-08 test f A100 \N t \N \N \N {} +3319 1151 2025-02-28 07:44:25.505195-08 2025-02-28 06:17:10.426155-08 test t L4 \N f \N \N \N {} +3569 1212 2025-02-28 12:31:15.223201-08 2025-02-28 12:02:50.307398-08 benchmark f H100 \N t \N \N \N {} +3327 1153 2025-02-28 07:48:24.592846-08 2025-02-28 06:08:43.919391-08 test t A100 \N f \N \N \N {} +3328 1153 2025-02-28 06:13:02.122552-08 2025-02-28 07:59:20.324753-08 test f A100 \N f \N \N \N {} +3535 1206 2025-02-28 11:07:39.749164-08 2025-02-28 11:05:51.393804-08 test f A100 \N t \N \N \N {} +3330 1153 2025-02-28 06:48:38.54272-08 2025-02-28 06:12:06.160611-08 test t H100 \N f \N \N \N {} +3331 1153 2025-02-28 06:44:57.334042-08 2025-02-28 06:27:45.550938-08 test f H100 \N f \N \N \N {} +3332 1153 2025-02-28 06:51:06.071549-08 2025-02-28 07:43:30.61761-08 test f T4 \N f \N \N \N {} +3333 1153 2025-02-28 07:22:29.016321-08 2025-02-28 07:25:37.384757-08 test t T4 \N f \N \N \N {} +3538 1206 2025-02-28 11:19:04.318559-08 2025-02-28 11:59:22.984501-08 test t A100 \N t \N \N \N {} +3337 1154 2025-02-28 06:12:20.751759-08 2025-02-28 07:24:13.732841-08 leaderboard t A100 0.003106905 t \N \N \N {} +3338 1154 2025-02-28 06:28:19.088439-08 2025-02-28 07:42:01.461767-08 test f A100 \N t \N \N \N {} +3339 1154 2025-02-28 07:24:35.655311-08 2025-02-28 07:21:32.784212-08 benchmark f A100 \N t \N \N \N {} +3340 1154 2025-02-28 06:30:11.131902-08 2025-02-28 07:23:31.696175-08 leaderboard f A100 0.0030856966666666665 t \N \N \N {} +3341 1155 2025-02-28 06:29:19.511228-08 2025-02-28 07:29:02.42522-08 test t A100 \N t \N \N \N {} +3342 1155 2025-02-28 07:51:44.284677-08 2025-02-28 07:48:29.018028-08 benchmark t A100 \N t \N \N \N {} +3343 1155 2025-02-28 06:47:28.573079-08 2025-02-28 06:44:05.949627-08 leaderboard t A100 0.0030834483333333335 t \N \N \N {} +3363 1161 2025-02-28 09:17:07.230642-08 2025-02-28 08:40:19.665408-08 test t T4 \N f \N \N \N {} +3348 1156 2025-02-28 07:16:58.973512-08 2025-02-28 07:33:23.078046-08 benchmark f A100 \N t \N \N \N {} +6188 1904 2025-03-11 18:10:38.388791-07 2025-03-11 18:14:58.210124-07 test t T4 \N f \N \N \N {} +6354 1970 2025-03-13 12:17:14.735768-07 2025-03-13 13:49:45.117322-07 test f T4 \N f \N \N \N {} +6508 2017 2025-03-14 11:42:27.682678-07 2025-03-14 12:36:52.887607-07 test f A100 \N t \N \N \N {} +6610 2047 2025-03-14 13:00:31.603918-07 2025-03-14 13:30:16.003681-07 test f A100 \N t \N \N \N {} +3570 1213 2025-02-28 11:12:09.580297-08 2025-02-28 11:15:38.840673-08 benchmark f H100 \N t \N \N \N {} +3541 1207 2025-02-28 11:14:12.554775-08 2025-02-28 11:14:24.949948-08 test t A100 \N t \N \N \N {} +3364 1161 2025-02-28 08:18:06.851362-08 2025-02-28 09:42:01.27446-08 test f T4 \N f \N \N \N {} +3365 1162 2025-02-28 09:22:47.122104-08 2025-02-28 08:23:26.975718-08 test f L4 \N f \N \N \N {} +3051 1093 2025-02-27 15:52:12.940276-08 2025-02-27 17:09:05.153707-08 leaderboard f A100 0.0031512003333333334 t \N \N \N {} +1451 485 2025-02-25 04:52:22.63147-08 2025-02-25 05:18:27.658437-08 benchmark t A100 \N t \N \N \N {} +1452 485 2025-02-25 05:30:56.8532-08 2025-02-25 05:31:55.77683-08 leaderboard t A100 0.0030837706666666663 t \N \N \N {} +2945 1032 2025-02-27 08:03:40.62355-08 2025-02-27 07:23:04.933128-08 test f A100 \N f \N \N \N {} +3151 1115 2025-02-28 03:28:35.730379-08 2025-02-28 02:37:28.944792-08 test t A100 \N t \N \N \N {} +3178 1120 2025-02-28 04:33:49.102329-08 2025-02-28 04:08:21.310974-08 test f A100 \N t \N \N \N {} +1454 485 2025-02-25 04:56:31.907725-08 2025-02-25 05:13:12.842041-08 benchmark f A100 \N t \N \N \N {} +1455 485 2025-02-25 04:05:06.520603-08 2025-02-25 04:56:19.51449-08 leaderboard f A100 0.003085375 t \N \N \N {} +2946 1033 2025-02-27 08:49:30.211741-08 2025-02-27 09:05:35.900691-08 test f T4 \N f \N \N \N {} +3003 1070 2025-02-27 13:00:06.212495-08 2025-02-27 11:57:37.768801-08 test f A100 \N f \N \N \N {} +3053 1094 2025-02-27 16:12:39.715401-08 2025-02-27 16:16:10.311482-08 benchmark f A100 \N t \N \N \N {} +3366 1162 2025-02-28 09:33:20.151769-08 2025-02-28 08:19:11.959347-08 test t L4 \N f \N \N \N {} +3571 1214 2025-02-28 12:01:55.895534-08 2025-02-28 12:13:51.232283-08 benchmark f H100 \N f \N \N \N {} +3373 1164 2025-02-28 09:54:50.944709-08 2025-02-28 09:48:36.826131-08 leaderboard f L4 0.01711390133333333 t \N \N \N {} +3374 1164 2025-02-28 10:03:17.913215-08 2025-02-28 08:48:38.575349-08 test t L4 \N t \N \N \N {} +3375 1164 2025-02-28 10:12:12.736365-08 2025-02-28 09:31:26.847036-08 benchmark t L4 \N t \N \N \N {} +3376 1164 2025-02-28 10:13:36.426125-08 2025-02-28 10:11:53.721613-08 leaderboard t L4 0.01711174266666667 t \N \N \N {} +3054 1094 2025-02-27 16:06:27.122167-08 2025-02-27 16:37:13.76371-08 leaderboard f A100 0.003111287 t \N \N \N {} +3154 1116 2025-02-28 02:29:06.631341-08 2025-02-28 01:58:59.085604-08 test f A100 \N t \N \N \N {} +1457 486 2025-02-25 04:36:15.216458-08 2025-02-25 05:15:34.163173-08 benchmark f A100 \N t \N \N \N {} +1458 486 2025-02-25 04:14:50.938805-08 2025-02-25 05:01:24.288432-08 leaderboard f A100 0.0030878703333333335 t \N \N \N {} +2947 1034 2025-02-27 09:13:44.098036-08 2025-02-27 09:11:18.488648-08 test f A100 \N f \N \N \N {} +3056 1094 2025-02-27 16:41:39.934588-08 2025-02-27 16:25:19.394991-08 benchmark t A100 \N t \N \N \N {} +3213 1125 2025-02-28 06:48:12.252064-08 2025-02-28 06:03:15.742155-08 leaderboard f A100 0.0030832093333333335 t \N \N \N {} +1460 486 2025-02-25 04:41:59.887933-08 2025-02-25 04:23:57.454866-08 benchmark t A100 \N t \N \N \N {} +1461 486 2025-02-25 05:49:45.663044-08 2025-02-25 04:12:57.109398-08 leaderboard t A100 0.003092806 t \N \N \N {} +2948 1035 2025-02-27 08:17:18.304303-08 2025-02-27 07:43:31.506965-08 test f T4 \N f \N \N \N {} +3005 1072 2025-02-27 13:09:50.152666-08 2025-02-27 12:57:15.8831-08 test f A100 \N t \N \N \N {} +3057 1094 2025-02-27 17:34:54.982672-08 2025-02-27 16:26:07.35543-08 leaderboard t A100 0.0031275906666666667 t \N \N \N {} +3080 1103 2025-02-28 02:02:56.286012-08 2025-02-28 02:38:01.79013-08 benchmark t A100 \N t \N \N \N {} +3081 1103 2025-02-28 01:56:35.783923-08 2025-02-28 01:56:41.958069-08 leaderboard t A100 0.0031360743333333336 t \N \N \N {} +3219 1126 2025-02-28 05:38:06.586102-08 2025-02-28 05:05:43.074581-08 leaderboard f A100 0.003108399 t \N \N \N {} +1463 487 2025-02-25 04:52:33.166187-08 2025-02-25 04:41:41.65243-08 benchmark t H100 \N t \N \N \N {} +1464 487 2025-02-25 05:44:02.421962-08 2025-02-25 04:30:44.563192-08 leaderboard t H100 0.0013963186666666667 t \N \N \N {} +2949 1036 2025-02-27 09:05:30.430291-08 2025-02-27 08:16:44.717534-08 test f A100 \N f \N \N \N {} +3006 1073 2025-02-27 14:03:17.549065-08 2025-02-27 12:33:09.482347-08 benchmark f A100 \N t \N \N \N {} +3583 1228 2025-02-28 13:36:44.96965-08 2025-02-28 13:58:50.097299-08 leaderboard f A100 0.0021488616666666665 t \N \N \N {} +3082 1104 2025-02-28 01:20:46.117646-08 2025-02-28 01:48:56.099424-08 test t A100 \N t \N \N \N {} +3083 1104 2025-02-28 02:40:27.969759-08 2025-02-28 03:00:37.086929-08 benchmark t A100 \N t \N \N \N {} +1466 487 2025-02-25 06:00:31.318157-08 2025-02-25 05:21:10.22327-08 benchmark f H100 \N t \N \N \N {} +1467 487 2025-02-25 05:08:04.83803-08 2025-02-25 05:35:28.456713-08 leaderboard f H100 0.0013990763333333333 t \N \N \N {} +1468 488 2025-02-25 04:11:00.167833-08 2025-02-25 05:53:43.66447-08 test f A100 \N f \N \N \N {} +3386 1166 2025-02-28 08:58:24.708767-08 2025-02-28 08:59:35.546096-08 test f L4 \N t \N \N \N {} +3572 1216 2025-02-28 13:25:32.928609-08 2025-02-28 13:06:59.918087-08 test f H100 \N f \N \N \N {} +3402 1173 2025-02-28 08:50:27.553378-08 2025-02-28 09:25:30.916359-08 leaderboard f L4 0.017073246333333333 t \N \N \N {} +3403 1173 2025-02-28 09:24:43.249263-08 2025-02-28 10:19:27.328955-08 test t L4 \N f \N \N \N {} +3404 1174 2025-02-28 09:36:18.604104-08 2025-02-28 10:01:25.201171-08 test t L4 \N t \N \N \N {} +3407 1174 2025-02-28 10:04:35.458188-08 2025-02-28 09:06:27.208576-08 test f L4 \N f \N \N \N {} +3409 1175 2025-02-28 10:42:07.749021-08 2025-02-28 10:23:25.763853-08 test t L4 \N f \N \N \N {} +3410 1176 2025-02-28 09:35:38.594297-08 2025-02-28 10:42:52.909956-08 test f L4 \N t \N \N \N {} +3411 1176 2025-02-28 10:40:17.639715-08 2025-02-28 09:51:35.220213-08 benchmark f L4 \N t \N \N \N {} +3412 1176 2025-02-28 10:11:43.370341-08 2025-02-28 10:27:44.06876-08 leaderboard f L4 0.017120271333333333 t \N \N \N {} +3547 1208 2025-02-28 12:27:27.702985-08 2025-02-28 11:17:47.788966-08 test f A100 \N t \N \N \N {} +3413 1176 2025-02-28 11:01:01.943548-08 2025-02-28 09:36:24.833158-08 test t L4 \N t \N \N \N {} +3414 1176 2025-02-28 09:58:29.179458-08 2025-02-28 09:26:37.706314-08 benchmark t L4 \N t \N \N \N {} +3415 1176 2025-02-28 10:55:29.876048-08 2025-02-28 09:14:25.427957-08 leaderboard t L4 0.017113951333333332 t \N \N \N {} +3416 1177 2025-02-28 10:07:18.386046-08 2025-02-28 10:14:01.64194-08 test f L4 \N f \N \N \N {} +3417 1177 2025-02-28 09:37:22.22405-08 2025-02-28 10:32:18.953094-08 test t L4 \N f \N \N \N {} +3550 1208 2025-02-28 11:50:19.101588-08 2025-02-28 12:45:50.950117-08 test t A100 \N t \N \N \N {} +3422 1180 2025-02-28 10:56:47.404757-08 2025-02-28 10:56:22.71621-08 test f L4 \N f \N \N \N {} +1470 490 2025-02-25 04:41:11.741201-08 2025-02-25 06:11:53.932837-08 benchmark f A100 \N t \N \N \N {} +2950 1037 2025-02-27 07:35:16.658999-08 2025-02-27 09:21:21.845387-08 test f A100 \N t \N \N \N {} +3007 1074 2025-02-27 13:18:44.138568-08 2025-02-27 13:54:19.684997-08 test t A100 \N t \N \N \N {} +3008 1074 2025-02-27 12:10:25.097092-08 2025-02-27 12:22:43.495573-08 benchmark t A100 \N t \N \N \N {} +1472 491 2025-02-25 04:16:51.40767-08 2025-02-25 04:44:20.032912-08 benchmark t A100 \N t \N \N \N {} +1473 491 2025-02-25 04:39:56.631178-08 2025-02-25 04:50:34.949043-08 leaderboard t A100 0.02900779833333333 t \N \N \N {} +3084 1104 2025-02-28 01:25:02.828823-08 2025-02-28 01:40:54.029102-08 leaderboard t A100 0.003145336 t \N \N \N {} +3092 1105 2025-02-28 03:07:23.548326-08 2025-02-28 02:21:49.2888-08 benchmark f A100 \N t \N \N \N {} +3093 1105 2025-02-28 03:14:38.771483-08 2025-02-28 02:41:25.038891-08 leaderboard f A100 0.003120996 t \N \N \N {} +3792 1279 2025-03-01 02:43:50.248573-08 2025-03-01 02:16:59.092747-08 test t A100 \N t \N \N \N {} +1475 491 2025-02-25 05:21:21.516015-08 2025-02-25 05:04:36.503808-08 benchmark f A100 \N t \N \N \N {} +1476 491 2025-02-25 04:41:54.691799-08 2025-02-25 05:44:48.514036-08 leaderboard f A100 0.02915152266666667 t \N \N \N {} +3423 1180 2025-02-28 09:29:33.8094-08 2025-02-28 10:27:43.704792-08 test t L4 \N f \N \N \N {} +3424 1181 2025-02-28 09:43:15.513902-08 2025-02-28 09:32:49.900783-08 test f L4 \N f \N \N \N {} +3425 1181 2025-02-28 09:43:45.066653-08 2025-02-28 10:25:58.378242-08 test t L4 \N f \N \N \N {} +3426 1182 2025-02-28 10:47:18.289906-08 2025-02-28 10:12:47.391263-08 test f L4 \N f \N \N \N {} +3427 1182 2025-02-28 09:47:39.824926-08 2025-02-28 11:31:24.156151-08 test t L4 \N f \N \N \N {} +3430 1184 2025-02-28 10:56:20.592407-08 2025-02-28 09:53:03.031846-08 test t L4 \N t \N \N \N {} +3431 1184 2025-02-28 11:16:58.29005-08 2025-02-28 10:48:40.540813-08 benchmark t L4 \N t \N \N \N {} +3432 1184 2025-02-28 09:40:03.831648-08 2025-02-28 11:23:08.875773-08 leaderboard t L4 0.017144596666666668 t \N \N \N {} +3433 1184 2025-02-28 10:51:16.362382-08 2025-02-28 11:01:47.958995-08 test f L4 \N f \N \N \N {} +3553 1209 2025-02-28 12:15:04.393062-08 2025-02-28 12:20:51.635003-08 test t A100 \N t \N \N \N {} +3436 1185 2025-02-28 10:58:38.735282-08 2025-02-28 09:55:56.205615-08 leaderboard t L4 0.017078157666666666 t \N \N \N {} +3437 1185 2025-02-28 09:43:25.915772-08 2025-02-28 11:05:27.656365-08 test f L4 \N f \N \N \N {} +3438 1186 2025-02-28 10:35:22.023921-08 2025-02-28 10:01:02.327904-08 test f L4 \N t \N \N \N {} +3443 1186 2025-02-28 10:41:33.074483-08 2025-02-28 10:48:09.890989-08 leaderboard t L4 0.017077551 t \N \N \N {} +3455 1190 2025-02-28 11:31:02.465158-08 2025-02-28 11:33:20.889525-08 benchmark f A100 \N t \N \N \N {} +3444 1187 2025-02-28 11:17:11.801344-08 2025-02-28 10:45:20.9261-08 test f L4 \N f \N \N \N {} +3445 1187 2025-02-28 11:46:40.120384-08 2025-02-28 11:16:52.048543-08 test t L4 \N f \N \N \N {} +3446 1188 2025-02-28 12:11:52.882164-08 2025-02-28 11:38:47.364032-08 test t A100 \N t \N \N \N {} +3447 1188 2025-02-28 11:53:16.182886-08 2025-02-28 11:57:38.888939-08 benchmark t A100 \N t \N \N \N {} +3448 1188 2025-02-28 11:31:39.142552-08 2025-02-28 10:34:32.539419-08 leaderboard t A100 0.003089445 t \N \N \N {} +3460 1191 2025-02-28 10:50:29.600852-08 2025-02-28 10:55:28.066601-08 test f A100 \N t \N \N \N {} +3451 1188 2025-02-28 11:25:05.48421-08 2025-02-28 11:26:09.847138-08 leaderboard f A100 0.003099992 t \N \N \N {} +3452 1189 2025-02-28 11:27:07.929922-08 2025-02-28 10:27:25.882031-08 test f A100 \N f \N \N \N {} +2956 1040 2025-02-27 07:53:09.243149-08 2025-02-27 08:42:54.201109-08 test t A100 \N t \N \N \N {} +3014 1076 2025-02-27 13:56:41.106043-08 2025-02-27 13:46:16.794281-08 benchmark f A100 \N t \N \N \N {} +1487 496 2025-02-25 06:02:20.071956-08 2025-02-25 06:20:28.361733-08 test t A100 \N f \N \N \N {} +1488 496 2025-02-25 06:37:53.255009-08 2025-02-25 07:06:16.467792-08 test f A100 \N f \N \N \N {} +3453 1189 2025-02-28 11:50:14.581562-08 2025-02-28 10:31:03.166396-08 test t A100 \N f \N \N \N {} +3463 1191 2025-02-28 10:39:12.659548-08 2025-02-28 11:48:27.810322-08 test t A100 \N t \N \N \N {} +3556 1209 2025-02-28 12:30:43.675467-08 2025-02-28 12:41:17.817806-08 test f A100 \N t \N \N \N {} +6514 2018 2025-03-14 12:18:15.299016-07 2025-03-14 11:04:40.892319-07 test f A100 \N t \N \N \N {} +6515 2018 2025-03-14 11:49:32.366043-07 2025-03-14 11:56:59.199313-07 benchmark f A100 \N t \N \N \N {} +6360 1972 2025-03-13 12:35:05.799956-07 2025-03-13 13:55:16.538902-07 benchmark f T4 \N f \N \N \N {} +6517 2019 2025-03-14 11:49:17.362182-07 2025-03-14 10:59:40.482475-07 benchmark f A100 \N t \N \N \N {} +6363 1973 2025-03-13 15:12:13.132853-07 2025-03-13 13:39:11.582645-07 test t T4 \N t \N \N \N {} +6364 1973 2025-03-13 13:54:16.988636-07 2025-03-13 14:15:30.672524-07 benchmark t T4 \N f \N \N \N {} +6521 2020 2025-03-14 11:11:59.364683-07 2025-03-14 12:39:27.185178-07 test t A100 \N t \N \N \N {} +3479 1196 2025-02-28 10:53:45.214965-08 2025-02-28 11:40:18.287407-08 benchmark f A100 \N t \N \N \N {} +3480 1196 2025-02-28 12:21:44.871556-08 2025-02-28 12:18:32.615765-08 leaderboard f A100 0.0030916253333333333 t \N \N \N {} +3481 1195 2025-02-28 10:38:20.149926-08 2025-02-28 11:37:11.075833-08 test f A100 \N t \N \N \N {} +3482 1195 2025-02-28 11:40:32.30581-08 2025-02-28 11:07:23.741189-08 benchmark f A100 \N f \N \N \N {} +3483 1195 2025-02-28 11:06:17.163408-08 2025-02-28 11:09:41.125501-08 test t A100 \N t \N \N \N {} +6196 1908 2025-03-11 17:42:13.201789-07 2025-03-11 16:36:53.748973-07 test f T4 \N f \N \N \N {} +6394 1987 2025-03-13 16:17:38.428534-07 2025-03-13 17:49:29.688605-07 test t T4 \N f \N \N \N {} +6367 1974 2025-03-13 15:18:15.442709-07 2025-03-13 14:52:02.574384-07 test t T4 \N t \N \N \N {} +6611 2047 2025-03-14 12:25:13.352133-07 2025-03-14 12:54:20.14153-07 benchmark f A100 \N t \N \N \N {} +3492 1198 2025-02-28 10:31:42.778359-08 2025-02-28 12:29:56.888072-08 benchmark f A100 \N t \N \N \N {} +3493 1198 2025-02-28 10:35:47.467941-08 2025-02-28 11:50:52.415689-08 leaderboard f A100 0.0030801673333333336 t \N \N \N {} +6198 1909 2025-03-11 17:57:18.149458-07 2025-03-11 17:27:06.275858-07 test f T4 \N f \N \N \N {} +6369 1975 2025-03-13 16:40:00.881683-07 2025-03-13 16:06:45.585849-07 test f T4 \N f \N \N \N {} +6916 2156 2025-03-16 15:11:39.340389-07 2025-03-16 16:54:21.426913-07 test f T4 \N t \N \N \N {} +3495 1198 2025-02-28 12:22:48.855653-08 2025-02-28 11:22:36.934387-08 benchmark t A100 \N t \N \N \N {} +3496 1198 2025-02-28 11:02:01.041551-08 2025-02-28 10:55:18.662378-08 leaderboard t A100 0.0030712036666666665 t \N \N \N {} +6199 1910 2025-03-11 17:51:06.602448-07 2025-03-11 17:45:19.999575-07 test t T4 \N f \N \N \N {} +6370 1975 2025-03-13 17:09:02.372261-07 2025-03-13 15:49:54.86776-07 test t T4 \N f \N \N \N {} +6528 2021 2025-03-14 11:20:09.013884-07 2025-03-14 11:51:31.5343-07 benchmark f A100 \N t \N \N \N {} +6529 2021 2025-03-14 12:17:59.33897-07 2025-03-14 11:57:07.878145-07 leaderboard f A100 0.0024379966666666663 t \N \N \N {} +6613 2047 2025-03-14 13:28:02.649127-07 2025-03-14 13:08:42.984819-07 test t A100 \N t \N \N \N {} +6677 2066 2025-03-14 14:28:21.53377-07 2025-03-14 15:18:12.092115-07 test t T4 \N f \N \N \N {} +3498 1199 2025-02-28 10:55:47.187252-08 2025-02-28 11:02:06.984812-08 benchmark f A100 \N t \N \N \N {} +3499 1199 2025-02-28 11:57:15.471579-08 2025-02-28 11:00:10.750543-08 leaderboard f A100 0.0030867046666666663 t \N \N \N {} +6200 1910 2025-03-11 18:13:57.459745-07 2025-03-11 17:39:14.073278-07 test f T4 \N f \N \N \N {} +6371 1976 2025-03-13 16:10:15.976652-07 2025-03-13 16:59:38.423812-07 test t T4 \N f \N \N \N {} +6530 2022 2025-03-14 12:42:51.830687-07 2025-03-14 12:44:20.985583-07 test f A100 \N t \N \N \N {} +6531 2022 2025-03-14 12:43:29.503929-07 2025-03-14 11:48:37.299314-07 benchmark f A100 \N t \N \N \N {} +6532 2022 2025-03-14 12:33:03.009856-07 2025-03-14 11:10:10.079119-07 leaderboard f A100 0.0031564046666666666 t \N \N \N {} +6614 2047 2025-03-14 12:41:54.04883-07 2025-03-14 12:42:12.07854-07 benchmark t A100 \N t \N \N \N {} +6615 2047 2025-03-14 12:28:03.957753-07 2025-03-14 12:16:08.077601-07 leaderboard t A100 0.0025148893333333333 t \N \N \N {} +3501 1199 2025-02-28 10:44:00.815458-08 2025-02-28 12:25:29.005606-08 benchmark t A100 \N t \N \N \N {} +1489 497 2025-02-25 06:09:45.421735-08 2025-02-25 06:06:56.895044-08 test t A100 \N f \N \N \N {} +1490 497 2025-02-25 07:30:16.061013-08 2025-02-25 05:58:05.353035-08 test f A100 \N f \N \N \N {} +1493 499 2025-02-25 07:29:41.937766-08 2025-02-25 07:35:35.73218-08 test t A100 \N f \N \N \N {} +1494 499 2025-02-25 06:55:16.544585-08 2025-02-25 06:42:04.831851-08 test f A100 \N f \N \N \N {} +1495 500 2025-02-25 06:11:38.525649-08 2025-02-25 06:06:09.247788-08 test f A100 \N f \N \N \N {} +1496 500 2025-02-25 06:28:17.374206-08 2025-02-25 07:36:57.6453-08 test t A100 \N f \N \N \N {} +1497 501 2025-02-25 06:22:45.354033-08 2025-02-25 06:04:40.972205-08 test t A100 \N f \N \N \N {} +1498 501 2025-02-25 07:26:32.437486-08 2025-02-25 06:26:05.660958-08 test f A100 \N f \N \N \N {} +2957 1040 2025-02-27 08:15:29.867269-08 2025-02-27 08:02:02.503639-08 benchmark t A100 \N t \N \N \N {} +2958 1040 2025-02-27 08:46:34.959091-08 2025-02-27 08:49:51.207345-08 leaderboard t A100 0.0031735606666666665 t \N \N \N {} +3106 1108 2025-02-28 02:29:24.19387-08 2025-02-28 02:25:44.234748-08 test t A100 \N t \N \N \N {} +2772 966 2025-02-26 19:49:15.757238-08 2025-02-26 20:10:22.740071-08 leaderboard t A100 0.014294890199999999 t \N \N \N {} +3502 1199 2025-02-28 11:15:50.983592-08 2025-02-28 11:56:22.474618-08 leaderboard t A100 0.00308841 t \N \N \N {} +1500 502 2025-02-25 05:50:41.376697-08 2025-02-25 06:42:15.925754-08 benchmark f T4 \N t \N \N \N {} +1501 502 2025-02-25 06:35:11.719711-08 2025-02-25 06:16:59.633058-08 leaderboard f T4 0.016468595 t \N \N \N {} +3015 1077 2025-02-27 14:04:51.482078-08 2025-02-27 14:44:42.488782-08 benchmark f H100 \N t \N \N \N {} +3109 1108 2025-02-28 02:01:50.848448-08 2025-02-28 01:49:08.698992-08 test f A100 \N t \N \N \N {} +3157 1116 2025-02-28 02:17:56.372163-08 2025-02-28 02:21:38.342465-08 test t A100 \N t \N \N \N {} +6201 1911 2025-03-11 18:01:48.518324-07 2025-03-11 18:32:00.643951-07 test t T4 \N f \N \N \N {} +6372 1976 2025-03-13 15:35:23.586028-07 2025-03-13 16:38:44.74512-07 test f T4 \N f \N \N \N {} +6533 2022 2025-03-14 12:55:57.39393-07 2025-03-14 12:29:44.638225-07 test t A100 \N t \N \N \N {} +6534 2022 2025-03-14 11:24:57.714064-07 2025-03-14 12:27:54.539497-07 benchmark t A100 \N t \N \N \N {} +6535 2022 2025-03-14 11:34:47.019969-07 2025-03-14 11:00:15.310359-07 leaderboard t A100 0.0039016193333333334 t \N \N \N {} +6616 2048 2025-03-14 12:53:00.225497-07 2025-03-14 13:45:31.447067-07 test t A100 \N t \N \N \N {} +6617 2048 2025-03-14 12:44:51.583069-07 2025-03-14 13:05:24.372941-07 benchmark t A100 \N t \N \N \N {} +3504 1200 2025-02-28 10:37:28.152845-08 2025-02-28 12:03:13.176444-08 benchmark f A100 \N t \N \N \N {} +3505 1200 2025-02-28 11:14:32.252149-08 2025-02-28 11:47:51.650604-08 leaderboard f A100 0.003092401 t \N \N \N {} +6202 1911 2025-03-11 17:19:23.061602-07 2025-03-11 18:30:37.372737-07 test f T4 \N f \N \N \N {} +6373 1977 2025-03-13 16:45:46.79345-07 2025-03-13 17:04:17.571101-07 test t T4 \N t \N \N \N {} +6375 1977 2025-03-13 15:59:28.148453-07 2025-03-13 15:45:43.825301-07 test f T4 \N t \N \N \N {} +6376 1977 2025-03-13 15:47:35.023166-07 2025-03-13 15:51:13.628825-07 benchmark f T4 \N f \N \N \N {} +6539 2023 2025-03-14 12:34:47.676474-07 2025-03-14 11:26:38.083854-07 test f A100 \N t \N \N \N {} +6540 2023 2025-03-14 12:11:02.732324-07 2025-03-14 12:35:39.482955-07 benchmark f A100 \N t \N \N \N {} +3510 1201 2025-02-28 12:09:30.867012-08 2025-02-28 11:14:15.654068-08 benchmark f A100 \N t \N \N \N {} +3511 1201 2025-02-28 12:22:28.128389-08 2025-02-28 10:55:21.592984-08 leaderboard f A100 0.0030901086666666666 t \N \N \N {} +6204 1912 2025-03-11 18:30:54.347773-07 2025-03-11 18:09:47.823091-07 test t T4 \N f \N \N \N {} +6205 1913 2025-03-11 17:40:25.001398-07 2025-03-11 16:51:30.586544-07 test f T4 \N f \N \N \N {} +6542 2024 2025-03-14 11:14:42.747731-07 2025-03-14 12:11:19.545486-07 benchmark f A100 \N t \N \N \N {} +6619 2048 2025-03-14 12:04:08.070953-07 2025-03-14 12:48:51.357583-07 test f A100 \N t \N \N \N {} +6650 2054 2025-03-14 13:53:44.337385-07 2025-03-14 13:48:46.599335-07 test t A100 \N t \N \N \N {} +6656 2058 2025-03-14 13:54:10.740579-07 2025-03-14 12:34:46.676855-07 test t A100 \N t \N \N \N {} +3516 1202 2025-02-28 12:33:09.097002-08 2025-02-28 10:58:45.829133-08 benchmark f A100 \N t \N \N \N {} +3517 1202 2025-02-28 11:58:25.410318-08 2025-02-28 11:30:47.992124-08 leaderboard f A100 0.003084266 t \N \N \N {} +6206 1913 2025-03-11 18:05:22.9157-07 2025-03-11 17:36:14.408717-07 test t T4 \N f \N \N \N {} +6379 1980 2025-03-13 16:36:27.760868-07 2025-03-13 16:20:53.675757-07 test t T4 \N f \N \N \N {} +6548 2025 2025-03-14 11:20:57.236222-07 2025-03-14 12:24:25.970334-07 leaderboard f A100 0.002452236 t \N \N \N {} +3524 1204 2025-02-28 12:20:39.907459-08 2025-02-28 11:12:35.546414-08 benchmark f A100 \N t \N \N \N {} +1503 502 2025-02-25 06:45:45.886953-08 2025-02-25 06:50:12.165567-08 benchmark t T4 \N t \N \N \N {} +1504 502 2025-02-25 07:36:18.732222-08 2025-02-25 06:56:09.60856-08 leaderboard t T4 0.016514245333333333 t \N \N \N {} +1505 503 2025-02-25 07:24:08.994752-08 2025-02-25 07:42:21.014151-08 test t A100 \N f \N \N \N {} +1506 503 2025-02-25 07:27:58.277977-08 2025-02-25 06:58:20.391073-08 test f A100 \N f \N \N \N {} +3112 1109 2025-02-28 02:49:13.950386-08 2025-02-28 03:20:33.833093-08 test t A100 \N t \N \N \N {} +3525 1204 2025-02-28 10:57:17.218778-08 2025-02-28 12:11:28.522376-08 leaderboard f A100 0.0030873746666666667 t \N \N \N {} +6549 2026 2025-03-14 11:43:25.469312-07 2025-03-14 11:20:32.899243-07 test t A100 \N t \N \N \N {} +6550 2026 2025-03-14 12:20:36.010926-07 2025-03-14 12:14:30.57356-07 benchmark t A100 \N t \N \N \N {} +3527 1204 2025-02-28 11:04:54.217825-08 2025-02-28 11:23:20.827974-08 benchmark t A100 \N t \N \N \N {} +3528 1204 2025-02-28 12:01:09.198033-08 2025-02-28 12:15:21.496381-08 leaderboard t A100 0.003084299 t \N \N \N {} +6382 1981 2025-03-13 16:02:38.157698-07 2025-03-13 16:43:08.39172-07 test f T4 \N f \N \N \N {} +6551 2026 2025-03-14 11:33:10.201136-07 2025-03-14 12:55:26.704593-07 leaderboard t A100 0.0025385736666666664 t \N \N \N {} +6623 2049 2025-03-14 12:51:48.541281-07 2025-03-14 13:37:41.702339-07 benchmark f A100 \N t \N \N \N {} +3530 1205 2025-02-28 11:05:21.637922-08 2025-02-28 10:59:24.063245-08 benchmark f A100 \N t \N \N \N {} +3531 1205 2025-02-28 11:51:46.708741-08 2025-02-28 10:54:12.608529-08 leaderboard f A100 0.003087388 t \N \N \N {} +6383 1982 2025-03-13 16:30:23.243012-07 2025-03-13 17:19:59.354647-07 test f T4 \N f \N \N \N {} +6552 2026 2025-03-14 12:22:01.49041-07 2025-03-14 13:00:33.669546-07 test f A100 \N t \N \N \N {} +6553 2026 2025-03-14 11:20:49.862991-07 2025-03-14 11:11:51.945168-07 benchmark f A100 \N t \N \N \N {} +3533 1205 2025-02-28 10:59:21.335705-08 2025-02-28 11:32:43.758531-08 benchmark t A100 \N t \N \N \N {} +3534 1205 2025-02-28 11:42:18.774429-08 2025-02-28 11:30:52.036125-08 leaderboard t A100 0.0030747123333333334 t \N \N \N {} +6211 1916 2025-03-11 18:51:26.328567-07 2025-03-11 17:34:02.97454-07 test f T4 \N f \N \N \N {} +6384 1982 2025-03-13 16:29:22.478385-07 2025-03-13 16:29:50.49526-07 test t T4 \N f \N \N \N {} +6554 2026 2025-03-14 11:32:39.712087-07 2025-03-14 13:01:51.543072-07 leaderboard f A100 0.0024447563333333333 t \N \N \N {} +6624 2049 2025-03-14 13:31:46.298149-07 2025-03-14 13:02:46.422741-07 leaderboard f A100 0.003148123 t \N \N \N {} +6659 2058 2025-03-14 12:36:09.483207-07 2025-03-14 12:28:00.634473-07 test f A100 \N t \N \N \N {} +6971 2169 2025-03-16 19:08:50.226241-07 2025-03-16 19:52:33.539167-07 leaderboard t T4 0.00026675565000000004 t \N \N \N {} +3536 1206 2025-02-28 12:01:21.310531-08 2025-02-28 10:59:24.032043-08 benchmark f A100 \N t \N \N \N {} +1508 504 2025-02-25 07:36:12.320858-08 2025-02-25 06:45:09.929248-08 benchmark f A100 \N t \N \N \N {} +1509 504 2025-02-25 06:24:05.118745-08 2025-02-25 07:05:31.47031-08 leaderboard f A100 0.003201205285714286 t \N \N \N {} +3016 1078 2025-02-27 15:09:58.383782-08 2025-02-27 15:09:34.428187-08 benchmark f A100 \N t \N \N \N {} +3086 1104 2025-02-28 02:37:38.797917-08 2025-02-28 02:36:42.886532-08 benchmark f A100 \N t \N \N \N {} +3537 1206 2025-02-28 11:44:02.859148-08 2025-02-28 12:17:13.765459-08 leaderboard f A100 0.0030771996666666664 t \N \N \N {} +6555 2027 2025-03-14 11:55:38.194672-07 2025-03-14 13:12:55.354266-07 benchmark f A100 \N t \N \N \N {} +6625 2049 2025-03-14 12:03:46.633551-07 2025-03-14 13:14:01.521077-07 test t A100 \N t \N \N \N {} +3539 1206 2025-02-28 12:05:54.440089-08 2025-02-28 11:48:11.736334-08 benchmark t A100 \N t \N \N \N {} +3540 1206 2025-02-28 11:50:30.166906-08 2025-02-28 11:34:39.086558-08 leaderboard t A100 0.003082872 t \N \N \N {} +6626 2049 2025-03-14 12:41:30.656497-07 2025-03-14 12:04:51.089204-07 benchmark t A100 \N t \N \N \N {} +3542 1207 2025-02-28 10:49:11.612247-08 2025-02-28 12:30:07.673052-08 benchmark t A100 \N t \N \N \N {} +3543 1207 2025-02-28 11:35:24.228637-08 2025-02-28 11:37:10.231465-08 leaderboard t A100 0.0031054823333333333 t \N \N \N {} +3546 1207 2025-02-28 11:42:05.635418-08 2025-02-28 12:05:11.256023-08 leaderboard f A100 0.0030748303333333333 t \N \N \N {} +3593 1230 2025-02-28 13:36:11.392976-08 2025-02-28 15:07:41.559929-08 benchmark f A100 \N t \N \N \N {} +3594 1231 2025-02-28 13:56:55.748686-08 2025-02-28 14:50:55.966775-08 test f H100 \N t \N \N \N {} +3595 1231 2025-02-28 14:21:28.207811-08 2025-02-28 13:22:24.302597-08 benchmark f H100 \N t \N \N \N {} +3596 1231 2025-02-28 13:34:32.178088-08 2025-02-28 14:18:02.106124-08 leaderboard f H100 0.0012008532 t \N \N \N {} +3597 1231 2025-02-28 14:33:14.730553-08 2025-02-28 13:51:57.351344-08 test t H100 \N t \N \N \N {} +3598 1231 2025-02-28 13:27:10.531166-08 2025-02-28 14:13:56.712585-08 benchmark t H100 \N t \N \N \N {} +3650 1248 2025-03-01 01:03:42.861509-08 2025-03-01 01:06:00.433924-08 leaderboard t A100 0.0031414876666666664 t \N \N \N {} +3652 1248 2025-03-01 02:29:49.148831-08 2025-03-01 01:52:31.634785-08 benchmark f A100 \N t \N \N \N {} +3658 1249 2025-03-01 01:37:20.868883-08 2025-03-01 01:41:30.386305-08 benchmark t A100 \N t \N \N \N {} +3659 1249 2025-03-01 02:08:57.983273-08 2025-03-01 02:33:34.854721-08 leaderboard t A100 0.0031066326666666665 t \N \N \N {} +3660 1250 2025-03-01 00:47:36.371435-08 2025-03-01 02:29:41.827776-08 test t A100 \N t \N \N \N {} +1511 504 2025-02-25 07:09:54.962145-08 2025-02-25 06:23:56.896069-08 benchmark t A100 \N t \N \N \N {} +1512 504 2025-02-25 06:32:05.54038-08 2025-02-25 07:46:23.871842-08 leaderboard t A100 0.0032573723333333333 t \N \N \N {} +3017 1079 2025-02-27 15:33:40.79789-08 2025-02-27 15:03:19.237768-08 benchmark f H100 \N t \N \N \N {} +3087 1104 2025-02-28 01:35:18.867193-08 2025-02-28 03:11:29.495526-08 leaderboard f A100 0.0031234573333333336 t \N \N \N {} +3095 1106 2025-02-28 02:29:26.843816-08 2025-02-28 03:12:54.70341-08 benchmark t A100 \N t \N \N \N {} +3661 1250 2025-03-01 02:05:16.755613-08 2025-03-01 01:23:08.151871-08 benchmark t A100 \N t \N \N \N {} +3662 1250 2025-03-01 01:38:13.707281-08 2025-03-01 01:47:51.283498-08 leaderboard t A100 0.0031076263333333336 t \N \N \N {} +3663 1250 2025-03-01 01:59:32.222492-08 2025-03-01 01:21:35.81262-08 test f A100 \N t \N \N \N {} +3664 1250 2025-03-01 00:54:01.041112-08 2025-03-01 01:19:58.345142-08 benchmark f A100 \N t \N \N \N {} +3892 1297 2025-03-01 03:24:01.588752-08 2025-03-01 03:18:14.954764-08 test f A100 \N t \N \N \N {} +3665 1250 2025-03-01 01:14:04.759133-08 2025-03-01 02:40:17.88009-08 leaderboard f A100 0.0031095133333333335 t \N \N \N {} +3666 1251 2025-03-01 00:49:00.146116-08 2025-03-01 01:10:29.020161-08 test t A100 \N t \N \N \N {} +3667 1251 2025-03-01 01:22:57.30705-08 2025-03-01 01:48:27.82583-08 benchmark t A100 \N t \N \N \N {} +3668 1251 2025-03-01 00:53:18.345319-08 2025-03-01 01:17:18.281427-08 leaderboard t A100 0.0031184623333333334 t \N \N \N {} +3675 1252 2025-03-01 00:51:13.234543-08 2025-03-01 01:19:47.389964-08 test t A100 \N t \N \N \N {} +3684 1254 2025-03-01 01:05:05.887393-08 2025-03-01 02:05:56.520759-08 benchmark f A100 \N t \N \N \N {} +3685 1255 2025-03-01 01:33:40.444936-08 2025-03-01 02:40:57.193542-08 benchmark f A100 \N t \N \N \N {} +3686 1256 2025-03-01 02:04:57.939143-08 2025-03-01 01:28:58.395269-08 benchmark f A100 \N t \N \N \N {} +3687 1257 2025-03-01 02:11:44.828858-08 2025-03-01 02:14:45.504175-08 benchmark f A100 \N t \N \N \N {} +3703 1263 2025-03-01 01:53:07.320683-08 2025-03-01 01:53:17.511001-08 test f A100 \N t \N \N \N {} +3688 1259 2025-03-01 02:02:24.550405-08 2025-03-01 01:42:30.841662-08 benchmark f H100 \N t \N \N \N {} +3689 1259 2025-03-01 02:51:13.657136-08 2025-03-01 01:36:15.328544-08 benchmark f A100 \N t \N \N \N {} +3695 1261 2025-03-01 02:37:45.341146-08 2025-03-01 02:45:50.374217-08 benchmark f A100 \N t \N \N \N {} +3696 1261 2025-03-01 02:43:06.297215-08 2025-03-01 02:38:52.020845-08 leaderboard f A100 0.0030843926666666664 t \N \N \N {} +1514 505 2025-02-25 06:11:31.116371-08 2025-02-25 07:53:55.571435-08 benchmark f A100 \N t \N \N \N {} +1515 505 2025-02-25 06:32:19.052576-08 2025-02-25 07:10:25.937469-08 leaderboard f A100 0.0032101916 t \N \N \N {} +3018 1080 2025-02-27 14:22:23.426466-08 2025-02-27 14:44:08.206039-08 benchmark f H100 \N f \N \N \N {} +3088 1105 2025-02-28 02:12:41.458282-08 2025-02-28 02:42:49.673656-08 test t A100 \N t \N \N \N {} +3096 1106 2025-02-28 03:02:13.970567-08 2025-02-28 02:27:13.461777-08 leaderboard t A100 0.0030811183333333335 t \N \N \N {} +3115 1109 2025-02-28 02:52:03.948052-08 2025-02-28 02:24:21.911819-08 test f A100 \N t \N \N \N {} +3705 1263 2025-03-01 02:09:53.923033-08 2025-03-01 02:59:38.816636-08 leaderboard f A100 0.0031043356666666665 t \N \N \N {} +3706 1263 2025-03-01 02:45:35.826522-08 2025-03-01 01:31:39.66555-08 test t A100 \N t \N \N \N {} +3707 1263 2025-03-01 02:23:56.866303-08 2025-03-01 02:48:25.991039-08 benchmark t A100 \N t \N \N \N {} +3708 1263 2025-03-01 02:48:53.22977-08 2025-03-01 01:38:57.844846-08 leaderboard t A100 0.0030862586666666665 t \N \N \N {} +3709 1264 2025-03-01 02:07:56.190346-08 2025-03-01 01:40:21.086921-08 test f A100 \N t \N \N \N {} +3710 1264 2025-03-01 03:21:38.843116-08 2025-03-01 01:42:57.620283-08 benchmark f A100 \N t \N \N \N {} +3711 1264 2025-03-01 01:50:57.95035-08 2025-03-01 02:14:10.580881-08 leaderboard f A100 0.003111637 t \N \N \N {} +3922 1304 2025-03-01 04:37:37.399916-08 2025-03-01 05:09:08.597247-08 test t A100 \N t \N \N \N {} +3713 1264 2025-03-01 02:34:54.769991-08 2025-03-01 02:40:30.444044-08 benchmark t A100 \N t \N \N \N {} +3714 1264 2025-03-01 03:26:13.41665-08 2025-03-01 01:31:26.999441-08 leaderboard t A100 0.003092787 t \N \N \N {} +3715 1265 2025-03-01 02:52:42.719228-08 2025-03-01 03:08:21.753062-08 test f A100 \N t \N \N \N {} +3716 1265 2025-03-01 01:34:00.130632-08 2025-03-01 01:35:33.719501-08 benchmark f A100 \N t \N \N \N {} +3717 1265 2025-03-01 03:18:58.998043-08 2025-03-01 01:56:17.857259-08 leaderboard f A100 0.00310427 t \N \N \N {} +3718 1265 2025-03-01 02:49:09.783091-08 2025-03-01 01:54:04.181859-08 test t A100 \N t \N \N \N {} +3719 1265 2025-03-01 01:35:45.872919-08 2025-03-01 01:33:30.016652-08 benchmark t A100 \N t \N \N \N {} +3720 1265 2025-03-01 03:24:17.497602-08 2025-03-01 01:42:24.245018-08 leaderboard t A100 0.0030956306666666666 t \N \N \N {} +3925 1305 2025-03-01 04:56:09.680619-08 2025-03-01 04:07:20.707758-08 test t A100 \N t \N \N \N {} +5248 1613 2025-03-06 08:34:35.577688-08 2025-03-06 08:01:22.528601-08 benchmark f H100 \N t \N \N \N {} +3726 1267 2025-03-01 02:36:57.461321-08 2025-03-01 02:35:02.941022-08 benchmark f A100 \N t \N \N \N {} +3727 1267 2025-03-01 01:38:43.401017-08 2025-03-01 02:32:47.651112-08 leaderboard f A100 0.0030772653333333336 t \N \N \N {} +3728 1268 2025-03-01 02:49:19.195623-08 2025-03-01 02:53:55.254272-08 test t A100 \N t \N \N \N {} +1517 505 2025-02-25 07:05:29.686612-08 2025-02-25 06:37:54.120803-08 benchmark t A100 \N t \N \N \N {} +1518 505 2025-02-25 07:41:30.63433-08 2025-02-25 06:35:14.486014-08 leaderboard t A100 0.0031655055 t \N \N \N {} +3019 1081 2025-02-27 14:48:37.142158-08 2025-02-27 15:06:31.119054-08 benchmark f H100 \N t \N \N \N {} +3097 1106 2025-02-28 03:19:00.154781-08 2025-02-28 02:16:02.356601-08 test f A100 \N t \N \N \N {} +3640 1244 2025-03-01 00:18:40.054434-08 2025-02-28 23:54:18.664151-08 test t A100 \N t \N \N \N {} +3740 1270 2025-03-01 02:37:04.861998-08 2025-03-01 03:28:09.585563-08 benchmark f A100 \N t \N \N \N {} +3741 1271 2025-03-01 02:44:07.033471-08 2025-03-01 03:41:23.057192-08 test f A100 \N t \N \N \N {} +3742 1271 2025-03-01 02:44:35.686645-08 2025-03-01 02:00:04.66024-08 benchmark f A100 \N t \N \N \N {} +3743 1271 2025-03-01 02:38:32.681534-08 2025-03-01 02:37:50.930025-08 leaderboard f A100 0.0030790513333333333 t \N \N \N {} +3744 1271 2025-03-01 03:21:20.979999-08 2025-03-01 02:59:00.012827-08 test t A100 \N t \N \N \N {} +3961 1321 2025-03-01 05:32:54.083384-08 2025-03-01 05:23:18.742132-08 test f A100 \N t \N \N \N {} +3745 1271 2025-03-01 03:15:49.381338-08 2025-03-01 02:53:14.040077-08 benchmark t A100 \N t \N \N \N {} +3746 1271 2025-03-01 02:06:31.769676-08 2025-03-01 01:46:51.112379-08 leaderboard t A100 0.0031014993333333333 t \N \N \N {} +3747 1272 2025-03-01 02:53:41.383476-08 2025-03-01 01:51:32.778962-08 test f A100 \N t \N \N \N {} +3748 1272 2025-03-01 02:45:32.603439-08 2025-03-01 03:10:17.870749-08 benchmark f A100 \N t \N \N \N {} +3749 1272 2025-03-01 02:08:50.626266-08 2025-03-01 02:33:18.807788-08 leaderboard f A100 0.0030842223333333333 t \N \N \N {} +3750 1272 2025-03-01 02:05:35.450725-08 2025-03-01 01:53:09.147022-08 test t A100 \N t \N \N \N {} +3751 1272 2025-03-01 02:35:41.645448-08 2025-03-01 02:31:48.954685-08 benchmark t A100 \N t \N \N \N {} +3756 1273 2025-03-01 03:40:36.388523-08 2025-03-01 02:50:54.185135-08 test t A100 \N t \N \N \N {} +3757 1273 2025-03-01 03:17:02.112839-08 2025-03-01 03:24:26.296631-08 benchmark t A100 \N t \N \N \N {} +3758 1273 2025-03-01 02:23:04.796296-08 2025-03-01 03:14:47.333595-08 leaderboard t A100 0.0031108526666666666 t \N \N \N {} +3759 1274 2025-03-01 03:30:42.935942-08 2025-03-01 02:39:37.720415-08 test f A100 \N t \N \N \N {} +3760 1274 2025-03-01 01:53:05.004026-08 2025-03-01 03:03:30.821872-08 benchmark f A100 \N t \N \N \N {} +3967 1322 2025-03-01 06:17:47.572392-08 2025-03-01 05:09:34.491694-08 test t A100 \N t \N \N \N {} +3761 1274 2025-03-01 02:33:50.346797-08 2025-03-01 02:26:36.964124-08 leaderboard f A100 0.0030782356666666667 t \N \N \N {} +3762 1274 2025-03-01 01:58:07.815646-08 2025-03-01 01:59:41.282385-08 test t A100 \N t \N \N \N {} +3763 1274 2025-03-01 03:17:32.966317-08 2025-03-01 01:57:52.417038-08 benchmark t A100 \N t \N \N \N {} +3764 1274 2025-03-01 02:01:00.831839-08 2025-03-01 02:45:44.696937-08 leaderboard t A100 0.0030974313333333335 t \N \N \N {} +3765 1275 2025-03-01 01:54:45.759236-08 2025-03-01 02:30:33.498327-08 test t A100 \N t \N \N \N {} +3766 1275 2025-03-01 01:51:25.781893-08 2025-03-01 01:50:19.627497-08 benchmark t A100 \N t \N \N \N {} +3767 1275 2025-03-01 02:55:04.68876-08 2025-03-01 03:34:18.634501-08 leaderboard t A100 0.003100908 t \N \N \N {} +3769 1275 2025-03-01 02:04:44.900517-08 2025-03-01 01:53:44.454925-08 benchmark f A100 \N t \N \N \N {} +3770 1275 2025-03-01 03:44:33.933845-08 2025-03-01 03:07:56.729501-08 leaderboard f A100 0.003100629 t \N \N \N {} +3771 1276 2025-03-01 03:45:41.230682-08 2025-03-01 03:07:19.802554-08 test f A100 \N t \N \N \N {} +3772 1276 2025-03-01 02:49:49.866518-08 2025-03-01 02:09:15.199733-08 benchmark f A100 \N t \N \N \N {} +3773 1276 2025-03-01 01:49:21.076445-08 2025-03-01 03:19:58.2239-08 leaderboard f A100 0.0030967063333333334 t \N \N \N {} +1520 506 2025-02-25 07:09:15.225758-08 2025-02-25 07:45:41.962123-08 benchmark t H100 \N t \N \N \N {} +1521 506 2025-02-25 07:34:19.048083-08 2025-02-25 07:59:41.638877-08 leaderboard t H100 0.00147709 t \N \N \N {} +3020 1082 2025-02-27 16:12:37.20378-08 2025-02-27 16:11:58.805622-08 benchmark f H100 \N t \N \N \N {} +3118 1110 2025-02-28 01:59:42.263954-08 2025-02-28 02:02:39.491672-08 test t A100 \N t \N \N \N {} +3774 1276 2025-03-01 02:29:59.337188-08 2025-03-01 03:02:44.666583-08 test t A100 \N t \N \N \N {} +5249 1613 2025-03-06 08:14:23.750809-08 2025-03-06 08:08:33.670096-08 leaderboard f H100 0.0014276973333333333 t \N \N \N {} +3781 1277 2025-03-01 03:22:11.445902-08 2025-03-01 02:55:41.41361-08 benchmark f A100 \N t \N \N \N {} +3782 1277 2025-03-01 03:39:09.245513-08 2025-03-01 03:08:32.889614-08 leaderboard f A100 0.0030874316666666666 t \N \N \N {} +3783 1278 2025-03-01 03:31:43.822781-08 2025-03-01 02:37:43.947702-08 test t A100 \N t \N \N \N {} +6787 2100 2025-03-15 05:14:40.803878-07 2025-03-15 04:12:35.487565-07 test t A100 \N t \N \N \N {} +6239 1924 2025-03-12 01:52:17.600422-07 2025-03-12 01:58:47.314377-07 test f T4 \N f \N \N \N {} +6241 1925 2025-03-12 01:51:25.617795-07 2025-03-12 00:25:45.175368-07 benchmark f T4 \N t \N \N \N {} +6242 1925 2025-03-12 00:18:20.920266-07 2025-03-12 00:37:09.552561-07 leaderboard f T4 3.623034557 t \N \N \N {} +6400 1990 2025-03-13 16:37:41.150108-07 2025-03-13 17:03:46.340373-07 test f T4 \N f \N \N \N {} +6684 2070 2025-03-14 16:12:52.115243-07 2025-03-14 15:40:16.793308-07 test t T4 \N f \N \N \N {} +3796 1280 2025-03-01 03:59:00.997836-08 2025-03-01 02:29:44.953873-08 benchmark f A100 \N t \N \N \N {} +3797 1280 2025-03-01 03:51:51.435647-08 2025-03-01 02:43:07.654041-08 leaderboard f A100 0.0030720253333333335 t \N \N \N {} +6243 1925 2025-03-12 01:00:01.616489-07 2025-03-12 00:47:35.44717-07 test t T4 \N t \N \N \N {} +6402 1991 2025-03-13 17:32:53.433867-07 2025-03-13 18:39:52.759282-07 benchmark f T4 \N t \N \N \N {} +3799 1280 2025-03-01 02:05:15.848809-08 2025-03-01 03:19:03.034027-08 benchmark t A100 \N t \N \N \N {} +3800 1280 2025-03-01 03:51:27.480737-08 2025-03-01 03:58:54.299511-08 leaderboard t A100 0.0031044756666666665 t \N \N \N {} +6246 1926 2025-03-12 01:05:44.944448-07 2025-03-12 01:36:04.272554-07 test t T4 \N t \N \N \N {} +6247 1926 2025-03-12 02:16:02.972538-07 2025-03-12 00:53:47.157218-07 benchmark t T4 \N t \N \N \N {} +6248 1926 2025-03-12 02:04:35.195202-07 2025-03-12 00:39:34.3763-07 leaderboard t T4 3.6946682543333336 t \N \N \N {} +6403 1991 2025-03-13 17:33:19.394407-07 2025-03-13 18:00:50.499601-07 leaderboard f T4 0.012992361650000001 t \N \N \N {} +6564 2035 2025-03-14 13:11:15.918389-07 2025-03-14 13:28:42.918799-07 benchmark f A100 \N t \N \N \N {} +3802 1281 2025-03-01 02:49:11.571703-08 2025-03-01 03:03:34.155129-08 benchmark f A100 \N t \N \N \N {} +3803 1281 2025-03-01 03:11:47.348485-08 2025-03-01 02:42:24.967997-08 leaderboard f A100 0.0030754246666666665 t \N \N \N {} +6249 1926 2025-03-12 02:18:23.699919-07 2025-03-12 01:38:21.807805-07 test f T4 \N t \N \N \N {} +6641 2053 2025-03-14 13:13:01.717161-07 2025-03-14 12:56:42.018712-07 test t A100 \N t \N \N \N {} +3827 1286 2025-03-01 03:21:43.151398-08 2025-03-01 04:03:44.372245-08 benchmark f A100 \N t \N \N \N {} +3828 1286 2025-03-01 02:16:32.267354-08 2025-03-01 02:27:13.261291-08 leaderboard f A100 0.0032163636666666666 t \N \N \N {} +6258 1934 2025-03-12 07:02:26.8957-07 2025-03-12 06:48:21.923788-07 benchmark f A100 \N t \N \N \N {} +6412 1994 2025-03-13 17:40:48.083573-07 2025-03-13 17:33:28.539709-07 test f T4 \N f \N \N \N {} +6573 2039 2025-03-14 11:47:52.741123-07 2025-03-14 13:13:09.01997-07 test t A100 \N t \N \N \N {} +6574 2039 2025-03-14 13:30:31.134825-07 2025-03-14 12:56:58.461643-07 benchmark t A100 \N t \N \N \N {} +6575 2039 2025-03-14 13:14:01.573829-07 2025-03-14 13:06:52.578339-07 leaderboard t A100 0.0025201923333333337 t \N \N \N {} +1523 506 2025-02-25 07:26:02.548226-08 2025-02-25 07:25:46.716931-08 benchmark f H100 \N t \N \N \N {} +1529 510 2025-02-25 06:46:53.396184-08 2025-02-25 07:55:23.310917-08 benchmark f A100 \N t \N \N \N {} +1530 511 2025-02-25 08:01:36.384337-08 2025-02-25 07:34:22.650715-08 benchmark f A100 \N t \N \N \N {} +3021 1083 2025-02-27 15:20:59.403393-08 2025-02-27 15:45:28.074308-08 test t H100 \N t \N \N \N {} +3022 1083 2025-02-27 15:38:29.43689-08 2025-02-27 15:22:38.825121-08 benchmark t H100 \N t \N \N \N {} +3023 1083 2025-02-27 16:15:56.367001-08 2025-02-27 16:18:18.215051-08 leaderboard t H100 0.0025371098599999997 t \N \N \N {} +3070 1102 2025-02-28 02:33:54.637798-08 2025-02-28 01:22:56.978086-08 test f A100 \N t \N \N \N {} +1547 512 2025-02-25 06:52:50.899054-08 2025-02-25 06:49:27.130424-08 benchmark t T4 \N t \N \N \N {} +1548 512 2025-02-25 06:32:04.498659-08 2025-02-25 07:33:16.893589-08 leaderboard t T4 0.021058454833333334 t \N \N \N {} +1549 512 2025-02-25 07:27:14.309213-08 2025-02-25 07:47:52.099855-08 test f L4 \N t \N \N \N {} +1550 512 2025-02-25 06:57:32.191599-08 2025-02-25 07:03:02.042374-08 benchmark f L4 \N t \N \N \N {} +1552 512 2025-02-25 06:58:24.333124-08 2025-02-25 06:33:18.568674-08 test t L4 \N t \N \N \N {} +1556 513 2025-02-25 06:14:45.491004-08 2025-02-25 07:16:46.376307-08 benchmark t A100 \N t \N \N \N {} +1557 513 2025-02-25 07:34:02.46493-08 2025-02-25 08:06:10.020194-08 leaderboard t A100 0.007567747333333333 t \N \N \N {} +3024 1083 2025-02-27 14:39:16.285794-08 2025-02-27 16:33:31.980742-08 test f H100 \N t \N \N \N {} +3025 1083 2025-02-27 16:31:07.646609-08 2025-02-27 14:51:20.50896-08 benchmark f H100 \N t \N \N \N {} +3026 1083 2025-02-27 14:40:27.566259-08 2025-02-27 16:02:44.891034-08 leaderboard f H100 0.00193133143 t \N \N \N {} +3071 1102 2025-02-28 01:56:21.879786-08 2025-02-28 02:17:53.253173-08 benchmark f A100 \N t \N \N \N {} +1559 513 2025-02-25 07:00:51.226524-08 2025-02-25 06:53:52.941813-08 benchmark f A100 \N t \N \N \N {} +1560 513 2025-02-25 07:40:54.286852-08 2025-02-25 07:20:37.31234-08 leaderboard f A100 0.007643499333333333 t \N \N \N {} +3027 1084 2025-02-27 14:58:06.567407-08 2025-02-27 14:38:42.214639-08 benchmark f A100 \N t \N \N \N {} +3072 1102 2025-02-28 02:51:58.714497-08 2025-02-28 02:17:05.194701-08 leaderboard f A100 0.0031002953333333335 t \N \N \N {} +3089 1105 2025-02-28 03:09:52.010667-08 2025-02-28 01:20:13.790445-08 benchmark t A100 \N t \N \N \N {} +3090 1105 2025-02-28 02:51:45.323434-08 2025-02-28 02:33:52.770692-08 leaderboard t A100 0.0030956803333333336 t \N \N \N {} +1562 514 2025-02-25 07:35:28.616691-08 2025-02-25 06:29:07.554479-08 benchmark f A100 \N t \N \N \N {} +1563 514 2025-02-25 07:01:33.808022-08 2025-02-25 06:30:57.182348-08 leaderboard f A100 0.00319349475 t \N \N \N {} +3028 1085 2025-02-27 15:14:39.199509-08 2025-02-27 15:48:14.221616-08 test f H100 \N t \N \N \N {} +3029 1085 2025-02-27 15:48:18.376186-08 2025-02-27 15:01:22.031548-08 benchmark f H100 \N t \N \N \N {} +3030 1085 2025-02-27 15:34:36.352873-08 2025-02-27 15:03:55.584707-08 leaderboard f H100 0.0018411454 t \N \N \N {} +3073 1102 2025-02-28 02:05:44.512427-08 2025-02-28 02:46:15.64099-08 test t A100 \N t \N \N \N {} +1565 514 2025-02-25 06:29:07.165341-08 2025-02-25 07:51:19.989787-08 benchmark t A100 \N t \N \N \N {} +1566 514 2025-02-25 06:29:37.966737-08 2025-02-25 06:28:42.906056-08 leaderboard t A100 0.0032033456 t \N \N \N {} +1573 516 2025-02-25 06:33:48.857537-08 2025-02-25 07:43:18.501261-08 test t A100 \N t \N \N \N {} +1574 516 2025-02-25 08:15:17.851507-08 2025-02-25 08:32:05.853279-08 benchmark t A100 \N t \N \N \N {} +1579 517 2025-02-25 06:55:30.527319-08 2025-02-25 07:43:47.516317-08 test f T4 \N t \N \N \N {} +1581 519 2025-02-25 07:22:27.620876-08 2025-02-25 07:28:11.702548-08 test t H100 \N t \N \N \N {} +1589 522 2025-02-25 08:42:40.422194-08 2025-02-25 08:02:12.607584-08 benchmark f A100 \N t \N \N \N {} +1593 526 2025-02-25 07:56:16.683261-08 2025-02-25 08:23:55.488514-08 test f A100 \N t \N \N \N {} +1594 527 2025-02-25 07:07:18.475511-08 2025-02-25 08:55:21.597339-08 benchmark f A100 \N t \N \N \N {} +1609 533 2025-02-25 08:14:37.090411-08 2025-02-25 07:43:04.159111-08 test f H100 \N t \N \N \N {} +1625 539 2025-02-25 08:06:46.284758-08 2025-02-25 09:01:35.105248-08 test t H100 \N t \N \N \N {} +6919 2156 2025-03-16 16:25:14.914949-07 2025-03-16 16:52:24.968404-07 test t T4 \N t \N \N \N {} +3830 1287 2025-03-01 02:52:44.123034-08 2025-03-01 03:04:16.432487-08 benchmark f A100 \N t \N \N \N {} +3831 1287 2025-03-01 03:18:09.205414-08 2025-03-01 02:51:41.151421-08 leaderboard f A100 0.0030734603333333337 t \N \N \N {} +6414 1995 2025-03-13 18:08:53.756809-07 2025-03-13 17:29:29.189385-07 test f T4 \N f \N \N \N {} +6579 2040 2025-03-14 12:48:20.53274-07 2025-03-14 13:09:22.939558-07 test t A100 \N t \N \N \N {} +6580 2040 2025-03-14 12:55:54.163861-07 2025-03-14 12:30:14.281856-07 benchmark t A100 \N t \N \N \N {} +6581 2040 2025-03-14 12:55:49.972531-07 2025-03-14 13:31:53.117136-07 leaderboard t A100 0.0025155676666666666 t \N \N \N {} +6664 2060 2025-03-14 13:51:59.840678-07 2025-03-14 12:26:11.365457-07 test f T4 \N f \N \N \N {} +3836 1288 2025-03-01 04:15:45.238418-08 2025-03-01 03:47:16.55003-08 benchmark f A100 \N t \N \N \N {} +3837 1288 2025-03-01 03:57:06.451424-08 2025-03-01 02:21:13.360645-08 leaderboard f A100 0.0030780183333333337 t \N \N \N {} +3840 1288 2025-03-01 02:23:11.113845-08 2025-03-01 03:57:50.503081-08 leaderboard t A100 0.0030832926666666664 t \N \N \N {} +6263 1939 2025-03-12 07:51:31.81151-07 2025-03-12 07:26:02.800143-07 benchmark f A100 \N t \N \N \N {} +6264 1939 2025-03-12 08:16:12.289193-07 2025-03-12 08:05:41.403712-07 leaderboard f A100 0.00006743858 t \N \N \N {} +6416 1996 2025-03-13 18:45:10.175348-07 2025-03-13 18:56:40.818899-07 test t T4 \N f \N \N \N {} +3842 1289 2025-03-01 04:13:05.572388-08 2025-03-01 02:54:57.38981-08 benchmark f A100 \N t \N \N \N {} +3843 1289 2025-03-01 02:45:06.634242-08 2025-03-01 03:39:15.781501-08 leaderboard f A100 0.0030902456666666665 t \N \N \N {} +6265 1939 2025-03-12 08:51:35.314623-07 2025-03-12 08:24:58.103706-07 test t A100 \N t \N \N \N {} +6266 1939 2025-03-12 07:29:33.603048-07 2025-03-12 08:44:42.162501-07 benchmark t A100 \N t \N \N \N {} +6267 1939 2025-03-12 07:09:09.345344-07 2025-03-12 07:39:08.752326-07 leaderboard t A100 0.00006758480769230768 t \N \N \N {} +3845 1289 2025-03-01 04:09:16.526703-08 2025-03-01 02:35:11.693428-08 benchmark t A100 \N t \N \N \N {} +3846 1289 2025-03-01 03:33:27.94603-08 2025-03-01 02:24:48.469479-08 leaderboard t A100 0.0030774643333333334 t \N \N \N {} +6269 1940 2025-03-12 08:08:47.902846-07 2025-03-12 07:11:33.996302-07 benchmark f H100 \N t \N \N \N {} +6270 1940 2025-03-12 07:11:39.417039-07 2025-03-12 08:15:41.878762-07 leaderboard f H100 0.00004438749 t \N \N \N {} +6418 1997 2025-03-13 18:59:25.941543-07 2025-03-13 19:24:02.914954-07 test f T4 \N f \N \N \N {} +3848 1290 2025-03-01 03:36:59.507399-08 2025-03-01 03:50:52.826622-08 benchmark f A100 \N t \N \N \N {} +3849 1290 2025-03-01 03:20:17.52254-08 2025-03-01 04:10:31.201707-08 leaderboard f A100 0.0030958916666666667 t \N \N \N {} +6587 2041 2025-03-14 12:56:09.267865-07 2025-03-14 12:56:01.714341-07 leaderboard t A100 0.0025133303333333334 t \N \N \N {} +3857 1291 2025-03-01 03:54:20.136882-08 2025-03-01 02:57:32.144781-08 benchmark f A100 \N t \N \N \N {} +1595 528 2025-02-25 07:57:34.081957-08 2025-02-25 08:46:00.770848-08 benchmark f A100 \N t \N \N \N {} +1596 529 2025-02-25 08:42:32.027812-08 2025-02-25 08:28:47.379598-08 benchmark f H100 \N t \N \N \N {} +1597 530 2025-02-25 08:13:24.437551-08 2025-02-25 08:25:42.046848-08 benchmark f H100 \N t \N \N \N {} +1598 531 2025-02-25 07:18:37.587178-08 2025-02-25 07:26:28.050318-08 test t A100 \N f \N \N \N {} +1599 531 2025-02-25 07:10:05.242952-08 2025-02-25 07:51:39.460156-08 test f A100 \N f \N \N \N {} +1600 532 2025-02-25 07:30:03.467759-08 2025-02-25 07:48:58.024839-08 test f A100 \N t \N \N \N {} +1676 550 2025-02-25 08:20:23.376176-08 2025-02-25 09:14:51.808165-08 leaderboard f H100 0.0015442018125 t \N \N \N {} +1601 532 2025-02-25 09:03:02.38871-08 2025-02-25 09:05:51.634571-08 benchmark f A100 \N t \N \N \N {} +1602 532 2025-02-25 08:03:27.143172-08 2025-02-25 08:12:43.058027-08 leaderboard f A100 0.003081489 t \N \N \N {} +1610 533 2025-02-25 08:22:53.82146-08 2025-02-25 08:19:04.111413-08 benchmark f H100 \N t \N \N \N {} +1611 533 2025-02-25 08:59:03.973218-08 2025-02-25 08:01:47.560678-08 leaderboard f H100 0.0010464732307692308 t \N \N \N {} +1612 534 2025-02-25 08:13:49.798132-08 2025-02-25 07:38:47.976643-08 benchmark f A100 \N t \N \N \N {} +1613 535 2025-02-25 07:35:30.962867-08 2025-02-25 08:49:27.767886-08 benchmark f H100 \N f \N \N \N {} +1623 539 2025-02-25 09:08:19.427448-08 2025-02-25 07:54:22.18938-08 benchmark f H100 \N t \N \N \N {} +1624 539 2025-02-25 09:12:30.335139-08 2025-02-25 09:19:28.18725-08 leaderboard f H100 0.001406501 t \N \N \N {} +1632 540 2025-02-25 08:41:53.357227-08 2025-02-25 07:46:39.377359-08 benchmark t H100 \N t \N \N \N {} +1633 540 2025-02-25 07:58:37.599753-08 2025-02-25 08:48:17.13888-08 leaderboard t H100 0.0014054696666666667 t \N \N \N {} +1635 542 2025-02-25 07:55:32.669761-08 2025-02-25 08:25:28.836254-08 test f A100 \N t \N \N \N {} +1636 542 2025-02-25 07:43:32.658836-08 2025-02-25 08:54:50.899175-08 benchmark f A100 \N t \N \N \N {} +1637 542 2025-02-25 09:16:00.130562-08 2025-02-25 08:20:39.142553-08 leaderboard f A100 0.003090287 t \N \N \N {} +1638 542 2025-02-25 07:35:49.713063-08 2025-02-25 08:24:45.44148-08 test t A100 \N t \N \N \N {} +2773 966 2025-02-26 20:06:46.265649-08 2025-02-26 20:37:56.859709-08 test f A100 \N t \N \N \N {} +6281 1943 2025-03-12 08:27:24.754837-07 2025-03-12 09:00:16.045266-07 test t L4 \N t \N \N \N {} +6282 1943 2025-03-12 09:15:13.475839-07 2025-03-12 08:12:28.990502-07 benchmark t L4 \N t \N \N \N {} +6283 1943 2025-03-12 07:28:57.790932-07 2025-03-12 07:49:47.805831-07 leaderboard t L4 0.00009934762 t \N \N \N {} +6665 2060 2025-03-14 14:00:15.697848-07 2025-03-14 13:40:02.788256-07 test t T4 \N f \N \N \N {} +3863 1292 2025-03-01 04:08:38.155179-08 2025-03-01 03:06:47.8337-08 benchmark t A100 \N t \N \N \N {} +6287 1944 2025-03-12 10:01:22.337882-07 2025-03-12 10:02:16.243525-07 test f T4 \N t \N \N \N {} +6288 1944 2025-03-12 10:35:51.44506-07 2025-03-12 10:53:31.762253-07 benchmark f T4 \N t \N \N \N {} +6289 1944 2025-03-12 11:15:49.75933-07 2025-03-12 10:30:20.890743-07 leaderboard f T4 3.5460847016666666 t \N \N \N {} +6421 1999 2025-03-14 05:54:56.594885-07 2025-03-14 05:07:15.151352-07 benchmark f A100 \N t \N \N \N {} +6422 1999 2025-03-14 04:48:17.257143-07 2025-03-14 04:38:30.49237-07 leaderboard f A100 0.002516429 t \N \N \N {} +6666 2061 2025-03-14 15:01:59.053891-07 2025-03-14 14:18:38.868311-07 test t T4 \N f \N \N \N {} +6291 1944 2025-03-12 09:57:38.536736-07 2025-03-12 10:33:16.849388-07 benchmark t T4 \N t \N \N \N {} +1639 542 2025-02-25 09:00:22.32772-08 2025-02-25 07:59:32.226862-08 benchmark t A100 \N t \N \N \N {} +1640 542 2025-02-25 09:11:58.980108-08 2025-02-25 08:19:49.179574-08 leaderboard t A100 0.0030916486666666665 t \N \N \N {} +1641 543 2025-02-25 07:54:29.239831-08 2025-02-25 09:35:38.991324-08 test t A100 \N t \N \N \N {} +1646 543 2025-02-25 08:14:16.552698-08 2025-02-25 08:35:18.8746-08 leaderboard f A100 0.0030903186666666666 t \N \N \N {} +1656 545 2025-02-25 08:18:12.922307-08 2025-02-25 09:29:21.123215-08 leaderboard t A100 0.0031894605 t \N \N \N {} +1657 546 2025-02-25 08:26:28.000804-08 2025-02-25 09:50:00.709982-08 test t A100 \N t \N \N \N {} +1658 546 2025-02-25 09:48:18.886362-08 2025-02-25 09:10:41.999421-08 benchmark t A100 \N t \N \N \N {} +1659 546 2025-02-25 07:55:49.954375-08 2025-02-25 09:05:11.341596-08 leaderboard t A100 0.00063344525 t \N \N \N {} +1660 546 2025-02-25 08:01:12.970128-08 2025-02-25 08:24:15.501382-08 test f A100 \N t \N \N \N {} +1661 546 2025-02-25 09:18:23.952092-08 2025-02-25 08:15:13.029454-08 benchmark f A100 \N t \N \N \N {} +6292 1944 2025-03-12 10:58:18.817212-07 2025-03-12 10:46:59.456595-07 leaderboard t T4 3.53122528 t \N \N \N {} +1662 546 2025-02-25 08:35:51.067428-08 2025-02-25 08:31:39.935762-08 leaderboard f A100 0.0005320072 t \N \N \N {} +1663 547 2025-02-25 09:32:55.917437-08 2025-02-25 08:44:26.311971-08 benchmark f H100 \N f \N \N \N {} +1684 557 2025-02-25 09:44:40.003266-08 2025-02-25 08:13:24.412498-08 benchmark f A100 \N f \N \N \N {} +1664 548 2025-02-25 08:06:02.985208-08 2025-02-25 08:56:36.303961-08 benchmark f H100 \N t \N \N \N {} +1666 549 2025-02-25 09:38:40.901485-08 2025-02-25 09:32:01.788835-08 benchmark t H100 \N t \N \N \N {} +1667 549 2025-02-25 08:26:34.238735-08 2025-02-25 08:25:38.843118-08 leaderboard t H100 0.000413247 t \N \N \N {} +1668 549 2025-02-25 09:52:24.604979-08 2025-02-25 08:02:29.9005-08 test f H100 \N t \N \N \N {} +1685 557 2025-02-25 09:19:53.854754-08 2025-02-25 08:46:52.219004-08 test t A100 \N t \N \N \N {} +1669 549 2025-02-25 08:53:05.362645-08 2025-02-25 08:47:47.898667-08 benchmark f H100 \N t \N \N \N {} +1670 549 2025-02-25 08:59:06.217181-08 2025-02-25 09:09:07.897493-08 leaderboard f H100 0.0004161602 t \N \N \N {} +6423 1999 2025-03-14 04:42:51.926225-07 2025-03-14 04:23:11.348669-07 test t A100 \N t \N \N \N {} +1678 553 2025-02-25 10:02:18.136454-08 2025-02-25 08:09:06.800521-08 test f H100 \N f \N \N \N {} +1682 556 2025-02-25 08:53:34.265116-08 2025-02-25 08:39:01.371239-08 benchmark f A100 \N t \N \N \N {} +1686 557 2025-02-25 08:35:22.195064-08 2025-02-25 09:08:11.056354-08 benchmark t A100 \N f \N \N \N {} +6424 1999 2025-03-14 05:09:18.158024-07 2025-03-14 04:42:27.024761-07 benchmark t A100 \N t \N \N \N {} +6667 2061 2025-03-14 15:25:37.443228-07 2025-03-14 15:06:32.582085-07 test f T4 \N f \N \N \N {} +6643 2053 2025-03-14 13:12:40.43597-07 2025-03-14 13:44:06.267317-07 leaderboard t A100 0.0024720393333333337 t \N \N \N {} +6668 2062 2025-03-14 14:17:49.224385-07 2025-03-14 14:26:32.460019-07 test t T4 \N f \N \N \N {} +3876 1294 2025-03-01 04:14:10.442655-08 2025-03-01 02:49:35.269118-08 leaderboard t A100 0.0030766496666666666 t \N \N \N {} +6294 1945 2025-03-12 10:57:16.296692-07 2025-03-12 11:33:24.443736-07 test f T4 \N f \N \N \N {} +6426 2000 2025-03-14 06:30:53.7156-07 2025-03-14 06:12:43.209048-07 test f H100 \N t \N \N \N {} +6427 2000 2025-03-14 06:57:59.87542-07 2025-03-14 06:15:38.776251-07 benchmark f H100 \N t \N \N \N {} +6428 2000 2025-03-14 07:35:27.590998-07 2025-03-14 06:32:04.615938-07 leaderboard f H100 0.0014073136666666668 t \N \N \N {} +6591 2042 2025-03-14 11:50:38.099796-07 2025-03-14 12:40:25.372521-07 test f A100 \N t \N \N \N {} +6644 2053 2025-03-14 13:49:44.146284-07 2025-03-14 12:26:28.495965-07 test f A100 \N t \N \N \N {} +6669 2062 2025-03-14 16:02:16.728023-07 2025-03-14 14:51:42.161638-07 test f T4 \N f \N \N \N {} +6436 2001 2025-03-14 06:50:57.915641-07 2025-03-14 07:00:20.008226-07 benchmark t A100 \N t \N \N \N {} +6437 2001 2025-03-14 07:44:58.201024-07 2025-03-14 07:24:40.594633-07 leaderboard t A100 0.002543368 t \N \N \N {} +3887 1296 2025-03-01 04:21:55.785115-08 2025-03-01 04:09:44.553825-08 benchmark f A100 \N t \N \N \N {} +3888 1296 2025-03-01 03:39:58.6244-08 2025-03-01 03:09:40.552669-08 leaderboard f A100 0.00309473 t \N \N \N {} +6299 1948 2025-03-12 10:32:56.767467-07 2025-03-12 11:22:11.851694-07 test t T4 \N t \N \N \N {} +6300 1948 2025-03-12 10:31:11.093009-07 2025-03-12 11:06:33.740742-07 benchmark t T4 \N f \N \N \N {} +6438 2002 2025-03-14 07:39:39.918952-07 2025-03-14 07:42:19.454653-07 test t A100 \N t \N \N \N {} +3891 1297 2025-03-01 04:29:07.565859-08 2025-03-01 03:39:00.355245-08 leaderboard t A100 0.0030872086666666665 t \N \N \N {} +6301 1949 2025-03-12 11:11:45.381428-07 2025-03-12 11:41:44.803347-07 test f T4 \N t \N \N \N {} +6302 1949 2025-03-12 11:19:11.369574-07 2025-03-12 11:47:00.702215-07 benchmark f T4 \N t \N \N \N {} +6303 1949 2025-03-12 11:08:31.94301-07 2025-03-12 11:22:31.160474-07 leaderboard f T4 5.264521755 t \N \N \N {} +3894 1297 2025-03-01 03:08:30.539157-08 2025-03-01 04:20:39.317282-08 leaderboard f A100 0.003077314 t \N \N \N {} +3895 1298 2025-03-01 03:26:56.396066-08 2025-03-01 04:02:10.736561-08 benchmark f A100 \N t \N \N \N {} +3896 1298 2025-03-01 04:30:06.792705-08 2025-03-01 05:08:52.916106-08 benchmark f H100 \N t \N \N \N {} +3897 1299 2025-03-01 03:32:41.876039-08 2025-03-01 03:28:04.603739-08 benchmark f H100 \N t \N \N \N {} +3902 1301 2025-03-01 03:45:08.15341-08 2025-03-01 03:30:46.472923-08 benchmark t A100 \N t \N \N \N {} +3903 1301 2025-03-01 05:18:11.482755-08 2025-03-01 04:15:04.738792-08 leaderboard t A100 0.0017082903333333333 t \N \N \N {} +3904 1301 2025-03-01 05:20:05.351706-08 2025-03-01 04:57:15.691666-08 test f H100 \N t \N \N \N {} +3905 1301 2025-03-01 04:11:22.209627-08 2025-03-01 03:29:30.979359-08 benchmark f H100 \N t \N \N \N {} +3909 1301 2025-03-01 05:05:05.368527-08 2025-03-01 05:21:07.368377-08 leaderboard t H100 0.0009410016 t \N \N \N {} +3911 1302 2025-03-01 04:15:21.468517-08 2025-03-01 04:16:28.01241-08 benchmark f A100 \N t \N \N \N {} +3912 1302 2025-03-01 05:15:06.388507-08 2025-03-01 05:04:17.52923-08 benchmark f H100 \N f \N \N \N {} +3914 1303 2025-03-01 03:58:49.506515-08 2025-03-01 05:03:02.215649-08 benchmark t A100 \N t \N \N \N {} +3915 1303 2025-03-01 05:02:46.480424-08 2025-03-01 05:10:24.739066-08 leaderboard t A100 0.0017033548333333334 t \N \N \N {} +3916 1303 2025-03-01 03:49:31.466637-08 2025-03-01 04:14:50.882192-08 test f A100 \N t \N \N \N {} +3917 1303 2025-03-01 04:37:55.798922-08 2025-03-01 05:09:32.972724-08 benchmark f A100 \N t \N \N \N {} +3918 1303 2025-03-01 04:24:18.36739-08 2025-03-01 04:58:17.307429-08 leaderboard f A100 0.0017008582 t \N \N \N {} +6304 1949 2025-03-12 10:47:37.368806-07 2025-03-12 12:09:25.453957-07 test t T4 \N t \N \N \N {} +1687 558 2025-02-25 09:27:10.084906-08 2025-02-25 09:48:43.848525-08 test f A100 \N f \N \N \N {} +1688 558 2025-02-25 08:29:44.125579-08 2025-02-25 08:24:09.285219-08 test t A100 \N f \N \N \N {} +1689 559 2025-02-25 09:17:25.101881-08 2025-02-25 09:24:03.785066-08 test f A100 \N t \N \N \N {} +1690 559 2025-02-25 08:36:17.748573-08 2025-02-25 09:33:30.838585-08 benchmark f A100 \N f \N \N \N {} +1691 559 2025-02-25 08:55:41.256091-08 2025-02-25 08:48:26.806972-08 test t A100 \N t \N \N \N {} +1692 559 2025-02-25 10:04:14.443146-08 2025-02-25 08:43:41.839368-08 benchmark t A100 \N f \N \N \N {} +1694 561 2025-02-25 10:05:36.595463-08 2025-02-25 10:09:40.331096-08 test f A100 \N t \N \N \N {} +3567 1211 2025-02-28 11:38:42.437274-08 2025-02-28 11:48:25.289361-08 leaderboard f H100 0.0011942561428571429 t \N \N \N {} +1696 562 2025-02-25 09:44:07.680498-08 2025-02-25 09:28:05.067515-08 test f A100 \N t \N \N \N {} +1697 562 2025-02-25 09:28:26.771379-08 2025-02-25 09:08:24.955188-08 benchmark f A100 \N t \N \N \N {} +1698 562 2025-02-25 08:47:34.033962-08 2025-02-25 08:35:38.541602-08 leaderboard f A100 0.023603107 t \N \N \N {} +1699 562 2025-02-25 09:50:30.387686-08 2025-02-25 10:01:59.310649-08 test t A100 \N t \N \N \N {} +1700 562 2025-02-25 08:34:58.738598-08 2025-02-25 08:38:55.902288-08 benchmark t A100 \N t \N \N \N {} +1701 562 2025-02-25 09:37:56.413765-08 2025-02-25 10:02:25.234169-08 leaderboard t A100 0.02387513042857143 t \N \N \N {} +6305 1949 2025-03-12 11:11:14.460487-07 2025-03-12 11:31:45.219851-07 benchmark t T4 \N t \N \N \N {} +6306 1949 2025-03-12 10:28:32.79482-07 2025-03-12 10:15:38.766761-07 leaderboard t T4 5.288479138666667 t \N \N \N {} +6443 2002 2025-03-14 07:34:37.078718-07 2025-03-14 07:14:50.86738-07 leaderboard f A100 0.0025309973333333337 t \N \N \N {} +6595 2043 2025-03-14 11:56:33.434461-07 2025-03-14 12:42:20.017678-07 test t A100 \N f \N \N \N {} +6685 2070 2025-03-14 15:59:15.307918-07 2025-03-14 15:02:13.119994-07 test f T4 \N f \N \N \N {} +3962 1321 2025-03-01 05:24:43.049186-08 2025-03-01 05:35:56.69-08 benchmark f A100 \N t \N \N \N {} +3963 1321 2025-03-01 06:08:58.229359-08 2025-03-01 05:22:31.386839-08 leaderboard f A100 0.0030768063333333337 t \N \N \N {} +6310 1951 2025-03-12 12:07:14.331628-07 2025-03-12 12:08:49.086672-07 test f T4 \N f \N \N \N {} +6453 2004 2025-03-14 06:42:37.075587-07 2025-03-14 07:33:35.882343-07 test t A100 \N t \N \N \N {} +6454 2004 2025-03-14 06:29:13.454127-07 2025-03-14 07:27:34.849727-07 benchmark t A100 \N t \N \N \N {} +6455 2004 2025-03-14 07:29:30.832561-07 2025-03-14 06:24:40.619546-07 leaderboard t A100 0.0025143146666666664 t \N \N \N {} +3965 1321 2025-03-01 05:11:28.825751-08 2025-03-01 04:35:18.89017-08 benchmark t A100 \N t \N \N \N {} +3966 1321 2025-03-01 06:16:12.443097-08 2025-03-01 04:37:00.745753-08 leaderboard t A100 0.0030776773333333336 t \N \N \N {} +6456 2005 2025-03-14 07:23:16.64199-07 2025-03-14 07:01:50.750143-07 test f A100 \N t \N \N \N {} +6457 2005 2025-03-14 06:22:29.76973-07 2025-03-14 06:09:06.773695-07 benchmark f A100 \N t \N \N \N {} +1703 565 2025-02-25 08:37:05.35978-08 2025-02-25 08:21:52.880091-08 test t A100 \N f \N \N \N {} +1713 572 2025-02-25 10:08:35.53301-08 2025-02-25 09:47:59.416619-08 benchmark f A100 \N t \N \N \N {} +1714 574 2025-02-25 08:57:21.205944-08 2025-02-25 08:58:46.708203-08 test f T4 \N f \N \N \N {} +1715 574 2025-02-25 08:22:30.782939-08 2025-02-25 09:22:33.716306-08 test t T4 \N f \N \N \N {} +1716 577 2025-02-25 09:25:17.463083-08 2025-02-25 09:32:17.15906-08 test t T4 \N f \N \N \N {} +1717 577 2025-02-25 08:30:00.171838-08 2025-02-25 09:40:29.944237-08 test f T4 \N f \N \N \N {} +1719 576 2025-02-25 10:16:42.240136-08 2025-02-25 09:11:50.029271-08 test f A100 \N t \N \N \N {} +3983 1326 2025-03-01 05:25:23.898128-08 2025-03-01 04:57:04.509129-08 test t A100 \N t \N \N \N {} +3992 1327 2025-03-01 05:54:28.752918-08 2025-03-01 05:57:31.677252-08 test t A100 \N t \N \N \N {} +3984 1326 2025-03-01 06:08:48.324057-08 2025-03-01 05:49:30.098232-08 benchmark t A100 \N t \N \N \N {} +3985 1326 2025-03-01 05:42:14.249969-08 2025-03-01 04:34:37.024307-08 leaderboard t A100 0.0030766156666666663 t \N \N \N {} +3986 1326 2025-03-01 04:55:11.733421-08 2025-03-01 05:15:01.56334-08 test f A100 \N t \N \N \N {} +3987 1326 2025-03-01 04:56:41.549319-08 2025-03-01 06:23:31.903277-08 benchmark f A100 \N t \N \N \N {} +3988 1326 2025-03-01 06:05:23.530527-08 2025-03-01 05:11:56.630856-08 leaderboard f A100 0.003088671 t \N \N \N {} +3991 1327 2025-03-01 05:48:54.261383-08 2025-03-01 06:04:01.141671-08 leaderboard f A100 0.003079475 t \N \N \N {} +3993 1327 2025-03-01 06:15:07.619494-08 2025-03-01 05:26:34.114027-08 benchmark t A100 \N t \N \N \N {} +3994 1327 2025-03-01 05:16:38.809307-08 2025-03-01 05:49:08.846786-08 leaderboard t A100 0.0031005003333333335 t \N \N \N {} +3995 1328 2025-03-01 04:33:03.029344-08 2025-03-01 04:45:00.030881-08 test f A100 \N t \N \N \N {} +3996 1328 2025-03-01 05:09:28.825437-08 2025-03-01 06:19:26.3327-08 benchmark f A100 \N t \N \N \N {} +1720 576 2025-02-25 08:55:00.326542-08 2025-02-25 10:00:29.575335-08 benchmark f A100 \N t \N \N \N {} +1721 576 2025-02-25 08:21:46.705866-08 2025-02-25 10:00:43.93478-08 leaderboard f A100 0.00010041933333333332 t \N \N \N {} +1722 576 2025-02-25 10:17:49.927789-08 2025-02-25 08:37:55.11806-08 test t A100 \N t \N \N \N {} +1723 576 2025-02-25 09:33:22.088092-08 2025-02-25 09:24:57.641368-08 benchmark t A100 \N t \N \N \N {} +1724 576 2025-02-25 08:39:15.730368-08 2025-02-25 08:39:06.879053-08 leaderboard t A100 0.00009493443478260869 t \N \N \N {} +3997 1328 2025-03-01 05:05:03.87852-08 2025-03-01 05:17:53.273059-08 leaderboard f A100 0.0030810756666666667 t \N \N \N {} +4001 1329 2025-03-01 05:01:28.939311-08 2025-03-01 05:15:07.131981-08 test t A100 \N t \N \N \N {} +4002 1329 2025-03-01 06:12:19.11185-08 2025-03-01 06:29:57.519732-08 benchmark t A100 \N t \N \N \N {} +4003 1329 2025-03-01 05:41:46.640503-08 2025-03-01 05:30:00.149015-08 leaderboard t A100 0.003084419 t \N \N \N {} +4004 1329 2025-03-01 04:37:14.969684-08 2025-03-01 05:59:19.610966-08 test f A100 \N t \N \N \N {} +4005 1329 2025-03-01 05:09:39.146484-08 2025-03-01 05:15:15.340274-08 benchmark f A100 \N t \N \N \N {} +4022 1332 2025-03-01 06:29:41.552466-08 2025-03-01 06:00:54.805207-08 test t A100 \N t \N \N \N {} +2084 702 2025-02-25 14:51:18.373776-08 2025-02-25 14:07:09.597234-08 test t T4 \N t \N \N \N {} +1728 582 2025-02-25 09:02:10.715863-08 2025-02-25 08:32:41.855511-08 test f T4 \N t \N \N \N {} +1729 582 2025-02-25 10:08:04.120077-08 2025-02-25 09:31:28.613967-08 benchmark f T4 \N f \N \N \N {} +1730 582 2025-02-25 09:12:24.120001-08 2025-02-25 08:35:23.070755-08 test t T4 \N t \N \N \N {} +1731 582 2025-02-25 08:45:58.419751-08 2025-02-25 09:50:28.503841-08 benchmark t T4 \N f \N \N \N {} +1732 581 2025-02-25 08:57:10.342617-08 2025-02-25 09:35:43.172418-08 test f T4 \N f \N \N \N {} +4023 1332 2025-03-01 05:59:07.048515-08 2025-03-01 05:19:06.578904-08 benchmark t A100 \N t \N \N \N {} +4024 1332 2025-03-01 06:30:59.985589-08 2025-03-01 05:19:38.789542-08 leaderboard t A100 0.0032329573333333334 t \N \N \N {} +4026 1333 2025-03-01 04:39:47.738838-08 2025-03-01 06:18:45.96386-08 benchmark f A100 \N t \N \N \N {} +4027 1333 2025-03-01 06:14:49.837768-08 2025-03-01 04:40:08.352975-08 leaderboard f A100 0.003070329 t \N \N \N {} +4028 1333 2025-03-01 05:10:32.553371-08 2025-03-01 04:42:50.943725-08 test t A100 \N t \N \N \N {} +4038 1335 2025-03-01 04:42:30.486974-08 2025-03-01 06:10:26.032842-08 test f A100 \N f \N \N \N {} +4039 1336 2025-03-01 06:21:06.572554-08 2025-03-01 06:39:44.644728-08 test t A100 \N t \N \N \N {} +4040 1336 2025-03-01 05:35:17.533391-08 2025-03-01 05:00:53.904121-08 benchmark t A100 \N t \N \N \N {} +4041 1336 2025-03-01 04:42:43.164597-08 2025-03-01 05:32:09.887961-08 leaderboard t A100 0.003087946 t \N \N \N {} +4043 1336 2025-03-01 05:22:32.085041-08 2025-03-01 05:29:36.766261-08 benchmark f A100 \N t \N \N \N {} +4044 1336 2025-03-01 04:46:43.10424-08 2025-03-01 05:19:36.970936-08 leaderboard f A100 0.0030770363333333336 t \N \N \N {} +4047 1337 2025-03-01 06:26:58.612027-08 2025-03-01 05:02:55.827739-08 leaderboard t A100 0.0030799923333333333 t \N \N \N {} +4048 1337 2025-03-01 04:51:49.552059-08 2025-03-01 05:32:32.010537-08 test f A100 \N t \N \N \N {} +4049 1337 2025-03-01 06:28:22.821883-08 2025-03-01 05:36:52.041982-08 benchmark f A100 \N t \N \N \N {} +4050 1337 2025-03-01 06:44:28.006933-08 2025-03-01 06:34:44.60449-08 leaderboard f A100 0.0030728746666666665 t \N \N \N {} +1733 581 2025-02-25 09:41:55.102032-08 2025-02-25 09:45:47.156909-08 test t T4 \N f \N \N \N {} +1739 583 2025-02-25 08:29:33.716534-08 2025-02-25 09:20:58.544378-08 benchmark f H100 \N t \N \N \N {} +1740 583 2025-02-25 09:22:37.035257-08 2025-02-25 08:36:17.972523-08 leaderboard f H100 0.00007956952 t \N \N \N {} +1747 587 2025-02-25 10:20:06.796411-08 2025-02-25 09:35:43.20184-08 test f T4 \N f \N \N \N {} +1750 589 2025-02-25 09:17:20.905239-08 2025-02-25 08:39:09.677788-08 test f A100 \N f \N \N \N {} +1754 593 2025-02-25 10:35:01.237155-08 2025-02-25 09:42:17.876251-08 benchmark f H100 \N f \N \N \N {} +2232 731 2025-02-25 20:55:56.356497-08 2025-02-25 21:54:01.459326-08 test f H100 \N f \N \N \N {} +1752 590 2025-02-25 09:26:44.50262-08 2025-02-25 09:02:57.304272-08 test f T4 \N t \N \N \N {} +1753 592 2025-02-25 10:16:57.848423-08 2025-02-25 09:02:46.758153-08 benchmark f H100 \N f \N \N \N {} +1755 594 2025-02-25 09:52:50.948573-08 2025-02-25 09:17:35.552434-08 test t T4 \N t \N \N \N {} +4051 1338 2025-03-01 04:53:46.779577-08 2025-03-01 05:51:05.617144-08 test t A100 \N t \N \N \N {} +4052 1338 2025-03-01 05:14:44.787303-08 2025-03-01 05:10:58.544283-08 benchmark t A100 \N t \N \N \N {} +4056 1338 2025-03-01 06:06:59.390985-08 2025-03-01 05:45:01.431744-08 leaderboard f A100 0.003124134 t \N \N \N {} +4057 1339 2025-03-01 05:45:53.840411-08 2025-03-01 04:57:01.712759-08 test f A100 \N t \N \N \N {} +4058 1339 2025-03-01 05:12:08.845443-08 2025-03-01 06:00:32.90691-08 benchmark f A100 \N t \N \N \N {} +4059 1339 2025-03-01 06:17:04.287969-08 2025-03-01 06:40:13.98261-08 leaderboard f A100 0.0030740783333333336 t \N \N \N {} +4060 1339 2025-03-01 05:30:41.282216-08 2025-03-01 06:19:05.26954-08 test t A100 \N t \N \N \N {} +4826 1490 2025-03-02 13:31:40.085228-08 2025-03-02 14:56:41.325187-08 test t A100 \N t \N \N \N {} +4069 1341 2025-03-01 06:46:40.043845-08 2025-03-01 05:08:54.482442-08 test t A100 \N t \N \N \N {} +4070 1341 2025-03-01 06:53:39.351776-08 2025-03-01 05:56:37.814647-08 benchmark t A100 \N t \N \N \N {} +4071 1341 2025-03-01 05:41:03.872073-08 2025-03-01 04:57:16.263647-08 leaderboard t A100 0.003135153 t \N \N \N {} +4072 1341 2025-03-01 06:36:12.592251-08 2025-03-01 05:33:09.049244-08 test f A100 \N t \N \N \N {} +4073 1341 2025-03-01 04:57:36.121576-08 2025-03-01 06:04:24.508424-08 benchmark f A100 \N t \N \N \N {} +4074 1341 2025-03-01 06:20:16.915193-08 2025-03-01 05:10:41.943093-08 leaderboard f A100 0.0031289636666666665 t \N \N \N {} +4079 1342 2025-03-01 06:28:17.155594-08 2025-03-01 05:18:07.512479-08 benchmark t A100 \N t \N \N \N {} +4080 1342 2025-03-01 05:17:17.167143-08 2025-03-01 06:19:18.451357-08 leaderboard t A100 0.003087757 t \N \N \N {} +1762 596 2025-02-25 09:48:18.872842-08 2025-02-25 09:08:03.874583-08 benchmark f H100 \N f \N \N \N {} +1763 596 2025-02-25 09:26:41.784533-08 2025-02-25 08:49:26.718913-08 benchmark f A100 \N f \N \N \N {} +1765 598 2025-02-25 10:31:00.584025-08 2025-02-25 09:31:07.814689-08 test f T4 \N f \N \N \N {} +4081 1343 2025-03-01 05:02:29.799238-08 2025-03-01 05:20:01.319572-08 test t A100 \N t \N \N \N {} +4082 1343 2025-03-01 06:56:56.021121-08 2025-03-01 05:37:48.321713-08 benchmark t A100 \N t \N \N \N {} +4083 1343 2025-03-01 05:51:20.985976-08 2025-03-01 06:10:38.11579-08 leaderboard t A100 0.003137047 t \N \N \N {} +4087 1344 2025-03-01 06:50:56.204942-08 2025-03-01 06:57:42.428539-08 test f A100 \N t \N \N \N {} +4088 1344 2025-03-01 05:59:31.101205-08 2025-03-01 05:10:00.245441-08 benchmark f A100 \N t \N \N \N {} +4089 1344 2025-03-01 06:31:19.255093-08 2025-03-01 05:37:19.624461-08 leaderboard f A100 0.0031286543333333334 t \N \N \N {} +4090 1344 2025-03-01 05:39:26.723837-08 2025-03-01 05:18:13.835391-08 test t A100 \N t \N \N \N {} +4091 1344 2025-03-01 05:16:22.102322-08 2025-03-01 05:57:13.821456-08 benchmark t A100 \N t \N \N \N {} +4092 1344 2025-03-01 06:48:00.846042-08 2025-03-01 06:36:07.876646-08 leaderboard t A100 0.0031320843333333334 t \N \N \N {} +4095 1346 2025-03-01 06:54:50.741501-08 2025-03-01 06:37:05.660285-08 test f A100 \N t \N \N \N {} +4096 1346 2025-03-01 06:43:56.667076-08 2025-03-01 05:03:01.487169-08 benchmark f A100 \N t \N \N \N {} +4097 1346 2025-03-01 06:20:12.926762-08 2025-03-01 05:15:59.317062-08 leaderboard f A100 0.0030682336666666664 t \N \N \N {} +4098 1346 2025-03-01 06:31:50.959915-08 2025-03-01 06:24:17.817323-08 test t A100 \N t \N \N \N {} +4099 1346 2025-03-01 05:34:30.160891-08 2025-03-01 05:36:09.807594-08 benchmark t A100 \N t \N \N \N {} +4100 1346 2025-03-01 06:40:10.997004-08 2025-03-01 06:45:08.049407-08 leaderboard t A100 0.003080214 t \N \N \N {} +4103 1348 2025-03-01 06:49:47.231825-08 2025-03-01 05:57:18.775954-08 test f A100 \N t \N \N \N {} +4104 1348 2025-03-01 06:30:42.302161-08 2025-03-01 06:25:51.821249-08 benchmark f A100 \N t \N \N \N {} +4105 1348 2025-03-01 05:19:35.876581-08 2025-03-01 05:20:21.682221-08 leaderboard f A100 0.0030814403333333336 t \N \N \N {} +4106 1348 2025-03-01 06:43:59.230765-08 2025-03-01 06:48:14.067513-08 test t A100 \N t \N \N \N {} +4107 1348 2025-03-01 06:33:05.642007-08 2025-03-01 05:34:35.401784-08 benchmark t A100 \N t \N \N \N {} +4108 1348 2025-03-01 06:33:53.834077-08 2025-03-01 06:05:52.452967-08 leaderboard t A100 0.0030692503333333335 t \N \N \N {} +4112 1349 2025-03-01 05:20:23.470116-08 2025-03-01 06:42:43.607444-08 test f A100 \N t \N \N \N {} +4113 1349 2025-03-01 06:18:41.622091-08 2025-03-01 06:59:47.322159-08 benchmark f A100 \N t \N \N \N {} +4114 1349 2025-03-01 06:55:36.386367-08 2025-03-01 06:13:36.564538-08 leaderboard f A100 0.0030793546666666666 t \N \N \N {} +4115 1350 2025-03-01 06:47:22.861306-08 2025-03-01 06:36:04.525113-08 test f A100 \N t \N \N \N {} +4116 1350 2025-03-01 05:51:42.091206-08 2025-03-01 06:44:09.234411-08 benchmark f A100 \N t \N \N \N {} +4117 1350 2025-03-01 07:00:25.183656-08 2025-03-01 06:56:10.192153-08 leaderboard f A100 0.003075028 t \N \N \N {} +4121 1351 2025-03-01 07:03:16.315538-08 2025-03-01 05:35:39.265352-08 test f A100 \N t \N \N \N {} +4122 1351 2025-03-01 06:26:56.34005-08 2025-03-01 06:15:23.856925-08 benchmark f A100 \N t \N \N \N {} +4123 1351 2025-03-01 06:27:06.070522-08 2025-03-01 05:20:19.58787-08 leaderboard f A100 0.003077125 t \N \N \N {} +4124 1351 2025-03-01 06:56:18.609617-08 2025-03-01 06:45:35.932387-08 test t A100 \N t \N \N \N {} +4125 1351 2025-03-01 05:59:31.787912-08 2025-03-01 05:12:15.522556-08 benchmark t A100 \N t \N \N \N {} +4127 1352 2025-03-01 06:15:58.415656-08 2025-03-01 05:15:59.770051-08 test t A100 \N t \N \N \N {} +4128 1352 2025-03-01 05:38:07.3835-08 2025-03-01 05:42:40.878001-08 benchmark t A100 \N t \N \N \N {} +4129 1352 2025-03-01 06:36:57.941443-08 2025-03-01 06:23:59.790542-08 leaderboard t A100 0.0030807613333333336 t \N \N \N {} +4130 1352 2025-03-01 06:43:58.284545-08 2025-03-01 05:17:08.992478-08 test f A100 \N t \N \N \N {} +4131 1352 2025-03-01 05:18:15.942457-08 2025-03-01 06:36:47.992588-08 benchmark f A100 \N t \N \N \N {} +4132 1352 2025-03-01 05:45:57.090043-08 2025-03-01 05:13:09.294944-08 leaderboard f A100 0.003084765 t \N \N \N {} +4133 1353 2025-03-01 05:45:21.57909-08 2025-03-01 05:50:08.24199-08 test t A100 \N t \N \N \N {} +4142 1354 2025-03-01 05:19:50.397498-08 2025-03-01 06:24:38.013703-08 test t A100 \N t \N \N \N {} +1767 600 2025-02-25 09:57:39.280293-08 2025-02-25 09:01:18.92218-08 benchmark f H100 \N t \N \N \N {} +1769 602 2025-02-25 09:26:39.998077-08 2025-02-25 10:45:34.040607-08 benchmark f H100 \N t \N \N \N {} +1770 603 2025-02-25 09:14:10.5262-08 2025-02-25 10:34:36.545027-08 benchmark f H100 \N t \N \N \N {} +1771 604 2025-02-25 09:18:20.318954-08 2025-02-25 09:47:55.570928-08 test t H100 \N t \N \N \N {} +4141 1354 2025-03-01 05:54:11.96859-08 2025-03-01 06:24:29.905592-08 leaderboard f A100 0.0030812046666666665 t \N \N \N {} +4145 1355 2025-03-01 05:52:29.99551-08 2025-03-01 05:50:58.136374-08 test f A100 \N t \N \N \N {} +4146 1355 2025-03-01 06:50:16.294841-08 2025-03-01 06:10:42.413759-08 benchmark f A100 \N t \N \N \N {} +4147 1355 2025-03-01 06:54:23.821235-08 2025-03-01 07:02:38.84073-08 leaderboard f A100 0.00309179 t \N \N \N {} +4148 1355 2025-03-01 05:36:48.581155-08 2025-03-01 06:55:13.189694-08 test t A100 \N t \N \N \N {} +4149 1355 2025-03-01 06:20:18.889242-08 2025-03-01 06:29:20.71451-08 benchmark t A100 \N t \N \N \N {} +4150 1355 2025-03-01 05:46:07.870361-08 2025-03-01 06:38:31.9395-08 leaderboard t A100 0.0030817543333333335 t \N \N \N {} +4151 1356 2025-03-01 07:33:56.778034-08 2025-03-01 06:51:39.921874-08 test f A100 \N f \N \N \N {} +4154 1359 2025-03-01 09:55:27.574174-08 2025-03-01 08:42:50.314131-08 benchmark f A100 \N t \N \N \N {} +4155 1359 2025-03-01 09:47:12.768835-08 2025-03-01 09:52:39.842141-08 benchmark f H100 \N t \N \N \N {} +4156 1360 2025-03-01 09:34:23.502047-08 2025-03-01 09:57:57.454449-08 benchmark f A100 \N t \N \N \N {} +4157 1360 2025-03-01 08:24:34.434752-08 2025-03-01 08:43:59.423015-08 benchmark f H100 \N t \N \N \N {} +4158 1361 2025-03-01 09:17:52.106156-08 2025-03-01 10:06:57.89908-08 test t H100 \N t \N \N \N {} +4159 1361 2025-03-01 09:16:48.1668-08 2025-03-01 08:15:25.825071-08 benchmark t H100 \N t \N \N \N {} +4160 1361 2025-03-01 09:01:03.843546-08 2025-03-01 09:36:32.754952-08 leaderboard t H100 0.0014479743333333333 t \N \N \N {} +4161 1361 2025-03-01 08:25:39.569213-08 2025-03-01 08:40:52.057697-08 test f H100 \N t \N \N \N {} +4162 1361 2025-03-01 08:47:33.253096-08 2025-03-01 09:19:00.121841-08 benchmark f H100 \N t \N \N \N {} +4163 1361 2025-03-01 09:21:19.831956-08 2025-03-01 09:37:55.855839-08 leaderboard f H100 0.001452041 t \N \N \N {} +4164 1363 2025-03-01 08:46:09.799691-08 2025-03-01 10:24:44.599745-08 benchmark f H100 \N t \N \N \N {} +4211 1390 2025-03-01 15:10:09.501721-08 2025-03-01 15:41:51.166433-08 benchmark f A100 \N t \N \N \N {} +4168 1364 2025-03-01 09:03:53.682811-08 2025-03-01 10:14:06.09417-08 leaderboard t A100 0.0031122263333333336 t \N \N \N {} +4169 1364 2025-03-01 09:49:16.863307-08 2025-03-01 10:08:01.584412-08 test f A100 \N t \N \N \N {} +1772 604 2025-02-25 10:42:51.901096-08 2025-02-25 10:19:33.430206-08 benchmark t H100 \N t \N \N \N {} +1773 604 2025-02-25 09:32:09.301008-08 2025-02-25 10:21:06.499152-08 leaderboard t H100 0.006142186 t \N \N \N {} +1774 604 2025-02-25 10:43:27.145802-08 2025-02-25 09:56:58.387594-08 test f H100 \N t \N \N \N {} +1775 604 2025-02-25 10:39:50.506911-08 2025-02-25 09:47:48.72776-08 benchmark f H100 \N t \N \N \N {} +1776 604 2025-02-25 09:10:42.599596-08 2025-02-25 09:30:41.654648-08 leaderboard f H100 0.006143060666666667 t \N \N \N {} +2414 816 2025-02-26 12:08:46.674357-08 2025-02-26 11:49:40.827855-08 benchmark f H100 \N f \N \N \N {} +1777 605 2025-02-25 10:52:46.64778-08 2025-02-25 09:29:41.278017-08 benchmark f H100 \N t \N \N \N {} +4213 1391 2025-03-01 15:19:09.143259-08 2025-03-01 14:01:35.420716-08 benchmark f H100 \N f \N \N \N {} +1778 605 2025-02-25 10:28:01.040475-08 2025-02-25 09:43:43.248513-08 benchmark f A100 \N t \N \N \N {} +1779 608 2025-02-25 10:43:12.211893-08 2025-02-25 10:07:05.017448-08 benchmark f H100 \N t \N \N \N {} +1780 609 2025-02-25 10:20:07.015553-08 2025-02-25 09:42:49.0735-08 benchmark f H100 \N t \N \N \N {} +2233 732 2025-02-25 22:08:54.161836-08 2025-02-25 22:20:50.391923-08 test f H100 \N t \N \N \N {} +1781 610 2025-02-25 10:34:24.601084-08 2025-02-25 10:19:19.681462-08 benchmark f H100 \N f \N \N \N {} +4677 1467 2025-03-02 05:46:48.283452-08 2025-03-02 06:33:48.786791-08 test f A100 \N t \N \N \N {} +1782 611 2025-02-25 10:20:28.964054-08 2025-02-25 09:52:02.934599-08 benchmark f H100 \N t \N \N \N {} +1791 616 2025-02-25 10:57:56.194437-08 2025-02-25 11:14:23.934399-08 benchmark t A100 \N t \N \N \N {} +1797 616 2025-02-25 10:18:29.893893-08 2025-02-25 10:05:32.012644-08 benchmark t T4 \N t \N \N \N {} +1802 616 2025-02-25 10:35:10.93135-08 2025-02-25 10:50:54.582852-08 test t H100 \N t \N \N \N {} +1807 616 2025-02-25 10:38:00.594029-08 2025-02-25 09:44:40.215164-08 leaderboard f L4 0.0002189721 t \N \N \N {} +1808 616 2025-02-25 10:30:31.534914-08 2025-02-25 10:05:10.928409-08 test t L4 \N t \N \N \N {} +1809 616 2025-02-25 10:42:40.135466-08 2025-02-25 10:28:37.438567-08 benchmark t L4 \N t \N \N \N {} +1813 619 2025-02-25 10:40:45.957535-08 2025-02-25 10:54:37.204319-08 benchmark f H100 \N t \N \N \N {} +1814 620 2025-02-25 10:47:50.841428-08 2025-02-25 09:37:03.436577-08 test t A100 \N t \N \N \N {} +1815 620 2025-02-25 11:04:50.249753-08 2025-02-25 09:57:25.703303-08 benchmark t A100 \N t \N \N \N {} +1820 621 2025-02-25 10:25:46.173072-08 2025-02-25 11:00:06.872432-08 test f A100 \N t \N \N \N {} +1825 621 2025-02-25 10:28:18.242991-08 2025-02-25 10:32:06.378285-08 leaderboard f H100 0.00006850659090909092 t \N \N \N {} +1826 621 2025-02-25 09:41:56.614729-08 2025-02-25 10:07:25.694167-08 test t A100 \N t \N \N \N {} +1827 621 2025-02-25 09:37:04.460998-08 2025-02-25 09:48:35.57183-08 benchmark t A100 \N t \N \N \N {} +1828 621 2025-02-25 10:08:41.300361-08 2025-02-25 11:30:23.969988-08 leaderboard t A100 0.00012062903571428571 t \N \N \N {} +1830 621 2025-02-25 09:45:11.698381-08 2025-02-25 10:09:44.153963-08 benchmark t H100 \N t \N \N \N {} +1838 621 2025-02-25 10:17:56.497108-08 2025-02-25 10:28:25.597743-08 test f L4 \N t \N \N \N {} +1841 621 2025-02-25 10:04:54.287311-08 2025-02-25 10:49:27.389838-08 test t L4 \N t \N \N \N {} +1842 621 2025-02-25 10:15:18.832896-08 2025-02-25 09:43:06.885167-08 benchmark t L4 \N t \N \N \N {} +1843 621 2025-02-25 11:14:37.289396-08 2025-02-25 09:37:11.924231-08 leaderboard t L4 0.00015086871428571428 t \N \N \N {} +1846 624 2025-02-25 10:33:21.190201-08 2025-02-25 11:11:47.766913-08 benchmark f A100 \N t \N \N \N {} +1847 625 2025-02-25 11:32:53.839235-08 2025-02-25 11:35:34.878827-08 benchmark f H100 \N t \N \N \N {} +1848 626 2025-02-25 10:29:31.639414-08 2025-02-25 09:50:52.046112-08 test f H100 \N t \N \N \N {} +1849 626 2025-02-25 10:56:50.659567-08 2025-02-25 11:09:07.456975-08 benchmark f H100 \N t \N \N \N {} +1850 626 2025-02-25 11:01:56.450365-08 2025-02-25 11:13:04.781975-08 leaderboard f H100 0.0014884195 t \N \N \N {} +3801 1281 2025-03-01 02:21:25.649213-08 2025-03-01 03:35:34.124629-08 test f A100 \N t \N \N \N {} +1852 626 2025-02-25 09:52:11.744323-08 2025-02-25 10:17:21.865184-08 benchmark t H100 \N t \N \N \N {} +1853 626 2025-02-25 11:11:09.345105-08 2025-02-25 09:56:58.318051-08 leaderboard t H100 0.0015167958125 t \N \N \N {} +1864 629 2025-02-25 10:03:17.408394-08 2025-02-25 10:31:15.377248-08 test t T4 \N f \N \N \N {} +1865 630 2025-02-25 10:33:02.54222-08 2025-02-25 09:57:27.268701-08 benchmark f A100 \N t \N \N \N {} +1866 631 2025-02-25 11:40:59.404486-08 2025-02-25 11:06:13.593002-08 benchmark f A100 \N t \N \N \N {} +1867 632 2025-02-25 10:30:21.975844-08 2025-02-25 10:10:22.159184-08 benchmark f A100 \N t \N \N \N {} +4171 1364 2025-03-01 09:02:25.076208-08 2025-03-01 09:47:26.827613-08 leaderboard f A100 0.003101713 t \N \N \N {} +4172 1365 2025-03-01 10:16:31.355759-08 2025-03-01 09:11:20.987034-08 benchmark f A100 \N t \N \N \N {} +4173 1366 2025-03-01 10:41:44.391735-08 2025-03-01 10:08:59.397316-08 test t A100 \N t \N \N \N {} +4174 1366 2025-03-01 10:41:36.980935-08 2025-03-01 10:58:56.581662-08 benchmark t A100 \N t \N \N \N {} +4686 1469 2025-03-02 07:07:48.25556-08 2025-03-02 07:20:29.156987-08 test f A100 \N t \N \N \N {} +4177 1366 2025-03-01 10:12:47.989298-08 2025-03-01 09:39:27.09578-08 benchmark f A100 \N t \N \N \N {} +1868 633 2025-02-25 11:43:07.576973-08 2025-02-25 10:22:50.962065-08 benchmark f A100 \N t \N \N \N {} +2454 860 2025-02-26 14:41:46.094612-08 2025-02-26 14:44:15.460594-08 test f A100 \N t \N \N \N {} +1869 634 2025-02-25 10:28:18.751593-08 2025-02-25 11:15:24.986518-08 benchmark f A100 \N t \N \N \N {} +1870 635 2025-02-25 11:53:39.07753-08 2025-02-25 11:27:22.45986-08 test f A100 \N t \N \N \N {} +1871 635 2025-02-25 11:53:25.443777-08 2025-02-25 10:35:30.145788-08 benchmark f A100 \N t \N \N \N {} +1874 635 2025-02-25 11:16:09.682168-08 2025-02-25 11:49:38.387512-08 benchmark t A100 \N t \N \N \N {} +1876 636 2025-02-25 11:49:31.647654-08 2025-02-25 11:20:21.285756-08 test f H100 \N t \N \N \N {} +1882 637 2025-02-25 11:02:50.678205-08 2025-02-25 11:53:07.5339-08 test f A100 \N t \N \N \N {} +1883 637 2025-02-25 12:15:08.69237-08 2025-02-25 12:05:12.143404-08 benchmark f A100 \N t \N \N \N {} +1884 637 2025-02-25 12:15:07.966893-08 2025-02-25 12:16:54.89578-08 leaderboard f A100 0.003200573 t \N \N \N {} +2419 822 2025-02-26 13:19:13.535475-08 2025-02-26 13:18:00.548275-08 test f T4 \N f \N \N \N {} +1889 639 2025-02-25 13:15:11.381768-08 2025-02-25 11:52:40.943412-08 benchmark f A100 \N t \N \N \N {} +1890 640 2025-02-25 13:03:11.780508-08 2025-02-25 11:39:48.194084-08 benchmark f A100 \N t \N \N \N {} +1892 641 2025-02-25 12:41:04.706804-08 2025-02-25 12:31:14.054076-08 benchmark t A100 \N t \N \N \N {} +1893 641 2025-02-25 12:48:58.704728-08 2025-02-25 12:33:09.050832-08 leaderboard t A100 0.003076926 t \N \N \N {} +1894 641 2025-02-25 13:10:49.145744-08 2025-02-25 11:53:53.607428-08 test f A100 \N t \N \N \N {} +1899 644 2025-02-25 12:19:53.732245-08 2025-02-25 13:40:01.838553-08 benchmark f A100 \N t \N \N \N {} +1900 646 2025-02-25 12:52:16.315942-08 2025-02-25 12:55:52.154965-08 test f T4 \N f \N \N \N {} +1901 647 2025-02-25 12:46:38.219456-08 2025-02-25 13:22:45.626265-08 benchmark f A100 \N t \N \N \N {} +1913 658 2025-02-25 13:14:34.483591-08 2025-02-25 12:48:18.641973-08 leaderboard f L4 0.017395948 t \N \N \N {} +1902 648 2025-02-25 13:22:19.470394-08 2025-02-25 13:20:10.149185-08 benchmark f A100 \N t \N \N \N {} +1903 649 2025-02-25 13:39:40.813885-08 2025-02-25 13:36:01.166239-08 benchmark f A100 \N t \N \N \N {} +1917 659 2025-02-25 12:18:43.146652-08 2025-02-25 13:44:37.824554-08 test t H100 \N t \N \N \N {} +1918 659 2025-02-25 12:38:33.511656-08 2025-02-25 12:09:10.447116-08 benchmark t H100 \N t \N \N \N {} +1919 659 2025-02-25 12:30:56.836878-08 2025-02-25 12:57:56.451178-08 leaderboard t H100 0.0014114212 t \N \N \N {} +1920 659 2025-02-25 13:27:03.426084-08 2025-02-25 14:05:30.708212-08 test f H100 \N t \N \N \N {} +1921 659 2025-02-25 13:32:15.353997-08 2025-02-25 13:54:42.055867-08 benchmark f H100 \N t \N \N \N {} +1922 659 2025-02-25 13:10:35.530509-08 2025-02-25 13:20:46.182913-08 leaderboard f H100 0.00142643575 t \N \N \N {} +3807 1283 2025-03-01 03:58:57.932103-08 2025-03-01 02:04:21.585952-08 test t A100 \N t \N \N \N {} +4178 1366 2025-03-01 09:15:06.396332-08 2025-03-01 09:50:59.628509-08 leaderboard f A100 0.0031052183333333335 t \N \N \N {} +4179 1367 2025-03-01 10:00:51.737363-08 2025-03-01 10:00:20.686025-08 benchmark f A100 \N t \N \N \N {} +4245 1401 2025-03-01 17:40:55.269982-08 2025-03-01 17:02:11.828026-08 benchmark f H100 \N t \N \N \N {} +4180 1368 2025-03-01 09:53:14.622803-08 2025-03-01 10:45:13.461477-08 benchmark f A100 \N t \N \N \N {} +4181 1369 2025-03-01 10:19:49.557836-08 2025-03-01 10:31:18.303541-08 benchmark f A100 \N t \N \N \N {} +4182 1370 2025-03-01 10:05:46.891703-08 2025-03-01 10:47:36.638039-08 benchmark f A100 \N t \N \N \N {} +4183 1371 2025-03-01 10:09:50.741844-08 2025-03-01 09:32:36.999656-08 benchmark f A100 \N t \N \N \N {} +4246 1401 2025-03-01 16:27:23.159511-08 2025-03-01 16:38:26.273265-08 benchmark f A100 \N t \N \N \N {} +4186 1374 2025-03-01 10:29:07.179112-08 2025-03-01 10:42:10.711955-08 benchmark f A100 \N f \N \N \N {} +4187 1375 2025-03-01 10:45:16.587627-08 2025-03-01 10:28:33.902324-08 benchmark f A100 \N f \N \N \N {} +4188 1376 2025-03-01 12:01:30.707689-08 2025-03-01 11:24:17.478908-08 test f A100 \N t \N \N \N {} +4189 1377 2025-03-01 12:12:57.585435-08 2025-03-01 11:53:33.240089-08 benchmark f A100 \N t \N \N \N {} +4190 1379 2025-03-01 12:16:36.769477-08 2025-03-01 13:18:08.829651-08 test f A100 \N t \N \N \N {} +4199 1388 2025-03-01 12:52:38.473441-08 2025-03-01 13:32:16.90025-08 test f A100 \N t \N \N \N {} +4200 1388 2025-03-01 13:20:31.349585-08 2025-03-01 12:39:30.194675-08 benchmark f A100 \N t \N \N \N {} +4201 1388 2025-03-01 13:30:38.204623-08 2025-03-01 12:55:51.306591-08 leaderboard f A100 0.4955258506666667 t \N \N \N {} +4805 1487 2025-03-02 12:59:51.989286-08 2025-03-02 12:35:17.947042-08 benchmark t T4 \N f \N \N \N {} +4202 1388 2025-03-01 12:42:31.113413-08 2025-03-01 13:17:21.163086-08 test t A100 \N t \N \N \N {} +4207 1389 2025-03-01 13:01:25.628609-08 2025-03-01 14:02:58.147439-08 leaderboard f H100 0.3205130943333333 t \N \N \N {} +4208 1389 2025-03-01 12:35:43.992201-08 2025-03-01 13:54:37.702884-08 test t H100 \N t \N \N \N {} +4209 1389 2025-03-01 13:46:31.426364-08 2025-03-01 14:02:32.562267-08 benchmark t H100 \N t \N \N \N {} +4210 1389 2025-03-01 12:35:26.710521-08 2025-03-01 13:30:46.516965-08 leaderboard t H100 0.31866500166666667 t \N \N \N {} +4212 1390 2025-03-01 14:35:10.227405-08 2025-03-01 15:03:07.466266-08 benchmark f H100 \N f \N \N \N {} +4214 1392 2025-03-01 15:54:46.023585-08 2025-03-01 15:50:44.87514-08 benchmark f A100 \N t \N \N \N {} +4215 1393 2025-03-01 15:05:15.318883-08 2025-03-01 15:06:05.433626-08 benchmark f A100 \N t \N \N \N {} +4216 1394 2025-03-01 16:24:31.141388-08 2025-03-01 16:30:42.940699-08 test f A100 \N t \N \N \N {} +4217 1394 2025-03-01 15:20:57.053534-08 2025-03-01 15:01:30.702462-08 benchmark f A100 \N t \N \N \N {} +2651 906 2025-02-26 14:32:29.620169-08 2025-02-26 14:03:45.689019-08 benchmark f A100 \N f \N \N \N {} +4773 1485 2025-03-02 12:07:24.955555-08 2025-03-02 12:12:01.099174-08 test t T4 \N t \N \N \N {} +4774 1485 2025-03-02 12:20:07.259834-08 2025-03-02 12:27:22.223302-08 benchmark t T4 \N t \N \N \N {} +4775 1485 2025-03-02 13:25:29.463588-08 2025-03-02 12:38:52.204635-08 leaderboard t T4 0.013066945 t \N \N \N {} +4776 1486 2025-03-02 13:33:31.030105-08 2025-03-02 12:15:35.189097-08 test t A100 \N t \N \N \N {} +4777 1486 2025-03-02 12:13:19.346928-08 2025-03-02 12:46:44.113151-08 benchmark t A100 \N f \N \N \N {} +4778 1486 2025-03-02 13:36:11.751592-08 2025-03-02 12:27:50.077759-08 test f A100 \N t \N \N \N {} +4779 1486 2025-03-02 13:14:12.733535-08 2025-03-02 13:18:37.639912-08 benchmark f A100 \N f \N \N \N {} +6738 2086 2025-03-15 03:23:36.712806-07 2025-03-15 04:23:06.297428-07 test f A100 \N t \N \N \N {} +4781 1486 2025-03-02 12:38:20.279542-08 2025-03-02 13:10:45.650515-08 benchmark t L4 \N t \N \N \N {} +1947 661 2025-02-25 13:28:58.74373-08 2025-02-25 13:47:27.943218-08 benchmark f H100 \N t \N \N \N {} +1949 663 2025-02-25 14:01:25.359523-08 2025-02-25 14:10:06.975147-08 benchmark f H100 \N t \N \N \N {} +1951 665 2025-02-25 12:52:25.628404-08 2025-02-25 13:09:46.048653-08 test f T4 \N t \N \N \N {} +1952 666 2025-02-25 13:17:01.172423-08 2025-02-25 13:04:42.018743-08 test f T4 \N t \N \N \N {} +5081 1553 2025-03-04 16:37:31.290979-08 2025-03-04 17:11:58.415963-08 leaderboard f H100 0.0014990966666666666 t \N \N \N {} +5082 1554 2025-03-04 17:14:21.826358-08 2025-03-04 18:23:09.521817-08 test f A100 \N f \N \N \N {} +5106 1565 2025-03-04 17:28:02.642565-08 2025-03-04 17:19:33.683442-08 leaderboard t A100 0.006331331333333333 t \N \N \N {} +5083 1555 2025-03-04 16:57:56.662828-08 2025-03-04 17:02:59.352083-08 test f A100 \N f \N \N \N {} +5258 1613 2025-03-06 07:52:29.561261-08 2025-03-06 07:25:55.418568-08 leaderboard t T4 0.016500428666666667 t \N \N \N {} +5120 1569 2025-03-04 23:12:31.354705-08 2025-03-04 21:49:58.854631-08 test t A100 \N t \N \N \N {} +5121 1569 2025-03-04 23:09:51.663868-08 2025-03-04 21:37:24.887769-08 benchmark t A100 \N t \N \N \N {} +5122 1569 2025-03-04 22:02:16.559653-08 2025-03-04 22:32:25.782751-08 leaderboard t A100 0.001608786 t \N \N \N {} +5123 1569 2025-03-04 22:05:47.583362-08 2025-03-04 21:33:49.87689-08 test t L4 \N t \N \N \N {} +5124 1569 2025-03-04 21:35:17.104808-08 2025-03-04 22:09:20.389921-08 benchmark t L4 \N t \N \N \N {} +5125 1569 2025-03-04 22:38:25.160967-08 2025-03-04 21:26:50.027334-08 leaderboard t L4 0.009029581666666666 t \N \N \N {} +5126 1569 2025-03-04 22:29:13.641854-08 2025-03-04 23:15:17.197089-08 test f H100 \N t \N \N \N {} +5127 1569 2025-03-04 22:01:12.379517-08 2025-03-04 23:10:54.429836-08 benchmark f H100 \N t \N \N \N {} +5128 1569 2025-03-04 23:11:31.285632-08 2025-03-04 21:42:40.185619-08 leaderboard f H100 0.0010705946666666668 t \N \N \N {} +5129 1569 2025-03-04 21:57:38.963122-08 2025-03-04 21:31:36.054672-08 test t H100 \N t \N \N \N {} +5130 1569 2025-03-04 21:27:09.873699-08 2025-03-04 22:20:40.268062-08 benchmark t H100 \N t \N \N \N {} +6778 2099 2025-03-15 03:30:57.66264-07 2025-03-15 04:43:08.964646-07 test f A100 \N t \N \N \N {} +5134 1569 2025-03-04 22:04:14.888682-08 2025-03-04 22:46:38.905995-08 leaderboard f T4 0.014848202 t \N \N \N {} +5135 1569 2025-03-04 22:03:01.49793-08 2025-03-04 22:36:42.090475-08 test t T4 \N t \N \N \N {} +5136 1569 2025-03-04 22:08:23.328887-08 2025-03-04 22:16:18.514342-08 benchmark t T4 \N t \N \N \N {} +5137 1569 2025-03-04 23:14:20.184391-08 2025-03-04 22:39:50.12814-08 leaderboard t T4 0.014794430666666665 t \N \N \N {} +5141 1570 2025-03-04 23:13:42.914895-08 2025-03-04 21:59:06.843057-08 test f A100 \N t \N \N \N {} +5142 1570 2025-03-04 21:36:21.746898-08 2025-03-04 22:16:20.82538-08 benchmark f A100 \N t \N \N \N {} +5143 1570 2025-03-04 22:33:56.277934-08 2025-03-04 22:10:31.797238-08 leaderboard f A100 0.0017140163333333332 t \N \N \N {} +5144 1570 2025-03-04 23:21:19.677221-08 2025-03-04 22:48:43.348393-08 test t A100 \N t \N \N \N {} +5145 1570 2025-03-04 23:16:49.330286-08 2025-03-04 22:14:55.550056-08 benchmark t A100 \N t \N \N \N {} +5146 1570 2025-03-04 22:23:09.667416-08 2025-03-04 21:43:24.622016-08 leaderboard t A100 0.0017186463333333333 t \N \N \N {} +5147 1570 2025-03-04 23:10:37.127588-08 2025-03-04 22:50:15.83577-08 test t H100 \N t \N \N \N {} +5148 1570 2025-03-04 22:15:05.089919-08 2025-03-04 21:59:44.284611-08 benchmark t H100 \N t \N \N \N {} +5149 1570 2025-03-04 22:40:39.695868-08 2025-03-04 22:51:12.192064-08 leaderboard t H100 0.0010933593333333333 t \N \N \N {} +5150 1570 2025-03-04 22:06:34.697175-08 2025-03-04 22:22:03.050409-08 test f H100 \N t \N \N \N {} +5151 1570 2025-03-04 23:14:32.375663-08 2025-03-04 23:16:06.022274-08 benchmark f H100 \N t \N \N \N {} +6781 2099 2025-03-15 04:17:18.910592-07 2025-03-15 04:27:25.440486-07 test t A100 \N t \N \N \N {} +5154 1571 2025-03-04 21:29:49.913428-08 2025-03-04 22:56:49.726281-08 benchmark t A100 \N t \N \N \N {} +5155 1571 2025-03-04 22:09:17.380828-08 2025-03-04 23:17:01.528387-08 leaderboard t A100 0.00149756 t \N \N \N {} +5156 1571 2025-03-04 22:42:35.691391-08 2025-03-04 22:30:28.371632-08 test f A100 \N t \N \N \N {} +1953 666 2025-02-25 13:11:27.663789-08 2025-02-25 12:56:49.679792-08 benchmark f T4 \N t \N \N \N {} +1954 666 2025-02-25 14:01:28.591074-08 2025-02-25 12:36:59.206687-08 leaderboard f T4 0.03533333425 t \N \N \N {} +1955 666 2025-02-25 13:24:34.96066-08 2025-02-25 12:23:48.496925-08 test t T4 \N t \N \N \N {} +1956 666 2025-02-25 13:11:21.334723-08 2025-02-25 13:18:01.451228-08 benchmark t T4 \N t \N \N \N {} +1957 666 2025-02-25 14:11:36.256788-08 2025-02-25 12:59:40.696196-08 leaderboard t T4 0.035418843285714284 t \N \N \N {} +1958 667 2025-02-25 13:33:38.816072-08 2025-02-25 13:27:18.187004-08 benchmark f A100 \N t \N \N \N {} +1975 679 2025-02-25 13:39:40.730805-08 2025-02-25 12:47:36.780104-08 leaderboard f T4 0.053832773 t \N \N \N {} +5170 1574 2025-03-04 22:47:12.679139-08 2025-03-04 22:51:50.789451-08 test t H100 \N f \N \N \N {} +5177 1575 2025-03-04 23:31:30.176049-08 2025-03-04 23:39:24.402655-08 benchmark t H100 \N t \N \N \N {} +5175 1575 2025-03-04 23:09:05.999145-08 2025-03-04 23:03:45.299121-08 leaderboard f H100 0.0010994093333333334 t \N \N \N {} +5176 1575 2025-03-04 21:58:21.866372-08 2025-03-04 23:01:50.540448-08 test t H100 \N t \N \N \N {} +5197 1589 2025-03-05 04:48:37.908985-08 2025-03-05 05:42:21.123456-08 test f H100 \N f \N \N \N {} +5178 1575 2025-03-04 23:49:34.473234-08 2025-03-04 22:47:13.576304-08 leaderboard t H100 0.001105298 t \N \N \N {} +5179 1576 2025-03-05 05:05:54.983075-08 2025-03-05 04:15:53.893439-08 benchmark f H100 \N t \N \N \N {} +1969 674 2025-02-25 14:18:09.782068-08 2025-02-25 12:29:36.512538-08 benchmark f A100 \N t \N \N \N {} +1971 675 2025-02-25 13:57:33.707833-08 2025-02-25 14:11:11.249992-08 benchmark f A100 \N t \N \N \N {} +1973 679 2025-02-25 13:04:53.521829-08 2025-02-25 14:18:59.793772-08 test f T4 \N t \N \N \N {} +1974 679 2025-02-25 14:13:13.965846-08 2025-02-25 13:57:26.833503-08 benchmark f T4 \N t \N \N \N {} +5180 1577 2025-03-05 03:52:35.002755-08 2025-03-05 04:21:45.713683-08 benchmark f H100 \N t \N \N \N {} +5181 1578 2025-03-05 05:17:31.803633-08 2025-03-05 05:50:42.297338-08 test t H100 \N t \N \N \N {} +5182 1578 2025-03-05 04:56:30.788431-08 2025-03-05 05:45:31.103522-08 benchmark t H100 \N t \N \N \N {} +5183 1578 2025-03-05 05:01:35.150294-08 2025-03-05 05:14:55.594706-08 leaderboard t H100 0.0014070166666666668 t \N \N \N {} +5184 1578 2025-03-05 05:28:07.773821-08 2025-03-05 05:16:37.68584-08 test f H100 \N t \N \N \N {} +5233 1611 2025-03-06 07:25:53.030783-08 2025-03-06 07:55:02.790842-08 test f T4 \N f \N \N \N {} +5190 1582 2025-03-05 04:44:16.876495-08 2025-03-05 04:31:50.278622-08 test f H100 \N f \N \N \N {} +5199 1591 2025-03-05 05:58:19.154295-08 2025-03-05 05:51:19.562708-08 test f H100 \N f \N \N \N {} +5200 1592 2025-03-05 06:51:32.869767-08 2025-03-05 07:03:41.586486-08 test f A100 \N f \N \N \N {} +5201 1593 2025-03-05 11:53:54.672796-08 2025-03-05 10:22:24.681764-08 test f T4 \N f \N \N \N {} +5202 1593 2025-03-05 11:34:29.199094-08 2025-03-05 11:32:56.415861-08 test f H100 \N f \N \N \N {} +5203 1594 2025-03-05 10:15:31.539292-08 2025-03-05 11:41:36.518701-08 test f T4 \N f \N \N \N {} +5204 1595 2025-03-05 11:50:17.097474-08 2025-03-05 10:50:25.088498-08 test f T4 \N f \N \N \N {} +5205 1596 2025-03-05 10:17:15.840178-08 2025-03-05 11:21:12.907749-08 test f T4 \N t \N \N \N {} +5206 1597 2025-03-05 10:38:59.781202-08 2025-03-05 11:53:31.710828-08 benchmark f T4 \N t \N \N \N {} +5207 1598 2025-03-05 15:43:10.18498-08 2025-03-05 15:44:26.256063-08 test f T4 \N f \N \N \N {} +5208 1599 2025-03-05 16:08:47.877222-08 2025-03-05 16:17:45.165244-08 test f T4 \N f \N \N \N {} +5215 1605 2025-03-05 22:28:57.064698-08 2025-03-05 22:38:29.672952-08 test t A100 \N t \N \N \N {} +5216 1605 2025-03-06 00:10:40.145439-08 2025-03-05 23:04:41.009576-08 benchmark t A100 \N t \N \N \N {} +5217 1605 2025-03-06 00:13:35.847747-08 2025-03-05 23:40:14.544266-08 leaderboard t A100 0.0022575486666666666 t \N \N \N {} +5244 1613 2025-03-06 07:19:13.628319-08 2025-03-06 06:35:37.599294-08 test f A100 \N t \N \N \N {} +5245 1613 2025-03-06 06:54:50.780479-08 2025-03-06 06:45:07.226442-08 benchmark f A100 \N t \N \N \N {} +5246 1613 2025-03-06 07:28:04.913981-08 2025-03-06 08:10:28.487649-08 leaderboard f A100 0.0025635133333333335 t \N \N \N {} +5276 1614 2025-03-06 07:19:59.675744-08 2025-03-06 07:43:31.134657-08 leaderboard f H100 0.00140798875 t \N \N \N {} +5277 1614 2025-03-06 08:41:38.090423-08 2025-03-06 08:37:03.818704-08 test t H100 \N t \N \N \N {} +5278 1614 2025-03-06 08:18:49.218286-08 2025-03-06 08:16:42.816157-08 benchmark t H100 \N t \N \N \N {} +5279 1614 2025-03-06 07:03:16.465007-08 2025-03-06 06:46:29.690376-08 leaderboard t H100 0.0014296095 t \N \N \N {} +5280 1615 2025-03-06 08:22:03.432718-08 2025-03-06 08:24:28.297651-08 test f A100 \N f \N \N \N {} +5281 1617 2025-03-06 12:18:02.879373-08 2025-03-06 11:00:55.592702-08 test f L4 \N f \N \N \N {} +5286 1616 2025-03-06 12:36:10.700755-08 2025-03-06 11:50:29.347443-08 test t T4 \N t \N \N \N {} +5287 1616 2025-03-06 12:25:41.61897-08 2025-03-06 11:16:54.998394-08 benchmark t T4 \N t \N \N \N {} +5312 1620 2025-03-06 12:33:59.587669-08 2025-03-06 11:40:59.729965-08 test f L4 \N t \N \N \N {} +5313 1620 2025-03-06 10:43:35.438642-08 2025-03-06 12:38:29.763377-08 benchmark f L4 \N t \N \N \N {} +5314 1620 2025-03-06 12:06:29.829934-08 2025-03-06 12:34:56.969836-08 leaderboard f L4 0.017135117666666668 t \N \N \N {} +1977 679 2025-02-25 12:51:05.905455-08 2025-02-25 12:53:16.176109-08 benchmark t T4 \N t \N \N \N {} +1978 679 2025-02-25 13:37:32.257654-08 2025-02-25 13:03:09.630302-08 leaderboard t T4 0.05386631333333334 t \N \N \N {} +3812 1283 2025-03-01 02:43:07.132486-08 2025-03-01 03:36:19.063234-08 test f A100 \N t \N \N \N {} +5584 1707 2025-03-09 09:22:46.945115-07 2025-03-09 08:37:58.159049-07 test t T4 \N t \N \N \N {} +5550 1693 2025-03-08 16:00:57.23753-08 2025-03-08 17:10:45.887607-08 test f H100 \N f \N \N \N {} +5551 1694 2025-03-08 17:58:29.601266-08 2025-03-08 17:28:23.434527-08 test t H100 \N f \N \N \N {} +5552 1694 2025-03-08 16:17:00.585648-08 2025-03-08 17:26:10.548997-08 test f H100 \N f \N \N \N {} +5553 1695 2025-03-08 17:31:35.366001-08 2025-03-08 16:43:40.087454-08 test t H100 \N f \N \N \N {} +5568 1702 2025-03-08 17:12:40.242062-08 2025-03-08 18:24:17.952735-08 test f H100 \N f \N \N \N {} +5582 1707 2025-03-09 08:41:44.387525-07 2025-03-09 09:18:33.589319-07 benchmark f T4 \N t \N \N \N {} +5557 1697 2025-03-08 18:09:07.446602-08 2025-03-08 16:49:50.71993-08 test f H100 \N f \N \N \N {} +5558 1697 2025-03-08 16:45:59.890505-08 2025-03-08 17:49:20.795913-08 test t H100 \N f \N \N \N {} +5559 1698 2025-03-08 16:44:33.058529-08 2025-03-08 16:55:51.404262-08 test t H100 \N f \N \N \N {} +5560 1698 2025-03-08 17:45:21.683334-08 2025-03-08 17:40:25.662216-08 test f H100 \N f \N \N \N {} +5561 1699 2025-03-08 17:23:07.204592-08 2025-03-08 16:29:40.8521-08 test f H100 \N f \N \N \N {} +5562 1699 2025-03-08 17:14:04.744552-08 2025-03-08 16:45:33.633138-08 test t H100 \N f \N \N \N {} +5565 1701 2025-03-08 17:19:00.706175-08 2025-03-08 16:50:25.370782-08 test t H100 \N f \N \N \N {} +5566 1701 2025-03-08 18:00:39.905743-08 2025-03-08 18:04:31.681329-08 test f H100 \N f \N \N \N {} +5583 1707 2025-03-09 10:07:02.952329-07 2025-03-09 08:33:06.895971-07 leaderboard f T4 0.016582775333333334 t \N \N \N {} +5569 1703 2025-03-08 17:21:28.286711-08 2025-03-08 17:59:25.372338-08 test f H100 \N f \N \N \N {} +5570 1703 2025-03-08 18:05:15.430458-08 2025-03-08 17:19:42.190919-08 test t H100 \N f \N \N \N {} +5571 1704 2025-03-09 09:07:05.945234-07 2025-03-09 09:40:55.738939-07 test f T4 \N f \N \N \N {} +5572 1704 2025-03-09 08:38:56.049572-07 2025-03-09 09:19:08.106745-07 test t T4 \N f \N \N \N {} +5573 1705 2025-03-09 09:08:47.834128-07 2025-03-09 08:44:55.26167-07 test t T4 \N f \N \N \N {} +6852 2125 2025-03-15 10:54:23.765567-07 2025-03-15 10:56:50.760245-07 test t A100 \N t \N \N \N {} +5574 1705 2025-03-09 08:19:09.822926-07 2025-03-09 08:17:05.627722-07 test f T4 \N f \N \N \N {} +5575 1706 2025-03-09 08:15:37.716285-07 2025-03-09 09:07:48.253727-07 test t T4 \N t \N \N \N {} +5576 1706 2025-03-09 09:03:21.820355-07 2025-03-09 09:23:13.212892-07 benchmark t T4 \N t \N \N \N {} +5577 1706 2025-03-09 09:24:37.224862-07 2025-03-09 09:06:00.619832-07 leaderboard t T4 0.017249751 t \N \N \N {} +5578 1706 2025-03-09 09:20:25.180918-07 2025-03-09 08:18:24.424228-07 test f T4 \N t \N \N \N {} +5579 1706 2025-03-09 08:21:53.936645-07 2025-03-09 08:29:28.573712-07 benchmark f T4 \N t \N \N \N {} +5580 1706 2025-03-09 08:30:06.61725-07 2025-03-09 09:22:13.972549-07 leaderboard f T4 0.017213831 t \N \N \N {} +6855 2126 2025-03-15 10:01:43.438198-07 2025-03-15 09:26:54.425537-07 test f A100 \N t \N \N \N {} +5586 1707 2025-03-09 08:33:05.304746-07 2025-03-09 08:45:18.547459-07 leaderboard t T4 0.016620549 t \N \N \N {} +2800 976 2025-02-26 21:36:15.417887-08 2025-02-26 21:39:28.329229-08 leaderboard f A100 0.0031626593333333335 t \N \N \N {} +5634 1718 2025-03-09 09:13:24.672764-07 2025-03-09 10:18:48.792588-07 test f T4 \N t \N \N \N {} +5635 1718 2025-03-09 10:16:44.041935-07 2025-03-09 09:08:48.252549-07 benchmark f T4 \N t \N \N \N {} +5641 1719 2025-03-09 10:29:41.062663-07 2025-03-09 10:42:23.185635-07 benchmark t T4 \N t \N \N \N {} +5642 1719 2025-03-09 10:03:38.725028-07 2025-03-09 10:55:41.903081-07 leaderboard t T4 0.01683973533333333 t \N \N \N {} +5643 1720 2025-03-09 09:57:20.289686-07 2025-03-09 10:34:13.152652-07 test f T4 \N t \N \N \N {} +5644 1720 2025-03-09 09:16:46.353693-07 2025-03-09 10:33:41.020996-07 benchmark f T4 \N t \N \N \N {} +5650 1721 2025-03-09 10:43:52.72403-07 2025-03-09 09:09:04.6489-07 benchmark t T4 \N t \N \N \N {} +5651 1721 2025-03-09 10:26:40.021059-07 2025-03-09 10:38:45.997727-07 leaderboard t T4 0.016620553333333333 t \N \N \N {} +5653 1722 2025-03-09 09:41:53.232041-07 2025-03-09 10:03:19.288174-07 benchmark t T4 \N t \N \N \N {} +5659 1723 2025-03-09 10:23:53.251832-07 2025-03-09 10:13:49.143494-07 benchmark f T4 \N t \N \N \N {} +2801 976 2025-02-26 21:49:50.078075-08 2025-02-26 21:02:25.183977-08 test t A100 \N t \N \N \N {} +6096 1844 2025-03-10 22:34:55.225767-07 2025-03-10 23:16:53.927941-07 leaderboard t A100 0.02345483125 t \N \N \N {} +6097 1845 2025-03-10 22:27:27.260452-07 2025-03-10 23:10:29.127667-07 test f H100 \N t \N \N \N {} +6098 1845 2025-03-10 22:33:31.470385-07 2025-03-10 23:33:08.346801-07 benchmark f H100 \N t \N \N \N {} +6099 1845 2025-03-10 23:19:50.390365-07 2025-03-10 23:55:52.423957-07 leaderboard f H100 0.007574126333333333 t \N \N \N {} +6100 1845 2025-03-10 23:23:59.756697-07 2025-03-10 22:36:01.810723-07 test t H100 \N t \N \N \N {} +6103 1846 2025-03-10 23:34:33.401447-07 2025-03-10 22:38:46.785595-07 test t L4 \N t \N \N \N {} +6104 1846 2025-03-10 22:40:13.180982-07 2025-03-10 22:03:25.587196-07 benchmark t L4 \N t \N \N \N {} +6105 1846 2025-03-10 22:07:31.471617-07 2025-03-10 22:36:23.340517-07 leaderboard t L4 0.289734965 t \N \N \N {} +6106 1846 2025-03-10 22:19:05.931425-07 2025-03-10 22:31:11.627074-07 test f L4 \N t \N \N \N {} +225 37 2025-02-23 10:59:25.499888-08 2025-02-23 11:56:22.371923-08 test f T4 \N t \N \N \N {} +332 80 2025-02-23 15:08:05.190515-08 2025-02-23 14:44:23.958644-08 test f H100 \N t \N \N \N {} +203 36 2025-02-23 11:46:05.335042-08 2025-02-23 12:11:22.163693-08 benchmark f H100 \N t \N \N \N {} +358 94 2025-02-23 18:49:28.760988-08 2025-02-23 19:12:31.385313-08 leaderboard f H100 0.012098538666666665 t \N \N \N {} +359 95 2025-02-23 17:48:36.820226-08 2025-02-23 19:17:34.067307-08 test f H100 \N t \N \N \N {} +1206 374 2025-02-24 20:28:11.986011-08 2025-02-24 19:36:30.92237-08 benchmark f A100 \N f \N \N \N {} +1207 375 2025-02-24 20:12:22.417421-08 2025-02-24 20:46:01.292845-08 benchmark f A100 \N f \N \N \N {} +1414 471 2025-02-25 03:42:52.544828-08 2025-02-25 04:20:26.74565-08 test f A100 \N t \N \N \N {} +1208 376 2025-02-24 19:48:12.808077-08 2025-02-24 20:45:57.879274-08 benchmark f A100 \N t \N \N \N {} +1209 377 2025-02-24 21:24:44.702336-08 2025-02-24 20:39:40.644905-08 benchmark f A100 \N f \N \N \N {} +1210 378 2025-02-24 21:34:59.47251-08 2025-02-24 21:22:02.439541-08 benchmark f A100 \N t \N \N \N {} +1211 379 2025-02-24 21:22:12.927474-08 2025-02-24 19:58:49.367481-08 benchmark f A100 \N t \N \N \N {} +1243 404 2025-02-24 22:06:51.766358-08 2025-02-24 22:05:43.599472-08 benchmark f A100 \N t \N \N \N {} +1216 384 2025-02-24 21:02:40.893555-08 2025-02-24 20:57:25.702865-08 benchmark f A100 \N f \N \N \N {} +1229 393 2025-02-24 20:52:22.785486-08 2025-02-24 21:45:46.13494-08 leaderboard t A100 0.0033359293333333337 t \N \N \N {} +1230 393 2025-02-24 21:16:21.658637-08 2025-02-24 20:25:01.054235-08 test f A100 \N t \N \N \N {} +1231 393 2025-02-24 22:03:36.537687-08 2025-02-24 21:08:30.465614-08 benchmark f A100 \N t \N \N \N {} +1240 401 2025-02-24 21:15:55.733324-08 2025-02-24 21:57:58.972958-08 test f A100 \N f \N \N \N {} +1241 402 2025-02-24 20:44:20.914092-08 2025-02-24 21:56:00.068563-08 test f A100 \N f \N \N \N {} +1248 409 2025-02-24 22:06:04.453015-08 2025-02-24 22:17:03.583941-08 test f A100 \N f \N \N \N {} +1417 471 2025-02-25 03:51:19.78766-08 2025-02-25 03:47:58.900665-08 test t A100 \N t \N \N \N {} +1249 410 2025-02-24 21:39:05.838922-08 2025-02-24 20:58:53.013953-08 test f A100 \N f \N \N \N {} +1250 411 2025-02-24 22:40:19.562086-08 2025-02-24 21:27:25.950857-08 test f A100 \N t \N \N \N {} +1404 469 2025-02-25 03:53:45.5055-08 2025-02-25 04:07:42.563768-08 leaderboard f A100 0.003393497 t \N \N \N {} +2922 1024 2025-02-27 04:31:40.23618-08 2025-02-27 02:56:46.161479-08 test f T4 \N t \N \N \N {} +2923 1024 2025-02-27 02:49:24.009049-08 2025-02-27 03:11:16.56453-08 benchmark f T4 \N t \N \N \N {} +2924 1024 2025-02-27 04:11:37.18876-08 2025-02-27 04:34:12.87404-08 leaderboard f T4 0.00039354559999999996 t \N \N \N {} +3121 1110 2025-02-28 03:11:43.352425-08 2025-02-28 01:42:01.408142-08 test f A100 \N t \N \N \N {} +3466 1192 2025-02-28 10:34:17.327676-08 2025-02-28 12:02:48.511506-08 test t A100 \N f \N \N \N {} +2926 1024 2025-02-27 03:07:44.576399-08 2025-02-27 04:23:59.379675-08 benchmark t T4 \N t \N \N \N {} +2927 1024 2025-02-27 03:35:18.806103-08 2025-02-27 04:01:32.334755-08 leaderboard t T4 0.000564142375 t \N \N \N {} +3124 1111 2025-02-28 03:22:39.920088-08 2025-02-28 03:21:57.983095-08 test f A100 \N t \N \N \N {} +3174 1119 2025-02-28 03:45:25.024721-08 2025-02-28 03:11:06.974641-08 leaderboard f A100 0.0031492663333333335 t \N \N \N {} +1409 470 2025-02-25 03:16:55.220474-08 2025-02-25 04:31:56.202607-08 benchmark t A100 \N t \N \N \N {} +1410 470 2025-02-25 04:11:58.013793-08 2025-02-25 03:21:32.360753-08 leaderboard t A100 0.0030943103333333334 t \N \N \N {} +2928 1025 2025-02-27 04:12:51.460546-08 2025-02-27 02:49:12.29781-08 test f L4 \N t \N \N \N {} +2929 1025 2025-02-27 03:26:34.775483-08 2025-02-27 04:12:58.357761-08 benchmark f L4 \N t \N \N \N {} +2930 1025 2025-02-27 03:19:52.147083-08 2025-02-27 03:27:21.649998-08 leaderboard f L4 0.00011601083783783784 t \N \N \N {} +3047 1093 2025-02-27 15:53:15.961217-08 2025-02-27 15:41:20.80553-08 benchmark t A100 \N t \N \N \N {} +3048 1093 2025-02-27 15:45:02.22711-08 2025-02-27 15:31:35.36741-08 leaderboard t A100 0.0031404533333333337 t \N \N \N {} +1433 475 2025-02-25 04:29:39.664763-08 2025-02-25 05:08:55.732423-08 test f A100 \N f \N \N \N {} +1436 477 2025-02-25 05:11:23.154044-08 2025-02-25 03:41:03.100879-08 test f A100 \N f \N \N \N {} +1437 478 2025-02-25 05:37:15.695274-08 2025-02-25 03:55:07.870743-08 test f A100 \N f \N \N \N {} +1441 481 2025-02-25 04:15:28.477993-08 2025-02-25 05:06:41.311478-08 test f A100 \N f \N \N \N {} +1442 481 2025-02-25 05:43:19.547838-08 2025-02-25 05:02:59.725963-08 test t A100 \N f \N \N \N {} +2941 1030 2025-02-27 04:33:34.388412-08 2025-02-27 03:16:04.017304-08 test t A100 \N t \N \N \N {} +3145 1114 2025-02-28 02:20:48.546337-08 2025-02-28 03:25:08.187009-08 test f A100 \N t \N \N \N {} +3002 1069 2025-02-27 11:57:26.315579-08 2025-02-27 12:26:49.008997-08 test f A100 \N f \N \N \N {} +3052 1094 2025-02-27 17:23:36.313978-08 2025-02-27 17:02:03.79401-08 test f A100 \N t \N \N \N {} +3004 1071 2025-02-27 12:13:32.872228-08 2025-02-27 13:48:22.621332-08 test f A100 \N f \N \N \N {} +3055 1094 2025-02-27 15:46:34.78066-08 2025-02-27 17:32:46.992293-08 test t A100 \N t \N \N \N {} +2951 1038 2025-02-27 08:08:02.949594-08 2025-02-27 07:45:52.672741-08 test f A100 \N t \N \N \N {} +1539 512 2025-02-25 07:42:24.70614-08 2025-02-25 07:55:31.991399-08 leaderboard t H100 0.0014827313333333333 t \N \N \N {} +1540 512 2025-02-25 06:33:27.810792-08 2025-02-25 07:40:47.978218-08 test f H100 \N t \N \N \N {} +1541 512 2025-02-25 07:57:36.75449-08 2025-02-25 07:43:37.029383-08 benchmark f H100 \N t \N \N \N {} +1542 512 2025-02-25 06:29:41.231954-08 2025-02-25 06:14:39.960451-08 leaderboard f H100 0.0014742606666666668 t \N \N \N {} +1543 512 2025-02-25 07:38:34.722488-08 2025-02-25 07:23:26.785114-08 test f T4 \N t \N \N \N {} +1544 512 2025-02-25 06:25:49.430545-08 2025-02-25 07:43:59.473865-08 benchmark f T4 \N t \N \N \N {} +1545 512 2025-02-25 07:44:49.156876-08 2025-02-25 07:09:53.612336-08 leaderboard f T4 0.021663530142857143 t \N \N \N {} +1546 512 2025-02-25 06:35:54.036675-08 2025-02-25 07:10:22.696453-08 test t T4 \N t \N \N \N {} +1561 514 2025-02-25 08:21:20.423985-08 2025-02-25 07:53:25.914837-08 test f A100 \N t \N \N \N {} +1564 514 2025-02-25 06:26:23.245478-08 2025-02-25 07:52:14.438492-08 test t A100 \N t \N \N \N {} +1551 512 2025-02-25 07:08:46.613751-08 2025-02-25 06:42:56.111896-08 leaderboard f L4 0.017496731666666668 t \N \N \N {} +1553 512 2025-02-25 06:58:42.387657-08 2025-02-25 08:00:31.070279-08 benchmark t L4 \N t \N \N \N {} +1554 512 2025-02-25 07:24:12.688624-08 2025-02-25 06:31:02.770404-08 leaderboard t L4 0.017451008666666667 t \N \N \N {} +3231 1130 2025-02-28 05:20:47.157439-08 2025-02-28 06:11:32.587664-08 leaderboard t A100 0.0031105466666666664 t \N \N \N {} +3091 1105 2025-02-28 02:11:54.38507-08 2025-02-28 02:14:17.819311-08 test f A100 \N t \N \N \N {} +3250 1137 2025-02-28 07:15:32.403653-08 2025-02-28 05:44:54.894859-08 test f A100 \N t \N \N \N {} +1567 515 2025-02-25 06:40:37.056239-08 2025-02-25 08:18:23.071234-08 test f A100 \N t \N \N \N {} +1568 515 2025-02-25 07:51:44.723769-08 2025-02-25 06:52:35.487372-08 benchmark f A100 \N t \N \N \N {} +1569 515 2025-02-25 08:12:57.053326-08 2025-02-25 07:23:24.825804-08 leaderboard f A100 0.00322647325 t \N \N \N {} +1570 515 2025-02-25 07:59:41.537278-08 2025-02-25 08:18:25.884866-08 test t A100 \N t \N \N \N {} +1571 515 2025-02-25 07:33:12.913858-08 2025-02-25 07:10:10.306586-08 benchmark t A100 \N t \N \N \N {} +1572 515 2025-02-25 07:41:19.245397-08 2025-02-25 07:29:16.962881-08 leaderboard t A100 0.003220757 t \N \N \N {} +1575 516 2025-02-25 07:41:18.473174-08 2025-02-25 07:29:29.868748-08 leaderboard t A100 0.0032304103333333336 t \N \N \N {} +1738 583 2025-02-25 09:31:47.936241-08 2025-02-25 09:08:32.636598-08 test f H100 \N t \N \N \N {} +1748 587 2025-02-25 10:24:59.666061-08 2025-02-25 09:27:46.691352-08 test t T4 \N f \N \N \N {} +1749 588 2025-02-25 08:59:07.160327-08 2025-02-25 08:38:22.92218-08 benchmark f H100 \N f \N \N \N {} +4803 1487 2025-03-02 12:55:09.203143-08 2025-03-02 13:27:27.333426-08 benchmark t A100 \N f \N \N \N {} +1756 594 2025-02-25 09:11:38.300189-08 2025-02-25 10:28:52.150779-08 benchmark t T4 \N t \N \N \N {} +1757 594 2025-02-25 08:46:53.338218-08 2025-02-25 09:57:23.906953-08 leaderboard t T4 0.017254103 t \N \N \N {} +1758 594 2025-02-25 08:57:48.338091-08 2025-02-25 09:10:25.874419-08 test f T4 \N t \N \N \N {} +1759 594 2025-02-25 09:38:33.456671-08 2025-02-25 09:43:17.759144-08 benchmark f T4 \N t \N \N \N {} +1760 594 2025-02-25 09:40:53.716064-08 2025-02-25 10:08:25.184778-08 leaderboard f T4 0.017332067125 t \N \N \N {} +4804 1487 2025-03-02 12:53:07.905973-08 2025-03-02 13:20:31.048316-08 test t T4 \N t \N \N \N {} +1783 612 2025-02-25 10:30:16.608473-08 2025-02-25 10:08:59.631399-08 benchmark f A100 \N t \N \N \N {} +2280 767 2025-02-26 03:31:52.024635-08 2025-02-26 03:22:12.199287-08 leaderboard f A100 0.0008225438000000001 t \N \N \N {} +2281 767 2025-02-26 03:40:30.007645-08 2025-02-26 04:29:52.334337-08 test t A100 \N t \N \N \N {} +2300 774 2025-02-26 04:14:40.857031-08 2025-02-26 04:33:29.483727-08 test f A100 \N t \N \N \N {} +3588 1229 2025-02-28 14:32:08.489693-08 2025-02-28 14:05:23.563906-08 benchmark f H100 \N t \N \N \N {} +2282 767 2025-02-26 03:24:59.166944-08 2025-02-26 04:45:17.998433-08 benchmark t A100 \N t \N \N \N {} +2284 768 2025-02-26 04:18:53.859554-08 2025-02-26 03:24:39.46805-08 benchmark f A100 \N t \N \N \N {} +3283 1147 2025-02-28 06:11:40.284397-08 2025-02-28 07:19:05.74238-08 benchmark f H100 \N t \N \N \N {} +2483 870 2025-02-26 13:16:44.041088-08 2025-02-26 13:47:23.050449-08 benchmark f A100 \N t \N \N \N {} +2484 870 2025-02-26 13:10:26.731301-08 2025-02-26 14:42:14.746386-08 leaderboard f A100 0.0035295413333333333 t \N \N \N {} +2485 870 2025-02-26 13:33:50.524318-08 2025-02-26 14:10:40.249698-08 test f T4 \N t \N \N \N {} +2486 870 2025-02-26 13:05:54.760424-08 2025-02-26 13:38:28.475426-08 benchmark f T4 \N t \N \N \N {} +2499 869 2025-02-26 13:54:53.311947-08 2025-02-26 13:58:02.187272-08 leaderboard t H100 0.002322158 t \N \N \N {} +3392 1168 2025-02-28 09:12:51.277351-08 2025-02-28 09:23:36.312793-08 test f L4 \N f \N \N \N {} +2510 872 2025-02-26 13:10:41.998428-08 2025-02-26 13:52:20.643803-08 test f H100 \N t \N \N \N {} +2505 872 2025-02-26 14:18:21.923745-08 2025-02-26 14:06:00.875303-08 benchmark t H100 \N t \N \N \N {} +2506 872 2025-02-26 13:16:25.959445-08 2025-02-26 14:06:50.761927-08 leaderboard t H100 0.0014855651428571429 t \N \N \N {} +2507 872 2025-02-26 14:54:25.186275-08 2025-02-26 13:13:47.344332-08 test f A100 \N t \N \N \N {} +2593 884 2025-02-26 14:14:00.181524-08 2025-02-26 13:46:27.570181-08 benchmark f L4 \N t \N \N \N {} +2594 884 2025-02-26 13:32:06.001069-08 2025-02-26 15:05:08.151616-08 leaderboard f L4 0.017391052333333334 t \N \N \N {} +2595 884 2025-02-26 15:04:28.988097-08 2025-02-26 15:18:03.931934-08 test t L4 \N t \N \N \N {} +3420 1179 2025-02-28 10:40:09.83029-08 2025-02-28 10:08:07.480948-08 test f L4 \N f \N \N \N {} +2596 884 2025-02-26 14:56:24.039776-08 2025-02-26 14:30:30.751724-08 benchmark t L4 \N t \N \N \N {} +2597 884 2025-02-26 14:35:42.960818-08 2025-02-26 15:14:14.400532-08 leaderboard t L4 0.017414738333333332 t \N \N \N {} +4203 1388 2025-03-01 13:04:56.163242-08 2025-03-01 14:12:53.003168-08 benchmark t A100 \N t \N \N \N {} +4136 1353 2025-03-01 05:57:54.388305-08 2025-03-01 05:50:17.87171-08 test f A100 \N t \N \N \N {} +4137 1353 2025-03-01 05:16:52.584167-08 2025-03-01 06:12:22.385227-08 benchmark f A100 \N t \N \N \N {} +4138 1353 2025-03-01 05:33:09.219491-08 2025-03-01 06:16:54.189813-08 leaderboard f A100 0.003070895 t \N \N \N {} +4139 1354 2025-03-01 06:20:09.60044-08 2025-03-01 05:18:22.96192-08 test f A100 \N t \N \N \N {} +4140 1354 2025-03-01 05:58:29.731003-08 2025-03-01 05:41:56.193045-08 benchmark f A100 \N t \N \N \N {} +4218 1394 2025-03-01 15:09:41.391316-08 2025-03-01 15:57:05.307002-08 leaderboard f A100 0.003101874 t \N \N \N {} +4695 1470 2025-03-02 07:38:44.249494-08 2025-03-02 07:11:45.382141-08 test f A100 \N t \N \N \N {} +4219 1394 2025-03-01 14:45:22.969713-08 2025-03-01 14:48:00.593278-08 test f H100 \N t \N \N \N {} +4220 1394 2025-03-01 15:38:36.009293-08 2025-03-01 16:26:03.890826-08 benchmark f H100 \N t \N \N \N {} +4221 1394 2025-03-01 15:14:09.768318-08 2025-03-01 14:35:45.346475-08 leaderboard f H100 0.00139569175 t \N \N \N {} +4222 1394 2025-03-01 14:47:10.848925-08 2025-03-01 16:25:02.786375-08 test t A100 \N t \N \N \N {} +4430 1443 2025-03-01 23:16:02.466485-08 2025-03-01 23:58:09.823321-08 benchmark f T4 \N t \N \N \N {} +4431 1443 2025-03-01 23:17:08.006949-08 2025-03-02 00:41:05.844634-08 leaderboard f T4 0.009837714666666665 t \N \N \N {} +4432 1443 2025-03-02 00:29:59.441844-08 2025-03-02 00:59:33.051229-08 test t T4 \N t \N \N \N {} +4433 1443 2025-03-02 00:28:05.820758-08 2025-03-01 23:33:08.733358-08 benchmark t T4 \N t \N \N \N {} +4434 1443 2025-03-01 23:22:06.634593-08 2025-03-01 23:36:43.202641-08 leaderboard t T4 0.009814015333333334 t \N \N \N {} +4435 1444 2025-03-02 00:23:07.374461-08 2025-03-02 00:27:41.123548-08 test f A100 \N t \N \N \N {} +4436 1444 2025-03-02 00:26:01.958483-08 2025-03-01 23:49:21.361713-08 benchmark f A100 \N t \N \N \N {} +6459 2005 2025-03-14 06:48:41.779094-07 2025-03-14 06:41:15.666539-07 test t A100 \N t \N \N \N {} +4439 1444 2025-03-01 23:56:43.720976-08 2025-03-02 00:29:41.591684-08 benchmark f T4 \N t \N \N \N {} +4440 1444 2025-03-02 01:00:37.296922-08 2025-03-01 23:59:24.879229-08 leaderboard f T4 0.009901969333333333 t \N \N \N {} +4441 1444 2025-03-02 00:32:35.468671-08 2025-03-02 00:07:20.667198-08 test f H100 \N t \N \N \N {} +4442 1444 2025-03-02 00:23:25.716537-08 2025-03-01 23:07:19.512954-08 benchmark f H100 \N t \N \N \N {} +4443 1444 2025-03-01 23:26:32.233267-08 2025-03-02 00:51:40.562071-08 leaderboard f H100 0.0011824463333333333 t \N \N \N {} +4447 1444 2025-03-01 23:33:37.582909-08 2025-03-02 00:50:04.426217-08 test t A100 \N t \N \N \N {} +4448 1444 2025-03-01 23:21:44.542236-08 2025-03-02 00:08:03.502567-08 benchmark t A100 \N t \N \N \N {} +4449 1444 2025-03-01 23:37:38.569687-08 2025-03-02 00:55:50.035923-08 leaderboard t A100 0.002102921 t \N \N \N {} +4450 1444 2025-03-02 00:52:28.079251-08 2025-03-01 23:41:04.354527-08 test t H100 \N t \N \N \N {} +4451 1444 2025-03-01 23:53:41.734266-08 2025-03-02 00:14:25.295609-08 benchmark t H100 \N t \N \N \N {} +4452 1444 2025-03-01 23:41:52.983649-08 2025-03-02 00:12:57.932353-08 leaderboard t H100 0.001193107 t \N \N \N {} +4453 1444 2025-03-02 00:27:47.992493-08 2025-03-01 23:08:52.737482-08 test f L4 \N t \N \N \N {} +4454 1444 2025-03-01 23:32:46.550951-08 2025-03-01 23:59:44.887779-08 benchmark f L4 \N t \N \N \N {} +4455 1444 2025-03-02 01:06:16.006685-08 2025-03-02 01:03:15.272313-08 leaderboard f L4 0.009334652333333334 t \N \N \N {} +4456 1444 2025-03-02 00:34:05.739298-08 2025-03-01 23:57:09.516919-08 test t L4 \N t \N \N \N {} +4457 1444 2025-03-01 23:54:57.096397-08 2025-03-02 00:38:20.034987-08 benchmark t L4 \N t \N \N \N {} +6704 2080 2025-03-15 03:44:28.237409-07 2025-03-15 02:59:07.329339-07 test t A100 \N t \N \N \N {} +4460 1445 2025-03-02 00:36:15.053645-08 2025-03-02 00:04:32.394667-08 benchmark f A100 \N t \N \N \N {} +4461 1445 2025-03-02 00:39:57.735714-08 2025-03-01 23:20:00.105801-08 leaderboard f A100 0.0018449429166666666 t \N \N \N {} +4462 1445 2025-03-02 00:55:37.500529-08 2025-03-02 00:06:09.985896-08 test t H100 \N t \N \N \N {} +4463 1445 2025-03-02 01:06:57.541572-08 2025-03-02 00:21:30.815554-08 benchmark t H100 \N t \N \N \N {} +4464 1445 2025-03-02 00:13:41.720834-08 2025-03-01 23:43:07.361692-08 leaderboard t H100 0.0010767913333333333 t \N \N \N {} +4468 1445 2025-03-02 01:09:32.900149-08 2025-03-01 23:58:50.176924-08 test f T4 \N t \N \N \N {} +4469 1445 2025-03-02 00:07:37.426171-08 2025-03-02 01:02:05.35442-08 benchmark f T4 \N t \N \N \N {} +4470 1445 2025-03-02 00:31:13.200358-08 2025-03-02 00:29:49.702127-08 leaderboard f T4 0.009909986333333334 t \N \N \N {} +4471 1445 2025-03-02 00:16:25.62727-08 2025-03-02 00:16:09.130264-08 test t T4 \N t \N \N \N {} +4472 1445 2025-03-02 00:04:53.640853-08 2025-03-01 23:24:24.363564-08 benchmark t T4 \N t \N \N \N {} +4473 1445 2025-03-02 01:02:57.604673-08 2025-03-01 23:55:02.547237-08 leaderboard t T4 0.009850056666666666 t \N \N \N {} +4474 1445 2025-03-01 23:14:41.583535-08 2025-03-02 01:03:01.853819-08 test t A100 \N t \N \N \N {} +4475 1445 2025-03-01 23:54:31.749676-08 2025-03-02 00:19:06.090943-08 benchmark t A100 \N t \N \N \N {} +4476 1445 2025-03-01 23:28:11.845822-08 2025-03-02 00:35:45.681919-08 leaderboard t A100 0.001977769 t \N \N \N {} +4477 1445 2025-03-01 23:10:57.421619-08 2025-03-01 23:46:37.516111-08 test f L4 \N t \N \N \N {} +4478 1445 2025-03-02 00:00:13.580017-08 2025-03-01 23:29:52.639891-08 benchmark f L4 \N t \N \N \N {} +6707 2080 2025-03-15 03:00:01.742093-07 2025-03-15 02:58:02.613361-07 test f A100 \N t \N \N \N {} +4488 1447 2025-03-02 00:17:43.55991-08 2025-03-02 01:03:56.914497-08 test t H100 \N t \N \N \N {} +4489 1447 2025-03-02 01:08:54.903269-08 2025-03-01 23:34:35.298743-08 benchmark t H100 \N f \N \N \N {} +4490 1447 2025-03-01 23:20:16.331584-08 2025-03-02 01:02:40.427181-08 test f A100 \N t \N \N \N {} +4491 1447 2025-03-01 23:58:12.688525-08 2025-03-02 00:00:00.160252-08 benchmark f A100 \N f \N \N \N {} +4492 1447 2025-03-02 00:06:10.369337-08 2025-03-02 00:53:30.708303-08 test f T4 \N t \N \N \N {} +6710 2081 2025-03-15 03:34:31.806339-07 2025-03-15 04:12:10.063053-07 test f A100 \N t \N \N \N {} +4493 1447 2025-03-02 01:03:35.98745-08 2025-03-02 00:54:11.188019-08 benchmark f T4 \N f \N \N \N {} +4494 1447 2025-03-02 00:30:24.438417-08 2025-03-01 23:49:43.972048-08 test t T4 \N t \N \N \N {} +4495 1447 2025-03-02 00:06:52.894921-08 2025-03-02 00:53:07.74224-08 benchmark t T4 \N f \N \N \N {} +4496 1447 2025-03-01 23:56:15.387541-08 2025-03-02 01:11:05.860239-08 test f L4 \N t \N \N \N {} +4497 1447 2025-03-01 23:59:26.042618-08 2025-03-02 00:05:19.797422-08 benchmark f L4 \N f \N \N \N {} +4676 1467 2025-03-02 06:38:57.431816-08 2025-03-02 05:44:56.24281-08 leaderboard t A100 0.0030988743333333333 t \N \N \N {} +4679 1467 2025-03-02 06:07:05.077247-08 2025-03-02 06:27:34.618874-08 leaderboard f A100 0.0030814753333333333 t \N \N \N {} +4681 1468 2025-03-02 05:51:53.90541-08 2025-03-02 05:57:04.320667-08 benchmark f A100 \N t \N \N \N {} +4682 1468 2025-03-02 06:59:21.935782-08 2025-03-02 07:08:58.21826-08 leaderboard f A100 0.0030820743333333333 t \N \N \N {} +4683 1468 2025-03-02 07:33:48.301938-08 2025-03-02 05:51:18.662826-08 test t A100 \N t \N \N \N {} +4684 1468 2025-03-02 06:04:29.029074-08 2025-03-02 06:40:01.015883-08 benchmark t A100 \N t \N \N \N {} +4685 1468 2025-03-02 05:46:36.618897-08 2025-03-02 07:31:14.275548-08 leaderboard t A100 0.0030780586666666666 t \N \N \N {} +4689 1469 2025-03-02 05:56:50.321497-08 2025-03-02 05:54:49.230629-08 test t A100 \N t \N \N \N {} +4690 1469 2025-03-02 06:10:42.998637-08 2025-03-02 06:36:41.015153-08 benchmark t A100 \N t \N \N \N {} +4691 1469 2025-03-02 06:43:44.220952-08 2025-03-02 07:42:47.37538-08 leaderboard t A100 0.00307954 t \N \N \N {} +4692 1470 2025-03-02 07:27:34.955858-08 2025-03-02 07:19:49.777082-08 test t A100 \N t \N \N \N {} +4797 1487 2025-03-02 12:47:39.303587-08 2025-03-02 14:24:58.396542-08 benchmark t H100 \N f \N \N \N {} +4798 1487 2025-03-02 14:03:11.635121-08 2025-03-02 12:41:11.568145-08 test f A100 \N t \N \N \N {} +4799 1487 2025-03-02 14:16:43.466988-08 2025-03-02 13:43:02.452058-08 benchmark f A100 \N f \N \N \N {} +4800 1487 2025-03-02 12:52:53.590829-08 2025-03-02 13:24:26.405768-08 test f H100 \N t \N \N \N {} +4801 1487 2025-03-02 12:48:49.45642-08 2025-03-02 14:27:42.149075-08 benchmark f H100 \N f \N \N \N {} +6752 2093 2025-03-15 03:33:18.596707-07 2025-03-15 05:00:28.91036-07 test f A100 \N t \N \N \N {} +4846 1493 2025-03-02 14:07:28.816942-08 2025-03-02 13:42:53.598883-08 leaderboard f A100 0.001957988 t \N \N \N {} +4847 1493 2025-03-02 13:42:25.008987-08 2025-03-02 15:06:27.494787-08 test t A100 \N t \N \N \N {} +4848 1493 2025-03-02 15:03:52.371645-08 2025-03-02 15:22:56.10921-08 benchmark t A100 \N t \N \N \N {} +4849 1493 2025-03-02 13:36:12.4802-08 2025-03-02 13:52:29.977751-08 leaderboard t A100 0.0019754496666666665 t \N \N \N {} +4850 1494 2025-03-02 14:37:31.400329-08 2025-03-02 14:01:28.604796-08 test f L4 \N t \N \N \N {} +4851 1494 2025-03-02 14:43:42.095203-08 2025-03-02 13:36:47.15097-08 benchmark f L4 \N t \N \N \N {} +4974 1533 2025-03-03 22:57:29.38809-08 2025-03-03 21:55:26.67234-08 test f H100 \N t \N \N \N {} +4865 1495 2025-03-02 14:13:05.528764-08 2025-03-02 14:22:55.062967-08 benchmark t A100 \N t \N \N \N {} +4866 1495 2025-03-02 15:19:34.20592-08 2025-03-02 15:11:10.458129-08 leaderboard t A100 0.0017338363333333333 t \N \N \N {} +4867 1495 2025-03-02 13:51:41.741994-08 2025-03-02 14:01:52.182359-08 test f A100 \N t \N \N \N {} +4880 1501 2025-03-03 08:43:05.642759-08 2025-03-03 09:27:05.185793-08 benchmark f A100 \N t \N \N \N {} +4881 1502 2025-03-03 09:35:30.629754-08 2025-03-03 09:05:15.805475-08 benchmark f A100 \N t \N \N \N {} +4894 1507 2025-03-03 13:08:55.752365-08 2025-03-03 13:41:41.116144-08 test f A100 \N t \N \N \N {} +4895 1507 2025-03-03 14:18:00.216385-08 2025-03-03 14:01:18.093034-08 benchmark f A100 \N t \N \N \N {} +4896 1507 2025-03-03 12:36:28.706864-08 2025-03-03 13:53:27.430536-08 leaderboard f A100 0.023420895 t \N \N \N {} +4902 1508 2025-03-03 13:21:12.73487-08 2025-03-03 13:02:32.458846-08 leaderboard f H100 0.007582660333333333 t \N \N \N {} +4904 1510 2025-03-03 14:37:41.941114-08 2025-03-03 13:46:22.666922-08 test f T4 \N f \N \N \N {} +5251 1613 2025-03-06 07:16:47.168832-08 2025-03-06 08:00:37.45677-08 benchmark t L4 \N t \N \N \N {} +4942 1522 2025-03-03 14:30:23.866503-08 2025-03-03 15:41:40.516523-08 benchmark f H100 \N t \N \N \N {} +4943 1522 2025-03-03 14:48:33.754294-08 2025-03-03 15:31:43.342939-08 leaderboard f H100 0.0009028792280701754 t \N \N \N {} +5163 1571 2025-03-04 22:04:28.946672-08 2025-03-04 22:40:14.074477-08 benchmark t H100 \N t \N \N \N {} +5164 1571 2025-03-04 22:14:23.494998-08 2025-03-04 21:56:46.443828-08 leaderboard t H100 0.0010517243333333333 t \N \N \N {} +5165 1572 2025-03-04 22:44:09.245212-08 2025-03-04 22:55:47.144621-08 test f H100 \N f \N \N \N {} +5166 1572 2025-03-04 22:51:29.817905-08 2025-03-04 22:29:55.447229-08 test t H100 \N f \N \N \N {} +5167 1573 2025-03-04 23:41:43.36022-08 2025-03-04 22:29:50.136285-08 test f H100 \N f \N \N \N {} +5168 1573 2025-03-04 23:23:11.4983-08 2025-03-04 22:11:11.957865-08 test t H100 \N f \N \N \N {} +5169 1574 2025-03-04 22:13:23.349969-08 2025-03-04 22:18:11.101194-08 test f H100 \N f \N \N \N {} +5588 1708 2025-03-09 10:19:34.720879-07 2025-03-09 08:50:34.606837-07 test t T4 \N f \N \N \N {} +5589 1709 2025-03-09 08:35:57.715637-07 2025-03-09 08:57:16.363193-07 test f T4 \N t \N \N \N {} +5590 1709 2025-03-09 08:48:05.388801-07 2025-03-09 08:51:52.822296-07 benchmark f T4 \N t \N \N \N {} +5598 1710 2025-03-09 09:29:22.059881-07 2025-03-09 09:55:48.650399-07 test f T4 \N t \N \N \N {} +5599 1710 2025-03-09 08:55:39.716901-07 2025-03-09 09:35:45.035771-07 benchmark f T4 \N t \N \N \N {} +5600 1710 2025-03-09 08:57:39.621985-07 2025-03-09 09:06:05.154876-07 leaderboard f T4 0.016820244 t \N \N \N {} +5603 1712 2025-03-09 08:59:13.701641-07 2025-03-09 10:05:42.666778-07 test f T4 \N t \N \N \N {} +5604 1712 2025-03-09 08:36:53.940611-07 2025-03-09 09:15:28.314509-07 benchmark f T4 \N t \N \N \N {} +5605 1712 2025-03-09 09:42:44.562696-07 2025-03-09 08:51:10.667131-07 leaderboard f T4 0.017130335 t \N \N \N {} +5606 1712 2025-03-09 09:25:40.097472-07 2025-03-09 09:24:28.034725-07 test t T4 \N t \N \N \N {} +5609 1713 2025-03-09 09:05:10.861954-07 2025-03-09 09:44:03.851528-07 test f T4 \N f \N \N \N {} +5610 1713 2025-03-09 09:44:19.160845-07 2025-03-09 09:34:04.669254-07 test t T4 \N f \N \N \N {} +5611 1714 2025-03-09 08:42:31.949236-07 2025-03-09 08:41:53.417604-07 test f T4 \N t \N \N \N {} +5612 1714 2025-03-09 09:57:39.262941-07 2025-03-09 09:05:14.002676-07 benchmark f T4 \N f \N \N \N {} +5615 1715 2025-03-09 09:17:39.516615-07 2025-03-09 09:12:41.592177-07 test t T4 \N t \N \N \N {} +5616 1715 2025-03-09 10:38:09.131269-07 2025-03-09 10:32:40.256801-07 benchmark t T4 \N t \N \N \N {} +5617 1715 2025-03-09 10:30:21.604304-07 2025-03-09 09:39:45.046006-07 leaderboard t T4 0.020089393 t \N \N \N {} +5618 1715 2025-03-09 09:22:37.846053-07 2025-03-09 09:51:45.425382-07 test f T4 \N t \N \N \N {} +5619 1715 2025-03-09 10:06:35.483321-07 2025-03-09 08:49:05.978541-07 benchmark f T4 \N t \N \N \N {} +5620 1715 2025-03-09 09:57:04.884393-07 2025-03-09 09:09:40.630581-07 leaderboard f T4 0.020089969 t \N \N \N {} +5621 1716 2025-03-09 09:09:36.619819-07 2025-03-09 09:58:56.525307-07 test t T4 \N t \N \N \N {} +5922 1799 2025-03-10 12:08:29.726347-07 2025-03-10 12:16:05.005078-07 test t T4 \N f \N \N \N {} +5927 1802 2025-03-10 12:32:14.926187-07 2025-03-10 12:54:34.768223-07 test t T4 \N f \N \N \N {} +5928 1802 2025-03-10 12:50:16.888351-07 2025-03-10 13:21:42.853839-07 test f T4 \N f \N \N \N {} +5929 1803 2025-03-10 13:37:22.444553-07 2025-03-10 13:05:55.49148-07 test t T4 \N f \N \N \N {} +5930 1803 2025-03-10 12:23:55.172369-07 2025-03-10 12:26:21.880798-07 test f T4 \N f \N \N \N {} +5935 1806 2025-03-10 13:48:38.054827-07 2025-03-10 13:23:13.229669-07 test t T4 \N f \N \N \N {} +5978 1820 2025-03-10 13:29:06.016869-07 2025-03-10 14:22:13.274644-07 leaderboard f H100 0.0002589177142857143 t \N \N \N {} +5959 1814 2025-03-10 12:44:05.167824-07 2025-03-10 14:16:11.26654-07 test f T4 \N f \N \N \N {} +5979 1820 2025-03-10 14:17:15.853549-07 2025-03-10 13:56:33.556629-07 test t H100 \N t \N \N \N {} +5964 1816 2025-03-10 13:24:28.295909-07 2025-03-10 12:59:46.71286-07 test f T4 \N f \N \N \N {} +5987 1819 2025-03-10 12:59:46.080421-07 2025-03-10 13:46:59.872067-07 leaderboard t A100 0.0007810198461538461 t \N \N \N {} +5968 1818 2025-03-10 12:49:46.935079-07 2025-03-10 14:29:30.320975-07 benchmark f T4 \N t \N \N \N {} +5970 1818 2025-03-10 12:50:38.37402-07 2025-03-10 12:59:21.234188-07 test t T4 \N t \N \N \N {} +2078 702 2025-02-25 14:41:18.894451-08 2025-02-25 14:59:05.107708-08 test t H100 \N t \N \N \N {} +2079 702 2025-02-25 13:46:28.574004-08 2025-02-25 14:33:01.405183-08 benchmark t H100 \N t \N \N \N {} +2080 702 2025-02-25 14:30:42.768365-08 2025-02-25 13:11:14.979116-08 leaderboard t H100 0.0019116912857142856 t \N \N \N {} +2081 702 2025-02-25 14:40:48.443884-08 2025-02-25 13:54:57.498846-08 test f T4 \N t \N \N \N {} +2082 702 2025-02-25 14:30:54.980313-08 2025-02-25 14:44:49.165667-08 benchmark f T4 \N t \N \N \N {} +2085 702 2025-02-25 14:18:01.193831-08 2025-02-25 14:59:07.45264-08 benchmark t T4 \N t \N \N \N {} +2086 702 2025-02-25 14:20:00.930365-08 2025-02-25 13:58:38.993991-08 leaderboard t T4 0.02001272633333333 t \N \N \N {} +6950 2162 2025-03-16 16:48:51.39716-07 2025-03-16 15:12:01.465889-07 leaderboard f T4 0.00032559321276595747 t \N \N \N {} +6974 2169 2025-03-16 19:35:21.550002-07 2025-03-16 20:31:39.018156-07 leaderboard f T4 0.00024278 t \N \N \N {} +6976 2170 2025-03-16 20:19:56.143081-07 2025-03-16 19:14:22.00651-07 benchmark f T4 \N t \N \N \N {} +6977 2170 2025-03-16 20:16:18.708649-07 2025-03-16 20:36:29.899891-07 leaderboard f T4 0.00021283630303030305 t \N \N \N {} +6978 2170 2025-03-16 20:24:22.625394-07 2025-03-16 20:12:10.318919-07 test t T4 \N t \N \N \N {} +6979 2170 2025-03-16 20:24:19.649393-07 2025-03-16 19:07:58.941721-07 benchmark t T4 \N t \N \N \N {} +6980 2170 2025-03-16 19:29:57.914998-07 2025-03-16 20:10:27.993196-07 leaderboard t T4 0.00021353688461538462 t \N \N \N {} +6981 2171 2025-03-16 20:30:12.137849-07 2025-03-16 20:46:29.948675-07 test t T4 \N t \N \N \N {} +6983 2171 2025-03-16 20:50:17.763964-07 2025-03-16 19:14:31.186365-07 leaderboard t T4 0.0002254876842105263 t \N \N \N {} +6984 2171 2025-03-16 21:04:10.03094-07 2025-03-16 19:42:37.771415-07 test f T4 \N t \N \N \N {} +7060 2185 2025-03-16 21:00:49.129817-07 2025-03-16 20:45:01.000696-07 test t L4 \N t \N \N \N {} +6985 2171 2025-03-16 20:30:05.903023-07 2025-03-16 20:53:27.393446-07 benchmark f T4 \N t \N \N \N {} +6986 2171 2025-03-16 20:39:51.238598-07 2025-03-16 21:10:32.960644-07 leaderboard f T4 0.00024411110344827586 t \N \N \N {} +6987 2172 2025-03-16 20:16:03.916165-07 2025-03-16 20:44:26.852206-07 test t T4 \N t \N \N \N {} +6995 2173 2025-03-16 19:35:01.975689-07 2025-03-16 20:14:16.885975-07 leaderboard t T4 0.00023485917647058822 t \N \N \N {} +6996 2173 2025-03-16 20:21:16.631389-07 2025-03-16 20:29:29.668201-07 test f T4 \N t \N \N \N {} +6997 2173 2025-03-16 20:27:58.230216-07 2025-03-16 20:09:23.910606-07 benchmark f T4 \N t \N \N \N {} +6998 2173 2025-03-16 20:58:20.871815-07 2025-03-16 20:25:33.751661-07 leaderboard f T4 0.000249013375 t \N \N \N {} +6999 2174 2025-03-16 19:59:32.772964-07 2025-03-16 20:00:12.299702-07 test t T4 \N f \N \N \N {} +7000 2174 2025-03-16 20:57:34.509848-07 2025-03-16 20:51:30.601681-07 test f T4 \N f \N \N \N {} +7001 2175 2025-03-16 21:17:45.195814-07 2025-03-16 20:18:47.813213-07 test t T4 \N t \N \N \N {} +7002 2175 2025-03-16 21:20:26.692848-07 2025-03-16 19:32:57.604151-07 benchmark t T4 \N t \N \N \N {} +7003 2175 2025-03-16 20:44:57.359938-07 2025-03-16 19:58:30.404808-07 leaderboard t T4 0.0002332306 t \N \N \N {} +7004 2175 2025-03-16 21:10:00.970602-07 2025-03-16 21:02:06.246562-07 test f T4 \N t \N \N \N {} +7005 2175 2025-03-16 20:07:09.492932-07 2025-03-16 20:42:42.346737-07 benchmark f T4 \N t \N \N \N {} +7008 2176 2025-03-16 20:09:25.465826-07 2025-03-16 20:12:12.087915-07 benchmark t T4 \N t \N \N \N {} +7009 2176 2025-03-16 20:39:02.786771-07 2025-03-16 20:32:18.347951-07 leaderboard t T4 0.0003024322631578947 t \N \N \N {} +7010 2176 2025-03-16 20:09:27.479373-07 2025-03-16 19:53:27.585268-07 test f T4 \N t \N \N \N {} +7011 2176 2025-03-16 20:40:20.373215-07 2025-03-16 20:35:48.949342-07 benchmark f T4 \N t \N \N \N {} +7012 2176 2025-03-16 20:27:53.725911-07 2025-03-16 21:32:11.632172-07 leaderboard f T4 0.0003064759545454545 t \N \N \N {} +7014 2177 2025-03-16 20:47:48.960713-07 2025-03-16 21:32:50.346065-07 benchmark t T4 \N t \N \N \N {} +7015 2177 2025-03-16 20:22:27.227663-07 2025-03-16 21:27:05.019116-07 leaderboard t T4 0.00023873836363636365 t \N \N \N {} +7016 2177 2025-03-16 20:52:07.570894-07 2025-03-16 21:28:14.032602-07 test f T4 \N t \N \N \N {} +7017 2177 2025-03-16 21:04:02.088955-07 2025-03-16 20:13:59.587543-07 benchmark f T4 \N t \N \N \N {} +7018 2177 2025-03-16 20:52:17.778254-07 2025-03-16 21:07:11.801446-07 leaderboard f T4 0.00021685985 t \N \N \N {} +7019 2178 2025-03-16 20:51:58.999601-07 2025-03-16 20:03:37.893564-07 test f T4 \N t \N \N \N {} +7043 2182 2025-03-16 21:03:58.995548-07 2025-03-16 20:05:41.20667-07 test f H100 \N t \N \N \N {} +7049 2183 2025-03-16 21:44:08.747705-07 2025-03-16 20:09:06.554136-07 test f L4 \N t \N \N \N {} +7050 2183 2025-03-16 21:20:04.412484-07 2025-03-16 21:04:19.112094-07 benchmark f L4 \N t \N \N \N {} +7051 2183 2025-03-16 20:20:24.080976-07 2025-03-16 20:12:16.039498-07 leaderboard f L4 0.00013962746913580247 t \N \N \N {} +7052 2183 2025-03-16 21:06:27.300343-07 2025-03-16 21:35:48.036723-07 test t L4 \N t \N \N \N {} +7053 2183 2025-03-16 21:08:28.281165-07 2025-03-16 21:05:19.303361-07 benchmark t L4 \N t \N \N \N {} +7054 2183 2025-03-16 21:03:17.39211-07 2025-03-16 21:12:38.71214-07 leaderboard t L4 0.00013929977922077922 t \N \N \N {} +7057 2185 2025-03-16 20:57:18.459356-07 2025-03-16 20:58:40.616284-07 test f L4 \N t \N \N \N {} +7058 2185 2025-03-16 21:09:41.620917-07 2025-03-16 20:26:34.270924-07 benchmark f L4 \N t \N \N \N {} +7072 2187 2025-03-16 21:18:46.105748-07 2025-03-16 21:30:58.658604-07 test t A100 \N t \N \N \N {} +3823 1286 2025-03-01 02:51:18.738565-08 2025-03-01 02:35:56.135696-08 test t A100 \N t \N \N \N {} +2423 828 2025-02-26 14:04:34.908137-08 2025-02-26 12:48:44.157236-08 benchmark f H100 \N t \N \N \N {} +2844 989 2025-02-26 23:05:37.389856-08 2025-02-26 23:57:49.949237-08 test t H100 \N t \N \N \N {} +3826 1286 2025-03-01 03:10:29.776175-08 2025-03-01 02:15:26.657539-08 test f A100 \N t \N \N \N {} +2853 990 2025-02-26 23:48:13.530611-08 2025-02-27 00:27:37.952577-08 test f H100 \N t \N \N \N {} +3829 1287 2025-03-01 03:52:39.094858-08 2025-03-01 03:40:13.588698-08 test f A100 \N t \N \N \N {} +2860 992 2025-02-26 23:57:24.598857-08 2025-02-26 22:46:51.241065-08 leaderboard t H100 0.001483009888888889 t \N \N \N {} +3832 1287 2025-03-01 02:40:18.855128-08 2025-03-01 02:20:51.580001-08 test t A100 \N t \N \N \N {} +3575 1222 2025-02-28 14:25:08.626031-08 2025-02-28 13:57:58.851842-08 test f H100 \N t \N \N \N {} +2861 992 2025-02-27 00:10:48.414508-08 2025-02-27 00:15:26.826737-08 test f H100 \N t \N \N \N {} +3835 1288 2025-03-01 03:56:27.936941-08 2025-03-01 03:22:45.323479-08 test f A100 \N t \N \N \N {} +3838 1288 2025-03-01 02:49:58.666605-08 2025-03-01 03:28:40.936194-08 test t A100 \N t \N \N \N {} +3586 1228 2025-02-28 13:04:01.825429-08 2025-02-28 13:19:18.114759-08 leaderboard t A100 0.0018734915714285713 t \N \N \N {} +2208 723 2025-02-25 15:12:24.768432-08 2025-02-25 14:15:55.618693-08 benchmark f A100 \N t \N \N \N {} +3841 1289 2025-03-01 02:47:05.119415-08 2025-03-01 04:04:07.592057-08 test f A100 \N t \N \N \N {} +4808 1487 2025-03-02 13:15:32.005735-08 2025-03-02 14:08:45.412831-08 test t L4 \N t \N \N \N {} +2209 723 2025-02-25 15:18:48.649607-08 2025-02-25 15:21:36.817173-08 benchmark f H100 \N t \N \N \N {} +6755 2093 2025-03-15 04:31:09.91887-07 2025-03-15 04:32:31.426367-07 test t A100 \N t \N \N \N {} +2217 724 2025-02-25 14:40:36.314858-08 2025-02-25 15:59:23.436752-08 benchmark t H100 \N t \N \N \N {} +2227 726 2025-02-25 21:11:10.262768-08 2025-02-25 21:16:28.616268-08 test f H100 \N f \N \N \N {} +2971 1053 2025-02-27 10:22:46.566956-08 2025-02-27 11:14:00.450534-08 test f H100 \N f \N \N \N {} +2229 728 2025-02-25 21:49:59.544732-08 2025-02-25 21:13:13.071273-08 test f H100 \N f \N \N \N {} +2249 748 2025-02-26 01:20:54.630857-08 2025-02-26 01:48:02.906843-08 test f H100 \N f \N \N \N {} +2251 750 2025-02-26 01:02:46.596404-08 2025-02-26 01:40:29.396697-08 benchmark f H100 \N f \N \N \N {} +2203 721 2025-02-25 14:21:58.314111-08 2025-02-25 15:40:17.725093-08 test f A100 \N f \N \N \N {} +2204 721 2025-02-25 15:30:04.482461-08 2025-02-25 15:34:55.709429-08 test f H100 \N f \N \N \N {} +2205 721 2025-02-25 15:37:24.227133-08 2025-02-25 14:52:16.301155-08 test f T4 \N f \N \N \N {} +2206 721 2025-02-25 14:14:52.248428-08 2025-02-25 14:35:57.115198-08 test f L4 \N f \N \N \N {} +2207 722 2025-02-25 14:23:22.656547-08 2025-02-25 14:28:52.286564-08 test f A100 \N t \N \N \N {} +2231 730 2025-02-25 22:25:36.727385-08 2025-02-25 22:07:18.246829-08 test f H100 \N f \N \N \N {} +2252 751 2025-02-26 01:22:15.758845-08 2025-02-26 00:47:07.586743-08 benchmark f H100 \N f \N \N \N {} +2210 724 2025-02-25 15:01:55.831184-08 2025-02-25 15:27:25.721588-08 test f H100 \N t \N \N \N {} +2211 724 2025-02-25 16:00:18.379653-08 2025-02-25 15:14:19.774224-08 benchmark f H100 \N t \N \N \N {} +2212 724 2025-02-25 15:21:00.820815-08 2025-02-25 14:21:32.48071-08 leaderboard f H100 0.00347018762 t \N \N \N {} +2213 724 2025-02-25 15:55:13.720814-08 2025-02-25 14:39:28.319218-08 test t A100 \N t \N \N \N {} +2214 724 2025-02-25 15:15:10.350587-08 2025-02-25 15:20:05.920535-08 benchmark t A100 \N t \N \N \N {} +2222 724 2025-02-25 15:51:08.090008-08 2025-02-25 14:52:07.078563-08 test t L4 \N f \N \N \N {} +2223 724 2025-02-25 15:59:35.150135-08 2025-02-25 15:41:40.617138-08 test t T4 \N f \N \N \N {} +2224 724 2025-02-25 15:59:58.549919-08 2025-02-25 16:16:59.086077-08 test f T4 \N f \N \N \N {} +2225 724 2025-02-25 15:31:29.288566-08 2025-02-25 14:20:08.177759-08 test f L4 \N f \N \N \N {} +2236 735 2025-02-25 22:08:20.946921-08 2025-02-25 22:28:19.364454-08 test f H100 \N f \N \N \N {} +2241 740 2025-02-25 23:14:33.485835-08 2025-02-25 23:51:46.740346-08 benchmark f A100 \N t \N \N \N {} +2242 741 2025-02-25 23:35:50.055644-08 2025-02-25 22:49:24.455708-08 benchmark f A100 \N t \N \N \N {} +2243 742 2025-02-26 00:25:26.264646-08 2025-02-25 23:17:09.093175-08 benchmark f A100 \N f \N \N \N {} +2245 744 2025-02-26 00:42:02.958881-08 2025-02-26 00:45:16.278457-08 test f H100 \N f \N \N \N {} +2254 753 2025-02-26 02:08:05.477951-08 2025-02-26 01:13:20.051352-08 benchmark f H100 \N f \N \N \N {} +2255 754 2025-02-26 00:59:44.093208-08 2025-02-26 02:58:08.265275-08 benchmark f H100 \N f \N \N \N {} +2259 758 2025-02-26 01:17:58.081473-08 2025-02-26 01:34:49.549302-08 benchmark f H100 \N t \N \N \N {} +2260 759 2025-02-26 01:37:29.394797-08 2025-02-26 02:47:14.747208-08 benchmark f H100 \N t \N \N \N {} +2261 760 2025-02-26 01:39:24.386253-08 2025-02-26 02:18:01.358833-08 benchmark f H100 \N t \N \N \N {} +2262 761 2025-02-26 03:15:59.591174-08 2025-02-26 01:36:18.508508-08 benchmark f H100 \N t \N \N \N {} +2265 762 2025-02-26 02:01:34.231469-08 2025-02-26 03:54:41.609703-08 leaderboard t H100 0.0014416166666666667 t \N \N \N {} +2274 766 2025-02-26 04:05:07.529508-08 2025-02-26 04:58:07.255773-08 leaderboard f A100 0.0008179115 t \N \N \N {} +2275 766 2025-02-26 03:48:00.408098-08 2025-02-26 04:08:59.825239-08 test t A100 \N t \N \N \N {} +2301 774 2025-02-26 04:32:05.77669-08 2025-02-26 04:38:11.895003-08 benchmark f A100 \N t \N \N \N {} +2302 774 2025-02-26 04:13:31.801702-08 2025-02-26 04:01:09.656019-08 leaderboard f A100 0.0031047866666666663 t \N \N \N {} +2303 774 2025-02-26 04:24:37.740211-08 2025-02-26 03:39:00.774209-08 test t A100 \N t \N \N \N {} +2308 777 2025-02-26 04:25:35.388029-08 2025-02-26 03:30:27.492815-08 benchmark f A100 \N t \N \N \N {} +2309 778 2025-02-26 03:49:41.847565-08 2025-02-26 05:07:38.629246-08 test t A100 \N t \N \N \N {} +2310 778 2025-02-26 04:40:13.137449-08 2025-02-26 03:29:03.613607-08 benchmark t A100 \N t \N \N \N {} +2316 781 2025-02-26 08:04:59.695461-08 2025-02-26 08:20:17.559059-08 benchmark t A100 \N t \N \N \N {} +2317 781 2025-02-26 07:31:58.361931-08 2025-02-26 07:59:50.437989-08 leaderboard t A100 0.0031859682 t \N \N \N {} +2324 783 2025-02-26 08:10:22.678801-08 2025-02-26 07:07:39.819759-08 benchmark t A100 \N t \N \N \N {} +2325 783 2025-02-26 08:24:49.179778-08 2025-02-26 08:36:36.413259-08 leaderboard t A100 0.00325122275 t \N \N \N {} +2335 785 2025-02-26 08:25:39.641455-08 2025-02-26 06:58:31.005281-08 benchmark t A100 \N t \N \N \N {} +2336 785 2025-02-26 08:09:56.776695-08 2025-02-26 08:13:07.269228-08 leaderboard t A100 0.004335441666666667 t \N \N \N {} +2340 787 2025-02-26 07:39:15.682026-08 2025-02-26 08:18:25.113435-08 test f A100 \N f \N \N \N {} +2341 788 2025-02-26 07:31:19.708086-08 2025-02-26 08:57:53.961399-08 test t A100 \N f \N \N \N {} +2342 788 2025-02-26 08:25:02.656473-08 2025-02-26 08:25:08.005045-08 test f A100 \N f \N \N \N {} +2343 789 2025-02-26 08:43:37.93693-08 2025-02-26 08:07:41.07638-08 test t A100 \N t \N \N \N {} +2347 789 2025-02-26 09:06:31.119554-08 2025-02-26 07:11:28.595374-08 benchmark f A100 \N t \N \N \N {} +2348 789 2025-02-26 07:29:52.547362-08 2025-02-26 08:50:51.272086-08 leaderboard f A100 0.003266832777777778 t \N \N \N {} +2359 793 2025-02-26 09:11:48.758042-08 2025-02-26 08:04:27.206933-08 test t H100 \N f \N \N \N {} +2360 793 2025-02-26 09:01:14.507106-08 2025-02-26 08:36:00.01203-08 test f H100 \N f \N \N \N {} +2361 794 2025-02-26 10:31:01.311335-08 2025-02-26 10:24:32.132628-08 test f T4 \N t \N \N \N {} +2365 795 2025-02-26 10:42:54.506552-08 2025-02-26 10:33:48.974343-08 benchmark f T4 \N f \N \N \N {} +2366 796 2025-02-26 10:39:57.933478-08 2025-02-26 09:13:35.87411-08 test f T4 \N t \N \N \N {} +2367 796 2025-02-26 08:55:18.203941-08 2025-02-26 10:26:58.100193-08 test f A100 \N f \N \N \N {} +2368 796 2025-02-26 09:46:15.118953-08 2025-02-26 10:28:06.814641-08 test f L4 \N f \N \N \N {} +2373 800 2025-02-26 11:48:00.696003-08 2025-02-26 11:05:20.167368-08 test f L4 \N t \N \N \N {} +2374 800 2025-02-26 11:07:59.074012-08 2025-02-26 12:43:47.36544-08 benchmark f L4 \N t \N \N \N {} +2375 800 2025-02-26 12:41:54.306115-08 2025-02-26 11:06:41.948227-08 leaderboard f L4 0.019241309 t \N \N \N {} +2376 800 2025-02-26 11:45:20.290145-08 2025-02-26 12:31:25.711521-08 test t L4 \N t \N \N \N {} +2377 800 2025-02-26 12:39:01.06552-08 2025-02-26 12:57:56.856584-08 benchmark t L4 \N t \N \N \N {} +2378 800 2025-02-26 11:19:43.410096-08 2025-02-26 12:49:08.158954-08 leaderboard t L4 0.01928871 t \N \N \N {} +2379 801 2025-02-26 11:38:56.285241-08 2025-02-26 12:19:54.264843-08 test f A100 \N t \N \N \N {} +2382 804 2025-02-26 12:57:43.215115-08 2025-02-26 13:30:55.355298-08 benchmark f H100 \N t \N \N \N {} +2383 805 2025-02-26 13:12:00.965515-08 2025-02-26 12:47:43.718436-08 test t H100 \N t \N \N \N {} +2384 805 2025-02-26 12:02:09.112152-08 2025-02-26 13:33:00.538551-08 benchmark t H100 \N t \N \N \N {} +2385 805 2025-02-26 12:07:29.57397-08 2025-02-26 11:40:32.117689-08 leaderboard t H100 0.006281822 t \N \N \N {} +2386 805 2025-02-26 13:23:11.459374-08 2025-02-26 12:01:38.625408-08 test f H100 \N t \N \N \N {} +2387 805 2025-02-26 12:40:46.998681-08 2025-02-26 13:19:43.340082-08 benchmark f H100 \N t \N \N \N {} +2388 805 2025-02-26 12:28:02.085984-08 2025-02-26 12:00:56.151234-08 leaderboard f H100 0.006283131666666667 t \N \N \N {} +2400 812 2025-02-26 12:35:43.081119-08 2025-02-26 11:46:25.709461-08 leaderboard f A100 0.0073987305599999996 t \N \N \N {} +2399 812 2025-02-26 13:00:44.684656-08 2025-02-26 12:40:03.546738-08 benchmark f A100 \N t \N \N \N {} +2412 814 2025-02-26 11:55:21.072277-08 2025-02-26 13:21:18.388476-08 benchmark f H100 \N f \N \N \N {} +2406 812 2025-02-26 11:58:36.640565-08 2025-02-26 12:25:12.325022-08 benchmark t H100 \N t \N \N \N {} +2413 815 2025-02-26 12:34:40.3804-08 2025-02-26 12:01:23.839657-08 benchmark f H100 \N f \N \N \N {} +2407 812 2025-02-26 13:09:11.965272-08 2025-02-26 13:07:20.626098-08 leaderboard t H100 0.0034966516099999997 t \N \N \N {} +2408 813 2025-02-26 12:32:37.101507-08 2025-02-26 13:16:23.500546-08 benchmark f H100 \N t \N \N \N {} +2409 812 2025-02-26 13:29:54.187186-08 2025-02-26 13:23:28.632317-08 test t L4 \N f \N \N \N {} +2424 829 2025-02-26 13:07:19.553-08 2025-02-26 14:01:46.739578-08 benchmark f H100 \N f \N \N \N {} +2425 830 2025-02-26 14:02:43.362147-08 2025-02-26 12:33:07.345304-08 benchmark f H100 \N t \N \N \N {} +2426 831 2025-02-26 12:31:38.224085-08 2025-02-26 13:19:43.236285-08 benchmark f H100 \N t \N \N \N {} +3850 1290 2025-03-01 04:03:10.861329-08 2025-03-01 03:39:38.665791-08 test t A100 \N t \N \N \N {} +2428 834 2025-02-26 13:15:44.571141-08 2025-02-26 13:44:55.710615-08 benchmark f H100 \N t \N \N \N {} +2429 835 2025-02-26 13:42:00.820827-08 2025-02-26 13:22:37.497485-08 benchmark f H100 \N t \N \N \N {} +2430 836 2025-02-26 13:48:13.271344-08 2025-02-26 13:35:52.895605-08 benchmark f H100 \N t \N \N \N {} +2431 839 2025-02-26 12:27:35.852032-08 2025-02-26 13:54:55.127064-08 test f H100 \N t \N \N \N {} +2432 839 2025-02-26 13:13:16.24415-08 2025-02-26 12:31:35.696649-08 benchmark f H100 \N t \N \N \N {} +2433 839 2025-02-26 13:25:16.844207-08 2025-02-26 12:41:49.998945-08 leaderboard f H100 0.0014103766666666668 t \N \N \N {} +2434 839 2025-02-26 13:02:21.500262-08 2025-02-26 13:19:32.907466-08 test t H100 \N t \N \N \N {} +3275 1144 2025-02-28 05:52:25.859901-08 2025-02-28 06:09:38.762195-08 test f A100 \N t \N \N \N {} +2437 843 2025-02-26 14:26:52.109887-08 2025-02-26 14:21:54.916482-08 benchmark f A100 \N t \N \N \N {} +2438 845 2025-02-26 12:52:37.260995-08 2025-02-26 12:43:22.265476-08 test f L4 \N t \N \N \N {} +2439 845 2025-02-26 13:15:51.000933-08 2025-02-26 13:07:58.39425-08 benchmark f L4 \N t \N \N \N {} +2440 845 2025-02-26 13:28:47.225016-08 2025-02-26 13:45:32.97832-08 leaderboard f L4 0.043612971333333334 t \N \N \N {} +2443 845 2025-02-26 14:12:08.997237-08 2025-02-26 12:35:40.416269-08 leaderboard t L4 0.04366572966666667 t \N \N \N {} +2444 850 2025-02-26 14:13:58.990169-08 2025-02-26 12:46:54.480342-08 benchmark f A100 \N t \N \N \N {} +2445 851 2025-02-26 14:31:38.156136-08 2025-02-26 14:03:21.128088-08 test f A100 \N f \N \N \N {} +2446 851 2025-02-26 14:23:33.344316-08 2025-02-26 12:39:36.843848-08 test t A100 \N f \N \N \N {} +3853 1291 2025-03-01 03:13:24.343849-08 2025-03-01 02:57:41.913482-08 test t A100 \N t \N \N \N {} +2448 854 2025-02-26 14:40:16.012015-08 2025-02-26 13:30:53.984566-08 benchmark f A100 \N t \N \N \N {} +2449 855 2025-02-26 13:12:11.591988-08 2025-02-26 14:39:17.48303-08 benchmark f A100 \N t \N \N \N {} +2450 857 2025-02-26 13:02:50.277788-08 2025-02-26 13:15:55.340603-08 benchmark f A100 \N t \N \N \N {} +2456 860 2025-02-26 12:50:27.406788-08 2025-02-26 14:27:11.783431-08 leaderboard f A100 0.0030875686666666665 t \N \N \N {} +2457 860 2025-02-26 13:17:23.373759-08 2025-02-26 14:15:06.821417-08 test t A100 \N t \N \N \N {} +2458 860 2025-02-26 13:49:59.089223-08 2025-02-26 14:34:48.510454-08 benchmark t A100 \N t \N \N \N {} +2459 860 2025-02-26 12:54:53.87546-08 2025-02-26 13:27:24.625628-08 leaderboard t A100 0.003084496 t \N \N \N {} +2460 862 2025-02-26 14:25:24.115849-08 2025-02-26 14:24:38.388715-08 benchmark f A100 \N t \N \N \N {} +2461 863 2025-02-26 12:52:35.025536-08 2025-02-26 13:59:55.535093-08 benchmark f A100 \N t \N \N \N {} +3278 1144 2025-02-28 06:16:01.039147-08 2025-02-28 06:22:29.16157-08 test t A100 \N t \N \N \N {} +2467 865 2025-02-26 14:29:40.579994-08 2025-02-26 13:01:17.531306-08 benchmark t A100 \N t \N \N \N {} +2468 865 2025-02-26 14:27:27.068975-08 2025-02-26 13:15:03.336598-08 leaderboard t A100 0.0068730191799999995 t \N \N \N {} +2469 867 2025-02-26 14:38:45.220709-08 2025-02-26 13:20:49.410663-08 benchmark f H100 \N t \N \N \N {} +2470 870 2025-02-26 14:35:14.099811-08 2025-02-26 13:16:45.84219-08 test t L4 \N t \N \N \N {} +2471 870 2025-02-26 14:54:17.97184-08 2025-02-26 14:53:55.242909-08 benchmark t L4 \N t \N \N \N {} +3163 1117 2025-02-28 02:58:49.256531-08 2025-02-28 02:51:45.758919-08 test f A100 \N t \N \N \N {} +2472 870 2025-02-26 14:49:05.199571-08 2025-02-26 13:43:51.365921-08 leaderboard t L4 0.01752669466666667 t \N \N \N {} +2473 870 2025-02-26 14:29:38.067462-08 2025-02-26 13:42:20.183691-08 test t H100 \N t \N \N \N {} +2474 870 2025-02-26 14:28:13.427627-08 2025-02-26 14:43:39.03146-08 benchmark t H100 \N t \N \N \N {} +2475 870 2025-02-26 14:44:05.364863-08 2025-02-26 14:18:20.873024-08 leaderboard t H100 0.0014439003333333333 t \N \N \N {} +2476 870 2025-02-26 14:09:52.898678-08 2025-02-26 14:05:33.382355-08 test t T4 \N t \N \N \N {} +2482 870 2025-02-26 14:26:54.587486-08 2025-02-26 13:04:15.11736-08 test f A100 \N t \N \N \N {} +3391 1168 2025-02-28 09:57:12.308014-08 2025-02-28 09:07:40.869103-08 test t L4 \N f \N \N \N {} +2477 870 2025-02-26 14:11:49.901716-08 2025-02-26 13:57:28.933272-08 benchmark t T4 \N t \N \N \N {} +2478 870 2025-02-26 14:52:49.914318-08 2025-02-26 14:30:14.957771-08 leaderboard t T4 0.018135662333333333 t \N \N \N {} +2479 870 2025-02-26 13:04:33.014453-08 2025-02-26 13:28:38.801176-08 test f H100 \N t \N \N \N {} +2480 870 2025-02-26 13:05:56.396107-08 2025-02-26 13:11:26.670759-08 benchmark f H100 \N t \N \N \N {} +2487 870 2025-02-26 13:53:37.8607-08 2025-02-26 13:42:40.91502-08 leaderboard f T4 0.018169958333333333 t \N \N \N {} +3284 1147 2025-02-28 06:34:56.869895-08 2025-02-28 06:59:54.488727-08 benchmark f A100 \N t \N \N \N {} +2488 870 2025-02-26 13:55:29.990434-08 2025-02-26 13:42:48.566834-08 test t A100 \N t \N \N \N {} +2489 870 2025-02-26 13:33:21.862277-08 2025-02-26 13:18:14.133316-08 benchmark t A100 \N t \N \N \N {} +2490 870 2025-02-26 14:25:53.367799-08 2025-02-26 14:32:23.689127-08 leaderboard t A100 0.0035314313333333334 t \N \N \N {} +2491 870 2025-02-26 13:48:35.450822-08 2025-02-26 14:06:57.276021-08 test f L4 \N t \N \N \N {} +2492 870 2025-02-26 13:52:29.744988-08 2025-02-26 13:53:25.403361-08 benchmark f L4 \N t \N \N \N {} +3285 1147 2025-02-28 06:24:43.203152-08 2025-02-28 06:13:29.756699-08 benchmark f T4 \N t \N \N \N {} +2493 870 2025-02-26 13:59:31.991194-08 2025-02-26 13:59:26.579581-08 leaderboard f L4 0.017607324 t \N \N \N {} +2494 869 2025-02-26 13:53:41.469056-08 2025-02-26 14:38:54.569715-08 test f H100 \N t \N \N \N {} +2495 869 2025-02-26 13:23:26.566841-08 2025-02-26 14:00:54.453804-08 benchmark f H100 \N t \N \N \N {} +2496 869 2025-02-26 14:13:25.627702-08 2025-02-26 13:16:17.10637-08 leaderboard f H100 0.0029435355699999996 t \N \N \N {} +2497 869 2025-02-26 13:45:34.876263-08 2025-02-26 13:53:58.749847-08 test t H100 \N t \N \N \N {} +2498 869 2025-02-26 13:46:26.837661-08 2025-02-26 13:24:26.415607-08 benchmark t H100 \N t \N \N \N {} +2503 872 2025-02-26 14:19:49.973867-08 2025-02-26 13:58:04.924559-08 leaderboard t A100 0.0031960086666666665 t \N \N \N {} +2524 872 2025-02-26 14:59:20.978098-08 2025-02-26 14:04:05.964393-08 leaderboard t L4 0.01734968933333333 t \N \N \N {} +2525 873 2025-02-26 14:17:24.445616-08 2025-02-26 13:21:44.500572-08 benchmark f T4 \N t \N \N \N {} +3311 1149 2025-02-28 07:38:30.746822-08 2025-02-28 06:13:50.041675-08 benchmark f H100 \N t \N \N \N {} +2526 874 2025-02-26 14:02:11.631299-08 2025-02-26 14:54:22.504542-08 benchmark f H100 \N f \N \N \N {} +2528 875 2025-02-26 13:53:59.206204-08 2025-02-26 13:59:51.952233-08 benchmark t L4 \N t \N \N \N {} +2529 875 2025-02-26 13:45:06.978432-08 2025-02-26 14:27:23.069181-08 leaderboard t L4 0.017381977333333333 t \N \N \N {} +2530 875 2025-02-26 13:14:04.120622-08 2025-02-26 13:37:23.311234-08 test f H100 \N t \N \N \N {} +2536 875 2025-02-26 14:17:45.203727-08 2025-02-26 13:52:51.20816-08 test t A100 \N t \N \N \N {} +3398 1172 2025-02-28 10:30:25.502848-08 2025-02-28 09:32:32.438948-08 test t L4 \N f \N \N \N {} +2531 875 2025-02-26 13:27:29.331193-08 2025-02-26 13:29:35.204364-08 benchmark f H100 \N t \N \N \N {} +2538 875 2025-02-26 13:53:08.870239-08 2025-02-26 14:00:49.668667-08 leaderboard t A100 0.0032747976666666666 t \N \N \N {} +2539 875 2025-02-26 13:14:52.010055-08 2025-02-26 14:18:34.81441-08 test f T4 \N t \N \N \N {} +2540 875 2025-02-26 13:20:18.964191-08 2025-02-26 13:13:55.628835-08 benchmark f T4 \N t \N \N \N {} +2541 875 2025-02-26 14:54:26.113811-08 2025-02-26 13:43:25.579169-08 leaderboard f T4 0.018149821333333333 t \N \N \N {} +3349 1156 2025-02-28 06:29:55.568265-08 2025-02-28 08:02:02.535704-08 leaderboard f A100 0.0031082066666666664 t \N \N \N {} +2542 875 2025-02-26 14:51:18.161936-08 2025-02-26 14:37:36.137121-08 test t H100 \N t \N \N \N {} +2543 875 2025-02-26 14:35:54.200978-08 2025-02-26 13:13:31.055337-08 benchmark t H100 \N t \N \N \N {} +2544 875 2025-02-26 14:33:45.385769-08 2025-02-26 13:26:20.622935-08 leaderboard t H100 0.0014337893333333334 t \N \N \N {} +2545 875 2025-02-26 13:53:24.267369-08 2025-02-26 13:18:55.978128-08 test t T4 \N t \N \N \N {} +2546 875 2025-02-26 13:38:11.861473-08 2025-02-26 14:59:51.393224-08 benchmark t T4 \N t \N \N \N {} +2567 877 2025-02-26 15:03:59.772268-08 2025-02-26 14:40:45.912645-08 test t A100 \N t \N \N \N {} +2547 875 2025-02-26 14:32:26.84369-08 2025-02-26 13:13:58.565371-08 leaderboard t T4 0.01822390733333333 t \N \N \N {} +2552 877 2025-02-26 13:20:01.898564-08 2025-02-26 13:58:51.317431-08 benchmark f L4 \N t \N \N \N {} +2557 876 2025-02-26 15:02:00.99544-08 2025-02-26 13:29:31.499121-08 benchmark f T4 \N t \N \N \N {} +2558 877 2025-02-26 13:46:19.16509-08 2025-02-26 14:56:55.471863-08 test f A100 \N t \N \N \N {} +2562 877 2025-02-26 14:11:45.890202-08 2025-02-26 14:42:19.158457-08 benchmark f H100 \N t \N \N \N {} +2574 877 2025-02-26 14:53:25.554208-08 2025-02-26 14:16:55.579672-08 benchmark t T4 \N t \N \N \N {} +2580 880 2025-02-26 15:07:50.100053-08 2025-02-26 13:18:56.845732-08 benchmark f H100 \N t \N \N \N {} +2585 883 2025-02-26 15:03:39.651289-08 2025-02-26 14:42:53.129821-08 benchmark f T4 \N t \N \N \N {} +2586 884 2025-02-26 14:50:00.889114-08 2025-02-26 15:10:04.434058-08 test f A100 \N t \N \N \N {} +2590 885 2025-02-26 15:19:01.393865-08 2025-02-26 13:28:15.590615-08 benchmark f H100 \N t \N \N \N {} +2591 885 2025-02-26 13:26:24.818556-08 2025-02-26 13:50:12.168949-08 leaderboard f H100 0.0014732981818181819 t \N \N \N {} +2602 884 2025-02-26 14:57:40.130347-08 2025-02-26 14:36:36.467514-08 benchmark t H100 \N t \N \N \N {} +2603 884 2025-02-26 14:47:56.199118-08 2025-02-26 14:47:39.751177-08 leaderboard t H100 0.001451446 t \N \N \N {} +2608 884 2025-02-26 14:52:54.969875-08 2025-02-26 14:45:17.829971-08 benchmark t T4 \N t \N \N \N {} +2609 884 2025-02-26 14:00:42.000568-08 2025-02-26 14:07:23.322658-08 leaderboard t T4 0.01839805933333333 t \N \N \N {} +2623 885 2025-02-26 13:24:51.303162-08 2025-02-26 14:59:25.978603-08 benchmark f L4 \N t \N \N \N {} +2624 885 2025-02-26 14:46:25.13041-08 2025-02-26 14:01:16.028106-08 leaderboard f L4 0.018322243399999997 t \N \N \N {} +2632 885 2025-02-26 13:42:28.590895-08 2025-02-26 15:11:24.84455-08 benchmark f T4 \N t \N \N \N {} +2633 885 2025-02-26 14:17:07.144881-08 2025-02-26 13:42:07.341899-08 leaderboard f T4 0.017316619 t \N \N \N {} +2638 890 2025-02-26 14:38:01.800384-08 2025-02-26 14:21:29.502344-08 benchmark f H100 \N t \N \N \N {} +2639 891 2025-02-26 14:00:22.235154-08 2025-02-26 13:49:58.414404-08 benchmark f H100 \N t \N \N \N {} +2640 893 2025-02-26 14:44:28.627123-08 2025-02-26 14:56:02.319874-08 benchmark f H100 \N t \N \N \N {} +2641 892 2025-02-26 14:06:16.166538-08 2025-02-26 14:45:31.778383-08 benchmark f H100 \N t \N \N \N {} +2646 898 2025-02-26 15:10:17.627593-08 2025-02-26 14:28:58.359942-08 benchmark f H100 \N t \N \N \N {} +2647 899 2025-02-26 13:42:07.536404-08 2025-02-26 14:35:17.861549-08 benchmark f H100 \N t \N \N \N {} +2652 909 2025-02-26 15:08:14.043409-08 2025-02-26 14:36:11.735601-08 test f T4 \N f \N \N \N {} +2674 917 2025-02-26 14:23:43.794578-08 2025-02-26 14:36:33.755414-08 benchmark t T4 \N t \N \N \N {} +2686 922 2025-02-26 15:57:13.427621-08 2025-02-26 15:26:20.611904-08 test f T4 \N t \N \N \N {} +2696 925 2025-02-26 14:17:38.083972-08 2025-02-26 16:03:31.423476-08 leaderboard f A100 0.0032370253333333333 t \N \N \N {} +2713 927 2025-02-26 14:37:41.802897-08 2025-02-26 14:31:27.897832-08 test f H100 \N t \N \N \N {} +2714 927 2025-02-26 15:15:35.837289-08 2025-02-26 14:47:25.94265-08 benchmark f H100 \N t \N \N \N {} +2718 927 2025-02-26 15:59:35.075538-08 2025-02-26 15:41:14.515832-08 leaderboard t H100 0.0008215225714285714 t \N \N \N {} +2724 937 2025-02-26 16:47:17.193058-08 2025-02-26 16:23:22.453844-08 test f H100 \N t \N \N \N {} +2725 937 2025-02-26 15:50:22.227979-08 2025-02-26 16:45:26.76204-08 benchmark f H100 \N t \N \N \N {} +2726 937 2025-02-26 16:51:40.672188-08 2025-02-26 16:03:32.138312-08 leaderboard f H100 0.00079554 t \N \N \N {} +2727 937 2025-02-26 15:40:40.022004-08 2025-02-26 17:18:13.128244-08 test t H100 \N t \N \N \N {} +2728 937 2025-02-26 17:19:47.937296-08 2025-02-26 17:33:18.727563-08 benchmark t H100 \N t \N \N \N {} +2789 974 2025-02-26 20:55:25.229033-08 2025-02-26 20:10:10.409265-08 test t A100 \N t \N \N \N {} +3494 1198 2025-02-28 11:44:26.606686-08 2025-02-28 12:27:34.310314-08 test t A100 \N t \N \N \N {} +3877 1295 2025-03-01 04:30:13.818352-08 2025-03-01 03:53:15.973054-08 test f A100 \N t \N \N \N {} +2734 939 2025-02-26 16:52:57.1295-08 2025-02-26 16:57:05.775532-08 test t H100 \N t \N \N \N {} +2735 939 2025-02-26 16:51:34.603971-08 2025-02-26 16:14:14.968064-08 benchmark t H100 \N t \N \N \N {} +2736 939 2025-02-26 17:22:29.074864-08 2025-02-26 17:19:20.267253-08 leaderboard t H100 0.0004968643 t \N \N \N {} +2737 940 2025-02-26 17:36:57.572482-08 2025-02-26 17:38:12.608294-08 benchmark f H100 \N t \N \N \N {} +2738 941 2025-02-26 16:31:57.595239-08 2025-02-26 17:53:18.674166-08 benchmark f H100 \N t \N \N \N {} +2740 943 2025-02-26 18:29:20.73428-08 2025-02-26 18:00:14.467783-08 test f H100 \N f \N \N \N {} +2742 945 2025-02-26 19:02:41.462274-08 2025-02-26 17:30:22.202193-08 test f H100 \N f \N \N \N {} +2743 946 2025-02-26 18:48:22.350038-08 2025-02-26 17:37:45.056255-08 test f H100 \N t \N \N \N {} +2744 947 2025-02-26 17:40:41.628733-08 2025-02-26 18:01:07.85212-08 benchmark f H100 \N t \N \N \N {} +3497 1199 2025-02-28 10:52:33.356367-08 2025-02-28 11:45:48.622925-08 test f A100 \N t \N \N \N {} +2746 949 2025-02-26 19:17:31.915891-08 2025-02-26 17:48:54.299006-08 test f H100 \N t \N \N \N {} +3590 1229 2025-02-28 14:30:29.574017-08 2025-02-28 13:08:20.706884-08 test t H100 \N t \N \N \N {} +2748 951 2025-02-26 17:49:30.605776-08 2025-02-26 19:07:43.438503-08 benchmark f H100 \N t \N \N \N {} +2749 952 2025-02-26 18:03:40.668218-08 2025-02-26 17:46:00.26634-08 test t H100 \N t \N \N \N {} +3500 1199 2025-02-28 11:02:55.892242-08 2025-02-28 11:47:33.229851-08 test t A100 \N t \N \N \N {} +2750 952 2025-02-26 17:51:37.477609-08 2025-02-26 18:26:25.871776-08 benchmark t H100 \N t \N \N \N {} +2753 952 2025-02-26 18:36:33.794858-08 2025-02-26 18:27:26.243733-08 benchmark f H100 \N t \N \N \N {} +2754 952 2025-02-26 18:28:16.765992-08 2025-02-26 19:25:37.400542-08 leaderboard f H100 0.00005700789 t \N \N \N {} +3503 1200 2025-02-28 12:12:33.080144-08 2025-02-28 10:58:43.275181-08 test f A100 \N t \N \N \N {} +3880 1295 2025-03-01 02:41:14.083823-08 2025-03-01 02:48:54.508486-08 test t A100 \N t \N \N \N {} +2758 956 2025-02-26 19:12:28.514931-08 2025-02-26 19:22:57.495178-08 test f A100 \N f \N \N \N {} +2764 962 2025-02-26 19:58:47.609776-08 2025-02-26 20:12:54.160596-08 test f A100 \N f \N \N \N {} +2777 965 2025-02-26 19:44:07.999345-08 2025-02-26 19:22:11.183958-08 benchmark t A100 \N t \N \N \N {} +2778 965 2025-02-26 20:27:24.433649-08 2025-02-26 21:00:25.129429-08 leaderboard t A100 0.014223114 t \N \N \N {} +2779 967 2025-02-26 21:12:06.665368-08 2025-02-26 19:59:07.508513-08 test f A100 \N t \N \N \N {} +2783 971 2025-02-26 21:16:26.422875-08 2025-02-26 20:09:38.194507-08 test f A100 \N f \N \N \N {} +2784 972 2025-02-26 19:47:01.998273-08 2025-02-26 20:04:32.122274-08 test f A100 \N f \N \N \N {} +2793 975 2025-02-26 21:52:56.123062-08 2025-02-26 20:49:58.093155-08 benchmark t A100 \N t \N \N \N {} +2795 975 2025-02-26 21:40:13.75942-08 2025-02-26 21:41:56.411339-08 test f A100 \N t \N \N \N {} +2802 976 2025-02-26 20:52:48.46039-08 2025-02-26 21:31:20.714827-08 benchmark t A100 \N t \N \N \N {} +2803 976 2025-02-26 20:35:27.685284-08 2025-02-26 20:31:29.866823-08 leaderboard t A100 0.0031533973333333336 t \N \N \N {} +2808 981 2025-02-26 23:31:14.250849-08 2025-02-26 22:50:58.663532-08 test f H100 \N t \N \N \N {} +2809 981 2025-02-26 22:06:44.871153-08 2025-02-26 23:16:53.896715-08 benchmark f H100 \N t \N \N \N {} +2812 981 2025-02-26 23:27:33.898802-08 2025-02-26 22:27:35.757556-08 benchmark t H100 \N t \N \N \N {} +2813 981 2025-02-26 22:50:45.630362-08 2025-02-26 22:38:26.024532-08 leaderboard t H100 0.0014750091999999999 t \N \N \N {} +2821 983 2025-02-26 23:54:19.617224-08 2025-02-26 22:54:43.658755-08 benchmark f T4 \N t \N \N \N {} +2830 984 2025-02-26 22:48:33.644224-08 2025-02-26 23:28:42.682233-08 benchmark f L4 \N t \N \N \N {} +2831 984 2025-02-26 22:41:43.757041-08 2025-02-26 22:20:51.450588-08 leaderboard f L4 0.01786917866666667 t \N \N \N {} +2832 985 2025-02-26 22:21:51.307477-08 2025-02-26 23:57:27.92374-08 test t H100 \N f \N \N \N {} +2833 985 2025-02-26 23:24:09.942894-08 2025-02-26 23:56:01.814252-08 test f H100 \N f \N \N \N {} +2834 986 2025-02-27 00:15:23.058422-08 2025-02-26 22:20:39.444729-08 test t H100 \N f \N \N \N {} +2836 987 2025-02-26 23:41:37.112761-08 2025-02-26 23:11:03.419055-08 test t H100 \N f \N \N \N {} +2837 987 2025-02-26 23:42:42.038768-08 2025-02-26 22:42:24.323417-08 test f H100 \N f \N \N \N {} +2838 988 2025-02-27 00:16:37.194207-08 2025-02-26 23:55:57.063136-08 test t H100 \N t \N \N \N {} +2845 989 2025-02-26 23:26:00.442015-08 2025-02-27 00:38:35.106507-08 benchmark t H100 \N t \N \N \N {} +2846 989 2025-02-27 00:27:01.196646-08 2025-02-26 23:54:58.682026-08 leaderboard t H100 0.0016006286666666667 t \N \N \N {} +2854 990 2025-02-26 23:50:17.183107-08 2025-02-27 00:18:03.383314-08 benchmark f H100 \N t \N \N \N {} +2862 992 2025-02-27 00:36:55.131454-08 2025-02-27 00:15:38.507114-08 benchmark f H100 \N t \N \N \N {} +2863 992 2025-02-27 00:40:16.481455-08 2025-02-26 22:52:39.64442-08 leaderboard f H100 0.0014829375555555555 t \N \N \N {} +2870 994 2025-02-26 23:16:51.538974-08 2025-02-26 23:17:43.660758-08 test t H100 \N f \N \N \N {} +2871 994 2025-02-26 23:04:28.177098-08 2025-02-26 23:47:57.196289-08 test f H100 \N f \N \N \N {} +2872 995 2025-02-26 23:07:49.94189-08 2025-02-26 23:01:43.419125-08 test t H100 \N t \N \N \N {} +2890 1001 2025-02-27 00:11:31.886016-08 2025-02-27 00:30:39.383216-08 benchmark f T4 \N f \N \N \N {} +3098 1106 2025-02-28 01:45:37.330184-08 2025-02-28 01:23:52.672209-08 benchmark f A100 \N t \N \N \N {} +3099 1106 2025-02-28 02:14:12.999333-08 2025-02-28 01:30:27.761931-08 leaderboard f A100 0.003088477 t \N \N \N {} +6141 1883 2025-03-11 12:00:12.478656-07 2025-03-11 10:31:10.503903-07 test f T4 \N f \N \N \N {} +6313 1953 2025-03-12 19:49:16.980515-07 2025-03-12 19:36:25.891323-07 benchmark f A100 \N t \N \N \N {} +3101 1107 2025-02-28 03:01:32.467064-08 2025-02-28 01:43:34.556856-08 benchmark f A100 \N t \N \N \N {} +3104 1107 2025-02-28 02:08:46.087586-08 2025-02-28 03:09:33.504864-08 benchmark t A100 \N t \N \N \N {} +3105 1107 2025-02-28 02:13:06.321782-08 2025-02-28 02:29:03.356769-08 leaderboard t A100 0.003087981 t \N \N \N {} +6143 1884 2025-03-11 10:32:50.120634-07 2025-03-11 10:37:50.909508-07 benchmark f A100 \N t \N \N \N {} +6316 1954 2025-03-12 19:55:01.537607-07 2025-03-12 19:24:32.57323-07 leaderboard f A100 0.0013369068899999998 t \N \N \N {} +6463 2007 2025-03-14 06:32:12.249801-07 2025-03-14 06:46:07.006143-07 benchmark f A100 \N t \N \N \N {} +3107 1108 2025-02-28 02:23:54.191397-08 2025-02-28 03:14:14.624324-08 benchmark t A100 \N t \N \N \N {} +6464 2008 2025-03-14 06:36:56.11805-07 2025-03-14 07:46:19.266759-07 test f A100 \N t \N \N \N {} +3110 1108 2025-02-28 01:35:00.983298-08 2025-02-28 01:52:32.794497-08 benchmark f A100 \N t \N \N \N {} +3111 1108 2025-02-28 02:00:47.78852-08 2025-02-28 02:24:47.205564-08 leaderboard f A100 0.0030859636666666665 t \N \N \N {} +6145 1885 2025-03-11 11:00:03.267504-07 2025-03-11 12:18:13.420225-07 test t T4 \N f \N \N \N {} +6466 2008 2025-03-14 07:55:41.158642-07 2025-03-14 06:50:35.791318-07 leaderboard f A100 0.003151319 t \N \N \N {} +3113 1109 2025-02-28 03:14:09.084685-08 2025-02-28 03:13:52.452047-08 benchmark t A100 \N t \N \N \N {} +3114 1109 2025-02-28 02:43:35.282513-08 2025-02-28 02:18:55.469658-08 leaderboard t A100 0.0030930446666666664 t \N \N \N {} +6146 1886 2025-03-11 11:52:02.632791-07 2025-03-11 12:03:46.235678-07 test t T4 \N f \N \N \N {} +6320 1955 2025-03-12 20:34:48.835946-07 2025-03-12 20:33:18.170617-07 benchmark f A100 \N t \N \N \N {} +6469 2008 2025-03-14 06:40:05.438504-07 2025-03-14 06:15:33.296357-07 leaderboard t A100 0.0031540403333333335 t \N \N \N {} +3116 1109 2025-02-28 02:52:12.076786-08 2025-02-28 02:54:24.827665-08 benchmark f A100 \N t \N \N \N {} +3117 1109 2025-02-28 01:28:21.790912-08 2025-02-28 02:23:58.811177-08 leaderboard f A100 0.003085927 t \N \N \N {} +6147 1886 2025-03-11 12:54:26.920466-07 2025-03-11 11:44:08.259058-07 test f T4 \N f \N \N \N {} +6472 2009 2025-03-14 07:41:35.13284-07 2025-03-14 07:39:10.080386-07 leaderboard t A100 0.0024421096666666663 t \N \N \N {} +6148 1887 2025-03-11 12:01:43.87785-07 2025-03-11 12:26:23.003639-07 test f T4 \N f \N \N \N {} +6474 2009 2025-03-14 06:55:16.190554-07 2025-03-14 07:53:24.794099-07 benchmark f A100 \N t \N \N \N {} +3125 1111 2025-02-28 03:11:12.541581-08 2025-02-28 01:41:13.937321-08 benchmark f A100 \N t \N \N \N {} +3126 1111 2025-02-28 02:42:24.500879-08 2025-02-28 03:15:29.89987-08 leaderboard f A100 0.00316644475 t \N \N \N {} +6149 1887 2025-03-11 11:38:18.005859-07 2025-03-11 12:03:46.05906-07 test t T4 \N f \N \N \N {} +6323 1957 2025-03-13 09:54:02.414286-07 2025-03-13 09:52:30.795121-07 test t T4 \N f \N \N \N {} +6475 2009 2025-03-14 06:45:29.792426-07 2025-03-14 07:50:32.215984-07 leaderboard f A100 0.0024576166666666665 t \N \N \N {} +6597 2044 2025-03-14 13:34:41.655918-07 2025-03-14 12:08:56.842687-07 test t T4 \N f \N \N \N {} +6896 2145 2025-03-16 13:27:48.549328-07 2025-03-16 13:17:35.997934-07 benchmark f T4 \N f \N \N \N {} +6476 2010 2025-03-14 08:15:20.795494-07 2025-03-14 08:16:59.451399-07 test t A100 \N t \N \N \N {} +6477 2010 2025-03-14 08:08:47.27748-07 2025-03-14 06:59:19.727174-07 benchmark t A100 \N t \N \N \N {} +6478 2010 2025-03-14 06:48:33.525468-07 2025-03-14 08:02:14.948812-07 leaderboard t A100 0.002638912 t \N \N \N {} +3131 1112 2025-02-28 02:57:25.964429-08 2025-02-28 01:48:05.5249-08 benchmark f A100 \N t \N \N \N {} +6479 2010 2025-03-14 06:44:08.522027-07 2025-03-14 07:44:23.847039-07 test f A100 \N t \N \N \N {} +6480 2010 2025-03-14 07:56:38.205116-07 2025-03-14 07:54:00.333047-07 benchmark f A100 \N t \N \N \N {} +6152 1889 2025-03-11 13:30:29.989092-07 2025-03-11 12:48:46.455566-07 test t T4 \N t \N \N \N {} +6326 1958 2025-03-13 10:24:57.188611-07 2025-03-13 10:35:01.884206-07 test f T4 \N f \N \N \N {} +6482 2011 2025-03-14 08:02:31.143266-07 2025-03-14 06:41:16.991591-07 test f A100 \N t \N \N \N {} +6598 2045 2025-03-14 12:19:29.381029-07 2025-03-14 12:25:30.310096-07 test f A100 \N t \N \N \N {} +3137 1113 2025-02-28 03:08:43.17474-08 2025-02-28 02:45:51.821714-08 benchmark f A100 \N t \N \N \N {} +3138 1113 2025-02-28 02:20:25.9795-08 2025-02-28 02:34:44.310115-08 leaderboard f A100 0.0030921646666666664 t \N \N \N {} +6154 1889 2025-03-11 13:37:01.73686-07 2025-03-11 12:13:13.284253-07 test f T4 \N t \N \N \N {} +6327 1959 2025-03-13 10:26:24.667252-07 2025-03-13 11:45:14.442974-07 test t T4 \N f \N \N \N {} +3140 1113 2025-02-28 01:57:21.325728-08 2025-02-28 03:23:21.141542-08 benchmark t A100 \N t \N \N \N {} +6156 1890 2025-03-11 12:21:39.727111-07 2025-03-11 12:34:12.250351-07 test t T4 \N t \N \N \N {} +6157 1890 2025-03-11 13:12:21.4034-07 2025-03-11 12:39:13.895393-07 benchmark t T4 \N f \N \N \N {} +6328 1959 2025-03-13 12:08:12.60756-07 2025-03-13 12:05:32.463165-07 test f T4 \N f \N \N \N {} +6899 2148 2025-03-16 12:45:15.052291-07 2025-03-16 12:31:02.61805-07 test t T4 \N t \N \N \N {} +3143 1114 2025-02-28 01:39:46.938722-08 2025-02-28 02:11:45.255844-08 benchmark t A100 \N t \N \N \N {} +6159 1890 2025-03-11 13:25:06.856726-07 2025-03-11 12:59:34.598467-07 benchmark f T4 \N f \N \N \N {} +6329 1960 2025-03-13 11:46:43.371-07 2025-03-13 10:55:57.695267-07 test f T4 \N f \N \N \N {} +6902 2148 2025-03-16 12:34:32.187225-07 2025-03-16 12:33:04.36163-07 test f T4 \N t \N \N \N {} +3146 1114 2025-02-28 03:06:33.724337-08 2025-02-28 03:00:58.980906-08 benchmark f A100 \N t \N \N \N {} +6330 1960 2025-03-13 12:01:53.271866-07 2025-03-13 10:58:04.509016-07 test t T4 \N f \N \N \N {} +6483 2011 2025-03-14 07:37:56.702294-07 2025-03-14 07:18:30.959485-07 benchmark f A100 \N t \N \N \N {} +6331 1961 2025-03-13 12:07:41.574197-07 2025-03-13 11:12:52.328699-07 test f T4 \N f \N \N \N {} +6485 2011 2025-03-14 08:12:16.79755-07 2025-03-14 07:27:22.899891-07 test t A100 \N t \N \N \N {} +6671 2063 2025-03-14 14:46:30.836769-07 2025-03-14 14:56:13.217033-07 test t T4 \N f \N \N \N {} +6332 1961 2025-03-13 11:55:09.823065-07 2025-03-13 11:32:59.676251-07 test t T4 \N f \N \N \N {} +6486 2011 2025-03-14 07:56:31.390698-07 2025-03-14 07:47:31.714989-07 benchmark t A100 \N t \N \N \N {} +6163 1894 2025-03-11 12:16:32.845989-07 2025-03-11 13:15:29.765532-07 benchmark f T4 \N f \N \N \N {} +6333 1962 2025-03-13 12:24:14.859057-07 2025-03-13 12:20:08.73536-07 test t T4 \N f \N \N \N {} +3159 1116 2025-02-28 02:38:21.177305-08 2025-02-28 02:21:09.980476-08 leaderboard t A100 0.0030984103333333334 t \N \N \N {} +6164 1895 2025-03-11 12:39:00.764376-07 2025-03-11 12:37:33.970367-07 benchmark f T4 \N f \N \N \N {} +3162 1117 2025-02-28 03:41:30.098596-08 2025-02-28 03:20:42.331939-08 leaderboard t A100 0.0031041603333333335 t \N \N \N {} +6165 1896 2025-03-11 14:01:29.688942-07 2025-03-11 14:06:15.280779-07 benchmark f T4 \N t \N \N \N {} +6335 1963 2025-03-13 12:06:34.502766-07 2025-03-13 12:03:06.093899-07 test f T4 \N f \N \N \N {} +3169 1118 2025-02-28 03:31:50.129542-08 2025-02-28 03:36:06.764464-08 test f A100 \N t \N \N \N {} +3170 1118 2025-02-28 03:57:44.683588-08 2025-02-28 02:28:12.168907-08 benchmark f A100 \N t \N \N \N {} +3175 1119 2025-02-28 03:54:43.890351-08 2025-02-28 02:21:17.673941-08 test t A100 \N t \N \N \N {} +3176 1119 2025-02-28 03:25:21.208695-08 2025-02-28 02:53:54.097447-08 benchmark t A100 \N t \N \N \N {} +3179 1120 2025-02-28 03:19:52.64551-08 2025-02-28 04:03:22.512601-08 benchmark f A100 \N t \N \N \N {} +3180 1120 2025-02-28 02:37:44.833383-08 2025-02-28 03:49:05.485113-08 leaderboard f A100 0.0030965363333333336 t \N \N \N {} +3182 1120 2025-02-28 03:37:30.50022-08 2025-02-28 02:42:14.289029-08 benchmark t A100 \N t \N \N \N {} +3183 1120 2025-02-28 04:27:43.205983-08 2025-02-28 04:09:58.805071-08 leaderboard t A100 0.0030974113333333336 t \N \N \N {} +3188 1121 2025-02-28 05:16:45.018621-08 2025-02-28 04:16:05.328123-08 benchmark t A100 \N t \N \N \N {} +3189 1121 2025-02-28 03:32:46.838908-08 2025-02-28 04:24:02.880315-08 leaderboard t A100 0.0031272676666666663 t \N \N \N {} +6174 1899 2025-03-11 13:02:25.812706-07 2025-03-11 14:15:36.592099-07 test f T4 \N f \N \N \N {} +6340 1965 2025-03-13 12:42:09.960264-07 2025-03-13 12:51:36.764465-07 test f T4 \N f \N \N \N {} +6494 2013 2025-03-14 11:17:10.359439-07 2025-03-14 12:08:51.813768-07 test t H100 \N f \N \N \N {} +3191 1122 2025-02-28 04:31:59.29779-08 2025-02-28 04:47:11.70033-08 benchmark f A100 \N t \N \N \N {} +6175 1900 2025-03-11 14:09:24.209402-07 2025-03-11 14:27:29.662604-07 test t T4 \N f \N \N \N {} +3194 1122 2025-02-28 04:47:15.662542-08 2025-02-28 03:39:02.874427-08 benchmark t A100 \N t \N \N \N {} +3195 1122 2025-02-28 05:10:42.206283-08 2025-02-28 04:42:15.656228-08 leaderboard t A100 0.0031017193333333333 t \N \N \N {} +3196 1123 2025-02-28 05:13:41.323903-08 2025-02-28 04:55:50.819985-08 test f A100 \N t \N \N \N {} +3197 1123 2025-02-28 04:32:26.171448-08 2025-02-28 05:16:39.991067-08 benchmark f A100 \N t \N \N \N {} +3199 1123 2025-02-28 03:54:47.179034-08 2025-02-28 04:59:40.706458-08 test t A100 \N t \N \N \N {} +3208 1125 2025-02-28 05:52:39.581361-08 2025-02-28 05:55:19.83514-08 test t A100 \N t \N \N \N {} +3209 1125 2025-02-28 06:17:26.794554-08 2025-02-28 05:23:32.742402-08 benchmark t A100 \N t \N \N \N {} +3210 1125 2025-02-28 06:05:54.312077-08 2025-02-28 06:38:52.236143-08 leaderboard t A100 0.0030843956666666665 t \N \N \N {} +3211 1125 2025-02-28 06:16:05.768963-08 2025-02-28 06:25:41.001152-08 test f A100 \N t \N \N \N {} +3214 1126 2025-02-28 05:05:57.182176-08 2025-02-28 05:32:39.383884-08 test t A100 \N t \N \N \N {} +3215 1126 2025-02-28 05:52:39.479649-08 2025-02-28 06:47:24.562236-08 benchmark t A100 \N t \N \N \N {} +3216 1126 2025-02-28 05:10:50.786526-08 2025-02-28 04:55:01.414872-08 leaderboard t A100 0.0030879633333333336 t \N \N \N {} +3217 1126 2025-02-28 05:53:55.969095-08 2025-02-28 05:27:21.527994-08 test f A100 \N t \N \N \N {} +3220 1127 2025-02-28 06:54:22.094293-08 2025-02-28 06:25:04.727769-08 test t A100 \N t \N \N \N {} +3221 1127 2025-02-28 05:59:30.794599-08 2025-02-28 06:35:17.919421-08 benchmark t A100 \N t \N \N \N {} +3222 1127 2025-02-28 06:46:45.538801-08 2025-02-28 06:24:24.183739-08 leaderboard t A100 0.0031056823333333334 t \N \N \N {} +3223 1127 2025-02-28 05:37:12.475011-08 2025-02-28 05:15:06.135555-08 test f A100 \N t \N \N \N {} +3226 1128 2025-02-28 06:50:10.03441-08 2025-02-28 07:04:42.596407-08 benchmark f A100 \N t \N \N \N {} +3227 1129 2025-02-28 05:42:24.496681-08 2025-02-28 05:46:06.806343-08 benchmark f A100 \N t \N \N \N {} +3228 1131 2025-02-28 06:46:23.079469-08 2025-02-28 07:01:32.420227-08 benchmark f A100 \N t \N \N \N {} +3229 1130 2025-02-28 05:31:01.081051-08 2025-02-28 05:35:16.365184-08 test t A100 \N t \N \N \N {} +3232 1130 2025-02-28 05:56:38.654734-08 2025-02-28 06:03:33.120382-08 test f A100 \N t \N \N \N {} +3233 1130 2025-02-28 06:46:27.388033-08 2025-02-28 07:12:24.723128-08 benchmark f A100 \N t \N \N \N {} +3234 1130 2025-02-28 05:46:53.817871-08 2025-02-28 06:42:04.198827-08 leaderboard f A100 0.00310292 t \N \N \N {} +3235 1132 2025-02-28 06:21:32.053551-08 2025-02-28 07:14:16.857496-08 benchmark f A100 \N t \N \N \N {} +3236 1133 2025-02-28 07:07:04.755708-08 2025-02-28 06:46:05.801511-08 test t A100 \N t \N \N \N {} +3237 1133 2025-02-28 07:17:33.624616-08 2025-02-28 07:02:03.837416-08 benchmark t A100 \N t \N \N \N {} +3248 1135 2025-02-28 05:37:54.093967-08 2025-02-28 05:45:04.283128-08 benchmark t A100 \N t \N \N \N {} +3249 1135 2025-02-28 06:33:33.396017-08 2025-02-28 06:15:49.659969-08 leaderboard t A100 0.0031071463333333335 t \N \N \N {} +6177 1901 2025-03-11 14:17:16.056405-07 2025-03-11 12:39:24.253923-07 test t T4 \N f \N \N \N {} +6347 1967 2025-03-13 13:09:40.841714-07 2025-03-13 12:52:38.371098-07 test f T4 \N f \N \N \N {} +6496 2014 2025-03-14 11:32:31.174534-07 2025-03-14 10:45:59.582663-07 test f H100 \N f \N \N \N {} +6601 2045 2025-03-14 13:39:24.594274-07 2025-03-14 12:37:33.021169-07 test t A100 \N t \N \N \N {} +6602 2045 2025-03-14 13:09:25.371778-07 2025-03-14 12:02:22.248161-07 benchmark t A100 \N t \N \N \N {} +6603 2045 2025-03-14 12:21:14.812855-07 2025-03-14 13:54:36.938359-07 leaderboard t A100 0.0025770586666666664 t \N \N \N {} +6646 2053 2025-03-14 12:10:23.321882-07 2025-03-14 13:10:56.484171-07 leaderboard f A100 0.0024435633333333333 t \N \N \N {} +3251 1137 2025-02-28 05:50:43.617739-08 2025-02-28 06:49:39.360729-08 benchmark f A100 \N t \N \N \N {} +3257 1139 2025-02-28 06:31:44.654978-08 2025-02-28 05:38:26.805158-08 test f A100 \N t \N \N \N {} +3261 1140 2025-02-28 06:07:13.230828-08 2025-02-28 05:47:24.282991-08 benchmark f A100 \N t \N \N \N {} +3262 1141 2025-02-28 05:55:39.227255-08 2025-02-28 05:48:38.619947-08 benchmark f A100 \N t \N \N \N {} +6178 1901 2025-03-11 13:27:16.149666-07 2025-03-11 13:38:34.371907-07 test f T4 \N f \N \N \N {} +6348 1967 2025-03-13 13:02:53.563358-07 2025-03-13 13:01:27.915249-07 test t T4 \N f \N \N \N {} +6497 2014 2025-03-14 12:15:59.319003-07 2025-03-14 11:30:37.118914-07 test t H100 \N f \N \N \N {} +6604 2046 2025-03-14 13:43:57.061271-07 2025-03-14 13:07:50.209665-07 test t A100 \N t \N \N \N {} +6605 2046 2025-03-14 12:17:52.139426-07 2025-03-14 12:29:22.656383-07 benchmark t A100 \N t \N \N \N {} +6606 2046 2025-03-14 12:53:29.636257-07 2025-03-14 12:33:45.393318-07 leaderboard t A100 0.00256954 t \N \N \N {} +3264 1142 2025-02-28 07:20:46.488258-08 2025-02-28 06:11:17.956226-08 benchmark f A100 \N t \N \N \N {} +3265 1142 2025-02-28 07:11:08.133535-08 2025-02-28 06:09:41.048274-08 leaderboard f A100 0.0030866826666666666 t \N \N \N {} +6179 1902 2025-03-11 17:31:19.851556-07 2025-03-11 16:59:27.86489-07 test t T4 \N f \N \N \N {} +6349 1968 2025-03-13 12:16:15.487592-07 2025-03-13 13:01:52.748945-07 test t T4 \N f \N \N \N {} +6498 2015 2025-03-14 10:52:26.469597-07 2025-03-14 10:53:06.720164-07 test f H100 \N t \N \N \N {} +6499 2015 2025-03-14 11:34:32.855847-07 2025-03-14 10:47:26.771183-07 benchmark f H100 \N t \N \N \N {} +3267 1142 2025-02-28 07:07:31.550653-08 2025-02-28 06:39:21.840614-08 benchmark t A100 \N t \N \N \N {} +3268 1142 2025-02-28 06:19:53.446652-08 2025-02-28 05:31:10.708334-08 leaderboard t A100 0.0030884806666666665 t \N \N \N {} +6180 1902 2025-03-11 16:42:29.121174-07 2025-03-11 17:29:34.053097-07 test f T4 \N f \N \N \N {} +6350 1968 2025-03-13 11:36:52.447175-07 2025-03-13 11:56:08.746664-07 test f T4 \N f \N \N \N {} +6501 2015 2025-03-14 11:04:29.106551-07 2025-03-14 11:04:06.594233-07 test t H100 \N t \N \N \N {} +6502 2015 2025-03-14 10:46:29.195352-07 2025-03-14 11:29:56.856874-07 benchmark t H100 \N t \N \N \N {} +6503 2015 2025-03-14 11:02:59.180189-07 2025-03-14 11:37:31.767013-07 leaderboard t H100 0.006124215666666667 t \N \N \N {} +3270 1143 2025-02-28 07:20:25.006918-08 2025-02-28 06:39:34.217517-08 benchmark t A100 \N t \N \N \N {} +3271 1143 2025-02-28 06:14:53.755216-08 2025-02-28 05:57:39.774657-08 leaderboard t A100 0.0031000976666666663 t \N \N \N {} +6181 1903 2025-03-11 16:58:37.492878-07 2025-03-11 15:58:30.703546-07 test t T4 \N t \N \N \N {} +6182 1903 2025-03-11 17:18:44.559625-07 2025-03-11 15:57:14.031513-07 benchmark t T4 \N t \N \N \N {} +6183 1903 2025-03-11 16:37:25.986127-07 2025-03-11 16:28:32.33402-07 leaderboard t T4 3.1959500263333336 t \N \N \N {} +6351 1969 2025-03-13 11:55:17.679659-07 2025-03-13 12:30:30.977919-07 test t T4 \N f \N \N \N {} +3273 1143 2025-02-28 07:13:08.532252-08 2025-02-28 06:24:09.587417-08 benchmark f A100 \N t \N \N \N {} +6353 1970 2025-03-13 13:09:01.840724-07 2025-03-13 13:09:06.040381-07 test t T4 \N f \N \N \N {} +6507 2017 2025-03-14 12:15:09.410382-07 2025-03-14 12:08:16.887223-07 leaderboard t A100 0.0024410596666666665 t \N \N \N {} +3300 1148 2025-02-28 07:07:41.615297-08 2025-02-28 07:39:21.848265-08 benchmark t H100 \N t \N \N \N {} +3301 1148 2025-02-28 06:10:52.271101-08 2025-02-28 06:58:11.397489-08 leaderboard t H100 0.001857518 t \N \N \N {} +3302 1148 2025-02-28 06:00:16.905751-08 2025-02-28 06:14:31.098179-08 test t L4 \N t \N \N \N {} +3303 1148 2025-02-28 07:52:17.086381-08 2025-02-28 06:16:18.985978-08 benchmark t L4 \N t \N \N \N {} +3304 1148 2025-02-28 07:32:12.045626-08 2025-02-28 06:24:56.573657-08 leaderboard t L4 0.008826167333333334 t \N \N \N {} +3307 1148 2025-02-28 07:29:43.892232-08 2025-02-28 06:09:46.083173-08 leaderboard f L4 0.008876066 t \N \N \N {} +3308 1148 2025-02-28 06:13:12.466775-08 2025-02-28 07:21:14.017377-08 test f T4 \N t \N \N \N {} +3309 1148 2025-02-28 06:14:05.077253-08 2025-02-28 07:11:50.628783-08 benchmark f T4 \N t \N \N \N {} +3310 1148 2025-02-28 06:45:02.322758-08 2025-02-28 06:31:27.319188-08 leaderboard f T4 0.010512813666666666 t \N \N \N {} +3313 1151 2025-02-28 06:13:28.870279-08 2025-02-28 06:59:04.57498-08 test f H100 \N f \N \N \N {} +3314 1151 2025-02-28 06:53:49.334214-08 2025-02-28 07:39:29.099662-08 test f A100 \N f \N \N \N {} +3315 1151 2025-02-28 06:23:38.170481-08 2025-02-28 06:10:53.877168-08 test t A100 \N f \N \N \N {} +3320 1151 2025-02-28 07:07:06.317012-08 2025-02-28 07:47:22.348612-08 test f T4 \N f \N \N \N {} +3321 1152 2025-02-28 06:41:39.144469-08 2025-02-28 07:15:44.323297-08 test f A100 \N t \N \N \N {} +3322 1152 2025-02-28 06:33:30.747147-08 2025-02-28 07:19:24.710801-08 benchmark f A100 \N t \N \N \N {} +3323 1152 2025-02-28 06:13:17.846448-08 2025-02-28 07:16:53.101236-08 leaderboard f A100 0.003092475 t \N \N \N {} +3532 1205 2025-02-28 11:36:17.404133-08 2025-02-28 11:04:59.294896-08 test t A100 \N t \N \N \N {} +3324 1152 2025-02-28 07:18:58.437402-08 2025-02-28 06:19:11.553801-08 test t A100 \N t \N \N \N {} +3325 1152 2025-02-28 07:55:29.183414-08 2025-02-28 07:03:55.443509-08 benchmark t A100 \N t \N \N \N {} +3326 1152 2025-02-28 07:19:25.423303-08 2025-02-28 07:50:35.236223-08 leaderboard t A100 0.0031075596666666665 t \N \N \N {} +3329 1153 2025-02-28 06:25:00.374655-08 2025-02-28 07:30:25.54331-08 test t L4 \N f \N \N \N {} +3334 1153 2025-02-28 07:49:11.602135-08 2025-02-28 07:52:08.469201-08 test f L4 \N f \N \N \N {} +3335 1154 2025-02-28 07:00:59.730078-08 2025-02-28 06:49:51.977949-08 test t A100 \N t \N \N \N {} +3336 1154 2025-02-28 07:54:33.813629-08 2025-02-28 06:56:00.534875-08 benchmark t A100 \N t \N \N \N {} +3344 1155 2025-02-28 07:18:52.719651-08 2025-02-28 07:58:30.473969-08 test f A100 \N t \N \N \N {} +3345 1155 2025-02-28 06:18:08.185243-08 2025-02-28 06:59:20.358203-08 benchmark f A100 \N t \N \N \N {} +3346 1155 2025-02-28 07:02:45.365698-08 2025-02-28 06:33:19.989005-08 leaderboard f A100 0.00308959 t \N \N \N {} +3347 1156 2025-02-28 07:59:44.021356-08 2025-02-28 06:48:49.388888-08 test f A100 \N t \N \N \N {} +3350 1156 2025-02-28 06:29:45.868814-08 2025-02-28 06:37:20.655964-08 test t A100 \N t \N \N \N {} +3351 1156 2025-02-28 07:13:45.617041-08 2025-02-28 07:15:15.742271-08 benchmark t A100 \N t \N \N \N {} +3352 1156 2025-02-28 07:46:00.514913-08 2025-02-28 06:43:16.480409-08 leaderboard t A100 0.0031107886666666665 t \N \N \N {} +3354 1157 2025-02-28 06:33:28.648011-08 2025-02-28 06:43:24.152901-08 benchmark t A100 \N t \N \N \N {} +3355 1157 2025-02-28 07:45:51.654693-08 2025-02-28 07:44:31.010515-08 leaderboard t A100 0.0030852116666666663 t \N \N \N {} +6189 1905 2025-03-11 17:31:04.799276-07 2025-03-11 17:23:51.876169-07 test t T4 \N f \N \N \N {} +6355 1971 2025-03-13 13:24:12.401709-07 2025-03-13 13:32:14.413028-07 test f T4 \N f \N \N \N {} +6509 2017 2025-03-14 11:29:46.886636-07 2025-03-14 12:19:10.774689-07 benchmark f A100 \N t \N \N \N {} +3357 1157 2025-02-28 08:03:49.481801-08 2025-02-28 08:00:06.310818-08 benchmark f A100 \N t \N \N \N {} +3358 1157 2025-02-28 07:39:56.271986-08 2025-02-28 07:00:59.378014-08 leaderboard f A100 0.0030816896666666666 t \N \N \N {} +3360 1159 2025-02-28 07:39:08.622355-08 2025-02-28 08:38:10.734027-08 test f T4 \N f \N \N \N {} +3361 1160 2025-02-28 09:26:30.209671-08 2025-02-28 09:51:28.232565-08 test f L4 \N f \N \N \N {} +3362 1160 2025-02-28 08:13:42.830297-08 2025-02-28 08:36:53.136944-08 test t L4 \N f \N \N \N {} +3367 1163 2025-02-28 09:08:01.615266-08 2025-02-28 09:04:38.110604-08 test t T4 \N t \N \N \N {} +3368 1163 2025-02-28 08:47:34.73436-08 2025-02-28 09:40:07.599281-08 benchmark t T4 \N t \N \N \N {} +3370 1163 2025-02-28 10:03:37.527045-08 2025-02-28 10:11:34.393096-08 test f T4 \N f \N \N \N {} +3371 1164 2025-02-28 10:01:44.770216-08 2025-02-28 09:27:21.603234-08 test f L4 \N t \N \N \N {} +3372 1164 2025-02-28 08:47:05.293177-08 2025-02-28 10:23:25.384173-08 benchmark f L4 \N t \N \N \N {} +3377 1165 2025-02-28 09:21:40.284625-08 2025-02-28 10:30:06.106699-08 test f L4 \N t \N \N \N {} +3378 1165 2025-02-28 08:56:55.571472-08 2025-02-28 09:49:21.797242-08 benchmark f L4 \N t \N \N \N {} +3379 1165 2025-02-28 10:05:42.150638-08 2025-02-28 09:36:46.848902-08 leaderboard f L4 0.017076701333333333 t \N \N \N {} +3454 1190 2025-02-28 12:13:49.181929-08 2025-02-28 11:09:14.008399-08 test f A100 \N t \N \N \N {} +3380 1165 2025-02-28 09:57:10.477902-08 2025-02-28 08:59:43.970761-08 test t L4 \N t \N \N \N {} +3381 1165 2025-02-28 09:34:07.882518-08 2025-02-28 10:28:42.342832-08 benchmark t L4 \N t \N \N \N {} +3382 1165 2025-02-28 10:25:49.905778-08 2025-02-28 08:45:12.787062-08 leaderboard t L4 0.017087161666666666 t \N \N \N {} +3383 1166 2025-02-28 10:09:06.401999-08 2025-02-28 09:14:44.292073-08 test t L4 \N t \N \N \N {} +3384 1166 2025-02-28 09:27:54.175727-08 2025-02-28 08:41:58.508616-08 benchmark t L4 \N t \N \N \N {} +3387 1166 2025-02-28 09:50:29.259635-08 2025-02-28 10:07:29.279896-08 benchmark f L4 \N t \N \N \N {} +3388 1166 2025-02-28 09:42:07.63904-08 2025-02-28 10:09:52.762874-08 leaderboard f L4 0.01713320033333333 t \N \N \N {} +3389 1167 2025-02-28 10:08:48.950368-08 2025-02-28 09:00:40.626022-08 test t L4 \N f \N \N \N {} +3390 1167 2025-02-28 09:23:41.075458-08 2025-02-28 08:55:11.552134-08 test f L4 \N f \N \N \N {} +3544 1207 2025-02-28 12:21:19.174948-08 2025-02-28 11:46:49.287104-08 test f A100 \N t \N \N \N {} +3394 1169 2025-02-28 09:19:45.607358-08 2025-02-28 10:30:06.679512-08 test t L4 \N f \N \N \N {} +3395 1169 2025-02-28 10:06:46.387305-08 2025-02-28 09:43:30.292106-08 test f L4 \N f \N \N \N {} +3397 1171 2025-02-28 09:03:44.961516-08 2025-02-28 10:20:27.853337-08 test f L4 \N f \N \N \N {} +3405 1174 2025-02-28 10:39:36.975544-08 2025-02-28 09:52:14.8899-08 benchmark t L4 \N t \N \N \N {} +3400 1173 2025-02-28 10:29:49.463718-08 2025-02-28 10:21:22.102387-08 test f L4 \N t \N \N \N {} +3401 1173 2025-02-28 09:21:57.128462-08 2025-02-28 10:30:59.993678-08 benchmark f L4 \N t \N \N \N {} +3434 1185 2025-02-28 11:34:12.254431-08 2025-02-28 10:21:43.385984-08 test t L4 \N t \N \N \N {} +3435 1185 2025-02-28 11:18:57.057952-08 2025-02-28 11:28:37.311547-08 benchmark t L4 \N t \N \N \N {} +3439 1186 2025-02-28 10:55:26.766127-08 2025-02-28 11:07:39.44103-08 benchmark f L4 \N t \N \N \N {} +3440 1186 2025-02-28 10:10:12.112939-08 2025-02-28 11:10:32.049899-08 leaderboard f L4 0.01707861033333333 t \N \N \N {} +3441 1186 2025-02-28 09:42:05.101791-08 2025-02-28 10:27:34.733276-08 test t L4 \N t \N \N \N {} +3442 1186 2025-02-28 10:06:18.981477-08 2025-02-28 10:02:38.94075-08 benchmark t L4 \N t \N \N \N {} +3449 1188 2025-02-28 11:22:41.568508-08 2025-02-28 10:32:23.811511-08 test f A100 \N t \N \N \N {} +3450 1188 2025-02-28 10:37:43.385883-08 2025-02-28 11:17:39.7834-08 benchmark f A100 \N t \N \N \N {} +3456 1190 2025-02-28 10:41:28.207826-08 2025-02-28 12:14:51.122436-08 leaderboard f A100 0.002069882 t \N \N \N {} +3457 1190 2025-02-28 11:29:53.359486-08 2025-02-28 11:55:18.355744-08 test t A100 \N t \N \N \N {} +3458 1190 2025-02-28 10:37:33.37378-08 2025-02-28 11:30:33.125044-08 benchmark t A100 \N t \N \N \N {} +3459 1190 2025-02-28 11:02:25.492442-08 2025-02-28 11:05:52.473542-08 leaderboard t A100 0.002045183 t \N \N \N {} +6190 1905 2025-03-11 16:26:41.241921-07 2025-03-11 16:38:33.020666-07 test f T4 \N f \N \N \N {} +6510 2017 2025-03-14 12:01:53.782121-07 2025-03-14 10:58:53.397351-07 leaderboard f A100 0.0025043896666666664 t \N \N \N {} +3461 1191 2025-02-28 10:52:09.093285-08 2025-02-28 11:24:50.914766-08 benchmark f A100 \N t \N \N \N {} +3462 1191 2025-02-28 12:13:09.43073-08 2025-02-28 11:11:59.107864-08 leaderboard f A100 0.003088071 t \N \N \N {} +6191 1906 2025-03-11 16:40:31.677341-07 2025-03-11 17:30:01.999781-07 test t T4 \N f \N \N \N {} +6356 1971 2025-03-13 12:18:33.704663-07 2025-03-13 12:52:16.173084-07 test t T4 \N f \N \N \N {} +6511 2018 2025-03-14 11:30:51.790617-07 2025-03-14 12:46:33.976449-07 test t A100 \N t \N \N \N {} +6512 2018 2025-03-14 11:50:37.040812-07 2025-03-14 12:00:32.698091-07 benchmark t A100 \N t \N \N \N {} +6513 2018 2025-03-14 11:58:09.461167-07 2025-03-14 10:57:34.439949-07 leaderboard t A100 0.0024541096666666666 t \N \N \N {} +3464 1191 2025-02-28 11:06:30.414003-08 2025-02-28 10:53:55.756396-08 benchmark t A100 \N t \N \N \N {} +3465 1191 2025-02-28 10:54:02.19782-08 2025-02-28 11:07:04.11224-08 leaderboard t A100 0.0030866083333333334 t \N \N \N {} +6192 1906 2025-03-11 18:04:00.474946-07 2025-03-11 18:14:14.534699-07 test f T4 \N f \N \N \N {} +6357 1972 2025-03-13 13:34:07.292711-07 2025-03-13 14:11:37.509333-07 test t T4 \N t \N \N \N {} +6358 1972 2025-03-13 13:17:51.104355-07 2025-03-13 14:01:28.265004-07 benchmark t T4 \N f \N \N \N {} +6516 2018 2025-03-14 10:52:38.328-07 2025-03-14 12:27:11.650928-07 leaderboard f A100 0.0024775536666666667 t \N \N \N {} +3469 1193 2025-02-28 12:13:49.642175-08 2025-02-28 11:55:09.712002-08 benchmark f A100 \N t \N \N \N {} +3470 1193 2025-02-28 11:12:47.050744-08 2025-02-28 11:14:55.313376-08 leaderboard f A100 0.0031209246666666665 t \N \N \N {} +6193 1907 2025-03-11 16:49:57.729617-07 2025-03-11 17:48:28.815284-07 test f T4 \N f \N \N \N {} +6359 1972 2025-03-13 13:07:47.502676-07 2025-03-13 14:27:21.253575-07 test f T4 \N t \N \N \N {} +3472 1193 2025-02-28 10:47:16.91505-08 2025-02-28 11:18:49.513731-08 benchmark t A100 \N t \N \N \N {} +3473 1193 2025-02-28 11:05:06.759594-08 2025-02-28 12:04:22.05689-08 leaderboard t A100 0.003093471 t \N \N \N {} +6194 1907 2025-03-11 17:02:18.345331-07 2025-03-11 16:38:32.775938-07 test t T4 \N f \N \N \N {} +6361 1973 2025-03-13 13:34:46.334274-07 2025-03-13 14:01:13.147474-07 test f T4 \N t \N \N \N {} +6362 1973 2025-03-13 13:37:07.283208-07 2025-03-13 15:08:05.351976-07 benchmark f T4 \N f \N \N \N {} +6518 2020 2025-03-14 11:30:41.762022-07 2025-03-14 11:12:09.117466-07 test f A100 \N t \N \N \N {} +6519 2020 2025-03-14 11:13:39.719926-07 2025-03-14 11:25:21.536972-07 benchmark f A100 \N t \N \N \N {} +6520 2020 2025-03-14 12:06:50.863502-07 2025-03-14 12:44:38.653958-07 leaderboard f A100 0.0025181186666666665 t \N \N \N {} +3476 1196 2025-02-28 10:54:21.072266-08 2025-02-28 11:37:50.81538-08 benchmark t A100 \N t \N \N \N {} +3477 1196 2025-02-28 12:24:50.73577-08 2025-02-28 11:42:01.495948-08 leaderboard t A100 0.0030894043333333336 t \N \N \N {} +6195 1908 2025-03-11 16:33:13.825423-07 2025-03-11 17:00:19.916832-07 test t T4 \N f \N \N \N {} +3484 1195 2025-02-28 11:31:48.964942-08 2025-02-28 11:27:19.249047-08 benchmark t A100 \N f \N \N \N {} +6365 1974 2025-03-13 15:18:06.293763-07 2025-03-13 14:44:47.284631-07 test f T4 \N t \N \N \N {} +3486 1197 2025-02-28 12:26:33.136784-08 2025-02-28 11:24:39.011923-08 benchmark f A100 \N t \N \N \N {} +3487 1197 2025-02-28 11:54:54.56215-08 2025-02-28 12:26:07.45337-08 leaderboard f A100 0.0030942576666666663 t \N \N \N {} +6366 1974 2025-03-13 14:15:36.374645-07 2025-03-13 14:09:23.053095-07 benchmark f T4 \N f \N \N \N {} +6522 2020 2025-03-14 12:18:54.728315-07 2025-03-14 12:44:10.914453-07 benchmark t A100 \N t \N \N \N {} +3489 1197 2025-02-28 11:23:03.004299-08 2025-02-28 11:03:53.165609-08 benchmark t A100 \N t \N \N \N {} +3490 1197 2025-02-28 11:33:31.529975-08 2025-02-28 11:07:28.938258-08 leaderboard t A100 0.0030712816666666667 t \N \N \N {} +6197 1909 2025-03-11 18:17:26.032444-07 2025-03-11 17:19:28.72111-07 test t T4 \N f \N \N \N {} +6368 1974 2025-03-13 14:55:40.171664-07 2025-03-13 14:12:41.864308-07 benchmark t T4 \N f \N \N \N {} +6523 2020 2025-03-14 12:53:35.58379-07 2025-03-14 11:54:41.519798-07 leaderboard t A100 0.002498382 t \N \N \N {} +6524 2021 2025-03-14 12:36:39.402806-07 2025-03-14 11:08:03.626394-07 test t A100 \N t \N \N \N {} +6525 2021 2025-03-14 11:24:50.232214-07 2025-03-14 12:29:14.201976-07 benchmark t A100 \N t \N \N \N {} +6526 2021 2025-03-14 12:39:45.622642-07 2025-03-14 11:23:16.261268-07 leaderboard t A100 0.0025124666666666664 t \N \N \N {} +6612 2047 2025-03-14 13:18:33.788335-07 2025-03-14 13:24:29.736685-07 leaderboard f A100 0.0025605253333333333 t \N \N \N {} +6527 2021 2025-03-14 12:10:21.085092-07 2025-03-14 11:31:54.576597-07 test f A100 \N t \N \N \N {} +6374 1977 2025-03-13 16:02:01.798258-07 2025-03-13 15:38:46.117793-07 benchmark t T4 \N f \N \N \N {} +6536 2023 2025-03-14 12:00:31.556878-07 2025-03-14 12:46:54.788332-07 test t A100 \N t \N \N \N {} +6537 2023 2025-03-14 12:32:44.743863-07 2025-03-14 12:54:06.787263-07 benchmark t A100 \N t \N \N \N {} +6538 2023 2025-03-14 12:49:53.790312-07 2025-03-14 12:00:47.817767-07 leaderboard t A100 0.0025183806666666665 t \N \N \N {} +3507 1200 2025-02-28 12:16:30.548788-08 2025-02-28 11:43:54.947982-08 benchmark t A100 \N t \N \N \N {} +3508 1200 2025-02-28 11:28:38.825541-08 2025-02-28 11:10:09.664227-08 leaderboard t A100 0.003144041 t \N \N \N {} +6203 1912 2025-03-11 17:08:06.730232-07 2025-03-11 17:58:19.821741-07 test f T4 \N f \N \N \N {} +6377 1978 2025-03-13 15:54:45.454946-07 2025-03-13 16:33:05.355765-07 benchmark f T4 \N t \N \N \N {} +6541 2023 2025-03-14 11:06:04.095739-07 2025-03-14 11:57:33.138195-07 leaderboard f A100 0.0024532393333333334 t \N \N \N {} +6618 2048 2025-03-14 12:42:53.553414-07 2025-03-14 13:49:27.329988-07 leaderboard t A100 0.0025746103333333333 t \N \N \N {} +6648 2054 2025-03-14 12:06:53.802343-07 2025-03-14 13:37:02.957155-07 benchmark f A100 \N t \N \N \N {} +6649 2054 2025-03-14 12:36:50.625847-07 2025-03-14 12:35:25.488371-07 leaderboard f A100 0.0024409133333333334 t \N \N \N {} +3513 1201 2025-02-28 12:22:24.212195-08 2025-02-28 12:18:25.901829-08 benchmark t A100 \N t \N \N \N {} +3514 1201 2025-02-28 12:24:03.792716-08 2025-02-28 12:13:02.802944-08 leaderboard t A100 0.0031015336666666664 t \N \N \N {} +6378 1979 2025-03-13 17:23:58.186753-07 2025-03-13 16:06:55.720179-07 benchmark f T4 \N f \N \N \N {} +6543 2025 2025-03-14 12:41:00.561242-07 2025-03-14 11:46:24.982291-07 test t A100 \N t \N \N \N {} +6544 2025 2025-03-14 12:20:54.337724-07 2025-03-14 11:31:16.216919-07 benchmark t A100 \N t \N \N \N {} +6545 2025 2025-03-14 11:25:51.951167-07 2025-03-14 12:23:37.08102-07 leaderboard t A100 0.0025238436666666663 t \N \N \N {} +6620 2048 2025-03-14 12:30:34.657105-07 2025-03-14 12:13:05.701347-07 benchmark f A100 \N t \N \N \N {} +6621 2048 2025-03-14 12:33:05.301709-07 2025-03-14 13:37:25.906329-07 leaderboard f A100 0.002544002 t \N \N \N {} +3519 1202 2025-02-28 11:11:30.376958-08 2025-02-28 11:01:50.55658-08 benchmark t A100 \N t \N \N \N {} +3520 1202 2025-02-28 11:43:16.373234-08 2025-02-28 12:31:24.408715-08 leaderboard t A100 0.003084011 t \N \N \N {} +6207 1914 2025-03-11 17:36:02.063163-07 2025-03-11 18:44:14.758287-07 test f T4 \N f \N \N \N {} +6380 1980 2025-03-13 15:58:51.960023-07 2025-03-13 16:24:59.128925-07 test f T4 \N f \N \N \N {} +6546 2025 2025-03-14 12:53:00.880967-07 2025-03-14 12:23:03.100245-07 test f A100 \N t \N \N \N {} +6547 2025 2025-03-14 11:06:13.671532-07 2025-03-14 11:10:58.899444-07 benchmark f A100 \N t \N \N \N {} +6622 2049 2025-03-14 12:01:06.825573-07 2025-03-14 12:10:07.768513-07 test f A100 \N t \N \N \N {} +6208 1914 2025-03-11 17:46:09.416055-07 2025-03-11 18:43:51.806087-07 test t T4 \N f \N \N \N {} +6381 1981 2025-03-13 17:27:47.16164-07 2025-03-13 17:00:09.810894-07 test t T4 \N f \N \N \N {} +6209 1915 2025-03-11 17:02:30.670995-07 2025-03-11 18:34:35.253755-07 test f T4 \N f \N \N \N {} +6210 1915 2025-03-11 18:19:24.18935-07 2025-03-11 16:56:01.127871-07 test t T4 \N f \N \N \N {} +6212 1916 2025-03-11 18:37:26.217772-07 2025-03-11 18:26:08.828412-07 test t T4 \N f \N \N \N {} +6385 1983 2025-03-13 16:53:42.93878-07 2025-03-13 18:01:17.86942-07 test t T4 \N f \N \N \N {} +6213 1917 2025-03-11 18:49:08.446554-07 2025-03-11 18:23:51.890625-07 test t T4 \N f \N \N \N {} +6386 1983 2025-03-13 16:59:20.147802-07 2025-03-13 17:23:05.485124-07 test f T4 \N f \N \N \N {} +6556 2028 2025-03-14 13:11:22.628618-07 2025-03-14 12:15:44.55829-07 test f A100 \N t \N \N \N {} +6214 1917 2025-03-11 18:57:56.391885-07 2025-03-11 18:40:03.839455-07 test f T4 \N f \N \N \N {} +6387 1984 2025-03-13 17:51:21.236837-07 2025-03-13 16:43:28.14984-07 test t T4 \N f \N \N \N {} +6557 2029 2025-03-14 12:34:22.237304-07 2025-03-14 11:29:44.596311-07 test t T4 \N f \N \N \N {} +6627 2049 2025-03-14 13:14:24.077723-07 2025-03-14 12:11:10.400483-07 leaderboard t A100 0.0031591056666666665 t \N \N \N {} +3545 1207 2025-02-28 11:34:32.128279-08 2025-02-28 10:48:07.092517-08 benchmark f A100 \N t \N \N \N {} +6388 1984 2025-03-13 17:46:54.072499-07 2025-03-13 16:07:38.434852-07 test f T4 \N f \N \N \N {} +6558 2029 2025-03-14 13:24:40.101943-07 2025-03-14 12:23:21.049241-07 test f T4 \N f \N \N \N {} +6628 2050 2025-03-14 13:02:18.585241-07 2025-03-14 12:00:19.719197-07 test f A100 \N t \N \N \N {} +6680 2068 2025-03-14 15:33:14.472771-07 2025-03-14 16:02:51.078416-07 test f T4 \N f \N \N \N {} +3548 1208 2025-02-28 11:47:51.556932-08 2025-02-28 11:49:48.214291-08 benchmark f A100 \N t \N \N \N {} +3549 1208 2025-02-28 12:21:05.688454-08 2025-02-28 11:23:23.692653-08 leaderboard f A100 0.003099421 t \N \N \N {} +6389 1985 2025-03-13 16:57:14.22982-07 2025-03-13 17:30:21.244746-07 test t T4 \N f \N \N \N {} +7055 2184 2025-03-16 20:46:00.165975-07 2025-03-16 21:45:58.753749-07 test f L4 \N f \N \N \N {} +3551 1208 2025-02-28 12:08:51.038645-08 2025-02-28 11:50:46.059027-08 benchmark t A100 \N t \N \N \N {} +3552 1208 2025-02-28 11:11:38.66471-08 2025-02-28 12:18:11.410389-08 leaderboard t A100 0.0030721586666666665 t \N \N \N {} +6390 1985 2025-03-13 16:22:49.557696-07 2025-03-13 17:03:17.529052-07 test f T4 \N f \N \N \N {} +7056 2184 2025-03-16 21:28:43.042415-07 2025-03-16 21:41:54.202912-07 test t L4 \N f \N \N \N {} +3554 1209 2025-02-28 11:42:34.402738-08 2025-02-28 10:58:34.409829-08 benchmark t A100 \N t \N \N \N {} +3555 1209 2025-02-28 12:08:05.685399-08 2025-02-28 12:29:20.539116-08 leaderboard t A100 0.0030725803333333337 t \N \N \N {} +6391 1986 2025-03-13 17:33:05.58602-07 2025-03-13 17:03:31.170452-07 test f T4 \N f \N \N \N {} +6662 2059 2025-03-14 13:23:27.383679-07 2025-03-14 13:38:22.037388-07 test f T4 \N f \N \N \N {} +3557 1209 2025-02-28 10:54:49.669617-08 2025-02-28 12:37:28.504084-08 benchmark f A100 \N t \N \N \N {} +3558 1209 2025-02-28 12:46:50.125484-08 2025-02-28 10:53:05.190346-08 leaderboard f A100 0.003090619 t \N \N \N {} +6392 1986 2025-03-13 18:05:36.821373-07 2025-03-13 16:18:41.422227-07 test t T4 \N f \N \N \N {} +6559 2030 2025-03-14 11:43:33.554172-07 2025-03-14 11:45:17.444192-07 benchmark f A100 \N t \N \N \N {} +3560 1210 2025-02-28 12:25:24.784425-08 2025-02-28 11:26:41.263715-08 benchmark f A100 \N t \N \N \N {} +3561 1210 2025-02-28 10:49:57.408057-08 2025-02-28 10:50:18.625288-08 leaderboard f A100 0.0030909023333333336 t \N \N \N {} +6393 1987 2025-03-13 17:09:40.617657-07 2025-03-13 18:01:53.260744-07 test f T4 \N f \N \N \N {} +3563 1210 2025-02-28 11:47:01.61943-08 2025-02-28 11:03:40.113806-08 benchmark t A100 \N t \N \N \N {} +3592 1229 2025-02-28 14:16:18.378222-08 2025-02-28 14:41:43.485886-08 leaderboard t H100 0.0011789098333333332 t \N \N \N {} +3599 1231 2025-02-28 14:24:45.98762-08 2025-02-28 14:33:38.933844-08 leaderboard t H100 0.0012175512 t \N \N \N {} +3600 1232 2025-02-28 13:31:10.936218-08 2025-02-28 14:19:19.455477-08 test t A100 \N t \N \N \N {} +3601 1232 2025-02-28 14:40:09.495449-08 2025-02-28 13:28:50.540847-08 benchmark t A100 \N t \N \N \N {} +3602 1232 2025-02-28 14:11:02.148305-08 2025-02-28 13:20:25.431282-08 leaderboard t A100 0.0018670176 t \N \N \N {} +3603 1232 2025-02-28 13:32:32.0241-08 2025-02-28 14:31:24.527828-08 test f A100 \N t \N \N \N {} +3604 1232 2025-02-28 13:20:53.784469-08 2025-02-28 14:29:17.100087-08 benchmark f A100 \N t \N \N \N {} +3629 1240 2025-03-01 01:07:27.030682-08 2025-03-01 00:11:34.566732-08 test t A100 \N f \N \N \N {} +3648 1248 2025-03-01 01:50:58.464696-08 2025-03-01 00:44:11.917362-08 test t A100 \N t \N \N \N {} +3605 1232 2025-02-28 14:53:49.887413-08 2025-02-28 15:02:29.191933-08 leaderboard f A100 0.001868504 t \N \N \N {} +3606 1233 2025-02-28 14:00:44.074635-08 2025-02-28 14:09:25.557225-08 benchmark f A100 \N t \N \N \N {} +3607 1234 2025-02-28 14:42:03.73345-08 2025-02-28 15:08:49.030554-08 benchmark f A100 \N f \N \N \N {} +3608 1235 2025-02-28 15:06:39.526344-08 2025-02-28 15:15:55.847946-08 benchmark f A100 \N t \N \N \N {} +3609 1235 2025-02-28 14:36:31.855831-08 2025-02-28 14:45:08.753778-08 benchmark f H100 \N f \N \N \N {} +3610 1236 2025-02-28 14:22:25.170803-08 2025-02-28 15:09:04.222075-08 benchmark f H100 \N t \N \N \N {} +3611 1237 2025-02-28 14:59:01.171066-08 2025-02-28 15:22:44.422251-08 test t H100 \N t \N \N \N {} +3612 1237 2025-02-28 15:35:54.216346-08 2025-02-28 15:38:16.179312-08 benchmark t H100 \N t \N \N \N {} +3613 1237 2025-02-28 14:52:56.878083-08 2025-02-28 14:51:40.998403-08 leaderboard t H100 0.001161027 t \N \N \N {} +3614 1237 2025-02-28 16:12:12.194689-08 2025-02-28 16:07:13.458388-08 test f H100 \N f \N \N \N {} +3615 1238 2025-02-28 16:14:51.726429-08 2025-02-28 16:25:19.334709-08 benchmark f H100 \N t \N \N \N {} +3616 1238 2025-02-28 15:18:16.528728-08 2025-02-28 16:17:03.954782-08 benchmark f A100 \N t \N \N \N {} +3630 1240 2025-03-01 00:33:39.01249-08 2025-03-01 00:07:40.699626-08 test f A100 \N f \N \N \N {} +3617 1239 2025-02-28 15:32:38.229302-08 2025-02-28 15:57:09.263444-08 test t H100 \N t \N \N \N {} +3618 1239 2025-02-28 15:40:41.884458-08 2025-02-28 15:19:49.237295-08 benchmark t H100 \N t \N \N \N {} +3619 1239 2025-02-28 15:26:34.626489-08 2025-02-28 16:48:20.214012-08 leaderboard t H100 0.0011695108 t \N \N \N {} +3620 1239 2025-02-28 15:34:19.352077-08 2025-02-28 15:18:34.628786-08 test f A100 \N t \N \N \N {} +3621 1239 2025-02-28 15:34:17.50586-08 2025-02-28 15:42:25.985409-08 benchmark f A100 \N t \N \N \N {} +3622 1239 2025-02-28 15:22:37.115875-08 2025-02-28 15:55:56.769348-08 leaderboard f A100 0.002215739 t \N \N \N {} +3623 1239 2025-02-28 17:10:36.81778-08 2025-02-28 15:57:08.360846-08 test t A100 \N t \N \N \N {} +3625 1239 2025-02-28 16:44:19.965613-08 2025-02-28 15:49:32.46356-08 leaderboard t A100 0.0020725956666666667 t \N \N \N {} +3626 1239 2025-02-28 15:41:26.021642-08 2025-02-28 15:23:55.7378-08 test f H100 \N t \N \N \N {} +3627 1239 2025-02-28 16:33:01.867796-08 2025-02-28 15:24:47.499545-08 benchmark f H100 \N t \N \N \N {} +3628 1239 2025-02-28 16:49:49.757233-08 2025-02-28 16:35:02.023132-08 leaderboard f H100 0.00116604725 t \N \N \N {} +6234 1922 2025-03-12 00:22:49.917936-07 2025-03-12 00:13:52.488578-07 test t T4 \N f \N \N \N {} +6395 1988 2025-03-13 17:58:47.761639-07 2025-03-13 17:01:13.84194-07 test t T4 \N f \N \N \N {} +6560 2031 2025-03-14 13:05:32.105346-07 2025-03-14 11:29:34.501788-07 benchmark f A100 \N t \N \N \N {} +6629 2050 2025-03-14 12:58:46.09814-07 2025-03-14 12:52:50.872419-07 benchmark f A100 \N t \N \N \N {} +6630 2050 2025-03-14 12:08:39.259423-07 2025-03-14 13:48:19.785521-07 leaderboard f A100 0.002560023 t \N \N \N {} +3638 1243 2025-03-01 00:16:45.170175-08 2025-03-01 00:27:41.072541-08 test t A100 \N f \N \N \N {} +3639 1243 2025-02-28 23:56:11.182103-08 2025-03-01 00:14:59.572871-08 test f A100 \N f \N \N \N {} +6235 1922 2025-03-11 23:57:28.40312-07 2025-03-12 00:24:44.986697-07 test f T4 \N f \N \N \N {} +6396 1988 2025-03-13 16:20:45.984417-07 2025-03-13 16:27:56.674856-07 test f T4 \N f \N \N \N {} +6631 2050 2025-03-14 12:29:57.064337-07 2025-03-14 13:06:46.738173-07 test t A100 \N t \N \N \N {} +3641 1244 2025-03-01 01:27:10.713031-08 2025-03-01 01:18:07.544805-08 benchmark t A100 \N f \N \N \N {} +6236 1923 2025-03-12 01:04:23.501448-07 2025-03-12 00:05:29.54956-07 test t T4 \N f \N \N \N {} +6397 1989 2025-03-13 17:25:39.429146-07 2025-03-13 16:39:56.193057-07 test t T4 \N f \N \N \N {} +6561 2032 2025-03-14 12:04:24.116313-07 2025-03-14 12:06:21.091672-07 benchmark f A100 \N t \N \N \N {} +6632 2050 2025-03-14 12:39:52.486934-07 2025-03-14 13:43:25.534592-07 benchmark t A100 \N t \N \N \N {} +6633 2050 2025-03-14 13:36:06.263877-07 2025-03-14 13:20:17.386606-07 leaderboard t A100 0.002519189 t \N \N \N {} +3643 1244 2025-03-01 01:35:19.685312-08 2025-03-01 00:36:21.094893-08 benchmark f A100 \N f \N \N \N {} +3644 1245 2025-03-01 00:26:06.119792-08 2025-03-01 01:35:27.012514-08 test t A100 \N f \N \N \N {} +3645 1245 2025-03-01 00:43:41.46218-08 2025-02-28 23:55:12.934017-08 test f A100 \N f \N \N \N {} +3646 1246 2025-03-01 00:53:33.727311-08 2025-03-01 02:15:48.757393-08 benchmark f A100 \N f \N \N \N {} +3647 1247 2025-03-01 02:33:55.98627-08 2025-03-01 02:31:22.439703-08 benchmark f A100 \N t \N \N \N {} +6237 1923 2025-03-12 00:58:02.114277-07 2025-03-12 01:54:20.094067-07 test f T4 \N f \N \N \N {} +3649 1248 2025-03-01 01:25:05.925773-08 2025-03-01 01:09:17.783085-08 benchmark t A100 \N t \N \N \N {} +3651 1248 2025-03-01 02:17:03.556096-08 2025-03-01 02:09:10.130241-08 test f A100 \N t \N \N \N {} +3653 1248 2025-03-01 02:26:58.343458-08 2025-03-01 00:43:43.03041-08 leaderboard f A100 0.0031272273333333334 t \N \N \N {} +3654 1249 2025-03-01 02:12:30.998096-08 2025-03-01 00:50:24.715655-08 test f A100 \N t \N \N \N {} +3655 1249 2025-03-01 01:49:55.694137-08 2025-03-01 01:29:49.718531-08 benchmark f A100 \N t \N \N \N {} +3656 1249 2025-03-01 01:38:29.821048-08 2025-03-01 01:46:03.662966-08 leaderboard f A100 0.0031039466666666665 t \N \N \N {} +3889 1297 2025-03-01 02:52:44.650492-08 2025-03-01 03:23:37.6224-08 test t A100 \N t \N \N \N {} +3657 1249 2025-03-01 01:50:49.813103-08 2025-03-01 02:00:22.394174-08 test t A100 \N t \N \N \N {} +3669 1251 2025-03-01 01:21:22.679753-08 2025-03-01 02:00:57.562983-08 test f A100 \N t \N \N \N {} +3670 1251 2025-03-01 02:27:13.746914-08 2025-03-01 01:53:26.460343-08 benchmark f A100 \N t \N \N \N {} +3671 1251 2025-03-01 00:57:08.998138-08 2025-03-01 00:58:17.33663-08 leaderboard f A100 0.0031081036666666664 t \N \N \N {} +3672 1252 2025-03-01 01:11:34.754783-08 2025-03-01 00:52:36.138674-08 test f A100 \N t \N \N \N {} +3681 1253 2025-03-01 01:03:11.979635-08 2025-03-01 02:40:30.398743-08 test f A100 \N t \N \N \N {} +3910 1301 2025-03-01 03:46:47.874489-08 2025-03-01 03:33:33.484922-08 test f A100 \N f \N \N \N {} +3673 1252 2025-03-01 02:02:20.431435-08 2025-03-01 01:05:47.013515-08 benchmark f A100 \N t \N \N \N {} +3674 1252 2025-03-01 02:12:52.290378-08 2025-03-01 01:46:02.154693-08 leaderboard f A100 0.0031048003333333336 t \N \N \N {} +3676 1252 2025-03-01 01:03:12.897497-08 2025-03-01 01:26:39.062279-08 benchmark t A100 \N t \N \N \N {} +3677 1252 2025-03-01 01:38:06.493139-08 2025-03-01 02:09:28.677674-08 leaderboard t A100 0.0031104436666666664 t \N \N \N {} +3678 1253 2025-03-01 02:06:10.058974-08 2025-03-01 01:30:07.280628-08 test t A100 \N t \N \N \N {} +3679 1253 2025-03-01 02:03:05.966122-08 2025-03-01 00:56:13.504406-08 benchmark t A100 \N t \N \N \N {} +3680 1253 2025-03-01 02:45:57.725737-08 2025-03-01 02:34:27.95218-08 leaderboard t A100 0.0031090656666666666 t \N \N \N {} +3682 1253 2025-03-01 02:05:39.373314-08 2025-03-01 01:20:42.226463-08 benchmark f A100 \N t \N \N \N {} +3683 1253 2025-03-01 01:49:07.509476-08 2025-03-01 01:57:14.444777-08 leaderboard f A100 0.0031048203333333335 t \N \N \N {} +3690 1260 2025-03-01 01:35:12.029569-08 2025-03-01 01:45:52.929787-08 benchmark f A100 \N t \N \N \N {} +3691 1261 2025-03-01 01:38:16.278326-08 2025-03-01 01:36:09.051317-08 test t A100 \N t \N \N \N {} +3692 1261 2025-03-01 02:49:47.45514-08 2025-03-01 03:17:46.327605-08 benchmark t A100 \N t \N \N \N {} +3693 1261 2025-03-01 02:16:44.503505-08 2025-03-01 02:01:57.713906-08 leaderboard t A100 0.0030880283333333336 t \N \N \N {} +3694 1261 2025-03-01 02:18:25.475537-08 2025-03-01 02:21:41.927369-08 test f A100 \N t \N \N \N {} +3712 1264 2025-03-01 02:56:03.049752-08 2025-03-01 01:43:52.163817-08 test t A100 \N t \N \N \N {} +3697 1262 2025-03-01 03:19:09.843073-08 2025-03-01 01:30:49.179646-08 test t A100 \N t \N \N \N {} +3698 1262 2025-03-01 03:23:21.681606-08 2025-03-01 02:15:56.73891-08 benchmark t A100 \N t \N \N \N {} +3699 1262 2025-03-01 02:06:27.293399-08 2025-03-01 02:01:32.569519-08 leaderboard t A100 0.0030787253333333336 t \N \N \N {} +3700 1262 2025-03-01 03:02:04.699757-08 2025-03-01 01:27:30.191004-08 test f A100 \N t \N \N \N {} +3701 1262 2025-03-01 01:41:08.419018-08 2025-03-01 02:33:50.455395-08 benchmark f A100 \N t \N \N \N {} +3702 1262 2025-03-01 03:18:19.489873-08 2025-03-01 02:32:54.172929-08 leaderboard f A100 0.0030775323333333336 t \N \N \N {} +3919 1304 2025-03-01 05:14:59.512003-08 2025-03-01 04:15:31.250903-08 test f A100 \N t \N \N \N {} +3704 1263 2025-03-01 03:03:11.700889-08 2025-03-01 02:34:12.760558-08 benchmark f A100 \N t \N \N \N {} +3721 1266 2025-03-01 03:29:58.775718-08 2025-03-01 02:01:14.58848-08 benchmark f A100 \N t \N \N \N {} +3722 1267 2025-03-01 02:00:41.613096-08 2025-03-01 02:39:28.789934-08 test t A100 \N t \N \N \N {} +3723 1267 2025-03-01 02:08:23.198399-08 2025-03-01 02:24:17.413187-08 benchmark t A100 \N t \N \N \N {} +3724 1267 2025-03-01 02:58:51.225291-08 2025-03-01 01:53:44.033391-08 leaderboard t A100 0.0030839456666666666 t \N \N \N {} +3725 1267 2025-03-01 02:45:10.109595-08 2025-03-01 02:41:38.775413-08 test f A100 \N t \N \N \N {} +3928 1305 2025-03-01 05:09:48.841292-08 2025-03-01 05:01:55.591158-08 test f A100 \N t \N \N \N {} +3729 1268 2025-03-01 03:05:42.17627-08 2025-03-01 02:39:10.249741-08 benchmark t A100 \N t \N \N \N {} +3730 1268 2025-03-01 01:47:00.767793-08 2025-03-01 02:57:06.798112-08 leaderboard t A100 0.0031026586666666666 t \N \N \N {} +3731 1268 2025-03-01 02:41:00.300122-08 2025-03-01 03:30:29.20737-08 test f A100 \N t \N \N \N {} +3732 1268 2025-03-01 03:04:23.203277-08 2025-03-01 01:43:41.695278-08 benchmark f A100 \N t \N \N \N {} +3733 1268 2025-03-01 01:51:37.215302-08 2025-03-01 02:38:14.824507-08 leaderboard f A100 0.0030949656666666666 t \N \N \N {} +3734 1269 2025-03-01 02:58:59.331439-08 2025-03-01 02:02:26.027818-08 test t A100 \N t \N \N \N {} +3735 1269 2025-03-01 03:35:14.847212-08 2025-03-01 02:02:44.717076-08 benchmark t A100 \N t \N \N \N {} +3736 1269 2025-03-01 01:53:25.567276-08 2025-03-01 02:33:01.154365-08 leaderboard t A100 0.0031152113333333337 t \N \N \N {} +3737 1269 2025-03-01 02:30:37.175149-08 2025-03-01 02:34:29.93941-08 test f A100 \N t \N \N \N {} +3738 1269 2025-03-01 03:04:08.336127-08 2025-03-01 02:39:05.51646-08 benchmark f A100 \N t \N \N \N {} +3739 1269 2025-03-01 03:26:54.522181-08 2025-03-01 02:30:29.400292-08 leaderboard f A100 0.003116479 t \N \N \N {} +3752 1272 2025-03-01 03:35:25.32574-08 2025-03-01 03:42:34.897532-08 leaderboard t A100 0.003070418 t \N \N \N {} +3964 1321 2025-03-01 04:25:29.824066-08 2025-03-01 05:01:10.21614-08 test t A100 \N t \N \N \N {} +3753 1273 2025-03-01 01:58:03.710454-08 2025-03-01 03:21:45.806033-08 test f A100 \N t \N \N \N {} +3754 1273 2025-03-01 03:22:29.292453-08 2025-03-01 03:42:54.655064-08 benchmark f A100 \N t \N \N \N {} +3755 1273 2025-03-01 02:32:56.792637-08 2025-03-01 03:04:59.058769-08 leaderboard f A100 0.0031262986666666663 t \N \N \N {} +3768 1275 2025-03-01 02:46:08.326312-08 2025-03-01 02:22:21.610827-08 test f A100 \N t \N \N \N {} +3777 1277 2025-03-01 02:21:07.979234-08 2025-03-01 03:47:24.50701-08 test t A100 \N t \N \N \N {} +3775 1276 2025-03-01 01:55:51.302209-08 2025-03-01 02:01:49.922507-08 benchmark t A100 \N t \N \N \N {} +3776 1276 2025-03-01 02:08:35.267407-08 2025-03-01 01:54:26.716726-08 leaderboard t A100 0.0030983713333333335 t \N \N \N {} +3778 1277 2025-03-01 03:32:26.499294-08 2025-03-01 03:46:45.069319-08 benchmark t A100 \N t \N \N \N {} +3779 1277 2025-03-01 03:38:23.774189-08 2025-03-01 02:35:30.930272-08 leaderboard t A100 0.003084539 t \N \N \N {} +3780 1277 2025-03-01 03:22:55.155356-08 2025-03-01 02:43:56.628934-08 test f A100 \N t \N \N \N {} +3784 1278 2025-03-01 02:48:23.56591-08 2025-03-01 02:33:17.354544-08 benchmark t A100 \N t \N \N \N {} +3785 1278 2025-03-01 02:39:45.41231-08 2025-03-01 02:40:12.61527-08 leaderboard t A100 0.0031055783333333335 t \N \N \N {} +3789 1279 2025-03-01 02:01:53.626081-08 2025-03-01 02:47:46.086211-08 test f A100 \N t \N \N \N {} +3786 1278 2025-03-01 02:03:03.53279-08 2025-03-01 03:47:19.946226-08 test f A100 \N t \N \N \N {} +3787 1278 2025-03-01 03:29:33.298347-08 2025-03-01 03:34:20.277338-08 benchmark f A100 \N t \N \N \N {} +3788 1278 2025-03-01 03:00:05.752988-08 2025-03-01 01:56:16.762213-08 leaderboard f A100 0.003097914 t \N \N \N {} +6238 1924 2025-03-12 01:21:46.601814-07 2025-03-12 01:32:26.514257-07 test t T4 \N f \N \N \N {} +6398 1989 2025-03-13 17:08:21.233687-07 2025-03-13 17:06:06.404998-07 test f T4 \N f \N \N \N {} +6562 2033 2025-03-14 13:12:39.948827-07 2025-03-14 13:23:53.347312-07 benchmark f A100 \N t \N \N \N {} +6634 2051 2025-03-14 12:30:34.721734-07 2025-03-14 12:30:27.296794-07 test t A100 \N t \N \N \N {} +3790 1279 2025-03-01 03:32:19.769184-08 2025-03-01 03:50:26.829557-08 benchmark f A100 \N t \N \N \N {} +3791 1279 2025-03-01 02:54:07.655455-08 2025-03-01 02:19:13.748311-08 leaderboard f A100 0.0030992133333333336 t \N \N \N {} +6399 1990 2025-03-13 17:50:57.628826-07 2025-03-13 16:49:18.239868-07 test t T4 \N f \N \N \N {} +6563 2034 2025-03-14 12:07:55.631644-07 2025-03-14 12:27:16.784091-07 benchmark f A100 \N t \N \N \N {} +7059 2185 2025-03-16 22:03:27.033571-07 2025-03-16 21:36:29.376537-07 leaderboard f L4 0.00011869082456140351 t \N \N \N {} +3793 1279 2025-03-01 03:42:52.339097-08 2025-03-01 03:50:15.909886-08 benchmark t A100 \N t \N \N \N {} +3794 1279 2025-03-01 02:12:29.931818-08 2025-03-01 03:14:22.177181-08 leaderboard t A100 0.0030700836666666667 t \N \N \N {} +6240 1925 2025-03-12 01:13:55.791064-07 2025-03-12 00:29:24.037503-07 test f T4 \N t \N \N \N {} +6244 1925 2025-03-12 01:24:08.247115-07 2025-03-12 00:16:50.797071-07 benchmark t T4 \N t \N \N \N {} +6245 1925 2025-03-12 00:39:44.345705-07 2025-03-12 01:03:34.532126-07 leaderboard t T4 3.565283209 t \N \N \N {} +6401 1991 2025-03-13 18:14:55.765999-07 2025-03-13 16:54:11.131116-07 test f T4 \N t \N \N \N {} +6250 1926 2025-03-12 02:15:10.273238-07 2025-03-12 01:01:04.027885-07 benchmark f T4 \N t \N \N \N {} +6251 1926 2025-03-12 00:50:43.580637-07 2025-03-12 00:57:06.425599-07 leaderboard f T4 3.682825032 t \N \N \N {} +6404 1991 2025-03-13 18:23:53.350354-07 2025-03-13 17:41:06.459187-07 test t T4 \N t \N \N \N {} +6405 1991 2025-03-13 18:45:41.07329-07 2025-03-13 17:03:11.875615-07 benchmark t T4 \N t \N \N \N {} +3805 1281 2025-03-01 02:40:30.487878-08 2025-03-01 02:47:22.690155-08 benchmark t A100 \N t \N \N \N {} +3806 1281 2025-03-01 03:18:03.1251-08 2025-03-01 02:07:31.048245-08 leaderboard t A100 0.0030762023333333337 t \N \N \N {} +6406 1991 2025-03-13 16:56:16.674442-07 2025-03-13 17:32:22.195013-07 leaderboard t T4 0.01198132082 t \N \N \N {} +6565 2036 2025-03-14 13:26:34.659744-07 2025-03-14 12:28:45.901302-07 benchmark f A100 \N t \N \N \N {} +6635 2051 2025-03-14 12:12:47.169659-07 2025-03-14 12:30:19.462198-07 benchmark t A100 \N t \N \N \N {} +6636 2051 2025-03-14 13:22:32.747423-07 2025-03-14 13:46:45.493463-07 leaderboard t A100 0.0025760326666666666 t \N \N \N {} +3808 1283 2025-03-01 03:57:31.233254-08 2025-03-01 03:30:41.035131-08 benchmark t A100 \N t \N \N \N {} +3809 1283 2025-03-01 02:53:01.039251-08 2025-03-01 03:11:15.949496-08 leaderboard t A100 0.0030891856666666667 t \N \N \N {} +3810 1282 2025-03-01 02:47:29.668011-08 2025-03-01 03:06:52.828571-08 benchmark f A100 \N t \N \N \N {} +3811 1282 2025-03-01 02:30:35.994805-08 2025-03-01 02:24:05.263461-08 benchmark f H100 \N t \N \N \N {} +6407 1992 2025-03-13 18:08:55.397985-07 2025-03-13 17:16:33.413217-07 test t T4 \N f \N \N \N {} +6566 2037 2025-03-14 12:48:47.844486-07 2025-03-14 12:08:55.718904-07 test f A100 \N f \N \N \N {} +6637 2051 2025-03-14 12:40:08.734201-07 2025-03-14 12:40:23.426756-07 test f A100 \N t \N \N \N {} +3813 1283 2025-03-01 04:00:42.588176-08 2025-03-01 02:20:16.080762-08 benchmark f A100 \N t \N \N \N {} +3814 1283 2025-03-01 03:40:07.853236-08 2025-03-01 03:29:58.258385-08 leaderboard f A100 0.003101622 t \N \N \N {} +3815 1284 2025-03-01 02:25:33.95253-08 2025-03-01 02:28:22.525197-08 test t A100 \N f \N \N \N {} +3816 1284 2025-03-01 03:57:37.041465-08 2025-03-01 02:25:56.015805-08 test f A100 \N f \N \N \N {} +6408 1992 2025-03-13 19:09:40.136801-07 2025-03-13 17:14:34.611515-07 test f T4 \N f \N \N \N {} +6567 2037 2025-03-14 12:36:50.459533-07 2025-03-14 12:05:58.31517-07 test t A100 \N f \N \N \N {} +3818 1285 2025-03-01 02:34:29.255307-08 2025-03-01 02:41:27.552585-08 benchmark f A100 \N t \N \N \N {} +3819 1285 2025-03-01 03:02:33.900221-08 2025-03-01 02:39:16.471311-08 leaderboard f A100 0.003196064 t \N \N \N {} +6409 1993 2025-03-13 17:37:09.987891-07 2025-03-13 17:44:10.720084-07 test t T4 \N f \N \N \N {} +6568 2038 2025-03-14 13:27:33.060334-07 2025-03-14 11:46:55.719973-07 test t A100 \N f \N \N \N {} +6638 2051 2025-03-14 14:00:30.84485-07 2025-03-14 12:56:57.304408-07 benchmark f A100 \N t \N \N \N {} +6639 2051 2025-03-14 13:35:20.196957-07 2025-03-14 12:03:45.180778-07 leaderboard f A100 0.002520242 t \N \N \N {} +3821 1285 2025-03-01 03:35:11.06709-08 2025-03-01 03:50:33.861385-08 benchmark t A100 \N t \N \N \N {} +3822 1285 2025-03-01 04:05:45.187777-08 2025-03-01 03:19:06.671204-08 leaderboard t A100 0.003205114 t \N \N \N {} +6410 1993 2025-03-13 18:31:14.592561-07 2025-03-13 18:03:31.587714-07 test f T4 \N f \N \N \N {} +6569 2038 2025-03-14 11:54:10.44922-07 2025-03-14 12:14:26.005828-07 test f A100 \N f \N \N \N {} +6640 2052 2025-03-14 13:20:11.68013-07 2025-03-14 13:54:08.817793-07 benchmark f A100 \N t \N \N \N {} +6663 2059 2025-03-14 13:43:14.834484-07 2025-03-14 13:10:23.01354-07 test t T4 \N f \N \N \N {} +3824 1286 2025-03-01 03:48:51.460228-08 2025-03-01 03:05:00.868889-08 benchmark t A100 \N t \N \N \N {} +3825 1286 2025-03-01 03:13:52.902037-08 2025-03-01 03:59:39.680247-08 leaderboard t A100 0.0032052243333333336 t \N \N \N {} +6257 1933 2025-03-12 06:29:52.272103-07 2025-03-12 06:33:58.837779-07 benchmark f A100 \N t \N \N \N {} +6411 1994 2025-03-13 17:31:54.500515-07 2025-03-13 17:50:40.571354-07 test t T4 \N f \N \N \N {} +6570 2039 2025-03-14 12:43:54.474387-07 2025-03-14 11:46:53.304327-07 test f A100 \N t \N \N \N {} +6571 2039 2025-03-14 13:36:58.274258-07 2025-03-14 11:48:23.338015-07 benchmark f A100 \N t \N \N \N {} +6572 2039 2025-03-14 12:38:10.932323-07 2025-03-14 12:39:34.102441-07 leaderboard f A100 0.0025144643333333337 t \N \N \N {} +6259 1936 2025-03-12 07:33:01.179724-07 2025-03-12 08:00:02.311465-07 benchmark f A100 \N t \N \N \N {} +6413 1995 2025-03-13 18:23:54.614105-07 2025-03-13 19:13:53.587183-07 test t T4 \N f \N \N \N {} +6576 2040 2025-03-14 12:03:45.01229-07 2025-03-14 12:44:15.413186-07 test f A100 \N t \N \N \N {} +6577 2040 2025-03-14 11:54:53.53347-07 2025-03-14 12:54:34.179949-07 benchmark f A100 \N t \N \N \N {} +6578 2040 2025-03-14 13:10:32.037477-07 2025-03-14 12:17:48.109262-07 leaderboard f A100 0.002512107 t \N \N \N {} +3833 1287 2025-03-01 02:36:47.925069-08 2025-03-01 03:26:45.302835-08 benchmark t A100 \N t \N \N \N {} +3834 1287 2025-03-01 02:32:30.809129-08 2025-03-01 03:12:32.781049-08 leaderboard t A100 0.0030916056666666666 t \N \N \N {} +6260 1937 2025-03-12 07:23:06.50947-07 2025-03-12 08:23:19.792542-07 benchmark f A100 \N f \N \N \N {} +6261 1938 2025-03-12 08:35:16.313743-07 2025-03-12 08:05:05.626744-07 benchmark f A100 \N t \N \N \N {} +6415 1996 2025-03-13 17:38:08.027955-07 2025-03-13 18:18:31.168297-07 test f T4 \N f \N \N \N {} +6582 2041 2025-03-14 13:35:12.454784-07 2025-03-14 11:47:41.393176-07 test f A100 \N t \N \N \N {} +6583 2041 2025-03-14 13:00:57.639982-07 2025-03-14 12:50:35.650726-07 benchmark f A100 \N t \N \N \N {} +3839 1288 2025-03-01 02:49:11.125792-08 2025-03-01 03:21:23.235824-08 benchmark t A100 \N t \N \N \N {} +6262 1939 2025-03-12 07:09:38.373161-07 2025-03-12 08:48:50.014584-07 test f A100 \N t \N \N \N {} +6417 1997 2025-03-13 20:13:42.139063-07 2025-03-13 19:48:53.847265-07 test t T4 \N f \N \N \N {} +6268 1940 2025-03-12 08:48:23.158902-07 2025-03-12 07:34:48.110256-07 test f H100 \N t \N \N \N {} +6271 1940 2025-03-12 08:39:14.809321-07 2025-03-12 08:06:26.839197-07 test t H100 \N t \N \N \N {} +6272 1940 2025-03-12 08:53:57.603406-07 2025-03-12 08:56:46.092949-07 benchmark t H100 \N t \N \N \N {} +6273 1940 2025-03-12 08:30:27.185852-07 2025-03-12 07:53:33.36497-07 leaderboard t H100 0.00004756101351351351 t \N \N \N {} +6584 2041 2025-03-14 11:48:11.361349-07 2025-03-14 13:07:20.826594-07 leaderboard f A100 0.00251554 t \N \N \N {} +3851 1290 2025-03-01 02:42:29.071194-08 2025-03-01 03:06:56.033212-08 benchmark t A100 \N t \N \N \N {} +3852 1290 2025-03-01 03:17:02.156523-08 2025-03-01 02:24:26.38537-08 leaderboard t A100 0.0030752533333333336 t \N \N \N {} +6274 1941 2025-03-12 08:49:13.036616-07 2025-03-12 09:04:52.719316-07 benchmark f T4 \N t \N \N \N {} +6419 1998 2025-03-14 05:01:43.261969-07 2025-03-14 04:51:13.385755-07 benchmark f A100 \N t \N \N \N {} +6585 2041 2025-03-14 12:16:33.083304-07 2025-03-14 11:57:07.432446-07 test t A100 \N t \N \N \N {} +6586 2041 2025-03-14 12:55:15.401484-07 2025-03-14 13:17:33.140416-07 benchmark t A100 \N t \N \N \N {} +3854 1291 2025-03-01 03:39:45.47163-08 2025-03-01 04:08:17.677385-08 benchmark t A100 \N t \N \N \N {} +3855 1291 2025-03-01 04:06:18.714922-08 2025-03-01 04:05:24.645808-08 leaderboard t A100 0.0030727556666666667 t \N \N \N {} +6275 1942 2025-03-12 08:13:42.287391-07 2025-03-12 08:06:35.694977-07 test f T4 \N t \N \N \N {} +6276 1942 2025-03-12 08:50:04.371499-07 2025-03-12 07:30:05.01053-07 benchmark f T4 \N t \N \N \N {} +6277 1942 2025-03-12 08:59:14.826076-07 2025-03-12 08:14:40.196742-07 leaderboard f T4 0.00013039858 t \N \N \N {} +6420 1999 2025-03-14 05:43:54.110159-07 2025-03-14 04:25:26.908913-07 test f A100 \N t \N \N \N {} +3858 1291 2025-03-01 02:47:40.294969-08 2025-03-01 03:21:35.997134-08 leaderboard f A100 0.0030834156666666667 t \N \N \N {} +6278 1942 2025-03-12 07:53:39.313803-07 2025-03-12 08:52:00.346671-07 test t T4 \N t \N \N \N {} +6279 1942 2025-03-12 07:54:27.029152-07 2025-03-12 08:11:56.444947-07 benchmark t T4 \N t \N \N \N {} +6280 1942 2025-03-12 09:01:51.96126-07 2025-03-12 07:48:32.954993-07 leaderboard t T4 0.00017005301923076922 t \N \N \N {} +6588 2042 2025-03-14 12:41:32.755275-07 2025-03-14 13:27:47.933076-07 test t A100 \N t \N \N \N {} +3860 1292 2025-03-01 03:44:44.046571-08 2025-03-01 02:59:32.783521-08 benchmark f A100 \N t \N \N \N {} +3861 1292 2025-03-01 02:54:03.416387-08 2025-03-01 04:17:02.090714-08 leaderboard f A100 0.0030798846666666665 t \N \N \N {} +3864 1292 2025-03-01 03:01:12.811119-08 2025-03-01 03:57:53.126582-08 leaderboard t A100 0.0030685383333333336 t \N \N \N {} +6284 1943 2025-03-12 07:37:15.328744-07 2025-03-12 08:00:32.114323-07 test f L4 \N t \N \N \N {} +6285 1943 2025-03-12 09:15:51.836775-07 2025-03-12 08:18:34.03295-07 benchmark f L4 \N t \N \N \N {} +6286 1943 2025-03-12 07:18:39.964262-07 2025-03-12 08:42:15.886255-07 leaderboard f L4 0.00009671902499999999 t \N \N \N {} +6589 2042 2025-03-14 13:28:36.680878-07 2025-03-14 12:42:33.70216-07 benchmark t A100 \N t \N \N \N {} +3866 1293 2025-03-01 03:45:51.800351-08 2025-03-01 04:07:15.03461-08 benchmark f A100 \N t \N \N \N {} +3867 1293 2025-03-01 03:54:05.969256-08 2025-03-01 03:52:25.243297-08 leaderboard f A100 0.0030785423333333337 t \N \N \N {} +3869 1293 2025-03-01 03:05:36.772437-08 2025-03-01 03:41:52.212108-08 benchmark t A100 \N t \N \N \N {} +3870 1293 2025-03-01 03:43:37.103283-08 2025-03-01 03:30:28.463215-08 leaderboard t A100 0.0030768713333333333 t \N \N \N {} +6290 1944 2025-03-12 10:57:21.104648-07 2025-03-12 10:29:58.980609-07 test t T4 \N t \N \N \N {} +3872 1294 2025-03-01 02:49:18.306021-08 2025-03-01 03:07:55.017888-08 benchmark f A100 \N t \N \N \N {} +3873 1294 2025-03-01 03:18:49.888527-08 2025-03-01 02:34:53.156434-08 leaderboard f A100 0.0030735693333333335 t \N \N \N {} +6293 1945 2025-03-12 11:27:15.263361-07 2025-03-12 10:44:27.72857-07 test t T4 \N f \N \N \N {} +6425 1999 2025-03-14 04:01:43.503576-07 2025-03-14 04:06:07.830224-07 leaderboard t A100 0.0024771706666666666 t \N \N \N {} +6590 2042 2025-03-14 12:07:33.510979-07 2025-03-14 12:55:00.902598-07 leaderboard t A100 0.0031459183333333333 t \N \N \N {} +6642 2053 2025-03-14 13:53:44.288829-07 2025-03-14 13:50:05.960849-07 benchmark t A100 \N t \N \N \N {} +3875 1294 2025-03-01 03:43:43.782738-08 2025-03-01 04:24:15.592112-08 benchmark t A100 \N t \N \N \N {} +3878 1295 2025-03-01 04:12:08.121664-08 2025-03-01 04:27:53.16111-08 benchmark f A100 \N t \N \N \N {} +3879 1295 2025-03-01 03:26:16.469666-08 2025-03-01 03:58:31.78643-08 leaderboard f A100 0.0030710493333333333 t \N \N \N {} +6429 2000 2025-03-14 06:05:54.518003-07 2025-03-14 07:37:02.202589-07 test t H100 \N t \N \N \N {} +6430 2000 2025-03-14 06:35:29.934346-07 2025-03-14 07:22:55.35725-07 benchmark t H100 \N t \N \N \N {} +6431 2000 2025-03-14 06:03:20.051179-07 2025-03-14 06:00:31.398891-07 leaderboard t H100 0.00140618875 t \N \N \N {} +6592 2042 2025-03-14 12:08:29.246929-07 2025-03-14 13:42:58.5473-07 benchmark f A100 \N t \N \N \N {} +6593 2042 2025-03-14 12:19:09.742168-07 2025-03-14 13:19:22.260046-07 leaderboard f A100 0.0038880753333333335 t \N \N \N {} +3881 1295 2025-03-01 03:58:42.298448-08 2025-03-01 04:33:00.759565-08 benchmark t A100 \N t \N \N \N {} +3882 1295 2025-03-01 04:11:32.308003-08 2025-03-01 03:45:03.260733-08 leaderboard t A100 0.0030748093333333336 t \N \N \N {} +6432 2001 2025-03-14 06:20:47.16994-07 2025-03-14 06:30:53.126756-07 test f A100 \N t \N \N \N {} +6433 2001 2025-03-14 06:43:34.047031-07 2025-03-14 05:58:41.550022-07 benchmark f A100 \N t \N \N \N {} +6434 2001 2025-03-14 07:28:13.286534-07 2025-03-14 05:58:23.458927-07 leaderboard f A100 0.0025180643333333336 t \N \N \N {} +6594 2043 2025-03-14 12:13:34.198838-07 2025-03-14 13:41:30.590742-07 test f A100 \N f \N \N \N {} +5073 1550 2025-03-04 16:13:34.029481-08 2025-03-04 17:53:09.887209-08 test f H100 \N f \N \N \N {} +3884 1296 2025-03-01 03:12:53.853598-08 2025-03-01 03:21:33.361057-08 benchmark t A100 \N t \N \N \N {} +3885 1296 2025-03-01 02:52:45.802668-08 2025-03-01 04:33:27.422507-08 leaderboard t A100 0.003072869 t \N \N \N {} +6297 1948 2025-03-12 11:29:51.801512-07 2025-03-12 10:25:13.155691-07 test f T4 \N t \N \N \N {} +6298 1948 2025-03-12 10:35:30.201717-07 2025-03-12 10:27:15.46981-07 benchmark f T4 \N f \N \N \N {} +6435 2001 2025-03-14 07:04:41.915018-07 2025-03-14 07:08:00.946033-07 test t A100 \N t \N \N \N {} +6439 2002 2025-03-14 06:51:44.898139-07 2025-03-14 07:04:48.007592-07 benchmark t A100 \N t \N \N \N {} +6440 2002 2025-03-14 06:58:37.268782-07 2025-03-14 07:30:47.849941-07 leaderboard t A100 0.0025299696666666667 t \N \N \N {} +3890 1297 2025-03-01 04:08:21.671557-08 2025-03-01 03:21:10.925412-08 benchmark t A100 \N t \N \N \N {} +6441 2002 2025-03-14 06:48:37.342621-07 2025-03-14 07:23:19.449449-07 test f A100 \N t \N \N \N {} +6442 2002 2025-03-14 07:08:21.810282-07 2025-03-14 06:12:36.932788-07 benchmark f A100 \N t \N \N \N {} +3893 1297 2025-03-01 03:27:32.585689-08 2025-03-01 03:40:51.676057-08 benchmark f A100 \N t \N \N \N {} +3900 1299 2025-03-01 03:40:55.395999-08 2025-03-01 03:53:19.378987-08 benchmark f A100 \N f \N \N \N {} +3901 1301 2025-03-01 03:23:52.831997-08 2025-03-01 03:24:06.231506-08 test t A100 \N t \N \N \N {} +3906 1301 2025-03-01 04:15:07.380839-08 2025-03-01 03:58:42.535677-08 leaderboard f H100 0.000903244375 t \N \N \N {} +3907 1301 2025-03-01 03:58:13.621695-08 2025-03-01 03:35:37.1111-08 test t H100 \N t \N \N \N {} +3908 1301 2025-03-01 04:58:15.504982-08 2025-03-01 04:38:42.581718-08 benchmark t H100 \N t \N \N \N {} +3913 1303 2025-03-01 03:45:26.565256-08 2025-03-01 05:21:39.968163-08 test t A100 \N t \N \N \N {} +3920 1304 2025-03-01 04:39:50.463012-08 2025-03-01 05:01:19.916314-08 benchmark f A100 \N t \N \N \N {} +3921 1304 2025-03-01 04:39:17.669272-08 2025-03-01 03:55:58.328183-08 leaderboard f A100 0.0030727456666666663 t \N \N \N {} +6670 2063 2025-03-14 14:58:48.236812-07 2025-03-14 15:04:51.635193-07 test f T4 \N f \N \N \N {} +3923 1304 2025-03-01 04:23:40.082947-08 2025-03-01 04:52:37.416251-08 benchmark t A100 \N t \N \N \N {} +3924 1304 2025-03-01 05:06:09.579535-08 2025-03-01 04:30:25.040011-08 leaderboard t A100 0.0030747073333333335 t \N \N \N {} +6307 1950 2025-03-12 11:44:15.74066-07 2025-03-12 10:54:32.286656-07 test f T4 \N f \N \N \N {} +6444 2003 2025-03-14 06:32:45.509148-07 2025-03-14 07:18:07.617634-07 test t A100 \N t \N \N \N {} +6445 2003 2025-03-14 06:43:39.064772-07 2025-03-14 07:26:22.701906-07 benchmark t A100 \N t \N \N \N {} +6446 2003 2025-03-14 07:33:49.087001-07 2025-03-14 06:39:43.653159-07 leaderboard t A100 0.00251628 t \N \N \N {} +3926 1305 2025-03-01 05:27:38.253899-08 2025-03-01 04:35:13.150247-08 benchmark t A100 \N t \N \N \N {} +3927 1305 2025-03-01 04:30:10.546633-08 2025-03-01 04:32:49.219013-08 leaderboard t A100 0.003074227 t \N \N \N {} +6308 1950 2025-03-12 12:25:05.113988-07 2025-03-12 12:37:59.249778-07 test t T4 \N f \N \N \N {} +6447 2003 2025-03-14 07:25:35.263987-07 2025-03-14 05:57:18.436435-07 test f A100 \N t \N \N \N {} +6448 2003 2025-03-14 06:22:22.575249-07 2025-03-14 07:19:37.030539-07 benchmark f A100 \N t \N \N \N {} +6449 2003 2025-03-14 07:33:07.159548-07 2025-03-14 07:43:35.889465-07 leaderboard f A100 0.0025153743333333335 t \N \N \N {} +3929 1305 2025-03-01 05:24:53.102248-08 2025-03-01 05:27:25.918489-08 benchmark f A100 \N t \N \N \N {} +3930 1305 2025-03-01 04:56:51.217802-08 2025-03-01 04:41:26.360821-08 leaderboard f A100 0.0030731703333333337 t \N \N \N {} +6309 1951 2025-03-12 12:37:05.375541-07 2025-03-12 12:21:31.848581-07 test t T4 \N f \N \N \N {} +6450 2004 2025-03-14 07:29:50.054077-07 2025-03-14 06:54:48.788265-07 test f A100 \N t \N \N \N {} +6451 2004 2025-03-14 06:47:18.994761-07 2025-03-14 07:41:04.641274-07 benchmark f A100 \N t \N \N \N {} +6452 2004 2025-03-14 07:10:52.271141-07 2025-03-14 07:05:23.598203-07 leaderboard f A100 0.0025149163333333334 t \N \N \N {} +6686 2071 2025-03-14 15:07:32.035959-07 2025-03-14 16:41:36.954155-07 test t T4 \N f \N \N \N {} +6935 2159 2025-03-16 16:02:23.35434-07 2025-03-16 16:42:39.300711-07 test f T4 \N t \N \N \N {} +6311 1952 2025-03-12 11:40:24.149803-07 2025-03-12 11:50:01.838943-07 test t T4 \N f \N \N \N {} +6458 2005 2025-03-14 07:21:25.580439-07 2025-03-14 07:49:47.315286-07 leaderboard f A100 0.004558669333333333 t \N \N \N {} +6687 2071 2025-03-14 15:04:02.106962-07 2025-03-14 16:27:06.289153-07 test f T4 \N f \N \N \N {} +6883 2136 2025-03-15 15:12:53.005577-07 2025-03-15 15:45:03.98046-07 test f T4 \N t \N \N \N {} +3968 1322 2025-03-01 05:31:02.366882-08 2025-03-01 05:15:44.290903-08 benchmark t A100 \N t \N \N \N {} +3969 1322 2025-03-01 05:37:29.486548-08 2025-03-01 05:45:21.369833-08 leaderboard t A100 0.0030774476666666664 t \N \N \N {} +3970 1322 2025-03-01 05:40:01.603582-08 2025-03-01 05:45:11.281035-08 test f A100 \N t \N \N \N {} +3971 1322 2025-03-01 06:09:29.893232-08 2025-03-01 05:02:33.403442-08 benchmark f A100 \N t \N \N \N {} +3972 1322 2025-03-01 05:06:02.207814-08 2025-03-01 05:55:39.737325-08 leaderboard f A100 0.0030752193333333333 t \N \N \N {} +3973 1323 2025-03-01 05:58:27.485607-08 2025-03-01 05:32:24.84737-08 test f A100 \N t \N \N \N {} +3974 1323 2025-03-01 06:16:37.08018-08 2025-03-01 04:46:10.75807-08 benchmark f A100 \N t \N \N \N {} +3975 1323 2025-03-01 06:18:48.328453-08 2025-03-01 05:34:36.581312-08 leaderboard f A100 0.003101147 t \N \N \N {} +3976 1323 2025-03-01 06:03:28.859556-08 2025-03-01 05:45:30.216393-08 test t A100 \N t \N \N \N {} +3977 1323 2025-03-01 06:05:43.149319-08 2025-03-01 06:06:01.600121-08 benchmark t A100 \N t \N \N \N {} +3978 1323 2025-03-01 05:20:14.946-08 2025-03-01 06:09:26.3369-08 leaderboard t A100 0.0030720786666666665 t \N \N \N {} +3979 1324 2025-03-01 05:38:48.032681-08 2025-03-01 05:38:54.242522-08 test t A100 \N f \N \N \N {} +3980 1324 2025-03-01 05:03:07.857476-08 2025-03-01 05:56:18.026019-08 test f A100 \N f \N \N \N {} +3981 1325 2025-03-01 04:30:24.581687-08 2025-03-01 05:30:26.203251-08 test f A100 \N f \N \N \N {} +3982 1325 2025-03-01 04:56:35.77322-08 2025-03-01 05:06:42.117449-08 test t A100 \N f \N \N \N {} +3989 1327 2025-03-01 04:28:09.627783-08 2025-03-01 06:14:03.4485-08 test f A100 \N t \N \N \N {} +3990 1327 2025-03-01 04:35:12.535221-08 2025-03-01 06:16:55.791101-08 benchmark f A100 \N t \N \N \N {} +3998 1328 2025-03-01 05:02:22.872872-08 2025-03-01 05:53:01.111038-08 test t A100 \N t \N \N \N {} +3999 1328 2025-03-01 05:20:30.293816-08 2025-03-01 04:33:16.504511-08 benchmark t A100 \N t \N \N \N {} +4000 1328 2025-03-01 05:24:59.129999-08 2025-03-01 05:11:00.788762-08 leaderboard t A100 0.0030814433333333333 t \N \N \N {} +4006 1329 2025-03-01 05:03:38.237873-08 2025-03-01 05:49:05.125811-08 leaderboard f A100 0.0030727136666666667 t \N \N \N {} +4007 1330 2025-03-01 06:29:07.869426-08 2025-03-01 06:22:44.330161-08 test t A100 \N t \N \N \N {} +4008 1330 2025-03-01 06:01:42.053545-08 2025-03-01 04:58:15.207722-08 benchmark t A100 \N t \N \N \N {} +4009 1330 2025-03-01 05:03:24.750209-08 2025-03-01 05:57:23.562377-08 leaderboard t A100 0.0030764006666666667 t \N \N \N {} +4010 1330 2025-03-01 05:02:18.387184-08 2025-03-01 04:49:51.849261-08 test f A100 \N t \N \N \N {} +4011 1330 2025-03-01 04:34:41.853129-08 2025-03-01 05:27:20.630573-08 benchmark f A100 \N t \N \N \N {} +4012 1330 2025-03-01 04:49:25.539951-08 2025-03-01 05:57:06.277345-08 leaderboard f A100 0.0030913556666666664 t \N \N \N {} +4013 1331 2025-03-01 04:48:50.723394-08 2025-03-01 05:02:12.598261-08 test f A100 \N t \N \N \N {} +4014 1331 2025-03-01 05:46:30.40955-08 2025-03-01 04:34:39.633219-08 benchmark f A100 \N t \N \N \N {} +4015 1331 2025-03-01 05:38:54.065577-08 2025-03-01 05:25:23.854596-08 leaderboard f A100 0.0030761543333333334 t \N \N \N {} +4016 1331 2025-03-01 04:41:26.822773-08 2025-03-01 06:31:54.977434-08 test t A100 \N t \N \N \N {} +4025 1333 2025-03-01 05:50:11.13569-08 2025-03-01 06:05:47.809639-08 test f A100 \N t \N \N \N {} +4034 1334 2025-03-01 05:19:46.050467-08 2025-03-01 06:13:31.802821-08 test t A100 \N t \N \N \N {} +4042 1336 2025-03-01 06:19:43.447341-08 2025-03-01 05:51:53.054293-08 test f A100 \N t \N \N \N {} +4017 1331 2025-03-01 04:46:15.747877-08 2025-03-01 05:40:33.74129-08 benchmark t A100 \N t \N \N \N {} +4018 1331 2025-03-01 04:50:33.824408-08 2025-03-01 04:35:16.517834-08 leaderboard t A100 0.0030775386666666666 t \N \N \N {} +4019 1332 2025-03-01 06:15:51.207238-08 2025-03-01 06:21:40.29258-08 test f A100 \N t \N \N \N {} +4020 1332 2025-03-01 05:54:40.351278-08 2025-03-01 06:07:21.488972-08 benchmark f A100 \N t \N \N \N {} +4021 1332 2025-03-01 05:31:15.028107-08 2025-03-01 06:30:55.794956-08 leaderboard f A100 0.0032326943333333335 t \N \N \N {} +4029 1333 2025-03-01 04:43:11.484999-08 2025-03-01 06:18:08.597188-08 benchmark t A100 \N t \N \N \N {} +4030 1333 2025-03-01 05:38:23.948276-08 2025-03-01 05:39:32.998484-08 leaderboard t A100 0.0030748286666666667 t \N \N \N {} +4031 1334 2025-03-01 05:48:36.681273-08 2025-03-01 05:09:41.33705-08 test f A100 \N t \N \N \N {} +4032 1334 2025-03-01 05:41:35.653199-08 2025-03-01 05:12:26.846467-08 benchmark f A100 \N t \N \N \N {} +4033 1334 2025-03-01 05:50:46.305933-08 2025-03-01 05:16:24.499999-08 leaderboard f A100 0.0031010393333333335 t \N \N \N {} +4035 1334 2025-03-01 06:38:20.344372-08 2025-03-01 05:01:27.901596-08 benchmark t A100 \N t \N \N \N {} +4036 1334 2025-03-01 05:13:10.923007-08 2025-03-01 05:05:10.395318-08 leaderboard t A100 0.0030793333333333337 t \N \N \N {} +4037 1335 2025-03-01 05:22:53.382722-08 2025-03-01 05:25:53.811459-08 test t A100 \N f \N \N \N {} +4045 1337 2025-03-01 06:43:13.512286-08 2025-03-01 05:52:38.084808-08 test t A100 \N t \N \N \N {} +4046 1337 2025-03-01 06:44:42.843151-08 2025-03-01 06:01:44.654233-08 benchmark t A100 \N t \N \N \N {} +4053 1338 2025-03-01 05:49:19.310951-08 2025-03-01 05:36:21.556206-08 leaderboard t A100 0.0030786213333333333 t \N \N \N {} +4054 1338 2025-03-01 06:08:31.2478-08 2025-03-01 05:15:47.918409-08 test f A100 \N t \N \N \N {} +4055 1338 2025-03-01 06:00:55.317206-08 2025-03-01 05:42:35.691206-08 benchmark f A100 \N t \N \N \N {} +4061 1339 2025-03-01 04:55:54.708149-08 2025-03-01 06:20:05.506072-08 benchmark t A100 \N t \N \N \N {} +4062 1339 2025-03-01 06:29:35.459123-08 2025-03-01 06:23:22.438908-08 leaderboard t A100 0.0030713596666666664 t \N \N \N {} +4063 1340 2025-03-01 05:41:13.291902-08 2025-03-01 05:08:42.07891-08 test f A100 \N t \N \N \N {} +4064 1340 2025-03-01 05:33:54.804638-08 2025-03-01 06:49:34.790694-08 benchmark f A100 \N t \N \N \N {} +4065 1340 2025-03-01 06:22:42.948861-08 2025-03-01 06:31:27.337118-08 leaderboard f A100 0.003198704 t \N \N \N {} +4066 1340 2025-03-01 05:53:22.22062-08 2025-03-01 06:34:56.280809-08 test t A100 \N t \N \N \N {} +4075 1342 2025-03-01 06:23:06.476775-08 2025-03-01 06:20:15.612586-08 test f A100 \N t \N \N \N {} +4084 1343 2025-03-01 06:31:52.402656-08 2025-03-01 06:10:01.556153-08 test f A100 \N t \N \N \N {} +4109 1349 2025-03-01 05:50:58.123614-08 2025-03-01 07:02:47.190517-08 test t A100 \N t \N \N \N {} +4067 1340 2025-03-01 05:51:32.857224-08 2025-03-01 05:50:31.869282-08 benchmark t A100 \N t \N \N \N {} +4068 1340 2025-03-01 06:52:04.233379-08 2025-03-01 05:20:00.968896-08 leaderboard t A100 0.0032254873333333336 t \N \N \N {} +4076 1342 2025-03-01 05:52:15.30752-08 2025-03-01 05:16:01.171421-08 benchmark f A100 \N t \N \N \N {} +4077 1342 2025-03-01 06:48:06.395287-08 2025-03-01 06:20:52.98914-08 leaderboard f A100 0.003085711 t \N \N \N {} +4078 1342 2025-03-01 06:38:10.193601-08 2025-03-01 06:54:28.028198-08 test t A100 \N t \N \N \N {} +4085 1343 2025-03-01 06:30:45.755953-08 2025-03-01 05:42:14.015703-08 benchmark f A100 \N t \N \N \N {} +4086 1343 2025-03-01 05:14:21.820912-08 2025-03-01 06:41:27.76024-08 leaderboard f A100 0.0031491675 t \N \N \N {} +4093 1345 2025-03-01 05:54:34.018691-08 2025-03-01 06:48:03.464696-08 test f A100 \N f \N \N \N {} +4094 1345 2025-03-01 06:07:45.020264-08 2025-03-01 05:56:24.473964-08 test t A100 \N f \N \N \N {} +4101 1347 2025-03-01 06:44:45.774365-08 2025-03-01 05:10:23.773951-08 test f A100 \N f \N \N \N {} +4102 1347 2025-03-01 06:21:29.448139-08 2025-03-01 05:16:01.94432-08 test t A100 \N f \N \N \N {} +4110 1349 2025-03-01 06:50:52.832524-08 2025-03-01 06:21:34.412682-08 benchmark t A100 \N t \N \N \N {} +4111 1349 2025-03-01 05:37:38.785374-08 2025-03-01 05:39:45.670246-08 leaderboard t A100 0.0030744786666666666 t \N \N \N {} +4118 1350 2025-03-01 05:53:22.664423-08 2025-03-01 07:01:25.508428-08 test t A100 \N t \N \N \N {} +4119 1350 2025-03-01 07:06:15.020001-08 2025-03-01 05:36:11.046309-08 benchmark t A100 \N t \N \N \N {} +4120 1350 2025-03-01 06:20:34.501205-08 2025-03-01 07:07:39.442371-08 leaderboard t A100 0.003088505 t \N \N \N {} +4126 1351 2025-03-01 05:29:03.034314-08 2025-03-01 06:25:02.133816-08 leaderboard t A100 0.0030791646666666664 t \N \N \N {} +4134 1353 2025-03-01 06:04:43.547155-08 2025-03-01 05:13:34.33261-08 benchmark t A100 \N t \N \N \N {} +4135 1353 2025-03-01 07:05:13.871186-08 2025-03-01 06:41:07.602177-08 leaderboard t A100 0.003071621 t \N \N \N {} +4143 1354 2025-03-01 05:17:03.588452-08 2025-03-01 05:16:11.035298-08 benchmark t A100 \N t \N \N \N {} +4144 1354 2025-03-01 06:01:34.820165-08 2025-03-01 06:34:58.72374-08 leaderboard t A100 0.00309386 t \N \N \N {} +4165 1363 2025-03-01 09:39:07.8542-08 2025-03-01 09:06:36.459295-08 benchmark f A100 \N t \N \N \N {} +4166 1364 2025-03-01 10:45:48.93923-08 2025-03-01 10:33:36.557942-08 test t A100 \N t \N \N \N {} +4167 1364 2025-03-01 09:43:39.289368-08 2025-03-01 09:24:23.903656-08 benchmark t A100 \N t \N \N \N {} +4170 1364 2025-03-01 10:36:49.26316-08 2025-03-01 09:28:08.907873-08 benchmark f A100 \N t \N \N \N {} +4175 1366 2025-03-01 10:48:21.857968-08 2025-03-01 10:57:43.342896-08 leaderboard t A100 0.0030997733333333337 t \N \N \N {} +4176 1366 2025-03-01 10:27:28.925342-08 2025-03-01 09:25:05.826997-08 test f A100 \N t \N \N \N {} +4184 1372 2025-03-01 10:01:34.032339-08 2025-03-01 10:16:18.107861-08 benchmark f A100 \N t \N \N \N {} +4185 1373 2025-03-01 09:15:05.438331-08 2025-03-01 10:28:47.955472-08 benchmark f A100 \N t \N \N \N {} +4191 1380 2025-03-01 11:36:24.556989-08 2025-03-01 11:40:57.219225-08 benchmark f A100 \N t \N \N \N {} +4192 1381 2025-03-01 12:37:28.809727-08 2025-03-01 11:54:48.723704-08 benchmark f A100 \N f \N \N \N {} +4193 1382 2025-03-01 12:05:31.937467-08 2025-03-01 12:39:42.035292-08 benchmark f A100 \N f \N \N \N {} +4194 1383 2025-03-01 12:24:03.28573-08 2025-03-01 12:49:27.141084-08 benchmark f A100 \N f \N \N \N {} +4195 1384 2025-03-01 12:30:23.318874-08 2025-03-01 11:45:28.231808-08 benchmark f A100 \N t \N \N \N {} +4196 1385 2025-03-01 12:10:07.559639-08 2025-03-01 13:20:27.623427-08 benchmark f A100 \N t \N \N \N {} +4197 1386 2025-03-01 13:54:20.599915-08 2025-03-01 13:59:32.398597-08 test f A100 \N t \N \N \N {} +4198 1387 2025-03-01 14:14:19.137435-08 2025-03-01 13:16:59.246757-08 benchmark f A100 \N t \N \N \N {} +4204 1388 2025-03-01 12:23:09.987084-08 2025-03-01 13:22:39.985792-08 leaderboard t A100 0.496474646 t \N \N \N {} +4205 1389 2025-03-01 13:19:47.926437-08 2025-03-01 13:50:43.82625-08 test f H100 \N t \N \N \N {} +4206 1389 2025-03-01 12:57:36.960021-08 2025-03-01 13:42:33.039405-08 benchmark f H100 \N t \N \N \N {} +4224 1394 2025-03-01 16:02:06.026414-08 2025-03-01 15:58:14.959593-08 leaderboard t A100 0.0031078296666666666 t \N \N \N {} +4225 1394 2025-03-01 15:56:49.702024-08 2025-03-01 14:57:49.257679-08 test t H100 \N t \N \N \N {} +4226 1394 2025-03-01 16:23:12.233422-08 2025-03-01 16:09:26.381534-08 benchmark t H100 \N t \N \N \N {} +4227 1394 2025-03-01 15:22:20.9878-08 2025-03-01 15:51:31.402515-08 leaderboard t H100 0.00140471775 t \N \N \N {} +4230 1397 2025-03-01 17:11:50.531769-08 2025-03-01 17:06:47.495163-08 benchmark f H100 \N t \N \N \N {} +4235 1398 2025-03-01 16:50:13.330905-08 2025-03-01 16:05:18.289309-08 benchmark f H100 \N t \N \N \N {} +4240 1398 2025-03-01 15:52:44.51554-08 2025-03-01 15:40:06.215226-08 test f A100 \N f \N \N \N {} +4241 1399 2025-03-01 17:23:26.394202-08 2025-03-01 16:46:15.926611-08 benchmark f H100 \N f \N \N \N {} +4248 1402 2025-03-01 16:50:49.496325-08 2025-03-01 16:38:17.053665-08 benchmark t H100 \N t \N \N \N {} +4249 1402 2025-03-01 17:29:19.46759-08 2025-03-01 17:17:13.325643-08 leaderboard t H100 0.0013931105 t \N \N \N {} +4250 1403 2025-03-01 16:41:21.078394-08 2025-03-01 16:31:20.029427-08 test f H100 \N t \N \N \N {} +4251 1403 2025-03-01 17:01:41.343912-08 2025-03-01 17:09:49.148125-08 benchmark f H100 \N t \N \N \N {} +4252 1403 2025-03-01 16:09:08.072113-08 2025-03-01 16:30:28.83147-08 leaderboard f H100 0.00139343525 t \N \N \N {} +4415 1443 2025-03-01 23:27:54.443718-08 2025-03-01 23:44:48.408471-08 benchmark t L4 \N t \N \N \N {} +4254 1403 2025-03-01 16:28:43.322824-08 2025-03-01 16:12:28.94178-08 benchmark t H100 \N t \N \N \N {} +4255 1403 2025-03-01 16:59:30.637265-08 2025-03-01 16:54:54.864297-08 leaderboard t H100 0.0014084093333333332 t \N \N \N {} +4256 1402 2025-03-01 17:48:26.163752-08 2025-03-01 16:43:09.49428-08 test f H100 \N t \N \N \N {} +4257 1402 2025-03-01 17:31:46.113694-08 2025-03-01 16:07:29.034922-08 benchmark f H100 \N t \N \N \N {} +4258 1402 2025-03-01 16:44:05.583517-08 2025-03-01 16:25:07.353815-08 leaderboard f H100 0.0014460703333333332 t \N \N \N {} +4259 1404 2025-03-01 16:04:19.796606-08 2025-03-01 17:33:34.248936-08 test t H100 \N t \N \N \N {} +4260 1404 2025-03-01 16:49:13.841981-08 2025-03-01 17:51:00.007281-08 benchmark t H100 \N t \N \N \N {} +4261 1404 2025-03-01 17:21:32.526268-08 2025-03-01 16:39:22.981736-08 leaderboard t H100 0.00139580475 t \N \N \N {} +4262 1404 2025-03-01 16:31:24.233879-08 2025-03-01 17:38:25.925363-08 test f H100 \N t \N \N \N {} +4263 1404 2025-03-01 17:31:33.499326-08 2025-03-01 16:34:05.021142-08 benchmark f H100 \N t \N \N \N {} +4379 1433 2025-03-01 21:51:29.505052-08 2025-03-01 21:42:07.436975-08 benchmark f H100 \N t \N \N \N {} +4264 1404 2025-03-01 16:46:11.323414-08 2025-03-01 17:19:04.9435-08 leaderboard f H100 0.0014059293333333331 t \N \N \N {} +4265 1405 2025-03-01 17:04:29.433941-08 2025-03-01 17:19:54.898023-08 test f L4 \N t \N \N \N {} +4266 1405 2025-03-01 17:15:36.867761-08 2025-03-01 16:06:23.499666-08 benchmark f L4 \N t \N \N \N {} +4267 1405 2025-03-01 17:28:05.852436-08 2025-03-01 16:40:49.105619-08 leaderboard f L4 0.017067446 t \N \N \N {} +4268 1405 2025-03-01 17:14:37.360289-08 2025-03-01 16:02:42.08553-08 test t L4 \N t \N \N \N {} +4384 1437 2025-03-01 22:29:30.981358-08 2025-03-01 22:15:31.729879-08 benchmark f H100 \N f \N \N \N {} +4269 1405 2025-03-01 16:26:20.496455-08 2025-03-01 16:15:13.996431-08 benchmark t L4 \N t \N \N \N {} +4270 1405 2025-03-01 16:08:25.687965-08 2025-03-01 17:10:55.36684-08 leaderboard t L4 0.01706846266666667 t \N \N \N {} +4271 1406 2025-03-01 17:35:32.433009-08 2025-03-01 16:47:33.575921-08 test f T4 \N t \N \N \N {} +4272 1406 2025-03-01 16:29:17.767422-08 2025-03-01 16:51:54.661181-08 benchmark f T4 \N t \N \N \N {} +4273 1406 2025-03-01 16:08:36.107841-08 2025-03-01 16:30:12.375768-08 leaderboard f T4 0.009307217333333334 t \N \N \N {} +4385 1438 2025-03-01 22:33:51.295044-08 2025-03-01 22:12:55.046741-08 benchmark f H100 \N f \N \N \N {} +4274 1406 2025-03-01 17:26:10.061376-08 2025-03-01 16:28:55.30488-08 test t T4 \N f \N \N \N {} +4275 1406 2025-03-01 17:24:14.461681-08 2025-03-01 17:32:10.727373-08 test f L4 \N f \N \N \N {} +4276 1406 2025-03-01 16:51:43.765001-08 2025-03-01 17:08:41.224245-08 test t L4 \N f \N \N \N {} +4351 1425 2025-03-01 18:27:28.317296-08 2025-03-01 19:48:34.537155-08 test t A100 \N t \N \N \N {} +4360 1425 2025-03-01 18:55:29.934952-08 2025-03-01 20:05:32.79375-08 test f T4 \N t \N \N \N {} +4726 1479 2025-03-02 07:19:35.258294-08 2025-03-02 07:04:07.889836-08 test f A100 \N t \N \N \N {} +4341 1424 2025-03-01 19:12:19.828159-08 2025-03-01 20:11:36.50049-08 benchmark f H100 \N t \N \N \N {} +4342 1425 2025-03-01 19:30:03.137888-08 2025-03-01 20:13:32.937364-08 test f H100 \N t \N \N \N {} +4735 1480 2025-03-02 06:33:46.379677-08 2025-03-02 07:28:20.847048-08 test f A100 \N t \N \N \N {} +4343 1425 2025-03-01 19:07:06.678388-08 2025-03-01 19:01:17.242899-08 benchmark f H100 \N t \N \N \N {} +4344 1425 2025-03-01 19:36:47.591098-08 2025-03-01 20:05:13.137174-08 leaderboard f H100 0.0015068819090909092 t \N \N \N {} +4345 1425 2025-03-01 19:40:07.817557-08 2025-03-01 19:16:20.820213-08 test t L4 \N t \N \N \N {} +4355 1425 2025-03-01 19:40:23.407312-08 2025-03-01 18:36:43.639689-08 benchmark t T4 \N t \N \N \N {} +4356 1425 2025-03-01 19:34:42.848141-08 2025-03-01 19:21:17.019994-08 leaderboard t T4 0.019392106333333332 t \N \N \N {} +4364 1425 2025-03-01 19:44:49.950465-08 2025-03-01 19:52:00.834651-08 benchmark f L4 \N t \N \N \N {} +4365 1425 2025-03-01 18:56:32.12492-08 2025-03-01 19:36:54.329004-08 leaderboard f L4 0.018389386333333334 t \N \N \N {} +4366 1426 2025-03-01 22:15:53.709448-08 2025-03-01 21:41:09.208205-08 test f H100 \N f \N \N \N {} +4367 1426 2025-03-01 20:32:14.409145-08 2025-03-01 20:49:12.590979-08 test f A100 \N f \N \N \N {} +4368 1427 2025-03-01 22:21:41.20304-08 2025-03-01 21:10:43.83991-08 test f T4 \N f \N \N \N {} +4369 1427 2025-03-01 20:41:14.21784-08 2025-03-01 22:20:23.41131-08 test f L4 \N f \N \N \N {} +4370 1428 2025-03-01 21:22:14.660722-08 2025-03-01 21:39:17.638743-08 test f L4 \N f \N \N \N {} +4371 1429 2025-03-01 20:56:28.569982-08 2025-03-01 21:27:23.848244-08 test f L4 \N f \N \N \N {} +4375 1432 2025-03-01 21:12:25.058278-08 2025-03-01 21:46:33.33486-08 benchmark f H100 \N t \N \N \N {} +4376 1432 2025-03-01 23:10:02.333804-08 2025-03-01 21:49:56.568079-08 benchmark f L4 \N t \N \N \N {} +4377 1432 2025-03-01 22:54:44.213473-08 2025-03-01 21:21:30.556007-08 benchmark f A100 \N t \N \N \N {} +4876 1500 2025-03-03 09:35:56.137495-08 2025-03-03 09:52:48.005654-08 leaderboard t A100 0.003105324 t \N \N \N {} +4380 1434 2025-03-01 22:15:57.892727-08 2025-03-01 23:36:46.04608-08 benchmark f H100 \N t \N \N \N {} +4381 1435 2025-03-01 22:24:08.366722-08 2025-03-01 22:29:42.067258-08 benchmark f H100 \N t \N \N \N {} +4382 1436 2025-03-01 23:39:19.061091-08 2025-03-01 22:54:39.475489-08 benchmark f H100 \N f \N \N \N {} +4383 1437 2025-03-01 22:35:16.757658-08 2025-03-01 22:50:02.811266-08 benchmark f A100 \N t \N \N \N {} +4386 1439 2025-03-02 00:07:36.475999-08 2025-03-01 23:44:26.098199-08 benchmark f H100 \N t \N \N \N {} +4387 1440 2025-03-02 00:02:57.861102-08 2025-03-02 00:00:32.358477-08 test f H100 \N f \N \N \N {} +4414 1443 2025-03-01 23:54:21.846815-08 2025-03-01 23:32:53.796678-08 test t L4 \N t \N \N \N {} +4388 1440 2025-03-01 23:11:27.786009-08 2025-03-01 23:02:42.012476-08 test t A100 \N f \N \N \N {} +4393 1440 2025-03-01 23:15:26.635065-08 2025-03-01 23:08:51.999345-08 test t L4 \N f \N \N \N {} +4398 1441 2025-03-01 23:17:23.847393-08 2025-03-02 00:04:54.13285-08 test f A100 \N f \N \N \N {} +4403 1442 2025-03-01 22:53:39.143776-08 2025-03-01 23:56:44.359809-08 test t L4 \N f \N \N \N {} +4404 1442 2025-03-01 22:33:19.623771-08 2025-03-01 23:47:56.133494-08 test t H100 \N f \N \N \N {} +4405 1442 2025-03-02 00:04:31.80069-08 2025-03-01 23:26:12.331721-08 test f L4 \N f \N \N \N {} +4406 1442 2025-03-01 23:27:53.503574-08 2025-03-01 22:47:29.500397-08 test f H100 \N f \N \N \N {} +4407 1442 2025-03-02 00:07:32.568134-08 2025-03-01 23:00:08.696649-08 test t T4 \N f \N \N \N {} +4408 1442 2025-03-01 23:29:07.086459-08 2025-03-01 23:23:37.989186-08 test t A100 \N f \N \N \N {} +4409 1442 2025-03-01 22:51:12.097545-08 2025-03-01 22:56:56.425507-08 test f A100 \N f \N \N \N {} +4410 1442 2025-03-01 23:47:29.737111-08 2025-03-02 00:21:02.822694-08 test f T4 \N f \N \N \N {} +4416 1443 2025-03-01 23:58:25.027917-08 2025-03-01 23:05:04.128044-08 leaderboard t L4 0.009129896 t \N \N \N {} +4417 1443 2025-03-01 23:26:40.751758-08 2025-03-02 00:27:51.073423-08 test t A100 \N t \N \N \N {} +4423 1443 2025-03-02 00:04:09.394316-08 2025-03-02 00:13:48.201959-08 test f H100 \N t \N \N \N {} +4424 1443 2025-03-01 23:36:08.305635-08 2025-03-02 00:47:29.293128-08 benchmark f H100 \N t \N \N \N {} +4425 1443 2025-03-01 23:30:11.887008-08 2025-03-02 00:21:43.679513-08 leaderboard f H100 0.0015019263333333332 t \N \N \N {} +4437 1444 2025-03-01 23:22:12.599414-08 2025-03-02 00:09:39.877934-08 leaderboard f A100 0.0021369876666666667 t \N \N \N {} +4438 1444 2025-03-01 23:59:01.602753-08 2025-03-02 00:52:25.788903-08 test f T4 \N t \N \N \N {} +4444 1444 2025-03-01 23:27:58.287274-08 2025-03-01 23:34:31.637125-08 test t T4 \N t \N \N \N {} +4445 1444 2025-03-01 23:28:25.653915-08 2025-03-02 00:46:20.886868-08 benchmark t T4 \N t \N \N \N {} +4446 1444 2025-03-01 23:44:46.267002-08 2025-03-02 00:46:20.218889-08 leaderboard t T4 0.009888984666666666 t \N \N \N {} +4458 1444 2025-03-02 00:52:06.841349-08 2025-03-02 00:45:13.235451-08 leaderboard t L4 0.009366314666666667 t \N \N \N {} +4459 1445 2025-03-02 00:17:32.146735-08 2025-03-02 00:27:53.179825-08 test f A100 \N t \N \N \N {} +4465 1445 2025-03-01 23:25:17.275973-08 2025-03-01 23:51:30.13378-08 test f H100 \N t \N \N \N {} +4466 1445 2025-03-02 00:51:37.846267-08 2025-03-02 00:39:47.26155-08 benchmark f H100 \N t \N \N \N {} +4467 1445 2025-03-01 23:12:50.976391-08 2025-03-02 00:06:29.223376-08 leaderboard f H100 0.0010895946666666667 t \N \N \N {} +4479 1445 2025-03-02 00:18:32.388575-08 2025-03-01 23:33:06.524137-08 leaderboard f L4 0.009311576333333333 t \N \N \N {} +4480 1445 2025-03-02 00:23:10.771659-08 2025-03-02 00:25:16.79615-08 test t L4 \N t \N \N \N {} +4481 1445 2025-03-02 01:06:15.185297-08 2025-03-02 00:56:41.356199-08 benchmark t L4 \N t \N \N \N {} +4486 1447 2025-03-01 23:44:19.043343-08 2025-03-02 00:34:17.729776-08 test t A100 \N t \N \N \N {} +4500 1448 2025-03-01 23:26:04.292969-08 2025-03-02 00:24:20.318749-08 test f A100 \N t \N \N \N {} +4501 1448 2025-03-01 23:52:25.86203-08 2025-03-02 00:46:12.033575-08 benchmark f A100 \N t \N \N \N {} +4502 1448 2025-03-02 00:50:48.885087-08 2025-03-02 00:58:56.374508-08 leaderboard f A100 0.005635142019999999 t \N \N \N {} +4503 1448 2025-03-02 00:36:34.265162-08 2025-03-01 23:44:52.402347-08 test t A100 \N t \N \N \N {} +4514 1450 2025-03-02 00:08:27.757645-08 2025-03-02 00:02:03.781381-08 leaderboard t H100 0.00110452 t \N \N \N {} +4515 1450 2025-03-02 00:14:30.121063-08 2025-03-02 00:04:30.11163-08 test t L4 \N t \N \N \N {} +4516 1450 2025-03-02 00:00:00.221666-08 2025-03-01 23:29:45.13959-08 benchmark t L4 \N t \N \N \N {} +4521 1450 2025-03-02 00:02:47.686536-08 2025-03-01 23:56:26.155139-08 test f H100 \N t \N \N \N {} +4522 1450 2025-03-01 23:44:32.403338-08 2025-03-02 01:02:56.194528-08 benchmark f H100 \N t \N \N \N {} +4523 1450 2025-03-02 00:07:11.12717-08 2025-03-01 23:55:23.35683-08 leaderboard f H100 0.0010997553333333332 t \N \N \N {} +4535 1450 2025-03-02 00:15:33.808245-08 2025-03-02 00:39:03.802273-08 leaderboard f L4 0.009153131 t \N \N \N {} +4536 1451 2025-03-02 01:18:52.170604-08 2025-03-02 01:11:40.804771-08 test t H100 \N t \N \N \N {} +4900 1508 2025-03-03 12:44:25.017899-08 2025-03-03 13:29:37.01192-08 test f H100 \N t \N \N \N {} +4759 1485 2025-03-02 13:16:14.223315-08 2025-03-02 12:01:21.108531-08 benchmark f H100 \N t \N \N \N {} +4905 1511 2025-03-03 14:17:16.597745-08 2025-03-03 13:19:16.395123-08 test t T4 \N f \N \N \N {} +4623 1458 2025-03-02 06:38:45.060119-08 2025-03-02 05:30:45.356488-08 test t A100 \N t \N \N \N {} +6716 2082 2025-03-15 03:44:36.086784-07 2025-03-15 03:23:26.694084-07 test t A100 \N t \N \N \N {} +4632 1460 2025-03-02 06:51:07.426669-08 2025-03-02 06:04:55.597062-08 test t A100 \N t \N \N \N {} +6719 2082 2025-03-15 03:16:11.184399-07 2025-03-15 03:09:17.233425-07 test f A100 \N t \N \N \N {} +4641 1461 2025-03-02 05:59:49.230938-08 2025-03-02 06:17:49.715674-08 test f A100 \N t \N \N \N {} +6722 2083 2025-03-15 03:29:49.696607-07 2025-03-15 04:02:55.058125-07 test f A100 \N t \N \N \N {} +4650 1463 2025-03-02 06:51:19.039666-08 2025-03-02 05:32:00.49752-08 test t A100 \N t \N \N \N {} +6725 2083 2025-03-15 04:23:08.720635-07 2025-03-15 04:21:03.715823-07 test t A100 \N t \N \N \N {} +4659 1464 2025-03-02 07:24:45.335423-08 2025-03-02 05:47:41.040187-08 test f A100 \N t \N \N \N {} +6728 2084 2025-03-15 03:50:48.746611-07 2025-03-15 03:49:46.374297-07 test t A100 \N t \N \N \N {} +4668 1466 2025-03-02 07:09:02.875102-08 2025-03-02 05:37:12.011229-08 test t A100 \N t \N \N \N {} +6731 2084 2025-03-15 02:46:52.42645-07 2025-03-15 04:11:18.181663-07 test f A100 \N t \N \N \N {} +4620 1458 2025-03-02 07:19:02.28608-08 2025-03-02 07:06:35.957428-08 test f A100 \N t \N \N \N {} +4621 1458 2025-03-02 06:23:47.065884-08 2025-03-02 07:03:30.070237-08 benchmark f A100 \N t \N \N \N {} +4622 1458 2025-03-02 07:11:03.17852-08 2025-03-02 06:48:19.70623-08 leaderboard f A100 0.0030958753333333337 t \N \N \N {} +4624 1458 2025-03-02 06:26:45.847704-08 2025-03-02 05:52:44.331704-08 benchmark t A100 \N t \N \N \N {} +4625 1458 2025-03-02 06:51:27.902184-08 2025-03-02 06:23:56.736147-08 leaderboard t A100 0.003088996 t \N \N \N {} +4633 1460 2025-03-02 06:16:38.8786-08 2025-03-02 07:11:21.936323-08 benchmark t A100 \N t \N \N \N {} +4634 1460 2025-03-02 07:04:41.283428-08 2025-03-02 07:16:21.369857-08 leaderboard t A100 0.003092899 t \N \N \N {} +4642 1461 2025-03-02 06:09:41.284959-08 2025-03-02 06:22:42.202445-08 benchmark f A100 \N t \N \N \N {} +4643 1461 2025-03-02 06:39:57.63533-08 2025-03-02 06:46:22.813146-08 leaderboard f A100 0.0030704853333333336 t \N \N \N {} +4651 1463 2025-03-02 06:26:49.901591-08 2025-03-02 06:01:32.015488-08 benchmark t A100 \N t \N \N \N {} +4653 1463 2025-03-02 07:29:04.708213-08 2025-03-02 07:18:09.192245-08 test f A100 \N t \N \N \N {} +4660 1464 2025-03-02 05:32:51.24243-08 2025-03-02 06:13:17.003468-08 benchmark f A100 \N t \N \N \N {} +4661 1464 2025-03-02 07:16:41.081997-08 2025-03-02 05:48:25.892059-08 leaderboard f A100 0.0030758066666666667 t \N \N \N {} +4669 1466 2025-03-02 07:06:29.137455-08 2025-03-02 06:45:12.645699-08 benchmark t A100 \N t \N \N \N {} +4670 1466 2025-03-02 06:40:31.210834-08 2025-03-02 05:42:40.735546-08 leaderboard t A100 0.0030799263333333333 t \N \N \N {} +4678 1467 2025-03-02 06:32:38.464263-08 2025-03-02 06:16:47.589364-08 benchmark f A100 \N t \N \N \N {} +4680 1468 2025-03-02 06:22:28.004766-08 2025-03-02 07:35:00.897908-08 test f A100 \N t \N \N \N {} +4687 1469 2025-03-02 06:37:34.057103-08 2025-03-02 05:51:59.584284-08 benchmark f A100 \N t \N \N \N {} +4688 1469 2025-03-02 06:22:01.378836-08 2025-03-02 06:13:47.362128-08 leaderboard f A100 0.00307773 t \N \N \N {} +4696 1470 2025-03-02 05:54:10.720598-08 2025-03-02 06:58:21.045687-08 benchmark f A100 \N t \N \N \N {} +4697 1470 2025-03-02 06:56:21.605195-08 2025-03-02 07:04:27.428497-08 leaderboard f A100 0.003323739428571429 t \N \N \N {} +4704 1472 2025-03-02 06:51:21.142542-08 2025-03-02 07:19:12.17164-08 benchmark f A100 \N t \N \N \N {} +4705 1473 2025-03-02 06:59:02.14179-08 2025-03-02 06:18:41.378415-08 benchmark f A100 \N t \N \N \N {} +4706 1474 2025-03-02 06:32:28.531936-08 2025-03-02 07:28:17.021525-08 benchmark f A100 \N t \N \N \N {} +4707 1475 2025-03-02 07:43:56.768427-08 2025-03-02 07:18:05.844238-08 benchmark f A100 \N t \N \N \N {} +4709 1476 2025-03-02 07:29:45.620972-08 2025-03-02 06:53:02.850347-08 benchmark t A100 \N t \N \N \N {} +4710 1476 2025-03-02 06:48:36.930559-08 2025-03-02 07:09:21.284629-08 leaderboard t A100 0.003082332 t \N \N \N {} +4718 1477 2025-03-02 07:14:49.953547-08 2025-03-02 07:01:10.444489-08 benchmark f A100 \N t \N \N \N {} +4719 1477 2025-03-02 07:13:14.737688-08 2025-03-02 07:58:55.056874-08 leaderboard f A100 0.003080525 t \N \N \N {} +4727 1479 2025-03-02 08:08:16.530944-08 2025-03-02 06:25:59.616763-08 benchmark f A100 \N t \N \N \N {} +4736 1480 2025-03-02 07:47:58.351644-08 2025-03-02 06:49:23.461444-08 benchmark f A100 \N t \N \N \N {} +4737 1480 2025-03-02 08:02:53.103575-08 2025-03-02 08:13:30.327361-08 leaderboard f A100 0.0030852583333333336 t \N \N \N {} +4752 1485 2025-03-02 11:53:55.657606-08 2025-03-02 11:29:49.203485-08 test f A100 \N t \N \N \N {} +4753 1485 2025-03-02 11:58:43.743381-08 2025-03-02 12:04:12.552688-08 benchmark f A100 \N t \N \N \N {} +4754 1485 2025-03-02 12:59:42.33895-08 2025-03-02 12:36:29.8219-08 leaderboard f A100 0.0017007568181818182 t \N \N \N {} +4760 1485 2025-03-02 13:02:43.584587-08 2025-03-02 11:40:22.88528-08 leaderboard f H100 0.001025282 t \N \N \N {} +4761 1485 2025-03-02 11:54:09.417727-08 2025-03-02 13:05:23.177947-08 test t H100 \N t \N \N \N {} +4764 1485 2025-03-02 12:19:37.770033-08 2025-03-02 11:40:52.739277-08 test f T4 \N t \N \N \N {} +4767 1485 2025-03-02 11:34:48.399203-08 2025-03-02 11:27:10.480623-08 test f L4 \N t \N \N \N {} +4768 1485 2025-03-02 12:21:36.330234-08 2025-03-02 13:16:33.964583-08 benchmark f L4 \N t \N \N \N {} +4769 1485 2025-03-02 11:54:00.372056-08 2025-03-02 12:11:51.743238-08 leaderboard f L4 0.009055283666666665 t \N \N \N {} +4780 1486 2025-03-02 13:32:04.884765-08 2025-03-02 13:19:13.880894-08 test t L4 \N t \N \N \N {} +4788 1486 2025-03-02 12:38:25.692798-08 2025-03-02 12:40:07.438573-08 test f T4 \N t \N \N \N {} +4795 1486 2025-03-02 11:54:55.018029-08 2025-03-02 12:21:15.163724-08 leaderboard f L4 0.013281347 t \N \N \N {} +4827 1490 2025-03-02 14:04:50.955427-08 2025-03-02 14:34:08.371455-08 benchmark t A100 \N t \N \N \N {} +4828 1490 2025-03-02 14:33:00.642669-08 2025-03-02 14:35:15.849785-08 leaderboard t A100 0.0017312735 t \N \N \N {} +4829 1490 2025-03-02 15:19:06.654379-08 2025-03-02 14:54:43.856752-08 test f A100 \N t \N \N \N {} +4830 1490 2025-03-02 13:56:46.528544-08 2025-03-02 15:10:08.582329-08 benchmark f A100 \N t \N \N \N {} +4831 1490 2025-03-02 13:52:33.720668-08 2025-03-02 13:42:04.183005-08 leaderboard f A100 0.0017395216666666667 t \N \N \N {} +4832 1491 2025-03-02 13:31:50.812204-08 2025-03-02 15:31:56.835867-08 test t A100 \N t \N \N \N {} +4833 1491 2025-03-02 15:09:34.132728-08 2025-03-02 13:54:04.869362-08 benchmark t A100 \N t \N \N \N {} +4834 1491 2025-03-02 14:20:30.701842-08 2025-03-02 13:59:22.648224-08 leaderboard t A100 0.0017047336000000001 t \N \N \N {} +4835 1491 2025-03-02 14:45:24.03934-08 2025-03-02 15:10:20.533866-08 test f A100 \N t \N \N \N {} +4836 1491 2025-03-02 14:20:31.244621-08 2025-03-02 15:06:36.572076-08 benchmark f A100 \N t \N \N \N {} +4837 1491 2025-03-02 15:29:45.897744-08 2025-03-02 13:54:34.350922-08 leaderboard f A100 0.0017131206666666668 t \N \N \N {} +6758 2094 2025-03-15 03:45:32.92736-07 2025-03-15 04:07:37.272427-07 test f A100 \N t \N \N \N {} +4844 1493 2025-03-02 13:46:35.98177-08 2025-03-02 14:48:05.043588-08 test f A100 \N t \N \N \N {} +4845 1493 2025-03-02 14:00:59.163247-08 2025-03-02 14:35:29.575037-08 benchmark f A100 \N t \N \N \N {} +6761 2094 2025-03-15 03:27:08.411109-07 2025-03-15 04:01:20.079415-07 test t A100 \N t \N \N \N {} +4852 1494 2025-03-02 15:08:24.34101-08 2025-03-02 14:19:40.913664-08 leaderboard f L4 0.009482677 t \N \N \N {} +4853 1494 2025-03-02 13:50:54.659903-08 2025-03-02 13:57:25.615542-08 test t L4 \N t \N \N \N {} +4854 1494 2025-03-02 14:27:36.134951-08 2025-03-02 13:47:01.295492-08 benchmark t L4 \N t \N \N \N {} +4855 1494 2025-03-02 15:05:50.23843-08 2025-03-02 15:31:13.138166-08 leaderboard t L4 0.009465309666666666 t \N \N \N {} +4856 1494 2025-03-02 14:59:15.831079-08 2025-03-02 15:20:52.999421-08 test t A100 \N t \N \N \N {} +4857 1494 2025-03-02 14:03:52.80639-08 2025-03-02 14:22:25.374431-08 benchmark t A100 \N t \N \N \N {} +4858 1494 2025-03-02 14:26:35.800808-08 2025-03-02 14:28:06.519575-08 leaderboard t A100 0.0017436125 t \N \N \N {} +4859 1494 2025-03-02 15:29:00.03614-08 2025-03-02 14:28:22.752305-08 test f A100 \N t \N \N \N {} +4860 1494 2025-03-02 14:27:41.758642-08 2025-03-02 14:55:37.506929-08 benchmark f A100 \N t \N \N \N {} +4861 1494 2025-03-02 15:13:30.285322-08 2025-03-02 13:38:55.585292-08 leaderboard f A100 0.0017438063333333332 t \N \N \N {} +4862 1494 2025-03-02 15:33:33.758009-08 2025-03-02 14:29:38.081435-08 test f T4 \N f \N \N \N {} +4869 1495 2025-03-02 15:00:43.386949-08 2025-03-02 15:19:19.029978-08 leaderboard f A100 0.0017183623 t \N \N \N {} +4863 1494 2025-03-02 13:48:00.216726-08 2025-03-02 14:43:10.705646-08 test t T4 \N f \N \N \N {} +4864 1495 2025-03-02 14:14:21.117209-08 2025-03-02 14:50:17.471125-08 test t A100 \N t \N \N \N {} +4868 1495 2025-03-02 13:56:41.498396-08 2025-03-02 15:19:57.661184-08 benchmark f A100 \N t \N \N \N {} +4870 1496 2025-03-03 05:33:22.851475-08 2025-03-03 07:04:29.985456-08 test f T4 \N f \N \N \N {} +4872 1498 2025-03-03 10:02:30.253679-08 2025-03-03 10:00:42.925801-08 benchmark f A100 \N t \N \N \N {} +4873 1499 2025-03-03 08:49:19.901664-08 2025-03-03 10:04:25.898649-08 benchmark f A100 \N t \N \N \N {} +4874 1500 2025-03-03 10:15:34.816738-08 2025-03-03 09:04:15.56701-08 test t A100 \N t \N \N \N {} +4875 1500 2025-03-03 09:58:02.568244-08 2025-03-03 10:16:40.069607-08 benchmark t A100 \N t \N \N \N {} +4878 1500 2025-03-03 10:05:00.25862-08 2025-03-03 09:37:20.75377-08 benchmark f A100 \N t \N \N \N {} +4879 1500 2025-03-03 09:50:57.496245-08 2025-03-03 08:44:48.833862-08 leaderboard f A100 0.003117381 t \N \N \N {} +4882 1503 2025-03-03 10:01:52.486048-08 2025-03-03 10:04:44.602379-08 benchmark f A100 \N t \N \N \N {} +4883 1504 2025-03-03 09:35:17.850935-08 2025-03-03 10:09:25.356439-08 benchmark f A100 \N t \N \N \N {} +4885 1506 2025-03-03 13:09:09.154292-08 2025-03-03 13:05:14.933666-08 test f H100 \N t \N \N \N {} +4886 1506 2025-03-03 12:41:09.063313-08 2025-03-03 12:33:34.096179-08 benchmark f H100 \N t \N \N \N {} +4887 1506 2025-03-03 13:59:05.903613-08 2025-03-03 13:48:05.240853-08 leaderboard f H100 0.007589034 t \N \N \N {} +4888 1506 2025-03-03 13:41:18.845874-08 2025-03-03 13:57:20.398849-08 test t H100 \N t \N \N \N {} +4889 1506 2025-03-03 13:43:47.027609-08 2025-03-03 13:34:31.478831-08 benchmark t H100 \N t \N \N \N {} +4890 1506 2025-03-03 14:32:13.712927-08 2025-03-03 13:02:10.756259-08 leaderboard t H100 0.007640819666666667 t \N \N \N {} +4892 1507 2025-03-03 14:08:09.124545-08 2025-03-03 13:52:34.536612-08 benchmark t A100 \N t \N \N \N {} +4893 1507 2025-03-03 14:13:34.686495-08 2025-03-03 13:52:41.545312-08 leaderboard t A100 0.023358184 t \N \N \N {} +4897 1508 2025-03-03 14:08:08.277234-08 2025-03-03 12:42:40.071166-08 test t H100 \N t \N \N \N {} +4898 1508 2025-03-03 13:11:16.067265-08 2025-03-03 13:52:02.911783-08 benchmark t H100 \N t \N \N \N {} +4901 1508 2025-03-03 12:59:43.830263-08 2025-03-03 13:02:51.227677-08 benchmark f H100 \N t \N \N \N {} +4903 1510 2025-03-03 13:17:23.292196-08 2025-03-03 14:30:50.919066-08 test t T4 \N f \N \N \N {} +6766 2097 2025-03-15 03:49:11.850159-07 2025-03-15 04:26:16.901571-07 test t A100 \N t \N \N \N {} +5252 1613 2025-03-06 07:03:50.875686-08 2025-03-06 06:49:34.383024-08 leaderboard t L4 0.017109318 t \N \N \N {} +5253 1613 2025-03-06 07:08:11.029391-08 2025-03-06 07:01:52.953606-08 test f L4 \N t \N \N \N {} +4937 1521 2025-03-03 14:44:11.876868-08 2025-03-03 14:10:59.325044-08 leaderboard f A100 0.0014066483684210525 t \N \N \N {} +5254 1613 2025-03-06 07:19:32.162924-08 2025-03-06 08:10:28.115423-08 benchmark f L4 \N t \N \N \N {} +4938 1521 2025-03-03 15:24:12.517619-08 2025-03-03 14:20:06.658751-08 test t A100 \N t \N \N \N {} +4935 1521 2025-03-03 14:48:27.263394-08 2025-03-03 15:21:43.699775-08 test f A100 \N t \N \N \N {} +4936 1521 2025-03-03 14:31:08.393947-08 2025-03-03 14:45:16.89233-08 benchmark f A100 \N t \N \N \N {} +4939 1521 2025-03-03 15:07:14.225011-08 2025-03-03 14:04:11.041623-08 benchmark t A100 \N t \N \N \N {} +4940 1521 2025-03-03 15:16:14.240133-08 2025-03-03 14:50:14.153588-08 leaderboard t A100 0.001405596387755102 t \N \N \N {} +4941 1522 2025-03-03 15:05:35.069177-08 2025-03-03 14:28:42.867479-08 test f H100 \N t \N \N \N {} +4945 1522 2025-03-03 14:50:55.354936-08 2025-03-03 15:01:22.261798-08 benchmark t H100 \N t \N \N \N {} +4947 1523 2025-03-03 15:51:18.302976-08 2025-03-03 15:33:42.474228-08 benchmark f L4 \N f \N \N \N {} +4950 1524 2025-03-03 14:20:11.679881-08 2025-03-03 15:49:02.916005-08 leaderboard f A100 0.0025553103333333335 t \N \N \N {} +4951 1524 2025-03-03 14:08:29.207972-08 2025-03-03 14:22:21.528102-08 test t H100 \N t \N \N \N {} +4952 1524 2025-03-03 14:38:24.77646-08 2025-03-03 15:05:24.221642-08 benchmark t H100 \N t \N \N \N {} +4953 1524 2025-03-03 14:46:04.864855-08 2025-03-03 15:51:25.144507-08 leaderboard t H100 0.001393842 t \N \N \N {} +4954 1524 2025-03-03 14:30:39.536975-08 2025-03-03 14:17:44.438638-08 test t A100 \N t \N \N \N {} +4955 1524 2025-03-03 14:40:56.30983-08 2025-03-03 14:38:06.121372-08 benchmark t A100 \N t \N \N \N {} +4956 1524 2025-03-03 15:09:02.97402-08 2025-03-03 15:42:51.195857-08 leaderboard t A100 0.0025125443333333334 t \N \N \N {} +5003 1534 2025-03-03 23:49:46.367273-08 2025-03-03 22:04:46.20686-08 benchmark f T4 \N t \N \N \N {} +5256 1613 2025-03-06 07:45:45.096126-08 2025-03-06 08:18:22.747974-08 test t T4 \N t \N \N \N {} +4968 1533 2025-03-03 22:51:29.622688-08 2025-03-03 23:00:37.622546-08 test t A100 \N t \N \N \N {} +4969 1533 2025-03-03 21:55:51.670102-08 2025-03-03 22:25:04.572539-08 benchmark t A100 \N f \N \N \N {} +4970 1533 2025-03-03 22:42:07.031457-08 2025-03-03 23:50:25.299897-08 test f A100 \N t \N \N \N {} +4971 1533 2025-03-03 22:38:20.171762-08 2025-03-03 23:13:31.324985-08 benchmark f A100 \N f \N \N \N {} +4972 1533 2025-03-03 22:03:32.238956-08 2025-03-03 22:00:26.809337-08 test t H100 \N t \N \N \N {} +4973 1533 2025-03-03 23:37:08.619928-08 2025-03-03 22:52:08.648968-08 benchmark t H100 \N f \N \N \N {} +4975 1533 2025-03-03 22:42:28.912392-08 2025-03-03 22:08:16.911585-08 benchmark f H100 \N f \N \N \N {} +4976 1533 2025-03-03 22:57:31.735787-08 2025-03-03 22:02:40.663491-08 test t T4 \N t \N \N \N {} +4977 1533 2025-03-03 23:18:01.411543-08 2025-03-03 22:17:36.143751-08 benchmark t T4 \N f \N \N \N {} +4978 1533 2025-03-03 23:47:51.751613-08 2025-03-03 22:21:29.994607-08 test f T4 \N t \N \N \N {} +4979 1533 2025-03-03 23:38:54.802767-08 2025-03-03 23:19:22.073079-08 benchmark f T4 \N f \N \N \N {} +4980 1533 2025-03-03 22:10:43.459126-08 2025-03-03 23:21:01.183959-08 test f L4 \N t \N \N \N {} +4987 1534 2025-03-03 22:29:57.882848-08 2025-03-03 22:02:34.344571-08 test f A100 \N t \N \N \N {} +4988 1534 2025-03-03 23:17:09.194997-08 2025-03-03 22:00:28.272124-08 benchmark f A100 \N t \N \N \N {} +6772 2098 2025-03-15 05:05:11.153225-07 2025-03-15 03:30:05.714156-07 test t A100 \N t \N \N \N {} +4989 1534 2025-03-03 23:03:04.470836-08 2025-03-03 22:15:42.701149-08 leaderboard f A100 0.001491699 t \N \N \N {} +4990 1534 2025-03-03 23:01:12.271656-08 2025-03-03 23:25:12.093163-08 test f H100 \N t \N \N \N {} +4991 1534 2025-03-03 23:48:25.304808-08 2025-03-03 23:01:02.444844-08 benchmark f H100 \N t \N \N \N {} +4992 1534 2025-03-03 23:36:40.312813-08 2025-03-03 23:14:16.51261-08 leaderboard f H100 0.001046992 t \N \N \N {} +4993 1534 2025-03-03 22:43:08.623067-08 2025-03-03 23:34:08.683584-08 test f L4 \N t \N \N \N {} +4994 1534 2025-03-03 22:04:15.889744-08 2025-03-03 22:26:35.523219-08 benchmark f L4 \N t \N \N \N {} +4995 1534 2025-03-03 22:41:49.509135-08 2025-03-03 23:39:33.494802-08 leaderboard f L4 0.009082157 t \N \N \N {} +4996 1534 2025-03-03 23:19:47.000677-08 2025-03-03 23:51:43.362821-08 test t H100 \N t \N \N \N {} +5000 1534 2025-03-03 22:10:50.514317-08 2025-03-03 23:01:22.855314-08 benchmark t T4 \N t \N \N \N {} +5001 1534 2025-03-03 23:23:50.275013-08 2025-03-03 22:04:37.625675-08 leaderboard t T4 0.013584824666666667 t \N \N \N {} +5002 1534 2025-03-03 22:16:59.138409-08 2025-03-03 22:04:29.040324-08 test f T4 \N t \N \N \N {} +6775 2098 2025-03-15 04:57:02.889001-07 2025-03-15 04:18:28.535969-07 test f A100 \N t \N \N \N {} +5011 1539 2025-03-04 07:31:37.161177-08 2025-03-04 07:58:56.890059-08 test f T4 \N t \N \N \N {} +5012 1539 2025-03-04 08:16:38.896588-08 2025-03-04 07:18:18.877124-08 benchmark f T4 \N t \N \N \N {} +5013 1539 2025-03-04 08:20:34.258391-08 2025-03-04 07:24:16.514602-08 leaderboard f T4 0.009332833333333334 t \N \N \N {} +5014 1539 2025-03-04 06:44:13.355504-08 2025-03-04 07:28:03.052048-08 test t T4 \N t \N \N \N {} +5015 1539 2025-03-04 08:06:37.290401-08 2025-03-04 06:41:47.643875-08 benchmark t T4 \N t \N \N \N {} +5016 1539 2025-03-04 06:59:22.532084-08 2025-03-04 06:53:54.381226-08 leaderboard t T4 0.009299085333333333 t \N \N \N {} +5017 1540 2025-03-04 08:59:30.670557-08 2025-03-04 07:55:07.055003-08 test f A100 \N f \N \N \N {} +5027 1545 2025-03-04 14:11:42.47821-08 2025-03-04 13:44:28.274936-08 leaderboard f A100 0.00004787177777777778 t \N \N \N {} +5032 1545 2025-03-04 14:00:04.919683-08 2025-03-04 13:52:34.794499-08 benchmark f H100 \N t \N \N \N {} +5033 1545 2025-03-04 15:24:15.587724-08 2025-03-04 15:27:11.189949-08 leaderboard f H100 0.00004866111 t \N \N \N {} +5035 1545 2025-03-04 15:30:36.223103-08 2025-03-04 14:27:02.560929-08 benchmark t T4 \N t \N \N \N {} +5036 1545 2025-03-04 15:07:24.286467-08 2025-03-04 14:51:48.245831-08 leaderboard t T4 0.00023255714285714288 t \N \N \N {} +5257 1613 2025-03-06 06:53:37.987844-08 2025-03-06 08:14:43.956638-08 benchmark t T4 \N t \N \N \N {} +5037 1545 2025-03-04 14:19:47.708086-08 2025-03-04 15:37:55.178413-08 test f L4 \N f \N \N \N {} +5038 1545 2025-03-04 13:49:27.790553-08 2025-03-04 15:32:18.011451-08 test t L4 \N f \N \N \N {} +5039 1545 2025-03-04 15:35:59.738048-08 2025-03-04 14:26:26.882713-08 test f T4 \N f \N \N \N {} +5072 1549 2025-03-04 18:02:14.774593-08 2025-03-04 16:24:38.762227-08 test f H100 \N f \N \N \N {} +5075 1552 2025-03-04 16:26:06.712061-08 2025-03-04 17:05:50.91046-08 benchmark f H100 \N t \N \N \N {} +5076 1553 2025-03-04 17:19:12.024738-08 2025-03-04 17:13:44.312105-08 test t H100 \N t \N \N \N {} +5077 1553 2025-03-04 17:19:42.564719-08 2025-03-04 17:12:42.482126-08 benchmark t H100 \N t \N \N \N {} +5078 1553 2025-03-04 18:03:27.829582-08 2025-03-04 16:30:29.070429-08 leaderboard t H100 0.001479608 t \N \N \N {} +5079 1553 2025-03-04 16:43:40.87693-08 2025-03-04 17:28:08.937479-08 test f H100 \N t \N \N \N {} +5080 1553 2025-03-04 16:51:53.271335-08 2025-03-04 16:21:38.563765-08 benchmark f H100 \N t \N \N \N {} +5084 1556 2025-03-04 16:54:52.472201-08 2025-03-04 17:45:47.245193-08 test f A100 \N f \N \N \N {} +5085 1557 2025-03-04 18:28:01.276786-08 2025-03-04 17:44:42.993298-08 test f A100 \N f \N \N \N {} +5086 1558 2025-03-04 18:11:17.534507-08 2025-03-04 17:29:06.539472-08 test f A100 \N f \N \N \N {} +6790 2101 2025-03-15 03:49:21.74589-07 2025-03-15 04:16:48.818713-07 test f A100 \N t \N \N \N {} +5093 1560 2025-03-04 18:23:35.895658-08 2025-03-04 17:04:19.58105-08 test f H100 \N f \N \N \N {} +5094 1561 2025-03-04 18:05:24.892968-08 2025-03-04 18:46:27.229976-08 test f H100 \N t \N \N \N {} +5095 1562 2025-03-04 18:36:54.224297-08 2025-03-04 18:32:29.172193-08 test f A100 \N t \N \N \N {} +5096 1563 2025-03-04 18:52:59.239172-08 2025-03-04 18:15:38.074894-08 test f H100 \N t \N \N \N {} +5097 1563 2025-03-04 18:49:59.0744-08 2025-03-04 17:26:17.186125-08 benchmark f H100 \N t \N \N \N {} +5098 1563 2025-03-04 17:39:36.278703-08 2025-03-04 17:57:36.746337-08 leaderboard f H100 0.001524286 t \N \N \N {} +5099 1564 2025-03-04 18:14:59.270131-08 2025-03-04 18:51:49.99979-08 benchmark f A100 \N t \N \N \N {} +5100 1563 2025-03-04 18:12:08.43731-08 2025-03-04 17:24:24.25148-08 test t H100 \N t \N \N \N {} +5101 1563 2025-03-04 17:22:01.762045-08 2025-03-04 18:00:18.365591-08 benchmark t H100 \N t \N \N \N {} +5102 1563 2025-03-04 17:14:24.254997-08 2025-03-04 18:03:10.882073-08 leaderboard t H100 0.0015306753333333332 t \N \N \N {} +5104 1565 2025-03-04 18:29:07.767766-08 2025-03-04 17:23:19.898042-08 test t A100 \N t \N \N \N {} +5105 1565 2025-03-04 18:37:16.219751-08 2025-03-04 17:10:25.551997-08 benchmark t A100 \N t \N \N \N {} +5108 1565 2025-03-04 18:35:43.150886-08 2025-03-04 18:46:33.299334-08 benchmark f A100 \N t \N \N \N {} +5109 1565 2025-03-04 17:46:26.10294-08 2025-03-04 17:32:00.135585-08 leaderboard f A100 0.006325797333333333 t \N \N \N {} +5117 1569 2025-03-04 22:14:53.928421-08 2025-03-04 22:40:51.332237-08 test f A100 \N t \N \N \N {} +5118 1569 2025-03-04 22:24:33.021103-08 2025-03-04 21:34:40.758739-08 benchmark f A100 \N t \N \N \N {} +5119 1569 2025-03-04 22:58:48.215389-08 2025-03-04 22:50:16.105009-08 leaderboard f A100 0.0016024253333333334 t \N \N \N {} +5131 1569 2025-03-04 22:47:53.005147-08 2025-03-04 21:52:01.609325-08 leaderboard t H100 0.0010646286666666667 t \N \N \N {} +5132 1569 2025-03-04 23:21:58.974923-08 2025-03-04 21:27:57.937643-08 test f T4 \N t \N \N \N {} +5133 1569 2025-03-04 21:35:43.347052-08 2025-03-04 22:03:01.621549-08 benchmark f T4 \N t \N \N \N {} +5138 1569 2025-03-04 22:37:43.97357-08 2025-03-04 23:16:28.792385-08 test f L4 \N t \N \N \N {} +5139 1569 2025-03-04 21:55:16.016933-08 2025-03-04 23:05:51.261929-08 benchmark f L4 \N t \N \N \N {} +5140 1569 2025-03-04 23:05:20.733299-08 2025-03-04 22:29:48.217612-08 leaderboard f L4 0.009060729666666666 t \N \N \N {} +5152 1570 2025-03-04 22:28:21.72317-08 2025-03-04 23:05:56.534035-08 leaderboard f H100 0.0011025386666666667 t \N \N \N {} +5153 1571 2025-03-04 23:27:03.934327-08 2025-03-04 22:25:50.087612-08 test t A100 \N t \N \N \N {} +5171 1574 2025-03-04 22:56:14.353869-08 2025-03-04 23:19:35.268597-08 test t A100 \N f \N \N \N {} +5172 1574 2025-03-04 21:59:17.605165-08 2025-03-04 22:14:53.169362-08 test f A100 \N f \N \N \N {} +5173 1575 2025-03-04 22:04:24.384305-08 2025-03-04 22:14:55.882584-08 test f H100 \N t \N \N \N {} +5174 1575 2025-03-04 23:09:21.089194-08 2025-03-04 22:12:41.373209-08 benchmark f H100 \N t \N \N \N {} +5185 1578 2025-03-05 04:32:15.216004-08 2025-03-05 04:30:27.093417-08 benchmark f H100 \N t \N \N \N {} +5186 1578 2025-03-05 05:12:33.674089-08 2025-03-05 05:32:04.699339-08 leaderboard f H100 0.0014253056666666667 t \N \N \N {} +5187 1579 2025-03-05 05:33:08.325393-08 2025-03-05 04:46:50.693654-08 test f H100 \N t \N \N \N {} +5188 1580 2025-03-05 04:39:23.334662-08 2025-03-05 04:55:15.840654-08 test f H100 \N f \N \N \N {} +5189 1581 2025-03-05 05:09:09.30193-08 2025-03-05 04:30:58.000235-08 test f H100 \N f \N \N \N {} +5191 1583 2025-03-05 06:03:36.083056-08 2025-03-05 05:52:58.768087-08 test f H100 \N f \N \N \N {} +5192 1584 2025-03-05 04:53:13.978814-08 2025-03-05 06:01:29.534419-08 test f H100 \N f \N \N \N {} +5193 1585 2025-03-05 05:16:23.384842-08 2025-03-05 04:40:47.567792-08 test f H100 \N f \N \N \N {} +5194 1586 2025-03-05 05:55:30.771046-08 2025-03-05 04:56:37.274029-08 test f H100 \N f \N \N \N {} +5195 1587 2025-03-05 05:49:50.339689-08 2025-03-05 05:58:41.349328-08 test f H100 \N f \N \N \N {} +5241 1613 2025-03-06 08:16:58.083166-08 2025-03-06 07:04:03.546846-08 test t A100 \N t \N \N \N {} +5196 1588 2025-03-05 05:52:45.188962-08 2025-03-05 06:10:15.320706-08 test f H100 \N f \N \N \N {} +5198 1590 2025-03-05 06:35:32.564142-08 2025-03-05 05:59:51.088599-08 test f H100 \N f \N \N \N {} +5211 1604 2025-03-05 23:01:07.467624-08 2025-03-06 00:15:28.333028-08 benchmark f A100 \N t \N \N \N {} +5209 1600 2025-03-05 16:08:44.73907-08 2025-03-05 16:32:02.657585-08 test f T4 \N f \N \N \N {} +5210 1601 2025-03-05 17:19:12.616402-08 2025-03-05 16:16:23.001998-08 test f T4 \N f \N \N \N {} +5212 1605 2025-03-05 22:59:21.454362-08 2025-03-05 22:28:41.565705-08 test f A100 \N t \N \N \N {} +5213 1605 2025-03-05 23:20:06.529966-08 2025-03-05 23:11:34.874411-08 benchmark f A100 \N t \N \N \N {} +5214 1605 2025-03-05 22:21:25.7678-08 2025-03-05 22:43:21.731097-08 leaderboard f A100 0.00240375 t \N \N \N {} +5218 1606 2025-03-05 22:50:16.943212-08 2025-03-06 00:17:26.530067-08 test f H100 \N t \N \N \N {} +5219 1606 2025-03-05 23:56:01.568548-08 2025-03-05 23:45:15.745008-08 benchmark f H100 \N t \N \N \N {} +5220 1606 2025-03-05 23:02:21.7458-08 2025-03-06 00:12:54.212376-08 leaderboard f H100 0.001836295 t \N \N \N {} +5221 1606 2025-03-05 23:07:53.314931-08 2025-03-05 23:19:58.450174-08 test t H100 \N t \N \N \N {} +5222 1606 2025-03-05 22:21:37.603066-08 2025-03-05 23:33:48.373334-08 benchmark t H100 \N t \N \N \N {} +5223 1606 2025-03-05 23:56:17.695563-08 2025-03-05 23:31:36.226457-08 leaderboard t H100 0.001844128 t \N \N \N {} +5224 1608 2025-03-06 06:41:32.825228-08 2025-03-06 07:35:28.239062-08 test f T4 \N f \N \N \N {} +5226 1610 2025-03-06 07:18:42.460735-08 2025-03-06 06:35:06.778387-08 test t T4 \N t \N \N \N {} +5250 1613 2025-03-06 08:07:54.141701-08 2025-03-06 07:24:21.635977-08 test t L4 \N t \N \N \N {} +5227 1610 2025-03-06 07:38:46.614128-08 2025-03-06 06:58:40.087618-08 benchmark t T4 \N t \N \N \N {} +5228 1610 2025-03-06 07:51:50.188241-08 2025-03-06 06:58:42.083894-08 leaderboard t T4 0.017143785666666668 t \N \N \N {} +5229 1610 2025-03-06 06:31:06.739357-08 2025-03-06 06:37:17.472387-08 test f T4 \N t \N \N \N {} +5230 1610 2025-03-06 07:22:42.771965-08 2025-03-06 07:33:54.418655-08 benchmark f T4 \N t \N \N \N {} +5231 1610 2025-03-06 06:16:53.919416-08 2025-03-06 06:36:47.083912-08 leaderboard f T4 0.01720559766666667 t \N \N \N {} +5232 1611 2025-03-06 08:16:50.440029-08 2025-03-06 07:49:49.111467-08 test t T4 \N f \N \N \N {} +6784 2100 2025-03-15 04:40:09.589806-07 2025-03-15 04:00:29.456068-07 test f A100 \N t \N \N \N {} +5234 1612 2025-03-06 07:34:10.797478-08 2025-03-06 06:55:41.824879-08 benchmark f L4 \N t \N \N \N {} +5235 1612 2025-03-06 07:42:17.581429-08 2025-03-06 07:04:34.801114-08 benchmark f A100 \N t \N \N \N {} +5236 1612 2025-03-06 07:29:27.298493-08 2025-03-06 08:18:54.173232-08 benchmark f H100 \N t \N \N \N {} +5237 1612 2025-03-06 07:45:18.830048-08 2025-03-06 07:09:08.875473-08 benchmark f T4 \N t \N \N \N {} +5238 1613 2025-03-06 07:02:42.383443-08 2025-03-06 06:58:40.326617-08 test t H100 \N t \N \N \N {} +5239 1613 2025-03-06 06:58:41.561231-08 2025-03-06 07:26:52.163278-08 benchmark t H100 \N t \N \N \N {} +5240 1613 2025-03-06 08:17:32.781179-08 2025-03-06 07:19:03.090424-08 leaderboard t H100 0.0014283363333333333 t \N \N \N {} +5242 1613 2025-03-06 07:50:24.304771-08 2025-03-06 06:37:12.655596-08 benchmark t A100 \N t \N \N \N {} +5243 1613 2025-03-06 07:52:14.675021-08 2025-03-06 07:13:22.297664-08 leaderboard t A100 0.0025773366666666667 t \N \N \N {} +5260 1613 2025-03-06 08:18:47.261863-08 2025-03-06 06:59:45.373237-08 benchmark f T4 \N t \N \N \N {} +5261 1613 2025-03-06 06:55:11.400354-08 2025-03-06 07:22:29.939541-08 leaderboard f T4 0.016297266666666668 t \N \N \N {} +5262 1614 2025-03-06 07:10:48.778261-08 2025-03-06 07:33:58.158012-08 test t A100 \N t \N \N \N {} +5263 1614 2025-03-06 07:57:06.330893-08 2025-03-06 07:32:20.266324-08 benchmark t A100 \N t \N \N \N {} +5264 1614 2025-03-06 07:34:46.398663-08 2025-03-06 07:48:31.150592-08 leaderboard t A100 0.002518239 t \N \N \N {} +5265 1614 2025-03-06 08:22:10.296115-08 2025-03-06 08:10:01.676777-08 test f A100 \N t \N \N \N {} +5266 1614 2025-03-06 07:36:30.354753-08 2025-03-06 08:06:26.122608-08 benchmark f A100 \N t \N \N \N {} +6793 2101 2025-03-15 04:34:05.070783-07 2025-03-15 03:38:46.857954-07 test t A100 \N t \N \N \N {} +5267 1614 2025-03-06 08:17:25.703839-08 2025-03-06 07:44:04.763072-08 leaderboard f A100 0.0025364223333333336 t \N \N \N {} +5268 1614 2025-03-06 07:54:07.764514-08 2025-03-06 06:53:43.516588-08 test f L4 \N t \N \N \N {} +5269 1614 2025-03-06 06:49:11.954531-08 2025-03-06 08:09:48.887046-08 benchmark f L4 \N t \N \N \N {} +5270 1614 2025-03-06 07:58:12.691983-08 2025-03-06 07:16:48.763534-08 leaderboard f L4 0.01703991833333333 t \N \N \N {} +5271 1614 2025-03-06 07:29:09.154149-08 2025-03-06 06:51:28.330755-08 test t L4 \N t \N \N \N {} +5272 1614 2025-03-06 08:11:29.656544-08 2025-03-06 07:59:29.594078-08 benchmark t L4 \N t \N \N \N {} +5273 1614 2025-03-06 08:35:09.612272-08 2025-03-06 07:01:28.409958-08 leaderboard t L4 0.01704863566666667 t \N \N \N {} +5274 1614 2025-03-06 07:29:25.840467-08 2025-03-06 08:40:01.921695-08 test f H100 \N t \N \N \N {} +5275 1614 2025-03-06 07:16:27.972728-08 2025-03-06 07:22:01.065139-08 benchmark f H100 \N t \N \N \N {} +5282 1617 2025-03-06 11:49:00.194719-08 2025-03-06 12:03:16.273518-08 test t L4 \N f \N \N \N {} +5283 1616 2025-03-06 12:12:36.448946-08 2025-03-06 11:07:02.723415-08 test f T4 \N t \N \N \N {} +5284 1616 2025-03-06 12:25:43.203938-08 2025-03-06 10:58:59.548248-08 benchmark f T4 \N t \N \N \N {} +5285 1616 2025-03-06 12:14:49.086319-08 2025-03-06 11:05:00.869471-08 leaderboard f T4 0.009402661909090908 t \N \N \N {} +5288 1616 2025-03-06 11:15:57.5079-08 2025-03-06 12:34:26.281327-08 leaderboard t T4 0.009402624535714285 t \N \N \N {} +5289 1617 2025-03-06 11:04:48.91978-08 2025-03-06 11:41:19.26857-08 test f T4 \N f \N \N \N {} +5290 1617 2025-03-06 11:31:11.14058-08 2025-03-06 11:07:15.510427-08 test t T4 \N f \N \N \N {} +5291 1616 2025-03-06 11:20:35.3437-08 2025-03-06 11:41:25.791263-08 test t L4 \N t \N \N \N {} +5292 1616 2025-03-06 11:17:21.149159-08 2025-03-06 11:36:57.390953-08 benchmark t L4 \N t \N \N \N {} +5293 1616 2025-03-06 11:23:09.088018-08 2025-03-06 10:56:39.864963-08 leaderboard t L4 0.009685256699999999 t \N \N \N {} +5294 1616 2025-03-06 11:46:28.551612-08 2025-03-06 10:50:17.314532-08 test f L4 \N t \N \N \N {} +5295 1616 2025-03-06 11:20:19.815835-08 2025-03-06 11:22:30.677937-08 benchmark f L4 \N t \N \N \N {} +5296 1616 2025-03-06 11:38:50.355641-08 2025-03-06 11:50:15.455809-08 leaderboard f L4 0.009650051153846154 t \N \N \N {} +5309 1620 2025-03-06 12:17:08.411894-08 2025-03-06 11:23:40.680848-08 test t L4 \N t \N \N \N {} +6796 2102 2025-03-15 03:47:18.073866-07 2025-03-15 04:58:31.457625-07 test f A100 \N t \N \N \N {} +6799 2102 2025-03-15 03:49:39.535334-07 2025-03-15 04:47:47.163839-07 test t A100 \N t \N \N \N {} +5310 1620 2025-03-06 11:47:19.342547-08 2025-03-06 11:29:52.276452-08 benchmark t L4 \N t \N \N \N {} +5311 1620 2025-03-06 11:30:56.626007-08 2025-03-06 11:54:39.587711-08 leaderboard t L4 0.017108037 t \N \N \N {} +5315 1621 2025-03-06 12:21:24.243905-08 2025-03-06 11:09:28.475948-08 test t L4 \N f \N \N \N {} +5316 1621 2025-03-06 12:05:13.275699-08 2025-03-06 12:39:19.771477-08 test f L4 \N f \N \N \N {} +5591 1709 2025-03-09 08:38:21.814972-07 2025-03-09 08:31:41.454653-07 leaderboard f T4 0.017528556666666667 t \N \N \N {} +6819 2120 2025-03-15 09:28:09.69146-07 2025-03-15 10:53:19.142071-07 test f A100 \N t \N \N \N {} +5592 1709 2025-03-09 09:45:13.630538-07 2025-03-09 08:29:21.654913-07 test t T4 \N t \N \N \N {} +5637 1719 2025-03-09 10:37:18.14285-07 2025-03-09 09:06:06.681007-07 test f T4 \N t \N \N \N {} +5646 1720 2025-03-09 10:27:41.86582-07 2025-03-09 09:54:42.03058-07 test t T4 \N t \N \N \N {} +5665 1722 2025-03-09 09:32:47.436929-07 2025-03-09 09:26:45.271601-07 benchmark f T4 \N t \N \N \N {} +6822 2120 2025-03-15 10:43:09.477007-07 2025-03-15 09:32:55.270251-07 test t A100 \N t \N \N \N {} +6825 2121 2025-03-15 10:03:06.648384-07 2025-03-15 09:27:27.809952-07 test t A100 \N t \N \N \N {} +5666 1722 2025-03-09 09:52:43.506841-07 2025-03-09 10:58:18.20445-07 leaderboard f T4 0.016603317666666666 t \N \N \N {} +6828 2121 2025-03-15 09:33:59.003711-07 2025-03-15 10:39:36.450576-07 test f A100 \N t \N \N \N {} +5667 1724 2025-03-09 10:44:35.101417-07 2025-03-09 09:13:35.191271-07 test f A100 \N t \N \N \N {} +5655 1721 2025-03-09 10:15:26.439475-07 2025-03-09 09:13:07.504617-07 test f T4 \N t \N \N \N {} +5664 1722 2025-03-09 09:28:08.995402-07 2025-03-09 10:57:10.770407-07 test f T4 \N t \N \N \N {} +6831 2122 2025-03-15 09:55:36.80771-07 2025-03-15 09:14:46.211485-07 test t A100 \N t \N \N \N {} +5668 1724 2025-03-09 10:39:56.890456-07 2025-03-09 11:00:53.761033-07 benchmark f A100 \N t \N \N \N {} +5669 1724 2025-03-09 09:11:31.886775-07 2025-03-09 10:51:26.751854-07 leaderboard f A100 0.0026062246666666666 t \N \N \N {} +5673 1724 2025-03-09 10:17:36.739347-07 2025-03-09 09:34:01.324901-07 test t A100 \N t \N \N \N {} +5670 1725 2025-03-09 09:56:06.327259-07 2025-03-09 10:35:57.538152-07 test f H100 \N t \N \N \N {} +5682 1726 2025-03-09 09:24:13.10994-07 2025-03-09 09:44:04.129165-07 test f L4 \N t \N \N \N {} +5671 1725 2025-03-09 09:58:59.213519-07 2025-03-09 09:39:17.681959-07 benchmark f H100 \N t \N \N \N {} +6834 2122 2025-03-15 09:41:59.559997-07 2025-03-15 10:55:52.113033-07 test f A100 \N t \N \N \N {} +5672 1725 2025-03-09 09:41:17.366352-07 2025-03-09 09:41:18.800653-07 leaderboard f H100 0.0014690293333333333 t \N \N \N {} +6864 2127 2025-03-15 09:44:16.174491-07 2025-03-15 10:46:22.719823-07 test t A100 \N t \N \N \N {} +6837 2123 2025-03-15 11:04:53.025505-07 2025-03-15 10:47:38.423663-07 test f A100 \N t \N \N \N {} +5691 1728 2025-03-09 09:50:18.197376-07 2025-03-09 09:21:08.916986-07 test f A100 \N t \N \N \N {} +6840 2123 2025-03-15 09:53:02.595875-07 2025-03-15 09:54:52.862532-07 test t A100 \N t \N \N \N {} +6843 2124 2025-03-15 10:08:06.005806-07 2025-03-15 10:57:35.474858-07 test f A100 \N t \N \N \N {} +5674 1724 2025-03-09 09:30:02.411383-07 2025-03-09 09:10:48.029672-07 benchmark t A100 \N t \N \N \N {} +6846 2124 2025-03-15 09:34:42.825301-07 2025-03-15 10:21:34.275136-07 test t A100 \N t \N \N \N {} +5563 1700 2025-03-08 17:19:44.52692-08 2025-03-08 16:34:26.7346-08 test t H100 \N f \N \N \N {} +6849 2125 2025-03-15 09:45:34.633967-07 2025-03-15 09:27:25.430422-07 test f A100 \N t \N \N \N {} +5564 1700 2025-03-08 17:51:09.804861-08 2025-03-08 16:57:41.781475-08 test f H100 \N f \N \N \N {} +5547 1692 2025-03-08 16:29:46.214302-08 2025-03-08 16:20:04.501518-08 test f H100 \N f \N \N \N {} +5548 1692 2025-03-08 15:58:09.465006-08 2025-03-08 16:18:52.498393-08 test t H100 \N f \N \N \N {} +5549 1693 2025-03-08 16:52:18.575178-08 2025-03-08 17:37:26.953067-08 test t H100 \N f \N \N \N {} +5567 1702 2025-03-08 16:31:44.29566-08 2025-03-08 17:20:51.940485-08 test t H100 \N f \N \N \N {} +5554 1695 2025-03-08 16:24:17.191755-08 2025-03-08 18:01:05.802736-08 test f H100 \N f \N \N \N {} +5555 1696 2025-03-08 17:11:18.772884-08 2025-03-08 17:09:32.444232-08 test t H100 \N f \N \N \N {} +5556 1696 2025-03-08 17:57:34.2415-08 2025-03-08 17:37:04.496604-08 test f H100 \N f \N \N \N {} +5581 1707 2025-03-09 09:14:09.834023-07 2025-03-09 08:25:50.569029-07 test f T4 \N t \N \N \N {} +5585 1707 2025-03-09 09:38:59.880211-07 2025-03-09 08:38:21.050436-07 benchmark t T4 \N t \N \N \N {} +5587 1708 2025-03-09 09:24:39.948132-07 2025-03-09 08:27:28.337093-07 test f T4 \N f \N \N \N {} +5593 1709 2025-03-09 08:46:11.00316-07 2025-03-09 09:09:14.41678-07 benchmark t T4 \N t \N \N \N {} +5594 1709 2025-03-09 09:13:43.175362-07 2025-03-09 09:11:53.850526-07 leaderboard t T4 0.017543475333333333 t \N \N \N {} +5595 1710 2025-03-09 09:43:56.816043-07 2025-03-09 10:16:02.768519-07 test t T4 \N t \N \N \N {} +5596 1710 2025-03-09 08:28:21.825411-07 2025-03-09 09:26:32.812995-07 benchmark t T4 \N t \N \N \N {} +5597 1710 2025-03-09 09:14:02.814794-07 2025-03-09 09:20:14.126574-07 leaderboard t T4 0.016824632333333332 t \N \N \N {} +6858 2126 2025-03-15 10:43:39.789025-07 2025-03-15 09:34:45.620404-07 test t A100 \N t \N \N \N {} +5601 1711 2025-03-09 09:09:47.896726-07 2025-03-09 09:02:44.866972-07 test t T4 \N f \N \N \N {} +5602 1711 2025-03-09 10:23:41.056545-07 2025-03-09 09:36:00.891836-07 test f T4 \N f \N \N \N {} +5700 1729 2025-03-09 10:19:32.974097-07 2025-03-09 09:54:46.566098-07 test f H100 \N t \N \N \N {} +5607 1712 2025-03-09 10:24:03.045125-07 2025-03-09 09:51:21.11739-07 benchmark t T4 \N t \N \N \N {} +5608 1712 2025-03-09 08:34:08.27444-07 2025-03-09 10:22:37.533877-07 leaderboard t T4 0.01711488933333333 t \N \N \N {} +5613 1714 2025-03-09 09:30:39.959969-07 2025-03-09 10:16:30.712056-07 test t T4 \N t \N \N \N {} +5614 1714 2025-03-09 09:55:01.657622-07 2025-03-09 09:16:09.636467-07 benchmark t T4 \N f \N \N \N {} +5781 1751 2025-03-09 22:17:14.433037-07 2025-03-09 21:49:17.871196-07 test t L4 \N t \N \N \N {} +5629 1717 2025-03-09 08:54:48.324259-07 2025-03-09 10:08:29.038511-07 benchmark t T4 \N t \N \N \N {} +5630 1717 2025-03-09 10:42:26.967084-07 2025-03-09 09:41:58.61599-07 leaderboard t T4 0.016894572333333333 t \N \N \N {} +5631 1718 2025-03-09 09:27:03.051502-07 2025-03-09 10:10:12.243584-07 test t T4 \N t \N \N \N {} +5632 1718 2025-03-09 09:39:46.422345-07 2025-03-09 10:18:05.599135-07 benchmark t T4 \N t \N \N \N {} +5636 1718 2025-03-09 09:51:06.131421-07 2025-03-09 09:42:47.922533-07 leaderboard f T4 0.021235485666666668 t \N \N \N {} +5638 1719 2025-03-09 10:26:54.407345-07 2025-03-09 09:15:13.06042-07 benchmark f T4 \N t \N \N \N {} +5639 1719 2025-03-09 09:29:44.472627-07 2025-03-09 09:37:56.334139-07 leaderboard f T4 0.016808872333333332 t \N \N \N {} +5640 1719 2025-03-09 10:32:20.431198-07 2025-03-09 09:39:18.18504-07 test t T4 \N t \N \N \N {} +5645 1720 2025-03-09 09:55:57.197808-07 2025-03-09 09:40:52.349852-07 leaderboard f T4 0.016596006666666666 t \N \N \N {} +5647 1720 2025-03-09 10:17:27.374148-07 2025-03-09 10:50:14.82081-07 benchmark t T4 \N t \N \N \N {} +5648 1720 2025-03-09 09:16:50.615819-07 2025-03-09 10:21:27.988216-07 leaderboard t T4 0.016557458666666667 t \N \N \N {} +5649 1721 2025-03-09 09:42:37.64406-07 2025-03-09 10:22:42.1259-07 test t T4 \N t \N \N \N {} +5652 1722 2025-03-09 10:58:24.342857-07 2025-03-09 09:19:31.766123-07 test t T4 \N t \N \N \N {} +5654 1722 2025-03-09 10:21:05.107452-07 2025-03-09 09:25:54.295116-07 leaderboard t T4 0.016595996333333335 t \N \N \N {} +5656 1721 2025-03-09 09:56:16.90649-07 2025-03-09 09:16:05.410514-07 benchmark f T4 \N t \N \N \N {} +5657 1721 2025-03-09 09:29:05.785369-07 2025-03-09 10:35:50.223531-07 leaderboard f T4 0.016588513666666665 t \N \N \N {} +5658 1723 2025-03-09 09:19:55.392332-07 2025-03-09 09:53:19.722057-07 test f T4 \N t \N \N \N {} +5677 1725 2025-03-09 09:28:24.172642-07 2025-03-09 10:22:11.236381-07 benchmark t H100 \N t \N \N \N {} +5678 1725 2025-03-09 09:26:29.522614-07 2025-03-09 10:22:55.539844-07 leaderboard t H100 0.001474356 t \N \N \N {} +5679 1726 2025-03-09 10:03:52.222717-07 2025-03-09 09:33:44.331339-07 test t L4 \N t \N \N \N {} +5680 1726 2025-03-09 10:46:04.610414-07 2025-03-09 09:20:34.148072-07 benchmark t L4 \N t \N \N \N {} +5681 1726 2025-03-09 09:37:57.602752-07 2025-03-09 10:22:08.448087-07 leaderboard t L4 0.017242522333333333 t \N \N \N {} +5686 1727 2025-03-09 10:05:30.828856-07 2025-03-09 09:43:47.571121-07 benchmark f L4 \N t \N \N \N {} +5687 1727 2025-03-09 11:07:15.116394-07 2025-03-09 10:13:13.37959-07 leaderboard f L4 0.017209885666666667 t \N \N \N {} +5688 1727 2025-03-09 10:18:00.314232-07 2025-03-09 10:57:21.614871-07 test t L4 \N t \N \N \N {} +5689 1727 2025-03-09 10:01:49.26376-07 2025-03-09 09:25:21.45197-07 benchmark t L4 \N t \N \N \N {} +5690 1727 2025-03-09 10:45:23.99607-07 2025-03-09 09:43:33.892968-07 leaderboard t L4 0.017194495333333334 t \N \N \N {} +5695 1729 2025-03-09 11:12:01.892957-07 2025-03-09 10:53:34.36104-07 benchmark t H100 \N t \N \N \N {} +5696 1729 2025-03-09 10:25:09.517125-07 2025-03-09 10:31:41.836024-07 leaderboard t H100 0.00151662 t \N \N \N {} +5697 1728 2025-03-09 10:48:03.533453-07 2025-03-09 09:49:32.322572-07 test t A100 \N t \N \N \N {} +5698 1728 2025-03-09 10:47:46.442739-07 2025-03-09 10:50:32.307978-07 benchmark t A100 \N t \N \N \N {} +5699 1728 2025-03-09 11:11:38.205953-07 2025-03-09 09:57:38.117077-07 leaderboard t A100 0.0027480226666666665 t \N \N \N {} +5701 1729 2025-03-09 10:46:43.436224-07 2025-03-09 10:08:36.297082-07 benchmark f H100 \N t \N \N \N {} +5702 1729 2025-03-09 09:53:35.832862-07 2025-03-09 10:24:24.298067-07 leaderboard f H100 0.001514298 t \N \N \N {} +5790 1752 2025-03-09 21:29:49.172353-07 2025-03-09 21:20:16.735724-07 test t T4 \N t \N \N \N {} +5775 1749 2025-03-09 22:00:09.902505-07 2025-03-09 22:13:49.54977-07 test f A100 \N t \N \N \N {} +5776 1749 2025-03-09 23:05:49.965781-07 2025-03-09 22:51:52.170132-07 benchmark f A100 \N t \N \N \N {} +5777 1749 2025-03-09 21:24:00.00801-07 2025-03-09 22:47:05.154155-07 leaderboard f A100 0.002640859 t \N \N \N {} +5778 1750 2025-03-09 23:04:17.164077-07 2025-03-09 23:12:50.24992-07 test t H100 \N t \N \N \N {} +5779 1750 2025-03-09 21:43:55.396805-07 2025-03-09 22:41:37.388888-07 benchmark t H100 \N t \N \N \N {} +5782 1751 2025-03-09 21:58:32.972565-07 2025-03-09 22:18:54.173023-07 benchmark t L4 \N t \N \N \N {} +5784 1750 2025-03-09 22:33:29.942446-07 2025-03-09 22:47:34.687352-07 test f H100 \N t \N \N \N {} +5785 1750 2025-03-09 22:57:51.36389-07 2025-03-09 23:11:33.809283-07 benchmark f H100 \N t \N \N \N {} +5786 1750 2025-03-09 22:55:32.663609-07 2025-03-09 21:59:50.521514-07 leaderboard f H100 0.001517079 t \N \N \N {} +5787 1752 2025-03-09 21:22:25.235791-07 2025-03-09 22:13:25.850939-07 test f T4 \N t \N \N \N {} +5788 1752 2025-03-09 23:01:38.584323-07 2025-03-09 21:41:17.038278-07 benchmark f T4 \N t \N \N \N {} +5793 1749 2025-03-09 21:33:50.661901-07 2025-03-09 22:14:05.438845-07 test t A100 \N t \N \N \N {} +5794 1749 2025-03-09 23:11:26.826051-07 2025-03-09 22:00:03.831654-07 benchmark t A100 \N t \N \N \N {} +5795 1749 2025-03-09 21:37:34.837957-07 2025-03-09 22:12:24.604584-07 leaderboard t A100 0.0027676723333333337 t \N \N \N {} +5796 1751 2025-03-09 23:08:13.975436-07 2025-03-09 22:41:10.744916-07 test f L4 \N t \N \N \N {} +5797 1751 2025-03-09 22:51:47.852548-07 2025-03-09 21:35:35.48834-07 benchmark f L4 \N t \N \N \N {} +5798 1751 2025-03-09 22:37:22.743083-07 2025-03-09 21:36:38.099972-07 leaderboard f L4 0.017245750666666667 t \N \N \N {} +5997 1823 2025-03-10 13:44:13.361199-07 2025-03-10 14:17:08.320572-07 test f H100 \N t \N \N \N {} +6111 1849 2025-03-11 00:45:42.466684-07 2025-03-11 02:08:01.224061-07 benchmark f A100 \N f \N \N \N {} +5887 1782 2025-03-10 12:48:56.366283-07 2025-03-10 11:26:28.502927-07 test t T4 \N f \N \N \N {} +5888 1782 2025-03-10 11:20:36.181976-07 2025-03-10 11:07:40.511412-07 test f T4 \N f \N \N \N {} +6006 1824 2025-03-10 14:29:01.568504-07 2025-03-10 13:17:50.880972-07 test t L4 \N t \N \N \N {} +5931 1804 2025-03-10 13:24:49.874112-07 2025-03-10 13:12:59.214864-07 test t T4 \N f \N \N \N {} +5932 1804 2025-03-10 13:11:51.554353-07 2025-03-10 12:36:24.247696-07 test f T4 \N f \N \N \N {} +5933 1805 2025-03-10 12:33:56.206912-07 2025-03-10 12:22:23.193916-07 test t T4 \N f \N \N \N {} +5934 1805 2025-03-10 12:16:39.318057-07 2025-03-10 13:04:51.698492-07 test f T4 \N f \N \N \N {} +5885 1781 2025-03-10 10:56:08.127207-07 2025-03-10 12:40:40.827896-07 test f T4 \N f \N \N \N {} +5886 1781 2025-03-10 11:37:13.25838-07 2025-03-10 11:18:09.354031-07 test t T4 \N f \N \N \N {} +5889 1783 2025-03-10 12:26:17.975854-07 2025-03-10 11:35:06.825449-07 test t T4 \N f \N \N \N {} +5893 1785 2025-03-10 12:49:26.520475-07 2025-03-10 12:05:07.923004-07 test t T4 \N f \N \N \N {} +5894 1785 2025-03-10 11:21:13.907922-07 2025-03-10 12:13:55.484547-07 test f T4 \N f \N \N \N {} +5895 1786 2025-03-10 12:53:04.60948-07 2025-03-10 12:56:23.335782-07 test t T4 \N f \N \N \N {} +5896 1786 2025-03-10 11:52:35.543265-07 2025-03-10 12:47:37.44292-07 test f T4 \N f \N \N \N {} +5897 1787 2025-03-10 12:19:59.35342-07 2025-03-10 12:03:09.756897-07 test t T4 \N f \N \N \N {} +5898 1787 2025-03-10 12:26:38.384347-07 2025-03-10 12:38:55.659544-07 test f T4 \N f \N \N \N {} +5904 1790 2025-03-10 13:07:41.471622-07 2025-03-10 11:29:54.088642-07 test f T4 \N f \N \N \N {} +5905 1791 2025-03-10 12:11:05.954905-07 2025-03-10 11:56:04.212348-07 test t T4 \N f \N \N \N {} +5906 1791 2025-03-10 12:06:20.464482-07 2025-03-10 13:05:37.047802-07 test f T4 \N f \N \N \N {} +5936 1806 2025-03-10 12:25:19.66528-07 2025-03-10 13:25:54.809235-07 test f T4 \N f \N \N \N {} +5907 1792 2025-03-10 11:41:05.031269-07 2025-03-10 12:31:10.781699-07 test f T4 \N f \N \N \N {} +5908 1792 2025-03-10 13:24:19.84949-07 2025-03-10 12:05:30.566627-07 test t T4 \N f \N \N \N {} +5909 1793 2025-03-10 13:10:29.314729-07 2025-03-10 12:27:22.377821-07 test f T4 \N f \N \N \N {} +5910 1793 2025-03-10 11:56:32.644344-07 2025-03-10 12:01:46.701558-07 test t T4 \N f \N \N \N {} +5911 1794 2025-03-10 11:51:38.865118-07 2025-03-10 11:51:19.334553-07 test t T4 \N f \N \N \N {} +5943 1809 2025-03-10 14:18:40.194531-07 2025-03-10 13:13:02.518142-07 leaderboard f A100 0.0026386466666666664 t \N \N \N {} +5912 1794 2025-03-10 12:22:55.695011-07 2025-03-10 13:18:30.719097-07 test f T4 \N f \N \N \N {} +5913 1795 2025-03-10 12:18:06.516563-07 2025-03-10 11:38:14.754524-07 test f T4 \N f \N \N \N {} +5914 1795 2025-03-10 12:36:00.298953-07 2025-03-10 12:55:22.229312-07 test t T4 \N f \N \N \N {} +5923 1800 2025-03-10 11:55:59.030364-07 2025-03-10 12:53:27.690509-07 test f T4 \N f \N \N \N {} +5924 1800 2025-03-10 12:07:20.544617-07 2025-03-10 12:44:06.621555-07 test t T4 \N f \N \N \N {} +5925 1801 2025-03-10 12:06:30.111263-07 2025-03-10 12:53:12.669353-07 test t T4 \N f \N \N \N {} +5926 1801 2025-03-10 13:31:32.380898-07 2025-03-10 13:00:06.56229-07 test f T4 \N f \N \N \N {} +5946 1809 2025-03-10 13:29:37.323879-07 2025-03-10 13:21:30.63564-07 leaderboard t A100 0.002635759 t \N \N \N {} +5937 1807 2025-03-10 12:41:08.079867-07 2025-03-10 12:35:49.297728-07 test f T4 \N f \N \N \N {} +5938 1807 2025-03-10 13:35:12.048803-07 2025-03-10 12:54:21.189668-07 test t T4 \N f \N \N \N {} +5939 1808 2025-03-10 13:59:07.441287-07 2025-03-10 13:33:08.669422-07 test t T4 \N f \N \N \N {} +5940 1808 2025-03-10 14:15:23.166819-07 2025-03-10 13:28:24.134307-07 test f T4 \N f \N \N \N {} +5941 1809 2025-03-10 14:13:38.207591-07 2025-03-10 13:49:12.972948-07 test f A100 \N t \N \N \N {} +5942 1809 2025-03-10 13:54:14.281293-07 2025-03-10 13:15:36.646021-07 benchmark f A100 \N t \N \N \N {} +5953 1811 2025-03-10 13:30:04.564524-07 2025-03-10 12:55:33.446063-07 test t T4 \N f \N \N \N {} +5954 1811 2025-03-10 14:13:48.338956-07 2025-03-10 12:46:33.424518-07 test f T4 \N f \N \N \N {} +5955 1812 2025-03-10 12:31:35.951286-07 2025-03-10 14:17:08.111846-07 test f T4 \N f \N \N \N {} +5956 1812 2025-03-10 13:45:55.348085-07 2025-03-10 13:37:16.626985-07 test t T4 \N f \N \N \N {} +5957 1813 2025-03-10 14:19:03.474874-07 2025-03-10 12:40:48.028667-07 test f T4 \N f \N \N \N {} +5958 1813 2025-03-10 13:09:32.274357-07 2025-03-10 12:58:22.955703-07 test t T4 \N f \N \N \N {} +5960 1814 2025-03-10 13:48:21.644188-07 2025-03-10 13:41:37.383129-07 test t T4 \N f \N \N \N {} +5961 1815 2025-03-10 14:25:53.892843-07 2025-03-10 14:27:52.516911-07 test f T4 \N f \N \N \N {} +5962 1815 2025-03-10 13:39:34.9964-07 2025-03-10 14:26:34.410119-07 test t T4 \N f \N \N \N {} +5963 1816 2025-03-10 12:54:03.964782-07 2025-03-10 13:19:23.51163-07 test t T4 \N f \N \N \N {} +5965 1817 2025-03-10 14:33:23.123369-07 2025-03-10 13:20:30.62665-07 test t T4 \N f \N \N \N {} +5966 1817 2025-03-10 14:24:33.780553-07 2025-03-10 13:09:05.912817-07 test f T4 \N f \N \N \N {} +5967 1818 2025-03-10 12:58:03.382174-07 2025-03-10 13:01:07.893499-07 test f T4 \N t \N \N \N {} +5971 1818 2025-03-10 13:56:03.131162-07 2025-03-10 13:29:13.996141-07 benchmark t T4 \N t \N \N \N {} +5989 1821 2025-03-10 14:34:10.506362-07 2025-03-10 13:16:40.681009-07 benchmark t L4 \N t \N \N \N {} +5990 1821 2025-03-10 14:14:40.84149-07 2025-03-10 13:59:04.607394-07 leaderboard t L4 0.0023410633333333336 t \N \N \N {} +5998 1823 2025-03-10 14:14:38.086577-07 2025-03-10 14:04:27.238303-07 benchmark f H100 \N t \N \N \N {} +5999 1823 2025-03-10 13:02:04.67764-07 2025-03-10 13:38:00.727394-07 leaderboard f H100 0.006278458 t \N \N \N {} +6000 1823 2025-03-10 14:25:36.878836-07 2025-03-10 13:56:47.954868-07 test t H100 \N t \N \N \N {} +6001 1823 2025-03-10 14:33:56.104952-07 2025-03-10 13:27:44.07615-07 benchmark t H100 \N t \N \N \N {} +6002 1823 2025-03-10 13:55:54.754715-07 2025-03-10 14:28:21.040943-07 leaderboard t H100 0.006274267666666667 t \N \N \N {} +6007 1824 2025-03-10 14:28:35.829178-07 2025-03-10 14:56:43.794426-07 benchmark t L4 \N t \N \N \N {} +6008 1824 2025-03-10 14:47:33.073271-07 2025-03-10 13:02:30.850136-07 leaderboard t L4 0.07900226333333332 t \N \N \N {} +6009 1825 2025-03-10 13:08:30.376545-07 2025-03-10 14:58:22.56048-07 test t T4 \N t \N \N \N {} +6010 1825 2025-03-10 13:10:54.018143-07 2025-03-10 14:06:35.263338-07 benchmark t T4 \N t \N \N \N {} +6012 1825 2025-03-10 14:42:31.922476-07 2025-03-10 14:59:03.540136-07 test f T4 \N t \N \N \N {} +6093 1844 2025-03-10 22:33:26.258836-07 2025-03-10 23:34:33.63838-07 leaderboard f A100 0.023116982 t \N \N \N {} +6075 1838 2025-03-10 16:41:59.664667-07 2025-03-10 17:22:08.116083-07 test t T4 \N f \N \N \N {} +6076 1838 2025-03-10 17:26:55.475959-07 2025-03-10 17:59:15.681447-07 test f T4 \N f \N \N \N {} +6077 1839 2025-03-10 17:21:38.821552-07 2025-03-10 17:51:32.223604-07 test f T4 \N f \N \N \N {} +6078 1839 2025-03-10 16:49:21.182853-07 2025-03-10 17:57:15.491956-07 test t T4 \N f \N \N \N {} +6079 1840 2025-03-10 18:26:53.2575-07 2025-03-10 18:21:22.708514-07 test t T4 \N f \N \N \N {} +6080 1840 2025-03-10 16:58:04.162955-07 2025-03-10 16:54:29.133532-07 test f T4 \N f \N \N \N {} +6094 1844 2025-03-10 22:39:58.995586-07 2025-03-10 23:17:13.977931-07 test t A100 \N t \N \N \N {} +6081 1841 2025-03-10 17:37:46.462074-07 2025-03-10 17:23:29.564865-07 test f T4 \N f \N \N \N {} +6082 1841 2025-03-10 17:01:30.436669-07 2025-03-10 17:54:05.772936-07 test t T4 \N f \N \N \N {} +6083 1842 2025-03-10 17:25:05.119241-07 2025-03-10 17:23:54.474002-07 test t T4 \N f \N \N \N {} +6084 1842 2025-03-10 17:50:14.778236-07 2025-03-10 17:38:41.04272-07 test f T4 \N f \N \N \N {} +6085 1843 2025-03-10 23:26:01.96551-07 2025-03-10 22:45:08.618919-07 test f T4 \N t \N \N \N {} +6102 1845 2025-03-10 23:18:03.684504-07 2025-03-10 22:13:42.532972-07 leaderboard t H100 0.007583517333333333 t \N \N \N {} +6086 1843 2025-03-10 23:47:15.122747-07 2025-03-10 23:21:01.982853-07 benchmark f T4 \N t \N \N \N {} +6087 1843 2025-03-10 23:09:56.514137-07 2025-03-10 23:41:36.80451-07 leaderboard f T4 1.0437126283333333 t \N \N \N {} +6088 1843 2025-03-10 22:13:21.923671-07 2025-03-10 23:11:52.790625-07 test t T4 \N t \N \N \N {} +6089 1843 2025-03-10 23:06:17.150635-07 2025-03-10 21:59:26.386221-07 benchmark t T4 \N t \N \N \N {} +6090 1843 2025-03-10 22:53:29.015752-07 2025-03-10 22:55:46.841799-07 leaderboard t T4 0.9874019316666667 t \N \N \N {} +6091 1844 2025-03-10 22:58:35.828002-07 2025-03-10 22:04:49.298779-07 test f A100 \N t \N \N \N {} +6092 1844 2025-03-10 23:35:22.877612-07 2025-03-10 22:06:59.964007-07 benchmark f A100 \N t \N \N \N {} +6101 1845 2025-03-10 22:20:24.248784-07 2025-03-10 23:42:44.548166-07 benchmark t H100 \N t \N \N \N {} +6113 1852 2025-03-11 01:44:33.106318-07 2025-03-11 00:54:18.834861-07 test f A100 \N f \N \N \N {} +6114 1853 2025-03-11 02:21:59.844098-07 2025-03-11 00:42:46.671243-07 test f A100 \N t \N \N \N {} +6115 1854 2025-03-11 02:28:36.156334-07 2025-03-11 00:46:06.202296-07 benchmark f A100 \N t \N \N \N {} +6886 2139 2025-03-16 11:38:46.212648-07 2025-03-16 11:28:03.154972-07 test f H100 \N f \N \N \N {} +6312 1952 2025-03-12 12:28:15.470511-07 2025-03-12 11:10:36.793829-07 test f T4 \N f \N \N \N {} +6460 2005 2025-03-14 06:31:39.099707-07 2025-03-14 07:51:37.583527-07 benchmark t A100 \N t \N \N \N {} +6461 2005 2025-03-14 06:46:54.111465-07 2025-03-14 07:07:11.64108-07 leaderboard t A100 0.004546970333333333 t \N \N \N {} +6596 2044 2025-03-14 13:39:50.152918-07 2025-03-14 12:42:49.661409-07 test f T4 \N f \N \N \N {} +6887 2140 2025-03-16 11:24:30.571404-07 2025-03-16 10:17:15.135954-07 test f T4 \N t \N \N \N {} +6651 2054 2025-03-14 12:23:27.508379-07 2025-03-14 12:07:07.961699-07 benchmark t A100 \N t \N \N \N {} +6652 2054 2025-03-14 13:00:27.647397-07 2025-03-14 13:25:59.68305-07 leaderboard t A100 0.0024835623333333335 t \N \N \N {} +6653 2055 2025-03-14 13:40:44.971801-07 2025-03-14 12:14:34.48949-07 benchmark f A100 \N t \N \N \N {} +6654 2056 2025-03-14 12:23:48.644957-07 2025-03-14 13:52:44.535234-07 benchmark f A100 \N t \N \N \N {} +6657 2058 2025-03-14 13:30:54.732584-07 2025-03-14 12:53:01.468344-07 benchmark t A100 \N t \N \N \N {} +6658 2058 2025-03-14 13:18:02.16859-07 2025-03-14 13:30:04.076635-07 leaderboard t A100 0.002514722 t \N \N \N {} +6660 2058 2025-03-14 12:19:31.91691-07 2025-03-14 12:23:40.05458-07 benchmark f A100 \N t \N \N \N {} +6678 2067 2025-03-14 14:51:27.080477-07 2025-03-14 15:45:50.453303-07 test t T4 \N f \N \N \N {} +6679 2067 2025-03-14 16:00:05.640981-07 2025-03-14 15:45:05.704388-07 test f T4 \N f \N \N \N {} +6681 2068 2025-03-14 15:59:19.410815-07 2025-03-14 15:47:46.277659-07 test t T4 \N f \N \N \N {} +6682 2069 2025-03-14 14:50:20.787072-07 2025-03-14 16:19:00.44313-07 test t T4 \N f \N \N \N {} +6683 2069 2025-03-14 15:33:20.870757-07 2025-03-14 16:21:01.08373-07 test f T4 \N f \N \N \N {} +6688 2072 2025-03-14 16:45:06.05283-07 2025-03-14 15:41:49.353865-07 test f T4 \N f \N \N \N {} +6689 2072 2025-03-14 15:20:50.731476-07 2025-03-14 15:36:51.584067-07 test t T4 \N f \N \N \N {} +6888 2141 2025-03-16 11:25:39.687485-07 2025-03-16 10:00:50.121381-07 test f H100 \N t \N \N \N {} +6690 2073 2025-03-14 17:13:14.556768-07 2025-03-14 16:38:50.501123-07 test t T4 \N f \N \N \N {} +6691 2073 2025-03-14 16:16:09.058399-07 2025-03-14 16:49:34.474312-07 test f T4 \N f \N \N \N {} +6692 2074 2025-03-14 16:06:22.83602-07 2025-03-14 16:32:58.584801-07 test t T4 \N f \N \N \N {} +6695 2075 2025-03-14 16:07:22.505809-07 2025-03-14 16:41:15.832579-07 test t T4 \N f \N \N \N {} +6696 2076 2025-03-14 17:00:09.730989-07 2025-03-14 17:45:40.086325-07 test f T4 \N f \N \N \N {} +6697 2076 2025-03-14 16:27:20.361782-07 2025-03-14 17:11:01.011478-07 test t T4 \N f \N \N \N {} +6698 2077 2025-03-14 17:12:38.530745-07 2025-03-14 17:22:03.682351-07 test t T4 \N f \N \N \N {} +6699 2077 2025-03-14 17:57:47.071236-07 2025-03-14 17:32:18.120911-07 test f T4 \N f \N \N \N {} +6700 2078 2025-03-14 17:18:24.324855-07 2025-03-14 16:55:54.52059-07 test t T4 \N f \N \N \N {} +6701 2078 2025-03-14 16:20:14.880505-07 2025-03-14 16:15:25.529956-07 test f T4 \N f \N \N \N {} +6702 2079 2025-03-14 16:16:56.772414-07 2025-03-14 16:16:13.557071-07 test t T4 \N f \N \N \N {} +6703 2079 2025-03-14 16:04:46.914169-07 2025-03-14 17:47:21.761297-07 test f T4 \N f \N \N \N {} +6705 2080 2025-03-15 03:22:21.602325-07 2025-03-15 03:51:58.457278-07 benchmark t A100 \N t \N \N \N {} +6706 2080 2025-03-15 02:53:01.372004-07 2025-03-15 04:22:27.007219-07 leaderboard t A100 0.002516597 t \N \N \N {} +6712 2081 2025-03-15 02:40:30.575706-07 2025-03-15 03:31:55.583471-07 leaderboard f A100 0.0025119373333333337 t \N \N \N {} +6714 2081 2025-03-15 04:01:30.92787-07 2025-03-15 04:10:50.24876-07 benchmark t A100 \N t \N \N \N {} +6715 2081 2025-03-15 03:13:12.177222-07 2025-03-15 04:31:33.220267-07 leaderboard t A100 0.0025109593333333337 t \N \N \N {} +6717 2082 2025-03-15 04:18:09.428811-07 2025-03-15 04:24:16.122702-07 benchmark t A100 \N t \N \N \N {} +6718 2082 2025-03-15 02:49:24.552314-07 2025-03-15 02:42:15.529163-07 leaderboard t A100 0.0025148316666666667 t \N \N \N {} +6720 2082 2025-03-15 04:08:25.063554-07 2025-03-15 03:27:30.400048-07 benchmark f A100 \N t \N \N \N {} +6723 2083 2025-03-15 03:03:30.27571-07 2025-03-15 03:18:11.663245-07 benchmark f A100 \N t \N \N \N {} +6724 2083 2025-03-15 03:42:23.801801-07 2025-03-15 03:28:29.653584-07 leaderboard f A100 0.002519684 t \N \N \N {} +6726 2083 2025-03-15 04:21:45.565499-07 2025-03-15 04:16:51.069546-07 benchmark t A100 \N t \N \N \N {} +6727 2083 2025-03-15 04:07:46.449197-07 2025-03-15 04:25:25.23021-07 leaderboard t A100 0.002565036 t \N \N \N {} +6729 2084 2025-03-15 03:39:18.979856-07 2025-03-15 03:30:24.318469-07 benchmark t A100 \N t \N \N \N {} +6730 2084 2025-03-15 04:36:55.579596-07 2025-03-15 02:45:20.602588-07 leaderboard t A100 0.0025688876666666665 t \N \N \N {} +6732 2084 2025-03-15 03:14:12.489414-07 2025-03-15 02:51:03.824773-07 benchmark f A100 \N t \N \N \N {} +6733 2084 2025-03-15 03:22:20.346621-07 2025-03-15 03:11:13.814391-07 leaderboard f A100 0.00251581 t \N \N \N {} +6734 2085 2025-03-15 04:20:48.927946-07 2025-03-15 04:46:33.035612-07 benchmark f A100 \N t \N \N \N {} +6736 2086 2025-03-15 04:16:49.706067-07 2025-03-15 03:09:15.471405-07 benchmark t A100 \N t \N \N \N {} +6737 2086 2025-03-15 04:03:35.988704-07 2025-03-15 03:48:00.494296-07 leaderboard t A100 0.0025361823333333337 t \N \N \N {} +6739 2086 2025-03-15 03:15:14.837866-07 2025-03-15 04:10:52.660769-07 benchmark f A100 \N t \N \N \N {} +6740 2086 2025-03-15 03:44:14.062436-07 2025-03-15 04:17:31.002026-07 leaderboard f A100 0.0025177116666666665 t \N \N \N {} +6742 2087 2025-03-15 03:11:35.820391-07 2025-03-15 03:29:33.061134-07 benchmark t A100 \N t \N \N \N {} +6743 2087 2025-03-15 04:11:17.86842-07 2025-03-15 04:57:34.429343-07 leaderboard t A100 0.0025124136666666665 t \N \N \N {} +6745 2087 2025-03-15 04:40:18.217533-07 2025-03-15 04:45:54.791513-07 benchmark f A100 \N t \N \N \N {} +6746 2087 2025-03-15 03:26:08.981404-07 2025-03-15 03:26:01.506723-07 leaderboard f A100 0.0025170136666666666 t \N \N \N {} +6747 2088 2025-03-15 03:45:17.641686-07 2025-03-15 03:59:01.555077-07 benchmark f A100 \N t \N \N \N {} +6748 2089 2025-03-15 04:38:05.216274-07 2025-03-15 03:11:47.405569-07 benchmark f A100 \N t \N \N \N {} +6749 2090 2025-03-15 03:38:24.37619-07 2025-03-15 03:54:27.64572-07 benchmark f A100 \N t \N \N \N {} +6750 2091 2025-03-15 03:40:25.330357-07 2025-03-15 05:05:56.066665-07 benchmark f A100 \N t \N \N \N {} +6751 2092 2025-03-15 03:14:13.338645-07 2025-03-15 03:23:06.85116-07 benchmark f A100 \N t \N \N \N {} +6753 2093 2025-03-15 03:42:38.171903-07 2025-03-15 04:13:43.823972-07 benchmark f A100 \N t \N \N \N {} +6754 2093 2025-03-15 04:16:08.4764-07 2025-03-15 04:59:47.179425-07 leaderboard f A100 0.0025626343333333334 t \N \N \N {} +6756 2093 2025-03-15 03:19:26.506082-07 2025-03-15 03:33:18.400847-07 benchmark t A100 \N t \N \N \N {} +6757 2093 2025-03-15 03:50:47.54997-07 2025-03-15 04:41:08.193634-07 leaderboard t A100 0.0025123326666666667 t \N \N \N {} +6759 2094 2025-03-15 04:41:28.996534-07 2025-03-15 03:59:31.091742-07 benchmark f A100 \N t \N \N \N {} +6760 2094 2025-03-15 03:19:48.97712-07 2025-03-15 04:52:46.927488-07 leaderboard f A100 0.002587383 t \N \N \N {} +6762 2094 2025-03-15 03:21:46.748837-07 2025-03-15 03:42:35.174807-07 benchmark t A100 \N t \N \N \N {} +6763 2094 2025-03-15 04:43:41.011534-07 2025-03-15 04:19:37.015055-07 leaderboard t A100 0.0025829123333333333 t \N \N \N {} +6764 2095 2025-03-15 03:22:19.084993-07 2025-03-15 04:23:32.846181-07 benchmark f A100 \N t \N \N \N {} +6765 2096 2025-03-15 04:20:43.869135-07 2025-03-15 03:47:49.996079-07 benchmark f A100 \N t \N \N \N {} +6767 2097 2025-03-15 05:10:33.784233-07 2025-03-15 05:11:32.173991-07 benchmark t A100 \N t \N \N \N {} +6768 2097 2025-03-15 05:07:59.890466-07 2025-03-15 04:39:03.8974-07 leaderboard t A100 0.0024414366666666667 t \N \N \N {} +6770 2097 2025-03-15 04:36:24.390754-07 2025-03-15 04:02:31.398882-07 benchmark f A100 \N t \N \N \N {} +6771 2097 2025-03-15 04:12:36.697244-07 2025-03-15 03:55:22.254114-07 leaderboard f A100 0.0025765703333333334 t \N \N \N {} +6773 2098 2025-03-15 05:10:57.321488-07 2025-03-15 04:50:43.920081-07 benchmark t A100 \N t \N \N \N {} +6774 2098 2025-03-15 04:14:30.497647-07 2025-03-15 04:53:30.016091-07 leaderboard t A100 0.002577079 t \N \N \N {} +6776 2098 2025-03-15 05:09:55.996121-07 2025-03-15 03:56:57.017989-07 benchmark f A100 \N t \N \N \N {} +6777 2098 2025-03-15 04:11:54.507468-07 2025-03-15 03:43:49.234308-07 leaderboard f A100 0.002441317 t \N \N \N {} +6779 2099 2025-03-15 03:45:03.883023-07 2025-03-15 05:13:30.957419-07 benchmark f A100 \N t \N \N \N {} +6780 2099 2025-03-15 03:28:47.790669-07 2025-03-15 04:06:54.990019-07 leaderboard f A100 0.002574019 t \N \N \N {} +6782 2099 2025-03-15 05:16:01.58112-07 2025-03-15 04:49:37.793655-07 benchmark t A100 \N t \N \N \N {} +6783 2099 2025-03-15 04:17:13.541413-07 2025-03-15 05:01:49.023637-07 leaderboard t A100 0.0024406096666666666 t \N \N \N {} +6785 2100 2025-03-15 05:11:53.175254-07 2025-03-15 04:00:48.036801-07 benchmark f A100 \N t \N \N \N {} +6786 2100 2025-03-15 04:50:11.427307-07 2025-03-15 04:59:24.114334-07 leaderboard f A100 0.0025154266666666666 t \N \N \N {} +6788 2100 2025-03-15 03:58:09.913043-07 2025-03-15 04:10:04.670157-07 benchmark t A100 \N t \N \N \N {} +6789 2100 2025-03-15 04:27:52.506737-07 2025-03-15 05:09:40.763406-07 leaderboard t A100 0.0025164226666666667 t \N \N \N {} +6791 2101 2025-03-15 03:51:55.571727-07 2025-03-15 04:36:50.58781-07 benchmark f A100 \N t \N \N \N {} +6792 2101 2025-03-15 05:02:04.728096-07 2025-03-15 03:47:35.278382-07 leaderboard f A100 0.002513957 t \N \N \N {} +6794 2101 2025-03-15 04:13:17.847293-07 2025-03-15 05:12:57.162997-07 benchmark t A100 \N t \N \N \N {} +6795 2101 2025-03-15 04:32:46.623254-07 2025-03-15 04:14:33.053587-07 leaderboard t A100 0.0025157153333333336 t \N \N \N {} +6797 2102 2025-03-15 04:32:32.324876-07 2025-03-15 05:04:55.352884-07 benchmark f A100 \N t \N \N \N {} +6798 2102 2025-03-15 05:21:36.583203-07 2025-03-15 04:18:22.362049-07 leaderboard f A100 0.002519632 t \N \N \N {} +6800 2102 2025-03-15 04:46:37.876337-07 2025-03-15 05:18:37.22304-07 benchmark t A100 \N t \N \N \N {} +6801 2102 2025-03-15 04:37:43.517293-07 2025-03-15 04:33:17.157515-07 leaderboard t A100 0.0025164303333333336 t \N \N \N {} +6802 2103 2025-03-15 04:28:54.778992-07 2025-03-15 04:05:58.240583-07 benchmark f A100 \N t \N \N \N {} +6803 2104 2025-03-15 04:17:37.393026-07 2025-03-15 04:32:32.169131-07 benchmark f A100 \N t \N \N \N {} +6805 2106 2025-03-15 04:31:28.892695-07 2025-03-15 05:14:03.534628-07 benchmark f A100 \N t \N \N \N {} +6809 2110 2025-03-15 05:36:30.308746-07 2025-03-15 05:16:01.72196-07 benchmark f A100 \N f \N \N \N {} +6810 2111 2025-03-15 05:40:36.907231-07 2025-03-15 04:27:34.338625-07 benchmark f A100 \N t \N \N \N {} +6811 2112 2025-03-15 04:14:08.830754-07 2025-03-15 04:10:52.990865-07 benchmark f A100 \N t \N \N \N {} +6812 2113 2025-03-15 04:18:14.027105-07 2025-03-15 04:27:38.546365-07 benchmark f A100 \N t \N \N \N {} +6892 2142 2025-03-16 11:16:36.721079-07 2025-03-16 10:23:08.981733-07 benchmark f H100 \N f \N \N \N {} +6813 2114 2025-03-15 05:32:29.886643-07 2025-03-15 04:16:37.097189-07 benchmark f A100 \N f \N \N \N {} +6814 2115 2025-03-15 04:20:01.707962-07 2025-03-15 04:47:01.479607-07 benchmark f A100 \N t \N \N \N {} +6815 2116 2025-03-15 09:41:27.374128-07 2025-03-15 10:42:21.527333-07 benchmark f A100 \N f \N \N \N {} +6817 2118 2025-03-15 09:34:00.585802-07 2025-03-15 09:31:57.810416-07 benchmark f A100 \N t \N \N \N {} +6818 2119 2025-03-15 09:01:24.585482-07 2025-03-15 09:06:44.508444-07 benchmark f A100 \N t \N \N \N {} +6820 2120 2025-03-15 09:11:32.950497-07 2025-03-15 10:49:33.106269-07 benchmark f A100 \N t \N \N \N {} +6821 2120 2025-03-15 10:40:24.613571-07 2025-03-15 09:19:20.415657-07 leaderboard f A100 0.002443316 t \N \N \N {} +6824 2120 2025-03-15 09:48:57.993341-07 2025-03-15 10:57:27.863934-07 leaderboard t A100 0.0025090733333333333 t \N \N \N {} +6826 2121 2025-03-15 10:59:21.842552-07 2025-03-15 09:27:40.540091-07 benchmark t A100 \N t \N \N \N {} +6827 2121 2025-03-15 11:01:43.071093-07 2025-03-15 09:36:34.551049-07 leaderboard t A100 0.0025179616666666663 t \N \N \N {} +6829 2121 2025-03-15 10:06:55.697797-07 2025-03-15 09:52:44.863131-07 benchmark f A100 \N t \N \N \N {} +6830 2121 2025-03-15 10:04:37.365154-07 2025-03-15 10:01:47.995774-07 leaderboard f A100 0.00258409 t \N \N \N {} +6832 2122 2025-03-15 09:19:20.004818-07 2025-03-15 10:37:41.259917-07 benchmark t A100 \N t \N \N \N {} +6833 2122 2025-03-15 10:30:16.383706-07 2025-03-15 10:47:04.167114-07 leaderboard t A100 0.002514938 t \N \N \N {} +6835 2122 2025-03-15 09:23:17.985207-07 2025-03-15 10:19:25.750256-07 benchmark f A100 \N t \N \N \N {} +6836 2122 2025-03-15 09:33:25.125545-07 2025-03-15 09:07:15.935099-07 leaderboard f A100 0.002580424 t \N \N \N {} +6838 2123 2025-03-15 09:19:08.433444-07 2025-03-15 10:51:34.7913-07 benchmark f A100 \N t \N \N \N {} +6839 2123 2025-03-15 10:48:27.578624-07 2025-03-15 10:18:07.6516-07 leaderboard f A100 0.0025640053333333334 t \N \N \N {} +6841 2123 2025-03-15 09:57:51.450683-07 2025-03-15 11:08:21.326212-07 benchmark t A100 \N t \N \N \N {} +6842 2123 2025-03-15 10:58:54.403466-07 2025-03-15 10:55:26.439192-07 leaderboard t A100 0.0024363643333333335 t \N \N \N {} +6844 2124 2025-03-15 10:59:45.752999-07 2025-03-15 11:08:50.594752-07 benchmark f A100 \N t \N \N \N {} +6845 2124 2025-03-15 10:30:51.054594-07 2025-03-15 09:50:58.327798-07 leaderboard f A100 0.0025648486666666665 t \N \N \N {} +6847 2124 2025-03-15 10:17:13.326454-07 2025-03-15 11:08:25.750753-07 benchmark t A100 \N t \N \N \N {} +6848 2124 2025-03-15 10:50:00.948662-07 2025-03-15 09:49:49.033815-07 leaderboard t A100 0.0024406783333333335 t \N \N \N {} +6850 2125 2025-03-15 10:24:17.864597-07 2025-03-15 11:03:08.565844-07 benchmark f A100 \N t \N \N \N {} +6851 2125 2025-03-15 10:43:35.545505-07 2025-03-15 09:58:24.113231-07 leaderboard f A100 0.0025163956666666666 t \N \N \N {} +6853 2125 2025-03-15 10:00:18.694621-07 2025-03-15 09:47:13.923293-07 benchmark t A100 \N t \N \N \N {} +6854 2125 2025-03-15 09:28:35.589355-07 2025-03-15 09:48:08.961608-07 leaderboard t A100 0.0024417513333333334 t \N \N \N {} +6856 2126 2025-03-15 11:02:27.462138-07 2025-03-15 09:43:02.319651-07 benchmark f A100 \N t \N \N \N {} +6857 2126 2025-03-15 10:34:45.228313-07 2025-03-15 10:59:38.816203-07 leaderboard f A100 0.0025180366666666667 t \N \N \N {} +6859 2126 2025-03-15 10:50:57.421372-07 2025-03-15 10:57:16.908572-07 benchmark t A100 \N t \N \N \N {} +6860 2126 2025-03-15 09:48:05.47393-07 2025-03-15 11:11:47.912597-07 leaderboard t A100 0.0024680626666666663 t \N \N \N {} +6862 2127 2025-03-15 10:49:46.799765-07 2025-03-15 11:16:21.43097-07 benchmark f A100 \N t \N \N \N {} +6863 2127 2025-03-15 09:54:25.438285-07 2025-03-15 09:33:00.814065-07 leaderboard f A100 0.0025179106666666667 t \N \N \N {} +6865 2127 2025-03-15 09:30:50.965569-07 2025-03-15 10:48:04.990717-07 benchmark t A100 \N t \N \N \N {} +6866 2127 2025-03-15 10:18:20.707038-07 2025-03-15 11:07:56.251823-07 leaderboard t A100 0.00251993 t \N \N \N {} +6867 2128 2025-03-15 12:31:03.821683-07 2025-03-15 13:36:09.213881-07 test t T4 \N f \N \N \N {} +6873 2131 2025-03-15 13:22:04.183948-07 2025-03-15 14:24:48.619022-07 test f T4 \N f \N \N \N {} +6879 2134 2025-03-15 13:51:24.499821-07 2025-03-15 13:59:05.032478-07 test f T4 \N f \N \N \N {} +6880 2134 2025-03-15 13:38:48.357103-07 2025-03-15 13:25:18.378546-07 test t T4 \N f \N \N \N {} +6881 2135 2025-03-15 13:08:37.869004-07 2025-03-15 14:28:23.740745-07 test t T4 \N f \N \N \N {} +6882 2135 2025-03-15 14:19:21.411146-07 2025-03-15 13:47:56.79824-07 test f T4 \N f \N \N \N {} +6885 2138 2025-03-16 09:39:05.307836-07 2025-03-16 09:57:12.148158-07 test f H100 \N f \N \N \N {} +6898 2147 2025-03-16 12:12:19.701328-07 2025-03-16 12:45:49.124926-07 benchmark f T4 \N t \N \N \N {} +6900 2148 2025-03-16 12:59:18.78657-07 2025-03-16 12:12:58.201063-07 benchmark t T4 \N t \N \N \N {} +6901 2148 2025-03-16 13:01:51.449428-07 2025-03-16 13:37:59.705354-07 leaderboard t T4 0.04730965533333334 t \N \N \N {} +6903 2148 2025-03-16 12:58:16.904312-07 2025-03-16 12:26:52.007968-07 benchmark f T4 \N t \N \N \N {} +6904 2148 2025-03-16 13:30:00.224366-07 2025-03-16 12:52:44.892037-07 leaderboard f T4 0.04833626233333334 t \N \N \N {} +6908 2152 2025-03-16 15:44:14.854646-07 2025-03-16 16:27:57.619851-07 test t T4 \N f \N \N \N {} +6937 2159 2025-03-16 16:22:15.506089-07 2025-03-16 15:10:41.848628-07 test t T4 \N t \N \N \N {} +6909 2152 2025-03-16 16:12:17.885519-07 2025-03-16 16:07:41.651133-07 test f T4 \N f \N \N \N {} +6910 2153 2025-03-16 16:06:39.076718-07 2025-03-16 16:34:36.042625-07 test f T4 \N f \N \N \N {} +6911 2153 2025-03-16 16:28:04.258089-07 2025-03-16 15:53:40.235028-07 test t T4 \N f \N \N \N {} +6917 2156 2025-03-16 16:39:30.967226-07 2025-03-16 16:15:32.585871-07 benchmark f T4 \N t \N \N \N {} +6918 2156 2025-03-16 16:54:16.751464-07 2025-03-16 16:03:12.107749-07 leaderboard f T4 0.00024644342857142856 t \N \N \N {} +6920 2156 2025-03-16 14:58:37.646855-07 2025-03-16 15:33:11.455364-07 benchmark t T4 \N t \N \N \N {} +6921 2156 2025-03-16 15:16:42.101122-07 2025-03-16 16:12:37.070857-07 leaderboard t T4 0.00027644572222222224 t \N \N \N {} +6936 2159 2025-03-16 15:05:11.186583-07 2025-03-16 15:35:42.129511-07 benchmark f T4 \N f \N \N \N {} +6938 2159 2025-03-16 16:42:58.618407-07 2025-03-16 15:47:12.763026-07 benchmark t T4 \N f \N \N \N {} +6939 2161 2025-03-16 17:02:27.026204-07 2025-03-16 16:28:43.910502-07 test t T4 \N t \N \N \N {} +6940 2161 2025-03-16 16:06:50.499036-07 2025-03-16 15:50:15.342936-07 benchmark t T4 \N t \N \N \N {} +6944 2161 2025-03-16 16:05:29.503727-07 2025-03-16 16:16:37.57484-07 leaderboard f T4 0.00036897348214285714 t \N \N \N {} +6958 2165 2025-03-16 15:32:28.811938-07 2025-03-16 15:44:31.566608-07 test t T4 \N f \N \N \N {} +6959 2165 2025-03-16 15:57:30.46348-07 2025-03-16 17:11:51.584129-07 test f T4 \N f \N \N \N {} +6967 2168 2025-03-16 19:51:44.930831-07 2025-03-16 19:38:02.035729-07 test t T4 \N f \N \N \N {} +6968 2168 2025-03-16 19:59:18.445681-07 2025-03-16 20:46:51.847725-07 test f T4 \N f \N \N \N {} +6969 2169 2025-03-16 20:01:37.505709-07 2025-03-16 19:53:20.526714-07 test t T4 \N t \N \N \N {} +6970 2169 2025-03-16 18:55:53.752614-07 2025-03-16 19:43:27.875834-07 benchmark t T4 \N t \N \N \N {} +6972 2169 2025-03-16 20:01:43.044-07 2025-03-16 20:16:29.475064-07 test f T4 \N t \N \N \N {} +6973 2169 2025-03-16 20:12:25.898132-07 2025-03-16 20:12:59.112672-07 benchmark f T4 \N t \N \N \N {} +6975 2170 2025-03-16 20:55:02.810795-07 2025-03-16 19:05:21.943366-07 test f T4 \N t \N \N \N {} +6982 2171 2025-03-16 20:34:15.139307-07 2025-03-16 19:39:38.074903-07 benchmark t T4 \N t \N \N \N {} +6988 2172 2025-03-16 21:11:02.554591-07 2025-03-16 20:23:24.725167-07 benchmark t T4 \N t \N \N \N {} +6989 2172 2025-03-16 19:23:38.557607-07 2025-03-16 19:25:56.789059-07 leaderboard t T4 0.0008529994 t \N \N \N {} +6990 2172 2025-03-16 20:55:25.236794-07 2025-03-16 20:01:21.258987-07 test f T4 \N t \N \N \N {} +6991 2172 2025-03-16 20:19:48.287171-07 2025-03-16 19:24:39.531799-07 benchmark f T4 \N t \N \N \N {} +6992 2172 2025-03-16 20:07:21.615115-07 2025-03-16 20:13:42.640692-07 leaderboard f T4 0.0005998246363636364 t \N \N \N {} +6993 2173 2025-03-16 19:27:19.561439-07 2025-03-16 20:33:05.60488-07 test t T4 \N t \N \N \N {} +6994 2173 2025-03-16 20:10:47.945369-07 2025-03-16 19:25:09.306754-07 benchmark t T4 \N t \N \N \N {} +7006 2175 2025-03-16 20:10:27.360853-07 2025-03-16 20:32:19.626531-07 leaderboard f T4 0.00022696372727272726 t \N \N \N {} +7007 2176 2025-03-16 20:27:39.388715-07 2025-03-16 21:13:20.266181-07 test t T4 \N t \N \N \N {} +7013 2177 2025-03-16 20:52:50.82818-07 2025-03-16 20:52:32.181629-07 test t T4 \N t \N \N \N {} +7020 2178 2025-03-16 20:56:15.870132-07 2025-03-16 21:37:33.135945-07 benchmark f T4 \N t \N \N \N {} +7021 2178 2025-03-16 20:46:26.93908-07 2025-03-16 20:15:43.470303-07 leaderboard f T4 0.00023776616 t \N \N \N {} +7022 2178 2025-03-16 20:29:51.881141-07 2025-03-16 20:49:15.652665-07 test t T4 \N t \N \N \N {} +7023 2178 2025-03-16 19:44:17.51868-07 2025-03-16 20:29:57.33345-07 benchmark t T4 \N t \N \N \N {} +7024 2178 2025-03-16 20:46:38.473464-07 2025-03-16 19:46:05.014633-07 leaderboard t T4 0.00023401815384615384 t \N \N \N {} +7025 2179 2025-03-16 20:43:42.53621-07 2025-03-16 20:37:35.619442-07 test t T4 \N t \N \N \N {} +7026 2179 2025-03-16 20:01:15.232212-07 2025-03-16 20:15:45.919775-07 benchmark t T4 \N t \N \N \N {} +7027 2179 2025-03-16 20:05:15.564236-07 2025-03-16 20:03:37.77276-07 leaderboard t T4 0.00023105845833333333 t \N \N \N {} +7028 2179 2025-03-16 21:07:58.380332-07 2025-03-16 20:38:16.446317-07 test f T4 \N t \N \N \N {} +7029 2179 2025-03-16 21:12:36.483147-07 2025-03-16 20:50:41.542771-07 benchmark f T4 \N t \N \N \N {} +7030 2179 2025-03-16 21:41:50.345063-07 2025-03-16 20:30:53.023148-07 leaderboard f T4 0.0002410815 t \N \N \N {} +7031 2180 2025-03-16 20:01:10.433977-07 2025-03-16 21:48:38.104837-07 test f T4 \N t \N \N \N {} +7032 2180 2025-03-16 20:42:55.307852-07 2025-03-16 19:56:48.747118-07 benchmark f T4 \N t \N \N \N {} +7033 2180 2025-03-16 19:54:54.43732-07 2025-03-16 21:43:31.624944-07 leaderboard f T4 0.0010320996666666666 t \N \N \N {} +7034 2180 2025-03-16 21:08:01.480549-07 2025-03-16 20:04:50.559362-07 test t T4 \N t \N \N \N {} +7035 2180 2025-03-16 20:17:30.013044-07 2025-03-16 21:24:22.031756-07 benchmark t T4 \N t \N \N \N {} +7036 2180 2025-03-16 21:41:47.373413-07 2025-03-16 21:32:11.525076-07 leaderboard t T4 0.0010306484 t \N \N \N {} +7037 2181 2025-03-16 21:22:46.486398-07 2025-03-16 20:29:47.484582-07 test f A100 \N t \N \N \N {} +7038 2181 2025-03-16 21:38:35.851119-07 2025-03-16 21:51:47.560305-07 benchmark f A100 \N t \N \N \N {} +7039 2181 2025-03-16 20:38:02.61894-07 2025-03-16 20:02:26.204495-07 leaderboard f A100 0.0000866505 t \N \N \N {} +7040 2181 2025-03-16 20:21:30.439741-07 2025-03-16 20:59:46.729245-07 test t A100 \N t \N \N \N {} +7041 2181 2025-03-16 21:46:26.568583-07 2025-03-16 21:18:37.064553-07 benchmark t A100 \N t \N \N \N {} +7042 2181 2025-03-16 21:48:37.803054-07 2025-03-16 20:30:42.439254-07 leaderboard t A100 0.00008592785365853658 t \N \N \N {} +7044 2182 2025-03-16 21:06:13.034065-07 2025-03-16 20:08:36.728568-07 benchmark f H100 \N t \N \N \N {} +7045 2182 2025-03-16 21:28:36.786376-07 2025-03-16 21:47:18.527666-07 leaderboard f H100 0.00005831765454545455 t \N \N \N {} +7046 2182 2025-03-16 20:56:24.442304-07 2025-03-16 21:33:14.384165-07 test t H100 \N t \N \N \N {} +7047 2182 2025-03-16 21:05:38.501639-07 2025-03-16 20:55:27.320926-07 benchmark t H100 \N t \N \N \N {} +7048 2182 2025-03-16 21:24:08.431353-07 2025-03-16 20:32:33.148155-07 leaderboard t H100 0.00006414326 t \N \N \N {} +7061 2185 2025-03-16 21:59:53.418742-07 2025-03-16 21:21:24.148854-07 benchmark t L4 \N t \N \N \N {} +7062 2185 2025-03-16 21:59:20.390159-07 2025-03-16 20:48:54.636456-07 leaderboard t L4 0.00011960029545454545 t \N \N \N {} +7063 2186 2025-03-16 20:23:16.497019-07 2025-03-16 21:55:58.531742-07 test t H100 \N t \N \N \N {} +7064 2186 2025-03-16 22:01:45.798895-07 2025-03-16 20:34:14.059263-07 benchmark t H100 \N t \N \N \N {} +7065 2186 2025-03-16 20:21:16.357956-07 2025-03-16 22:00:26.089828-07 leaderboard t H100 0.00008572011000000001 t \N \N \N {} +7066 2186 2025-03-16 21:34:57.985674-07 2025-03-16 21:07:45.295076-07 test f H100 \N t \N \N \N {} +7067 2186 2025-03-16 20:33:02.195088-07 2025-03-16 20:29:30.445574-07 benchmark f H100 \N t \N \N \N {} +7068 2186 2025-03-16 22:14:09.367859-07 2025-03-16 21:19:31.416324-07 leaderboard f H100 0.00007322206 t \N \N \N {} +7069 2187 2025-03-16 21:45:45.648373-07 2025-03-16 20:53:58.681948-07 test f A100 \N t \N \N \N {} +7070 2187 2025-03-16 21:59:03.441533-07 2025-03-16 20:58:40.170299-07 benchmark f A100 \N t \N \N \N {} +7071 2187 2025-03-16 21:35:23.332245-07 2025-03-16 21:20:36.708794-07 leaderboard f A100 0.00008178825714285713 t \N \N \N {} +7073 2187 2025-03-16 20:17:23.698815-07 2025-03-16 21:16:53.360041-07 benchmark t A100 \N t \N \N \N {} +7074 2187 2025-03-16 21:35:59.086324-07 2025-03-16 21:27:19.519665-07 leaderboard t A100 0.00010883197674418605 t \N \N \N {} +7075 2188 2025-03-16 20:50:17.210026-07 2025-03-16 21:20:29.402014-07 test f T4 \N t \N \N \N {} +7076 2188 2025-03-16 21:33:43.489412-07 2025-03-16 20:58:46.157067-07 benchmark f T4 \N f \N \N \N {} +7077 2188 2025-03-16 21:27:45.9623-07 2025-03-16 20:47:40.647623-07 test t T4 \N t \N \N \N {} +7078 2188 2025-03-16 20:46:07.02613-07 2025-03-16 20:52:10.402659-07 benchmark t T4 \N f \N \N \N {} +\. + + +-- +-- Data for Name: submission; Type: TABLE DATA; Schema: leaderboard; Owner: - +-- + +COPY leaderboard.submission (id, leaderboard_id, file_name, user_id, code_id, submission_time, done) FROM stdin; +34 340 kernel.py 456789012345678 22 2025-02-23 14:06:19.847245-08 t +35 340 trial.py 234567890123456 22 2025-02-23 10:34:59.345289-08 t +36 340 kernel.py 012345678901234 22 2025-02-23 14:10:53.942002-08 t +37 340 trial.py 789012345678901 22 2025-02-23 11:31:30.819174-08 t +38 340 submission.py 456789012345678 23 2025-02-23 12:59:26.917155-08 t +39 340 impl.py 345678901234567 24 2025-02-23 11:29:53.494709-08 t +40 340 solution.py 456789012345678 25 2025-02-23 13:01:42.352117-08 t +41 340 impl.py 345678901234567 26 2025-02-23 10:25:26.751067-08 t +47 340 impl.py 123456789012345 29 2025-02-23 13:44:32.29949-08 t +48 340 trial.py 012345678901234 30 2025-02-23 11:51:10.320644-08 t +49 340 solution.py 789012345678901 31 2025-02-23 11:14:01.554187-08 t +50 340 submission.py 567890123456789 32 2025-02-23 11:30:07.746585-08 t 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901234567890123 1658 2025-03-16 14:46:22.028535-07 t +2154 341 solution.py 890123456789012 1659 2025-03-16 15:40:05.164974-07 t +2155 341 solution.py 234567890123456 1660 2025-03-16 16:08:52.91041-07 t +2156 341 trial.py 345678901234567 1661 2025-03-16 15:55:54.269054-07 t +2159 341 kernel.py 567890123456789 1663 2025-03-16 16:56:15.614343-07 t +2161 341 kernel.py 567890123456789 1664 2025-03-16 14:25:34.852162-07 t +2162 341 submission.py 456789012345678 1665 2025-03-16 15:45:01.981724-07 t +2165 341 trial.py 901234567890123 1667 2025-03-16 15:57:14.154203-07 t +2168 341 solution.py 567890123456789 1669 2025-03-16 19:42:03.407202-07 t +2169 341 kernel.py 123456789012345 1670 2025-03-16 21:40:28.730775-07 t +2170 341 impl.py 567890123456789 1671 2025-03-16 20:34:06.613897-07 t +2171 341 impl.py 234567890123456 1672 2025-03-16 20:25:54.773579-07 t +2172 341 kernel.py 901234567890123 1673 2025-03-16 19:13:58.425443-07 t +2173 341 kernel.py 678901234567890 1674 2025-03-16 20:19:59.98833-07 t +2174 341 impl.py 567890123456789 1675 2025-03-16 19:55:46.291501-07 t +2175 341 submission.py 234567890123456 1672 2025-03-16 21:40:33.525951-07 t +2176 341 impl.py 901234567890123 1676 2025-03-16 19:48:54.463167-07 t +2177 341 solution.py 789012345678901 1677 2025-03-16 19:20:32.110269-07 t +2178 341 kernel.py 890123456789012 1678 2025-03-16 20:39:30.587902-07 t +2179 341 kernel.py 901234567890123 1679 2025-03-16 19:21:10.332567-07 t +2180 341 trial.py 678901234567890 1680 2025-03-16 21:49:30.567938-07 t +2181 341 kernel.py 234567890123456 1681 2025-03-16 22:13:58.509615-07 t +2182 341 trial.py 789012345678901 1682 2025-03-16 20:36:13.558492-07 t +2183 341 solution.py 567890123456789 1683 2025-03-16 23:11:09.871429-07 t +2184 341 trial.py 345678901234567 1684 2025-03-16 21:43:03.928902-07 t +2185 341 solution.py 012345678901234 1685 2025-03-17 00:23:22.941377-07 t +456 343 trial.py 345678901234567 342 2025-02-25 02:16:40.106445-08 t +457 343 kernel.py 890123456789012 343 2025-02-25 02:53:28.950775-08 t +458 343 trial.py 234567890123456 344 2025-02-25 02:18:18.082127-08 t +459 340 solution.py 567890123456789 345 2025-02-25 04:59:31.823358-08 t +460 340 solution.py 234567890123456 346 2025-02-25 06:10:21.267295-08 t +461 340 kernel.py 234567890123456 347 2025-02-25 05:49:02.710954-08 t +462 343 submission.py 234567890123456 348 2025-02-25 03:38:49.445807-08 t +463 343 solution.py 012345678901234 349 2025-02-25 04:30:12.065474-08 t +464 340 kernel.py 012345678901234 350 2025-02-25 01:08:53.53109-08 t +465 340 submission.py 890123456789012 351 2025-02-25 03:59:24.602449-08 t +466 340 solution.py 567890123456789 352 2025-02-25 01:50:26.521436-08 t +2186 341 solution.py 901234567890123 1686 2025-03-16 21:59:27.745748-07 t +2187 341 submission.py 678901234567890 1687 2025-03-16 22:17:20.151817-07 t +2188 339 submission.py 901234567890123 1538 2025-03-16 21:59:59.819221-07 t +2111 340 impl.py 789012345678901 1635 2025-03-15 06:44:00.820768-07 t +2112 340 submission.py 901234567890123 1628 2025-03-15 04:47:49.044866-07 t +2113 340 submission.py 234567890123456 1621 2025-03-15 02:32:05.207331-07 t +2114 340 kernel.py 789012345678901 1582 2025-03-15 06:20:17.516971-07 t +2115 340 solution.py 012345678901234 1564 2025-03-15 05:24:26.402597-07 t +2116 340 solution.py 789012345678901 1636 2025-03-15 09:28:57.05897-07 t +2117 340 impl.py 890123456789012 1637 2025-03-15 08:03:21.946542-07 t +2118 340 impl.py 678901234567890 1638 2025-03-15 08:48:41.805515-07 t +349 340 impl.py 789012345678901 261 2025-02-24 17:53:42.081953-08 t +350 340 kernel.py 123456789012345 262 2025-02-24 22:18:21.499025-08 t +351 343 kernel.py 456789012345678 263 2025-02-24 18:52:15.666067-08 t +352 340 trial.py 890123456789012 264 2025-02-24 20:22:34.103361-08 t +353 340 solution.py 901234567890123 265 2025-02-24 21:49:20.140299-08 t +354 343 impl.py 456789012345678 266 2025-02-24 18:17:10.896171-08 t +355 343 solution.py 234567890123456 266 2025-02-24 20:30:54.809023-08 t +356 340 impl.py 901234567890123 267 2025-02-24 20:04:53.781775-08 t +357 340 impl.py 678901234567890 268 2025-02-24 20:42:03.419994-08 t +358 340 solution.py 789012345678901 269 2025-02-24 23:34:55.358489-08 t +359 343 kernel.py 234567890123456 266 2025-02-24 17:10:16.132019-08 t +360 340 solution.py 567890123456789 270 2025-02-24 22:10:07.962184-08 t +361 343 impl.py 234567890123456 266 2025-02-24 22:21:12.999201-08 t +362 343 trial.py 012345678901234 266 2025-02-24 18:25:02.337986-08 t +363 340 trial.py 890123456789012 271 2025-02-24 20:28:07.541467-08 t +364 340 kernel.py 901234567890123 272 2025-02-24 20:12:55.455942-08 t +365 340 kernel.py 789012345678901 272 2025-02-24 17:59:20.334685-08 t +366 340 solution.py 567890123456789 273 2025-02-24 22:08:53.1847-08 t +367 340 impl.py 234567890123456 274 2025-02-24 20:01:01.347547-08 t +368 340 solution.py 123456789012345 275 2025-02-24 20:09:18.416867-08 t +369 340 kernel.py 567890123456789 276 2025-02-24 17:50:27.889805-08 t +370 340 trial.py 678901234567890 277 2025-02-24 19:05:38.645246-08 t +371 340 impl.py 567890123456789 278 2025-02-24 21:33:43.68494-08 t +372 340 solution.py 345678901234567 277 2025-02-24 22:28:34.255238-08 t +373 340 kernel.py 678901234567890 278 2025-02-24 20:17:42.906-08 t +374 340 solution.py 678901234567890 276 2025-02-24 19:50:47.578074-08 t +375 340 solution.py 901234567890123 274 2025-02-24 22:39:39.439413-08 t +376 340 kernel.py 567890123456789 279 2025-02-24 22:38:33.284299-08 t +377 340 kernel.py 567890123456789 280 2025-02-24 23:01:26.796521-08 t +378 340 solution.py 890123456789012 281 2025-02-24 21:43:34.100549-08 t +379 340 trial.py 012345678901234 282 2025-02-24 21:16:44.359747-08 t +380 340 solution.py 345678901234567 283 2025-02-24 21:09:51.617995-08 t +381 340 trial.py 890123456789012 284 2025-02-24 21:46:35.897243-08 t +382 340 submission.py 678901234567890 282 2025-02-24 22:06:28.717211-08 t +383 340 trial.py 789012345678901 285 2025-02-24 20:08:30.927225-08 t +384 340 submission.py 123456789012345 286 2025-02-24 23:04:05.11528-08 t +385 340 kernel.py 567890123456789 287 2025-02-24 20:28:53.792403-08 t +386 340 trial.py 012345678901234 288 2025-02-24 18:29:59.318391-08 t +387 340 solution.py 901234567890123 289 2025-02-24 23:58:21.771671-08 t +504 340 impl.py 234567890123456 383 2025-02-25 05:18:11.284904-08 t +397 342 impl.py 012345678901234 297 2025-02-24 19:18:40.269216-08 t +475 340 submission.py 901234567890123 360 2025-02-25 02:31:30.333289-08 t +85 339 impl.py 901234567890123 60 2025-02-23 17:50:04.871466-08 t +476 340 trial.py 890123456789012 361 2025-02-25 06:38:35.973455-08 t +477 340 trial.py 567890123456789 362 2025-02-25 03:08:23.865636-08 t +478 340 impl.py 012345678901234 363 2025-02-25 04:23:16.192602-08 t +479 340 solution.py 890123456789012 364 2025-02-25 07:08:28.458738-08 t +480 340 submission.py 901234567890123 365 2025-02-25 05:56:27.07678-08 t +481 340 kernel.py 567890123456789 366 2025-02-25 04:43:04.936759-08 t +482 340 impl.py 012345678901234 367 2025-02-25 02:40:24.717191-08 t +483 340 kernel.py 012345678901234 364 2025-02-25 07:38:25.590003-08 t +484 340 kernel.py 012345678901234 368 2025-02-25 04:05:24.266333-08 t +485 340 kernel.py 234567890123456 368 2025-02-25 01:21:21.995918-08 t +486 340 impl.py 123456789012345 369 2025-02-25 05:15:42.322743-08 t +487 340 impl.py 890123456789012 370 2025-02-25 03:57:08.220114-08 t +488 340 kernel.py 890123456789012 371 2025-02-25 02:34:33.54953-08 t +489 340 kernel.py 345678901234567 372 2025-02-25 06:56:15.060357-08 t +490 340 submission.py 567890123456789 372 2025-02-25 05:18:46.28171-08 t +491 340 impl.py 567890123456789 372 2025-02-25 04:41:19.684912-08 t +492 340 kernel.py 456789012345678 373 2025-02-25 06:37:30.60971-08 t +495 340 impl.py 678901234567890 376 2025-02-25 06:51:34.935988-08 t +496 340 kernel.py 234567890123456 377 2025-02-25 04:42:39.816617-08 t +497 340 trial.py 012345678901234 378 2025-02-25 06:16:55.257084-08 t +788 340 kernel.py 456789012345678 593 2025-02-26 05:29:18.26114-08 t +2119 340 kernel.py 234567890123456 1625 2025-03-15 09:03:56.203676-07 t +2120 340 kernel.py 456789012345678 1625 2025-03-15 10:58:17.809272-07 t +2121 340 impl.py 890123456789012 1625 2025-03-15 09:51:45.663147-07 t +2122 340 solution.py 123456789012345 1625 2025-03-15 11:12:31.373245-07 t +2123 340 trial.py 345678901234567 1639 2025-03-15 12:46:17.341813-07 t +2124 340 kernel.py 567890123456789 1639 2025-03-15 09:46:35.467594-07 t +2125 340 impl.py 012345678901234 1621 2025-03-15 09:52:45.740845-07 t +2126 340 submission.py 789012345678901 1621 2025-03-15 09:58:33.640706-07 t +2127 340 solution.py 789012345678901 1623 2025-03-15 10:00:55.895166-07 t +2128 342 submission.py 234567890123456 1640 2025-03-15 14:34:13.003032-07 t +2129 342 trial.py 567890123456789 1641 2025-03-15 13:00:18.227657-07 t +2130 342 impl.py 012345678901234 1642 2025-03-15 12:29:47.042745-07 t +2131 342 submission.py 345678901234567 1643 2025-03-15 13:08:41.987409-07 t +2132 342 kernel.py 901234567890123 1644 2025-03-15 15:40:51.471691-07 t +2133 342 impl.py 789012345678901 1645 2025-03-15 15:31:01.440834-07 t +\. + + +-- +-- Data for Name: user_info; Type: TABLE DATA; Schema: leaderboard; Owner: - +-- + +COPY leaderboard.user_info (id, user_name) FROM stdin; +123456789012345 Alice +234567890123456 Bob +345678901234567 Charlie +456789012345678 Dave +567890123456789 Eve +678901234567890 Frank +789012345678901 Grace +890123456789012 Heidi +901234567890123 Ivan +012345678901234 James +\. + + +-- +-- Name: code_files_id_seq; Type: SEQUENCE SET; Schema: leaderboard; Owner: - +-- + +SELECT pg_catalog.setval('leaderboard.code_files_id_seq', 1687, true); + + +-- +-- Name: leaderboard_id_seq; Type: SEQUENCE SET; Schema: leaderboard; Owner: - +-- + +SELECT pg_catalog.setval('leaderboard.leaderboard_id_seq', 365, true); + + +-- +-- Name: runs_id_seq; Type: SEQUENCE SET; Schema: leaderboard; Owner: - +-- + +SELECT pg_catalog.setval('leaderboard.runs_id_seq', 7078, true); + + +-- +-- Name: submission_id_seq; Type: SEQUENCE SET; Schema: leaderboard; Owner: - +-- + +SELECT pg_catalog.setval('leaderboard.submission_id_seq', 2188, true); + + +-- +-- Name: code_files code_files_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.code_files + ADD CONSTRAINT code_files_pkey PRIMARY KEY (id); + + +-- +-- Name: gpu_type gpu_type_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.gpu_type + ADD CONSTRAINT gpu_type_pkey PRIMARY KEY (leaderboard_id, gpu_type); + + +-- +-- Name: leaderboard leaderboard_name_key; Type: CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.leaderboard + ADD CONSTRAINT leaderboard_name_key UNIQUE (name); + + +-- +-- Name: leaderboard leaderboard_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.leaderboard + ADD CONSTRAINT leaderboard_pkey PRIMARY KEY (id); + + +-- +-- Name: runs runs_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.runs + ADD CONSTRAINT runs_pkey PRIMARY KEY (id); + + +-- +-- Name: submission submission_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.submission + ADD CONSTRAINT submission_pkey PRIMARY KEY (id); + + +-- +-- Name: user_info user_info_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.user_info + ADD CONSTRAINT user_info_pkey PRIMARY KEY (id); + + +-- +-- Name: submission fk_user_info; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.submission + ADD CONSTRAINT fk_user_info FOREIGN KEY (user_id) REFERENCES leaderboard.user_info(id); + + +-- +-- Name: gpu_type gpu_type_leaderboard_id_fkey; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.gpu_type + ADD CONSTRAINT gpu_type_leaderboard_id_fkey FOREIGN KEY (leaderboard_id) REFERENCES leaderboard.leaderboard(id) ON DELETE CASCADE; + + +-- +-- Name: runs runs_submission_id_fkey; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.runs + ADD CONSTRAINT runs_submission_id_fkey FOREIGN KEY (submission_id) REFERENCES leaderboard.submission(id); + + +-- +-- Name: submission submission_code_id_fkey; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.submission + ADD CONSTRAINT submission_code_id_fkey FOREIGN KEY (code_id) REFERENCES leaderboard.code_files(id); + + +-- +-- Name: submission submission_leaderboard_id_fkey; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.submission + ADD CONSTRAINT submission_leaderboard_id_fkey FOREIGN KEY (leaderboard_id) REFERENCES leaderboard.leaderboard(id); + + +-- +-- PostgreSQL database dump complete +-- + diff --git a/pyproject.toml b/pyproject.toml index 2157d2c3..6a7eb9f9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -54,4 +54,4 @@ python_files = ["test_*.py", "*_test.py", "ci_test_*.py"] [tool.ruff] line-length = 120 -target-version = "py310" \ No newline at end of file +target-version = "py310" diff --git a/requirements.txt b/requirements.txt index 8569b10f..ad593132 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,4 +11,5 @@ better_profanity PyYAML fastapi[all] uvicorn -jinja2 \ No newline at end of file +jinja2 +pytest-asyncio==1.1.0 diff --git a/seed.sql b/seed.sql new file mode 100644 index 00000000..65982fa8 --- /dev/null +++ b/seed.sql @@ -0,0 +1,21 @@ +BEGIN; + +INSERT INTO leaderboard.leaderboard + (id, name, deadline, task, creator_id, forum_id, secret_seed, description) +VALUES +( + 339, + 'conv2d', + '2025-06-29 17:00:00-07', + $TASK$ +{ ... valid JSON starting with { and ending with } ... } +$TASK$, + 838132355075014667, + 1343279714277527582, + 1828004782, + $DESC$ +Multiline description goes here… +$DESC$ +); + +COMMIT; diff --git a/src/discord_cluster_manager.egg-info/PKG-INFO b/src/discord_cluster_manager.egg-info/PKG-INFO new file mode 100644 index 00000000..79bcae0c --- /dev/null +++ b/src/discord_cluster_manager.egg-info/PKG-INFO @@ -0,0 +1,408 @@ +Metadata-Version: 2.4 +Name: discord-cluster-manager +Version: 0.1.0 +Summary: Discord bot for managing compute clusters and running kernel benchmarks +Requires-Python: >=3.10 +Description-Content-Type: text/markdown +License-File: LICENSE +Requires-Dist: PyGithub +Requires-Dist: aiohttp +Requires-Dist: discord.py +Requires-Dist: audioop-lts; python_version >= "3.13" +Requires-Dist: python-dotenv +Requires-Dist: requests +Requires-Dist: modal +Requires-Dist: psycopg2-binary +Requires-Dist: yoyo-migrations +Requires-Dist: better_profanity +Requires-Dist: PyYAML +Requires-Dist: fastapi[all] +Requires-Dist: uvicorn +Requires-Dist: jinja2 +Provides-Extra: dev +Requires-Dist: ruff; extra == "dev" +Requires-Dist: pre-commit; extra == "dev" +Requires-Dist: pytest; extra == "dev" +Requires-Dist: pytest-coverage; extra == "dev" +Dynamic: license-file + +# discord-cluster-manager + +[![nvidia-arc](https://github.com/gpu-mode/discord-cluster-manager/actions/workflows/nvidia-arc-health.yml/badge.svg)](https://github.com/pytorch-labs/discord-cluster-manager/actions/workflows/nvidia-arc-health.yml) +[![amd](https://github.com/gpu-mode/discord-cluster-manager/actions/workflows/amd-health.yml/badge.svg)](https://github.com/pytorch-labs/discord-cluster-manager/actions/workflows/amd-health.yml) + +This is the code for the Discord bot we'll be using to queue jobs to a cluster of GPUs that our generous sponsors have provided. Our goal is to be able to queue kernels that can run end to end in seconds that way things feel interactive and social. + +The key idea is that we're using Github Actions as a job scheduling engine and primarily making the Discord bot interact with the cluster via issuing Github Actions and and monitoring their status and while we're focused on having a nice user experience on discord.gg/gpumode, [we're happy to accept PRs](#local-development) that make it easier for other Discord communities to hook GPUs. + +## Table of Contents + +- [Supported Schedulers](#supported-schedulers) +- [Local Development](#local-development) + - [Clone Repository](#clone-repository) + - [Discord Bot](#discord-bot) + - [Database](#database) + - [Environment Variables](#environment-variables) + - [Verify Setup](#verify-setup) +- [Available Commands](#available-commands) +- [Using the Leaderboard](#using-the-leaderboard) + - [Creating a New Leaderboard](#creating-a-new-leaderboard) + - [Reference Code Requirements (Python)](#reference-code-requirements-python) + - [Reference Code Requirements (CUDA)](#reference-code-requirements-cuda) + - [Submitting to a Leaderboard](#submitting-to-a-leaderboard) + - [Other Available Leaderboard Commands](#other-available-leaderboard-commands) + - [GPU Kernel-Specific Commands](#gpu-kernel-specific-commands) +- [Testing the Discord Bot](#testing-the-discord-bot) +- [How to Add a New GPU to the Cluster](#how-to-add-a-new-gpu-to-the-cluster) +- [Acknowledgements](#acknowledgements) + +## Supported schedulers + +- GitHub Actions +- Modal +- Slurm (not implemented yet) + +## Local Development + +### Clone Repository + +> [!IMPORTANT] +> Do not fork this repository. Instead, directly clone this repository to your local machine. + +> [!IMPORTANT] +> Python 3.11 or higher is required. + +After, install the dependencies with `pip install -r requirements.txt`. + +### Setup Discord Bot + +To run and develop the bot locally, you need to add it to your own "staging" server. Follow the steps [here](https://discordjs.guide/preparations/setting-up-a-bot-application.html#creating-your-bot) and [here](https://discordjs.guide/preparations/adding-your-bot-to-servers.html#bot-invite-links) to create a bot application and then add it to your staging server. + +Below is a visual walk-through of the steps linked above: + +- The bot needs the `Message Content Intent` and `Server Members Intent` permissions turned on. +
+ Click here for visual. + DCS_bot_perms +
+ +- The bot needs `applications.commands` and `bot` scopes. + +
+ Click here for visual. + Screenshot 2024-11-24 at 12 34 09 PM +
+ +- Finally, generate an invite link for the bot and enter it into any browser. + +
+ Click here for visual. + Screenshot 2024-11-24 at 12 44 08 PM +
+ +> [!NOTE] +> Bot permissions involving threads/mentions/messages should suffice, but you can naively give it `Administrator` since it's just a test bot in your own testing Discord server. + +### Database + +The leaderboard persists information in a Postgres database. To develop locally, set Postgres up on your machine. Then start a Postgres shell with `psql`, and create a database: + +``` +$ psql -U postgres +Password for user postgres: ******** +psql (16.6 (Ubuntu 16.6-1.pgdg22.04+1)) +Type "help" for help. + +postgres=# CREATE DATABASE clusterdev; +``` + +We are using [Yoyo Migrations](https://ollycope.com/software/yoyo/) to manage tables, indexes, etc. in our database. To create tables in your local database, apply the migrations in `src/discord-cluster-manager/migrations` with the following command line: + +``` +yoyo apply src/discord-cluster-manager/migrations \ + -d postgresql://user:password@localhost/clusterdev +``` + +
+ Click here for a transcript of a yoyo apply session + + $ yoyo apply . -d postgresql://user:password@localhost/clusterdev + + [20241208_01_p3yuR-initial-leaderboard-schema] + Shall I apply this migration? [Ynvdaqjk?]: y + + Selected 1 migration: + [20241208_01_p3yuR-initial-leaderboard-schema] + Apply this migration to postgresql://user:password@localhost/clusterdev [Yn]: y + Save migration configuration to yoyo.ini? + This is saved in plain text and contains your database password. + + Answering 'y' means you do not have to specify the migration source or database connection for future runs [yn]: n + +
+ +Applying migrations to our staging and prod environments also happens using `yoyo apply`, just with a different database URL. + +To make changes to the structure of the database, create a new migration: + +``` +yoyo new src/discord-cluster-manager/migrations -m "short_description" +``` + +...and then edit the generated file. Please do not edit existing migration files: the existing migration files form a sort of changelog that is supposed to be immutable, and so yoyo will refuse to reapply the changes. + +We are following an expand/migrate/contract pattern to allow database migrations without downtime. When you want to make a change to the structure of the database, first determine if it is expansive or contractive. + +- _Expansive changes_ are those that have no possibility of breaking a running application. Examples include: adding a new nullable column, adding a non-null column with a default value, adding an index, adding a table, etc. +- _Contractive changes_ are those that could break a running application. Examples include: dropping a table, dropping a column, adding a not null constraint to a column, adding a unique index, etc. + +After an expansive phase, data gets migrated to the newly added elements. Code also begins using the newly added elements. This is the migration step. Finally, when all code is no longer using elements that are obsolete, these can be removed. (Or, if adding a unique or not null constraint, after checking that the data satisfies the constraint, then the constraint can be safely added.) + +Expand, migrate, and contract steps may all be written using yoyo. + +### Environment Variables + +Create a `.env` file with the following environment variables: + +- `DISCORD_DEBUG_TOKEN` : The token of the bot you want to run locally +- `DISCORD_TOKEN` : The token of the bot you want to run in production +- `DISCORD_DEBUG_CLUSTER_STAGING_ID` : The ID of the "staging" server you want to connect to +- `DISCORD_CLUSTER_STAGING_ID` : The ID of the "production" server you want to connect to +- `GITHUB_TOKEN` : A Github token with permissions to trigger workflows, for now only new branches from [discord-cluster-manager](https://github.com/gpu-mode/discord-cluster-manager) are tested, since the bot triggers workflows on your behalf +- `GITHUB_REPO` : The repository where the cluster manager is hosted. +- `GITHUB_WORKFLOW_BRANCH` : The branch to start the GitHub Actions jobs from when submitting a task. +- `DATABASE_URL` : The URL you use to connect to Postgres. +- `DISABLE_SSL` : (Optional) set if you want to disable SSL when connecting to Postgres. + +Below is where to find these environment variables: + +> [!NOTE] +> For now, you can naively set `DISCORD_DEBUG_TOKEN` and `DISCORD_DEBUG_CLUSTER_STAGING_ID` to the same values as `DISCORD_TOKEN` and `DISCORD_CLUSTER_STAGING_ID` respectively. + +- `DISCORD_DEBUG_TOKEN` or `DISCORD_TOKEN`: Found in your bot's page within the [Discord Developer Portal](https://discord.com/developers/applications/): + +
+ Click here for visual. + Screenshot 2024-11-24 at 11 01 19 AM +
+ +- `DISCORD_DEBUG_CLUSTER_STAGING_ID` or `DISCORD_CLUSTER_STAGING_ID`: Right-click your staging Discord server and select `Copy Server ID`: + +
+ Click here for visual. + Screenshot 2024-11-24 at 10 58 27 AM +
+ +- `GITHUB_TOKEN`: Found in Settings -> Developer Settings (or [here](https://github.com/settings/tokens?type=beta)). Create a new (preferably classic) personal access token with an expiration date to any day less than a year from the current date, and the scopes `repo` and `workflow`. + +
+ Click here for visual. + Screenshot 2024-12-30 at 8 51 59 AM +
+ +- `GITHUB_REPO`: This should be set to this repository, which is usually `gpu-mode/discord-cluster-manager`. + +- `GITHUB_WORKFLOW_BRANCH`: Usually `main` or the branch you are working from. + +- `DATABASE_URL`: This contains the connection details for your local database, and has the form `postgresql://user:password@localhost/clusterdev`. + +- `DISABLE_SSL`: Set to `1` when developing. + +### Verify Setup + +Install the kernel bot as editable using `pip install -e .` + +Run the following command to run the bot: + +``` +python src/kernelbot/main.py --debug +``` + +Then in your staging server, use the `/verifyruns` command to test basic functionalities of the bot and the `/verifydb` command to check database connectivity. + +> [!NOTE] +> To test functionality of the Modal runner, you also need to be authenticated with Modal. Modal provides free credits to get started. +> To test functionality of the GitHub runner, you may need direct access to this repo which you can ping us for. + +## Available Commands + +TODO. This is currently a work in progress. + +`/run modal ` which you can use to pick a specific gpu, right now defaults to T4 + +`/run github ` which picks one of two workflow files + +`/resync` to clear all the commands and resync them + +`/ping` to check if the bot is online + +## Using the Leaderboard + +The main purpose of the Discord bot is to allow servers to host coding competitions through Discord. +The leaderboard was designed for evaluating GPU kernels, but can be adapted easily for other +competitions. The rest of this section will mostly refer to leaderboard submissions in the context +of our GPU Kernel competition. + +> [!NOTE] +> All leaderboard commands have the prefix `/leaderboard`, and center around creating, submitting to, +> and viewing leaderboard statistics and information. + +### Creating a new Leaderboard + +``` +/leaderboard create {name: str} {deadline: str} {reference_code: .cu or .py file} +``` + +The above command creates a leaderboard named `name` that ends at `deadline`. The `reference_code` +has strict function signature requirements, and is required to contain an input generator and a +reference implementation for the desired GPU kernel. We import these functions in our evaluation +scripts for verifying leaderboard submissions and measuring runtime. In the next mini-section, we +discuss the exact requirements for the `reference_code` script. + +Each leaderboard `name` can also specify the types of hardware that users can run their kernels on. +For example, a softmax kernel on an RTX 4090 can have different performance characteristics on an +H100. After running the leaderboard creation command, a prompt will pop up where the creator can +specify the available GPUs that the leaderboard evaluates on. + +![Leaderboard GPU](assets/img/lb_gpu.png) + +#### Reference Code Requirements (Python) + +The Discord bot internally contains an `eval.py` script that handles the correctness and timing +analysis for the leaderboard. The `reference_code` that the leaderboard creator submits must have +the following function signatures with their implementations filled out. `InputType` and +`OutputType` are generics that could be a `torch.Tensor`, `List[torch.Tensor]`, etc. +depending on the reference code specifications. We leave this flexibility to the leaderboard creator. + +```python +# Reference kernel implementation. +def ref_kernel(input: InputType) -> OutputType: + # Implement me... + +# Generate a list of tensors as input to the kernel +def generate_input() -> InputType: + # Implement me... + +# Verify correctness of reference and output +def check_implementation(custom_out: OutputType, reference_out: OutputType) -> bool: + # Implement me... +``` + +#### Reference Code Requirements (CUDA) + +The Discord bot internally contains an `eval.cu` script that handles the correctness and timing +analysis for the leaderboard. The difficult of CUDA evaluation scripts is we need to explicitly +handle the typing system for tensors. The `reference.cu` that the leaderboard creator submits must have +the following function signatures with their implementations filled out: + +The main difference is we now need to define an alias for the type that the input / outputs are. A +simple and common example is a list of FP32 tensors, which can be defined using a pre-defined array of +`const int`s called `N_SIZES`, then define an array of containers, e.g. +`std::array, N_SIZES>`. + +```cuda +// User-defined type for inputs, e.g. using input_t = std::array, IN_SIZES>; +using input_t = ...; + +// User-defined type for outputs, e.g. using output_t = std::array, OUT_SIZES>; +using output_t = ...; + +// Generate random data of type input_t +input_t generate_input() { + // Implement me... +} + + +// Reference kernel host code. +output_t reference(input_t data) { + // Implement me... +} + + +// Verify correctness of reference and output +bool check_implementation(output_t out, output_t ref) { + // Implement me... +} +``` + +### Submitting to a Leaderboard + +``` +/leaderboard submit {github / modal} {leaderboard_name: str} {script: .cu or .py file} +``` + +The leaderboard submission for _Python code_ requires the following function signatures, where +`InputType` and `OutputType` are generics that could be a `torch.Tensor`, `List[torch.Tensor]`, +etc. depending on the reference code specifications. + +```python +# User kernel implementation. +def custom_kernel(input: InputType) -> OutputType: + # Implement me... +``` + +### Other Available Leaderboard Commands + +Deleting a leaderboard: + +``` +/leaderboard delete {name: str} +``` + +List all active leaderboards and which GPUs they can run on: + +``` +/leaderboard list +``` + +List all leaderboard scores (runtime) for a particular leaderboard. (currently deprecated. Doesn't +support multiple GPU types yet) + +``` +/leaderboard show {name: str} +``` + +Display all personal scores (runtime) from a specific leaderboard. + +``` +/leaderboard show-personal {name: str} +``` + +### Submitting via a CLI + +Moving forward we also allow submissions without logging in to Discord via a CLI tool we wrote in Rust https://github.com/gpu-mode/popcorn-cli + +#### GPU Kernel-specific Commands + +We plan to add support for the PyTorch profiler and CUDA NSight Compute CLI to allow users to +profile their kernels. These commands are not specific to the leaderboard, but may be helpful for +leaderboard submissions. + +## How to add a new GPU to the cluster + +If you'd like to donate a GPU to our efforts, we can make you a CI admin in Github and have you add an org level runner https://github.com/organizations/gpu-mode/settings/actions/runners + +## Acknowledgements + +- Thank you to AMD for sponsoring an MI250 node +- Thank you to NVIDIA for sponsoring an H100 node +- Thank you to Nebius for sponsoring credits and an H100 node +- Thank you Modal for credits and speedy spartup times +- Luca Antiga did something very similar for the NeurIPS LLM efficiency competition, it was great! +- Midjourney was a similar inspiration in terms of UX + +## Citation + +If you used our software please cite it as + +``` +@misc{kernelbot2025, + title={KernelBot: A Discord-based GPU Kernel Evaluation and Competition Platform}, + author={Sirovatka, Matej and Zhang, Alex and Schultheis, Erik and Horowitz, Ben and Saroufim, Mark}, + year={2025}, + month={2}, + note={Equal contribution}, + url={https://github.com/gpu-mode/discord-cluster-manager}, + publisher={GitHub} +} +``` diff --git a/src/discord_cluster_manager.egg-info/SOURCES.txt b/src/discord_cluster_manager.egg-info/SOURCES.txt new file mode 100644 index 00000000..e6b21773 --- /dev/null +++ b/src/discord_cluster_manager.egg-info/SOURCES.txt @@ -0,0 +1,59 @@ +LICENSE +README.md +pyproject.toml +src/discord_cluster_manager.egg-info/PKG-INFO +src/discord_cluster_manager.egg-info/SOURCES.txt +src/discord_cluster_manager.egg-info/dependency_links.txt +src/discord_cluster_manager.egg-info/requires.txt +src/discord_cluster_manager.egg-info/top_level.txt +src/kernelbot/discord_reporter.py +src/kernelbot/discord_utils.py +src/kernelbot/env.py +src/kernelbot/main.py +src/kernelbot/api/__init__.py +src/kernelbot/api/api_utils.py +src/kernelbot/api/main.py +src/kernelbot/cogs/__init__.py +src/kernelbot/cogs/admin_cog.py +src/kernelbot/cogs/leaderboard_cog.py +src/kernelbot/cogs/misc_cog.py +src/kernelbot/cogs/verify_run_cog.py +src/kernelbot/ui/misc.py +src/kernelbot/ui/table.py +src/libkernelbot/__init__.py +src/libkernelbot/backend.py +src/libkernelbot/consts.py +src/libkernelbot/db_types.py +src/libkernelbot/leaderboard_db.py +src/libkernelbot/report.py +src/libkernelbot/run_eval.py +src/libkernelbot/submission.py +src/libkernelbot/task.py +src/libkernelbot/utils.py +src/libkernelbot/launchers/__init__.py +src/libkernelbot/launchers/github.py +src/libkernelbot/launchers/launcher.py +src/libkernelbot/launchers/modal.py +src/migrations/20241208_01_p3yuR-initial-leaderboard-schema.py +src/migrations/20241214_01_M62BX-drop-old-leaderboard-tables.py +src/migrations/20241221_01_54Oeg-rename-problem-table.py +src/migrations/20241222_01_ELxU5-add-gpu-types.py +src/migrations/20241224_01_Pg4FX-delete-cascade.py +src/migrations/20241226_01_ZQSOK-add_gpu_type_to_submission.py +src/migrations/20250106_01_Sgph3-add-leaderboard-creator-id.py +src/migrations/20250202_01_YYS3Q-leaderboard-rename-reference-to-task.py +src/migrations/20250221_01_GA8ro-submission-collection.py +src/migrations/20250228_01_9ANYn-submission-add-user-name.py +src/migrations/20250304_01_DzORz-collect-system-information-for-each-run.py +src/migrations/20250316_01_5oMi3-remember-forum-id.py +src/migrations/20250329_01_7VjJJ-add-a-secret-seed-column.py +src/migrations/20250406_01_ZXjWK-user-info-add-cli-id.py +src/migrations/20250412_01_l7Dra-user-info-fix-auth.py +src/migrations/20250412_02_NN9kK-user-info-cli-drop-old.py +src/migrations/20250506_01_38PkG-add-index-on-runs-runner-score.py +src/migrations/20250617_01_c5mrF-task-split.py +src/migrations/20250728_01_Q3jso-fix-code-table.py +src/migrations/20250822_01_UtXzl-website-submission.py +src/runners/github-runner.py +src/runners/modal_runner.py +src/runners/modal_runner_archs.py \ No newline at end of file diff --git a/src/discord_cluster_manager.egg-info/dependency_links.txt b/src/discord_cluster_manager.egg-info/dependency_links.txt new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/src/discord_cluster_manager.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/src/discord_cluster_manager.egg-info/requires.txt b/src/discord_cluster_manager.egg-info/requires.txt new file mode 100644 index 00000000..ac2cbf9b --- /dev/null +++ b/src/discord_cluster_manager.egg-info/requires.txt @@ -0,0 +1,22 @@ +PyGithub +aiohttp +discord.py +python-dotenv +requests +modal +psycopg2-binary +yoyo-migrations +better_profanity +PyYAML +fastapi[all] +uvicorn +jinja2 + +[:python_version >= "3.13"] +audioop-lts + +[dev] +ruff +pre-commit +pytest +pytest-coverage diff --git a/src/discord_cluster_manager.egg-info/top_level.txt b/src/discord_cluster_manager.egg-info/top_level.txt new file mode 100644 index 00000000..e90efa46 --- /dev/null +++ b/src/discord_cluster_manager.egg-info/top_level.txt @@ -0,0 +1,4 @@ +kernelbot +libkernelbot +migrations +runners diff --git a/src/kernelbot/api/api_utils.py b/src/kernelbot/api/api_utils.py index 57b764bb..27ce4d6e 100644 --- a/src/kernelbot/api/api_utils.py +++ b/src/kernelbot/api/api_utils.py @@ -11,6 +11,7 @@ from kernelbot.env import env from libkernelbot.backend import KernelBackend from libkernelbot.consts import SubmissionMode +from libkernelbot.leaderboard_db import LeaderboardDB from libkernelbot.report import ( Log, MultiProgressReporter, @@ -19,7 +20,6 @@ Text, ) from libkernelbot.submission import SubmissionRequest, prepare_submission -from src.libkernelbot.leaderboard_db import LeaderboardDB async def _handle_discord_oauth( @@ -197,7 +197,7 @@ async def display_report(self, title: str, report: RunResultReport): self.long_report += f"```\n{part.content}```" -async def to_submission_info( +async def to_submit_info( user_info: Any, submission_mode: str, file: UploadFile, @@ -334,30 +334,25 @@ def start_detached_run( submission_request: SubmissionRequest, submission_mode_enum: SubmissionMode, backend: KernelBackend, - db: "LeaderboardDB", background_tasks: BackgroundTasks, ) -> int: - """Starts a submission in the background and returns the submission id immediately.""" - # create submission id, so that it can be return to client before task is started - with db: - req = submission_request + with backend.db as db: sub_id = db.create_submission( - leaderboard=req.leaderboard, - file_name=req.file_name, - code=req.code, - user_id=req.user_id, - time=datetime.now(), - user_name=req.user_name, + leaderboard=submission_request.leaderboard, + file_name=submission_request.file_name, + code=submission_request.code, + user_id=submission_request.user_id, + time=datetime.datetime.now(), + user_name=submission_request.user_name, ) + db.upsert_submission_status(sub_id, "initial", None) - # makes the task run in the background background_tasks.add_task( - _run_submission, + backend.submit_with_status, submission_request, submission_mode_enum, backend, - db, sub_id, ) return sub_id diff --git a/src/kernelbot/api/main.py b/src/kernelbot/api/main.py index eda249c7..b6dfa20b 100644 --- a/src/kernelbot/api/main.py +++ b/src/kernelbot/api/main.py @@ -1,24 +1,26 @@ import asyncio import base64 -import datetime import json import os import time -from dataclasses import asdict -from typing import Annotated, Any, Optional, Tuple +from typing import Annotated, Any, Optional -from fastapi import Depends, FastAPI, Header, HTTPException, Request, UploadFile, BackgroundTasks +from fastapi import BackgroundTasks, Depends, FastAPI, Header, HTTPException, Request, UploadFile from fastapi.responses import JSONResponse, StreamingResponse - from libkernelbot.backend import KernelBackend -from libkernelbot.consts import SubmissionMode +from libkernelbot.db_types import IdentityType from libkernelbot.leaderboard_db import LeaderboardDB, LeaderboardRankedEntry -from libkernelbot.submission import SubmissionRequest -from libkernelbot.utils import KernelBotError, -from src.libkernelbot.db_types import IdentityType +from libkernelbot.utils import KernelBotError +from src.libkernelbot.submission import prepare_submission -from .api_utils import _handle_discord_oauth, _handle_github_oauth, sse_stream_submission, start_detached_run, to_submission_info +from .api_utils import ( + _handle_discord_oauth, + _handle_github_oauth, + sse_stream_submission, + start_detached_run, + to_submit_info, +) # yes, we do want ... = Depends() in function signatures # ruff: noqa: B008 @@ -97,6 +99,51 @@ async def validate_cli_header( return user_info +async def validate_user_header( + x_web_auth_id: Optional[str] = Header(None, alias="X-Web-Auth-Id"), + x_popcorn_cli_id: Optional[str] = Header(None, alias="X-Popcorn-Cli-Id"), + db_context: LeaderboardDB =Depends(get_db), +) -> Any: + """ + Validate either X-Web-Auth-Id or X-Popcorn-Cli-Id and return the associated user id. + Prefers X-Web-Auth-Id if both are provided. + """ + token = x_web_auth_id or x_popcorn_cli_id + if not token: + raise HTTPException( + status_code=400, + detail="Missing X-Web-Auth-Id or X-Popcorn-Cli-Id header", + ) + + if x_web_auth_id: + token = x_web_auth_id + id_type = IdentityType.WEB + elif x_popcorn_cli_id: + token = x_popcorn_cli_id + id_type = IdentityType.CLI + else: + raise HTTPException( + status_code=400, + detail="Missing header must be eother X-Web-Auth-Id or X-Popcorn-Cli-Id header", + ) + try: + with db_context as db: + user_info = db.validate_identity(token, id_type) + except Exception as e: + raise HTTPException( + status_code=500, + detail=f"Database error during validation: {e}", + ) from e + + if not user_info: + raise HTTPException( + status_code=401, + detail="Invalid or unauthorized auth header elaine", + ) + return user_info + + + @app.get("/auth/init") async def auth_init(provider: str, db_context=Depends(get_db)) -> dict: if provider not in ["discord", "github"]: @@ -218,48 +265,6 @@ async def cli_auth(auth_provider: str, code: str, state: str, db_context=Depends "is_reset": is_reset, } -async def validate_user_header( - x_web_auth_id: Optional[str] = Header(None, alias="X-Web-Auth-Id"), - x_popcorn_cli_id: Optional[str] = Header(None, alias="X-Popcorn-Cli-Id"), - db_context: LeaderboardDB =Depends(get_db), -) -> Any: - """ - Validate either X-Web-Auth-Id or X-Popcorn-Cli-Id and return the associated user id. - Prefers X-Web-Auth-Id if both are provided. - """ - token = x_web_auth_id or x_popcorn_cli_id - if not token: - raise HTTPException( - status_code=400, - detail="Missing X-Web-Auth-Id or X-Popcorn-Cli-Id header", - ) - - if x_web_auth_id: - token = x_web_auth_id - id_type = IdentityType.WEB - elif x_popcorn_cli_id: - token = x_popcorn_cli_id - id_type = IdentityType.CLI - else: - raise HTTPException( - status_code=400, - detail="Missing header must be eother X-Web-Auth-Id or X-Popcorn-Cli-Id header", - ) - try: - with db_context as db: - user_info = db.validate_identity(token, id_type) - except Exception as e: - raise HTTPException( - status_code=500, - detail=f"Database error during validation: {e}", - ) from e - - if not user_info: - raise HTTPException( - status_code=401, - detail="Invalid or unauthorized auth header", - ) - return user_info @app.post("/{leaderboard_name}/{gpu_type}/{submission_mode}") async def run_submission( # noqa: C901 @@ -289,7 +294,7 @@ async def run_submission( # noqa: C901 StreamingResponse: A streaming response containing the status and results of the submission. """ await simple_rate_limit() - submission_request,submission_mode_enum = await to_submission_info( + submission_request,submission_mode_enum = await to_submit_info( user_info, submission_mode, file, @@ -304,20 +309,20 @@ async def run_submission( # noqa: C901 ) return StreamingResponse(generator, media_type="text/event-stream") -@app.post("submission/{leaderboard_name}/{gpu_type}/{submission_mode}") -async def run_submission_v2( # noqa: C901 +@app.post("/submission/{leaderboard_name}/{gpu_type}/{submission_mode}") +async def run_submission_v2( leaderboard_name: str, gpu_type: str, submission_mode: str, file: UploadFile, + background_tasks: BackgroundTasks, user_info: Annotated[dict, Depends(validate_user_header)], - db_context=Depends(get_db), - background_tasks: BackgroundTasks = Depends() + db_context = Depends(get_db), ) -> Any: await simple_rate_limit() - submission_request,submission_mode_enum = await to_submission_info( + submission_request,submission_mode_enum = await to_submit_info( user_info, submission_mode, file, @@ -325,6 +330,14 @@ async def run_submission_v2( # noqa: C901 gpu_type, db_context) + try: + req = prepare_submission(submission_request, backend_instance) + except Exception as e: + raise HTTPException(status_code=400, detail=str(e)) from e + + if len(req.gpus) != 1: + raise HTTPException(status_code=400, detail="Invalid GPU type") + sub_id = start_detached_run(submission_request, submission_mode_enum, backend_instance, db_context, background_tasks) return JSONResponse( status_code=202, diff --git a/src/libkernelbot/backend.py b/src/libkernelbot/backend.py index 4cee7b8d..e61edb21 100644 --- a/src/libkernelbot/backend.py +++ b/src/libkernelbot/backend.py @@ -3,7 +3,7 @@ from datetime import datetime from types import SimpleNamespace from typing import Optional - +import json from libkernelbot.consts import GPU, GPU_TO_SM, SubmissionMode, get_gpu_by_name from libkernelbot.launchers import Launcher from libkernelbot.leaderboard_db import LeaderboardDB @@ -14,7 +14,7 @@ make_short_report, ) from libkernelbot.run_eval import FullResult -from libkernelbot.submission import ProcessedSubmissionRequest, compute_score +from libkernelbot.submission import ProcessedSubmissionRequest, compute_score, prepare_submission from libkernelbot.task import LeaderboardTask, build_task_config from libkernelbot.utils import setup_logging @@ -230,3 +230,42 @@ async def handle_submission( def _get_arch(self, gpu_type: GPU): return GPU_TO_SM[gpu_type.name] + + + async def submit_with_status( + self, + req: ProcessedSubmissionRequest, + mode: SubmissionMode, + reporter: MultiProgressReporter, + sub_id: int, + ): + + succeeded = False + try: + with self.db as db: + db.upsert_submission_status(sub_id, "prepare", None) + req = prepare_submission(req, self) + if not req.gpus or len(req.gpus) != 1: + raise ValueError("Invalid GPU type") + + with self.db as db: + db.upsert_submission_status(sub_id, "running", None) + sub_id2, results = await self.submit_full( + req, mode, reporter, pre_sub_id=sub_id + ) + with self.db as db: + db.upsert_submission_status(sub_id, "succeeded", None) + except Exception as e: + info = json.dumps({"error": str(e)}, ensure_ascii=False) + try: + with self.db as db: + db.upsert_submission_status(sub_id, "failed", info) + except Exception: + logger.exception("Failed to write failed status for submission %s", sub_id) + logger.exception("Detached run %s failed", sub_id) + finally: + with self.db as db: + if not succeeded: + db.upsert_submission_status(sub_id, "failed", None) + else: + db.upsert_submission_status(sub_id, "succeeded", None) diff --git a/src/libkernelbot/leaderboard_db.py b/src/libkernelbot/leaderboard_db.py index 0d3f69a7..d8c9af78 100644 --- a/src/libkernelbot/leaderboard_db.py +++ b/src/libkernelbot/leaderboard_db.py @@ -382,6 +382,28 @@ def mark_submission_done( self.connection.rollback() # Ensure rollback if error occurs raise KernelBotError("Error while finalizing submission") from e + def upsert_submission_status( + self, submission_id: int, status: str, info: Optional[str] = None + ) -> None: + try: + self.cursor.execute( + """ + INSERT INTO leaderboard.submission_status (submission_id, status, info) + VALUES (%s, %s, %s) + ON CONFLICT (submission_id) + DO UPDATE SET + status = EXCLUDED.status, + info = EXCLUDED.info, + updated_at = now() + """, + (submission_id, status, info), + ) + self.connection.commit() + except psycopg2.Error as e: + self.connection.rollback() + logger.exception("Error upserting submission status for %s", submission_id, exc_info=e) + raise KernelBotError("Error updating submission status") from e + def create_submission_run( self, submission: int, diff --git a/src/migrations/20250822_01_UtXzl-website-submission.py b/src/migrations/20250822_01_UtXzl-website-submission.py index 03bd9192..fe8bfaef 100644 --- a/src/migrations/20250822_01_UtXzl-website-submission.py +++ b/src/migrations/20250822_01_UtXzl-website-submission.py @@ -10,5 +10,13 @@ step( "ALTER TABLE leaderboard.user_info " "ADD COLUMN IF NOT EXISTS web_auth_id VARCHAR(255) DEFAULT NULL;" - ) + ), + step(""" + CREATE TABLE IF NOT EXISTS leaderboard.submission_status ( + id SERIAL PRIMARY KEY, + submission_id INTEGER NOT NULL REFERENCES leaderboard.submission(id), + status VARCHAR(255) DEFAULT NULL, + info TEXT DEFAULT NULL + ) + """), ] diff --git a/submission.py b/submission.py new file mode 100644 index 00000000..96be8b6d --- /dev/null +++ b/submission.py @@ -0,0 +1 @@ +print("love and peace") diff --git a/unit-tests/conftest.py b/unit-tests/conftest.py new file mode 100644 index 00000000..b9faa9b7 --- /dev/null +++ b/unit-tests/conftest.py @@ -0,0 +1,24 @@ +import os + +import pytest + +REQUIRED = { + "DISCORD_TOKEN": "dummy", + "GITHUB_TOKEN": "dummy", + "GITHUB_REPO": "dummy", +} + +for k, v in REQUIRED.items(): + os.environ.setdefault(k, v) + +@pytest.fixture(scope="session", autouse=True) +def _restore_env(): + old = {k: os.environ.get(k) for k in REQUIRED} + try: + yield + finally: + for k, v in old.items(): + if v is None: + os.environ.pop(k, None) + else: + os.environ[k] = v diff --git a/unit-tests/test_leaderboard_db.py b/unit-tests/test_leaderboard_db.py index 1f1cf6a7..c6874c73 100644 --- a/unit-tests/test_leaderboard_db.py +++ b/unit-tests/test_leaderboard_db.py @@ -441,10 +441,22 @@ def test_validate_identity_web_auth_happy_path(database, submit_leaderboard): """, ("1234", "sara_jojo","2345" ), ) - user_info = db.validate_identity("2345",IdentityType.WEB) - assert user_info["user_id"] =="1234" - assert user_info["user_name"] =="sara_jojo" - assert user_info["id_type"] ==IdentityType.WEB.value + user_info = db.validate_identity("2345",IdentityType.WEB) + assert user_info["user_id"] =="1234" + assert user_info["user_name"] =="sara_jojo" + assert user_info["id_type"] ==IdentityType.WEB.value + +def test_validate_identity_web_auth_not_found(database, submit_leaderboard): + with database as db: + db.cursor.execute( + """ + INSERT INTO leaderboard.user_info (id, user_name) + VALUES (%s, %s) + """, + ("1234", "sara_jojo"), + ) + user_info = db.validate_identity("2345",IdentityType.WEB) + assert user_info is None def test_validate_identity_web_auth_missing(database, submit_leaderboard): with database as db: @@ -459,7 +471,6 @@ def test_validate_identity_web_auth_missing(database, submit_leaderboard): assert res is None - def test_leaderboard_submission_deduplication(database, submit_leaderboard): """validate that identical submission codes are added just once""" with database as db: diff --git a/unit-tests/test_submission.py b/unit-tests/test_submission.py index 58f57c93..b93f906b 100644 --- a/unit-tests/test_submission.py +++ b/unit-tests/test_submission.py @@ -1,11 +1,14 @@ import datetime import re +from types import SimpleNamespace from unittest import mock import pytest +from fastapi import BackgroundTasks +from kernelbot.api import api_utils from libkernelbot import submission -from libkernelbot.consts import RankCriterion +from libkernelbot.consts import RankCriterion, SubmissionMode from libkernelbot.db_types import LeaderboardItem from libkernelbot.utils import KernelBotError @@ -106,7 +109,7 @@ def test_get_popcorn_directives_valid(): assert result == {"gpus": ["a100", "v100"], "leaderboard": "My_Board"} # Extra whitespace - code_whitespace = """#!POPCORN gpu A100 V100 + code_whitespace = """#!POPCORN gpu A100 V100 #!POPCORN leaderboard my_board """ # noqa: W291 result = submission._get_popcorn_directives(code_whitespace) assert result == {"gpus": ["A100", "V100"], "leaderboard": "my_board"} @@ -344,3 +347,77 @@ def test_compute_score(): mock_task.ranking_by = "WRONG" with pytest.raises(KernelBotError, match="Invalid ranking criterion WRONG"): submission.compute_score(mock_result, mock_task, 1) + + +@pytest.mark.asyncio +async def test_start_detached_run_uses_existing_mock_backend(monkeypatch, mock_backend): + db = mock_backend.db + + # Ensure the db mock has create_submission and returns a fixed id + if not hasattr(db, "create_submission"): + db.create_submission = mock.Mock(return_value=777) + else: + db.create_submission.return_value = 777 + + # Minimal submission_request: only the attributes accessed by start_detached_run + req = SimpleNamespace( + leaderboard="test_board", + file_name="hello.py", + code="print('hi')", + user_id="u-1", + user_name="alice", + ) + mode = SubmissionMode.LEADERBOARD + bg = BackgroundTasks() + + # Stub _run_submission to avoid running real work and capture the call + called = {} + async def fake_run_submission( + submission_request, + submission_mode_enum, + backend_arg, + db_arg, + sub_id_arg): + called["args"] = ( + submission_request, + submission_mode_enum, + backend_arg, db_arg, + sub_id_arg) + + monkeypatch.setattr(api_utils, "_run_submission", fake_run_submission) + + # Execute function under test + sub_id = api_utils.start_detached_run( + submission_request=req, + submission_mode_enum=mode, + backend=mock_backend, + db=db, + background_tasks=bg, + ) + + # Assert: returned id + assert sub_id == 777 + + # Assert: database context manager was entered/exited + db.__enter__.assert_called_once() + db.__exit__.assert_called_once() + + # Assert: create_submission was called with expected kwargs + db.create_submission.assert_called_once_with( + leaderboard="test_board", + file_name="hello.py", + code="print('hi')", + user_id="u-1", + time=mock.ANY, # time is dynamic + user_name="alice", + ) + + # Assert: task was enqueued into BackgroundTasks with correct function and args + assert len(bg.tasks) == 1 + task = bg.tasks[0] + assert task.func is fake_run_submission + assert task.args == (req, mode, mock_backend, db, 777) + + # verify the enqueued background task to verify the stub was called + await bg() + assert called["args"] == (req, mode, mock_backend, db, 777) diff --git a/unit-tests/test_validate_user_header.py b/unit-tests/test_validate_user_header.py new file mode 100644 index 00000000..54ab484d --- /dev/null +++ b/unit-tests/test_validate_user_header.py @@ -0,0 +1,70 @@ +from typing import Optional + +import pytest +from fastapi import HTTPException + +from kernelbot.api.main import validate_user_header +from libkernelbot.db_types import IdentityType + + +class DummyDBCtx: + def __init__(self, to_return=None, to_raise: Optional[Exception] = None): + self.to_return = to_return + self.to_raise = to_raise + self.seen = {} + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc, tb): + return False + + def validate_identity(self, token, id_type): + self.seen["token"] = token + self.seen["id_type"] = id_type + if self.to_raise: + raise self.to_raise + return self.to_return + +@pytest.mark.asyncio +async def test_cli_header_success(): + test_db = DummyDBCtx(to_return={"user_id": "u2", "user_name": "bob"}) + res = await validate_user_header( + x_web_auth_id=None, + x_popcorn_cli_id="clitok", + db_context=test_db, + ) + assert res["user_id"] == "u2" + assert test_db.seen["id_type"] == IdentityType.CLI + +@pytest.mark.asyncio +async def test_both_headers_prefers_web(): + test_db = DummyDBCtx(to_return={"user_id": "u3", "user_name": "c"}) + _ = await validate_user_header( + x_web_auth_id="webtok", + x_popcorn_cli_id="clitok", + db_context=test_db, + ) + assert test_db.seen["token"] == "webtok" + assert test_db.seen["id_type"] == IdentityType.WEB + +@pytest.mark.asyncio +async def test_missing_header_400(): + test_db = DummyDBCtx() + with pytest.raises(HTTPException) as ei: + await validate_user_header(None, None, test_db) + assert ei.value.status_code == 400 + +@pytest.mark.asyncio +async def test_db_error_500(): + test_db = DummyDBCtx(to_raise=RuntimeError("boom")) + with pytest.raises(HTTPException) as ei: + await validate_user_header("webtok", None, test_db) + assert ei.value.status_code == 500 + +@pytest.mark.asyncio +async def test_unauthorized_401(): + test_db = DummyDBCtx(to_return=None) + with pytest.raises(HTTPException) as ei: + await validate_user_header("webtok", None, test_db) + assert ei.value.status_code == 401 From de2d58937bb068d7ef12f0e7b622dd4e5c0a07f7 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 15:11:21 -0700 Subject: [PATCH 05/28] echo variables --- src/kernelbot/api/api_utils.py | 135 +--------- src/kernelbot/api/main.py | 235 +++++++++++++---- src/kernelbot/main.py | 18 +- src/libkernelbot/backend.py | 43 +--- .../background_submission_manager.py | 243 ++++++++++++++++++ src/libkernelbot/launchers/modal.py | 12 +- src/libkernelbot/leaderboard_db.py | 60 ++++- .../20250822_01_UtXzl-website-submission.py | 21 +- .../test_background_submission_manager.py | 148 +++++++++++ 9 files changed, 672 insertions(+), 243 deletions(-) create mode 100644 src/libkernelbot/background_submission_manager.py create mode 100644 unit-tests/test_background_submission_manager.py diff --git a/src/kernelbot/api/api_utils.py b/src/kernelbot/api/api_utils.py index 27ce4d6e..1228d7d2 100644 --- a/src/kernelbot/api/api_utils.py +++ b/src/kernelbot/api/api_utils.py @@ -1,15 +1,9 @@ -import asyncio -import datetime -import json -import time -from dataclasses import asdict -from typing import Any, Optional +from typing import Any import requests -from fastapi import BackgroundTasks, HTTPException, UploadFile +from fastapi import HTTPException, UploadFile from kernelbot.env import env -from libkernelbot.backend import KernelBackend from libkernelbot.consts import SubmissionMode from libkernelbot.leaderboard_db import LeaderboardDB from libkernelbot.report import ( @@ -19,7 +13,7 @@ RunResultReport, Text, ) -from libkernelbot.submission import SubmissionRequest, prepare_submission +from libkernelbot.submission import SubmissionRequest async def _handle_discord_oauth( @@ -204,7 +198,7 @@ async def to_submit_info( leaderboard_name: str, gpu_type: str, db_context: LeaderboardDB, -) -> tuple[SubmissionRequest, SubmissionMode]: +) -> tuple[SubmissionRequest, SubmissionMode]: # noqa: C901 user_name = user_info["user_name"] user_id = user_info["user_id"] @@ -296,124 +290,3 @@ async def to_submit_info( ) from e return submission_request, submission_mode_enum - - -def json_serializer(obj): - """JSON serializer for objects not serializable by default json code""" - if isinstance(obj, (datetime.datetime, datetime.date, datetime.time)): - return obj.isoformat() - raise TypeError(f"Type {type(obj)} not serializable") - - -async def _run_submission( - submission: SubmissionRequest, - mode: SubmissionMode, - backend: KernelBackend, - submission_id: Optional[int] = None, -): - try: - req = prepare_submission(submission, backend) - except Exception as e: - raise HTTPException(status_code=400, detail=str(e)) from e - - if len(req.gpus) != 1: - raise HTTPException(status_code=400, detail="Invalid GPU type") - - reporter = MultiProgressReporterAPI() - sub_id, results = await backend.submit_full( - req, mode, reporter, submission_id - ) - return ( - results, - [rep.get_message() + "\n" + rep.long_report for rep in reporter.runs], - sub_id, - ) - - -def start_detached_run( - submission_request: SubmissionRequest, - submission_mode_enum: SubmissionMode, - backend: KernelBackend, - background_tasks: BackgroundTasks, -) -> int: - - with backend.db as db: - sub_id = db.create_submission( - leaderboard=submission_request.leaderboard, - file_name=submission_request.file_name, - code=submission_request.code, - user_id=submission_request.user_id, - time=datetime.datetime.now(), - user_name=submission_request.user_name, - ) - db.upsert_submission_status(sub_id, "initial", None) - - background_tasks.add_task( - backend.submit_with_status, - submission_request, - submission_mode_enum, - backend, - sub_id, - ) - return sub_id - - -async def sse_stream_submission( - submission_request: SubmissionRequest, - submission_mode_enum: SubmissionMode, - backend: KernelBackend, -): - start_time = time.time() - task: asyncio.Task | None = None - try: - task = asyncio.create_task( - _run_submission(submission_request, submission_mode_enum, backend) - ) - - while not task.done(): - elapsed_time = time.time() - start_time - yield ( - "event: status\n" - f"data: {json.dumps({'status': 'processing','elapsed_time': round(elapsed_time, 2)}, default=json_serializer)}\n\n" - ) - try: - await asyncio.wait_for(asyncio.shield(task), timeout=15.0) - except asyncio.TimeoutError: - continue - except asyncio.CancelledError: - yield ( - "event: error\n" - f"data: {json.dumps({'status': 'error','detail': 'Submission cancelled'}, default=json_serializer)}\n\n" - ) - return - - result, reports = await task - result_data = { - "status": "success", - "results": [asdict(r) for r in result], - "reports": reports, - } - yield "event: result\n" f"data: {json.dumps(result_data, default=json_serializer)}\n\n" - - except HTTPException as http_exc: - error_data = { - "status": "error", - "detail": http_exc.detail, - "status_code": http_exc.status_code, - } - yield "event: error\n" f"data: {json.dumps(error_data, default=json_serializer)}\n\n" - except Exception as e: - error_type = type(e).__name__ - error_data = { - "status": "error", - "detail": f"An unexpected error occurred: {error_type}", - "raw_error": str(e), - } - yield "event: error\n" f"data: {json.dumps(error_data, default=json_serializer)}\n\n" - finally: - if task and not task.done(): - task.cancel() - try: - await task - except asyncio.CancelledError: - pass diff --git a/src/kernelbot/api/main.py b/src/kernelbot/api/main.py index b6dfa20b..c01ee917 100644 --- a/src/kernelbot/api/main.py +++ b/src/kernelbot/api/main.py @@ -1,24 +1,30 @@ import asyncio import base64 +import datetime import json import os import time +from dataclasses import asdict from typing import Annotated, Any, Optional -from fastapi import BackgroundTasks, Depends, FastAPI, Header, HTTPException, Request, UploadFile +from fastapi import Depends, FastAPI, Header, HTTPException, Request, UploadFile from fastapi.responses import JSONResponse, StreamingResponse from libkernelbot.backend import KernelBackend +from libkernelbot.background_submission_manager import BackgroundSubmissionManager +from libkernelbot.consts import SubmissionMode from libkernelbot.db_types import IdentityType from libkernelbot.leaderboard_db import LeaderboardDB, LeaderboardRankedEntry +from libkernelbot.submission import ( + ProcessedSubmissionRequest, + prepare_submission, +) from libkernelbot.utils import KernelBotError -from src.libkernelbot.submission import prepare_submission from .api_utils import ( + MultiProgressReporterAPI, _handle_discord_oauth, _handle_github_oauth, - sse_stream_submission, - start_detached_run, to_submit_info, ) @@ -27,13 +33,13 @@ app = FastAPI() - - backend_instance: KernelBackend = None +background_submission_manager: BackgroundSubmissionManager = None _last_action = time.time() _submit_limiter = asyncio.Semaphore(3) + async def simple_rate_limit(): """ A very primitive rate limiter. This function returns at most @@ -58,6 +64,12 @@ def init_api(_backend_instance: KernelBackend): backend_instance = _backend_instance +def init_background_submission_manager(_manager: BackgroundSubmissionManager): + global background_submission_manager + background_submission_manager = _manager + return background_submission_manager + + @app.exception_handler(KernelBotError) async def kernel_bot_error_handler(req: Request, exc: KernelBotError): return JSONResponse(status_code=exc.http_code, content={"message": str(exc)}) @@ -91,10 +103,14 @@ async def validate_cli_header( with db_context as db: user_info = db.validate_cli_id(x_popcorn_cli_id) except Exception as e: - raise HTTPException(status_code=500, detail=f"Database error during validation: {e}") from e + raise HTTPException( + status_code=500, detail=f"Database error during validation: {e}" + ) from e if user_info is None: - raise HTTPException(status_code=401, detail="Invalid or unauthorized X-Popcorn-Cli-Id") + raise HTTPException( + status_code=401, detail="Invalid or unauthorized X-Popcorn-Cli-Id" + ) return user_info @@ -102,7 +118,7 @@ async def validate_cli_header( async def validate_user_header( x_web_auth_id: Optional[str] = Header(None, alias="X-Web-Auth-Id"), x_popcorn_cli_id: Optional[str] = Header(None, alias="X-Popcorn-Cli-Id"), - db_context: LeaderboardDB =Depends(get_db), + db_context: LeaderboardDB = Depends(get_db), ) -> Any: """ Validate either X-Web-Auth-Id or X-Popcorn-Cli-Id and return the associated user id. @@ -143,7 +159,6 @@ async def validate_user_header( return user_info - @app.get("/auth/init") async def auth_init(provider: str, db_context=Depends(get_db)) -> dict: if provider not in ["discord", "github"]: @@ -171,16 +186,22 @@ async def auth_init(provider: str, db_context=Depends(get_db)) -> dict: db.init_user_from_cli(state_uuid, provider) except AttributeError as e: # Catch if leaderboard_db methods don't exist - raise HTTPException(status_code=500, detail=f"Database interface error: {e}") from e + raise HTTPException( + status_code=500, detail=f"Database interface error: {e}" + ) from e except Exception as e: # Catch other potential errors during DB interaction - raise HTTPException(status_code=500, detail=f"Failed to initialize auth in DB: {e}") from e + raise HTTPException( + status_code=500, detail=f"Failed to initialize auth in DB: {e}" + ) from e return {"state": state_uuid} @app.get("/auth/cli/{auth_provider}") -async def cli_auth(auth_provider: str, code: str, state: str, db_context=Depends(get_db)): # noqa: C901 +async def cli_auth( + auth_provider: str, code: str, state: str, db_context=Depends(get_db) +): # noqa: C901 """ Handle Discord/GitHub OAuth redirect. This endpoint receives the authorization code and state parameter from the OAuth flow. @@ -197,7 +218,9 @@ async def cli_auth(auth_provider: str, code: str, state: str, db_context=Depends ) if not code or not state: - raise HTTPException(status_code=400, detail="Missing authorization code or state") + raise HTTPException( + status_code=400, detail="Missing authorization code or state" + ) try: # Pad state if necessary for correct base64 decoding @@ -207,10 +230,14 @@ async def cli_auth(auth_provider: str, code: str, state: str, db_context=Depends cli_id = state_data["cli_id"] is_reset = state_data.get("is_reset", False) except (json.JSONDecodeError, KeyError, Exception) as e: - raise HTTPException(status_code=400, detail=f"Invalid state parameter: {e}") from None + raise HTTPException( + status_code=400, detail=f"Invalid state parameter: {e}" + ) from None # Determine API URL (handle potential None value) - api_base_url = os.environ.get("HEROKU_APP_DEFAULT_DOMAIN_NAME") or os.getenv("POPCORN_API_URL") + api_base_url = os.environ.get("HEROKU_APP_DEFAULT_DOMAIN_NAME") or os.getenv( + "POPCORN_API_URL" + ) if not api_base_url: raise HTTPException( status_code=500, @@ -240,7 +267,8 @@ async def cli_auth(auth_provider: str, code: str, state: str, db_context=Depends if not user_id or not user_name: raise HTTPException( - status_code=500, detail="Failed to retrieve user ID or username from provider." + status_code=500, + detail="Failed to retrieve user ID or username from provider.", ) try: @@ -255,7 +283,9 @@ async def cli_auth(auth_provider: str, code: str, state: str, db_context=Depends status_code=500, detail=f"Database interface error during update: {e}" ) from e except Exception as e: - raise HTTPException(status_code=400, detail=f"Database update failed: {e}") from e + raise HTTPException( + status_code=400, detail=f"Database update failed: {e}" + ) from e return { "status": "success", @@ -294,51 +324,54 @@ async def run_submission( # noqa: C901 StreamingResponse: A streaming response containing the status and results of the submission. """ await simple_rate_limit() - submission_request,submission_mode_enum = await to_submit_info( - user_info, - submission_mode, - file, - leaderboard_name, - gpu_type, - db_context) + submission_request, submission_mode_enum = await to_submit_info( + user_info, submission_mode, file, leaderboard_name, gpu_type, db_context + ) + + # prepare submission request before the submission is started + try: + req = prepare_submission(submission_request, backend_instance) + except Exception as e: + raise HTTPException(status_code=400, detail=str(e)) from e + + if not req.gpus or len(req.gpus) != 1: + raise HTTPException(status_code=400, detail="Invalid GPU type") generator = sse_stream_submission( - submission_request=submission_request, + req=req, submission_mode_enum=submission_mode_enum, backend=backend_instance, ) return StreamingResponse(generator, media_type="text/event-stream") + @app.post("/submission/{leaderboard_name}/{gpu_type}/{submission_mode}") async def run_submission_v2( leaderboard_name: str, gpu_type: str, submission_mode: str, file: UploadFile, - background_tasks: BackgroundTasks, user_info: Annotated[dict, Depends(validate_user_header)], - db_context = Depends(get_db), + db_context=Depends(get_db), ) -> Any: - await simple_rate_limit() - submission_request,submission_mode_enum = await to_submit_info( - user_info, - submission_mode, - file, - leaderboard_name, - gpu_type, - db_context) + submission_request, submission_mode_enum = await to_submit_info( + user_info, submission_mode, file, leaderboard_name, gpu_type, db_context + ) try: req = prepare_submission(submission_request, backend_instance) except Exception as e: raise HTTPException(status_code=400, detail=str(e)) from e - if len(req.gpus) != 1: + # prepare submission request before the submission is started + if not req.gpus or len(req.gpus) != 1: raise HTTPException(status_code=400, detail="Invalid GPU type") - sub_id = start_detached_run(submission_request, submission_mode_enum, backend_instance, db_context, background_tasks) + sub_id = await start_detached_run( + req, submission_mode_enum, backend_instance, background_submission_manager + ) return JSONResponse( status_code=202, content={"id": sub_id, "status": "accepted"}, @@ -359,7 +392,9 @@ async def get_leaderboards(db_context=Depends(get_db)): with db_context as db: return db.get_leaderboards() except Exception as e: - raise HTTPException(status_code=500, detail=f"Error fetching leaderboards: {e}") from e + raise HTTPException( + status_code=500, detail=f"Error fetching leaderboards: {e}" + ) from e @app.get("/gpus/{leaderboard_name}") @@ -379,7 +414,9 @@ async def get_gpus(leaderboard_name: str, db_context=Depends(get_db)) -> list[st return db.get_leaderboard_gpu_types(leaderboard_name) except Exception as e: - raise HTTPException(status_code=500, detail=f"Error fetching GPU data: {e}") from e + raise HTTPException( + status_code=500, detail=f"Error fetching GPU data: {e}" + ) from e @app.get("/submissions/{leaderboard_name}/{gpu_name}") @@ -398,18 +435,128 @@ async def get_submissions( leaderboard_name, gpu_name, limit=limit, offset=offset ) except Exception as e: - raise HTTPException(status_code=500, detail=f"Error fetching submissions: {e}") from e + raise HTTPException( + status_code=500, detail=f"Error fetching submissions: {e}" + ) from e @app.get("/submission_count/{leaderboard_name}/{gpu_name}") async def get_submission_count( - leaderboard_name: str, gpu_name: str, user_id: str = None, db_context=Depends(get_db) + leaderboard_name: str, + gpu_name: str, + user_id: str = None, + db_context=Depends(get_db), ) -> dict: """Get the total count of submissions for pagination""" await simple_rate_limit() try: with db_context as db: - count = db.get_leaderboard_submission_count(leaderboard_name, gpu_name, user_id) + count = db.get_leaderboard_submission_count( + leaderboard_name, gpu_name, user_id + ) return {"count": count} except Exception as e: - raise HTTPException(status_code=500, detail=f"Error fetching submission count: {e}") from e + raise HTTPException( + status_code=500, detail=f"Error fetching submission count: {e}" + ) from e + + +async def _run_submission( + req: ProcessedSubmissionRequest, + mode: SubmissionMode, + backend: KernelBackend, + submission_id: Optional[int] = None, +): + reporter = MultiProgressReporterAPI() + sub_id, results = await backend.submit_full(req, mode, reporter, submission_id) + return ( + results, + [rep.get_message() + "\n" + rep.long_report for rep in reporter.runs], + sub_id, + ) + + +async def start_detached_run( + req: ProcessedSubmissionRequest, + mode: SubmissionMode, + backend: KernelBackend, + manager: BackgroundSubmissionManager, +): + with backend.db as db: + sub_id = db.create_submission( + leaderboard=req.leaderboard, + file_name=req.file_name, + code=req.code, + user_id=req.user_id, + time=datetime.datetime.now(), + user_name=req.user_name, + ) + db.upsert_submission_job_status(sub_id, "initial", None) + await manager.enqueue(req, mode, sub_id) + return sub_id + + +async def sse_stream_submission( + req: ProcessedSubmissionRequest, + submission_mode_enum: SubmissionMode, + backend: KernelBackend, +): + start_time = time.time() + task: asyncio.Task | None = None + try: + task = asyncio.create_task(_run_submission(req, submission_mode_enum, backend)) + + while not task.done(): + elapsed_time = time.time() - start_time + yield ( + "event: status\n" + f"data: {json.dumps({'status': 'processing','elapsed_time': round(elapsed_time, 2)}, default=json_serializer)}\n\n" + ) + try: + await asyncio.wait_for(asyncio.shield(task), timeout=15.0) + except asyncio.TimeoutError: + continue + except asyncio.CancelledError: + yield ( + "event: error\n" + f"data: {json.dumps({'status': 'error','detail': 'Submission cancelled'}, default=json_serializer)}\n\n" + ) + return + + result, reports = await task + result_data = { + "status": "success", + "results": [asdict(r) for r in result], + "reports": reports, + } + yield "event: result\n" f"data: {json.dumps(result_data, default=json_serializer)}\n\n" + + except HTTPException as http_exc: + error_data = { + "status": "error", + "detail": http_exc.detail, + "status_code": http_exc.status_code, + } + yield "event: error\n" f"data: {json.dumps(error_data, default=json_serializer)}\n\n" + except Exception as e: + error_type = type(e).__name__ + error_data = { + "status": "error", + "detail": f"An unexpected error occurred: {error_type}", + "raw_error": str(e), + } + yield "event: error\n" f"data: {json.dumps(error_data, default=json_serializer)}\n\n" + finally: + if task and not task.done(): + task.cancel() + try: + await task + except asyncio.CancelledError: + pass + + +def json_serializer(obj): + """JSON serializer for objects not serializable by default json code""" + if isinstance(obj, (datetime.datetime, datetime.date, datetime.time)): + return obj.isoformat() + raise TypeError(f"Type {type(obj)} not serializable") diff --git a/src/kernelbot/main.py b/src/kernelbot/main.py index 20e40248..e0411096 100644 --- a/src/kernelbot/main.py +++ b/src/kernelbot/main.py @@ -4,7 +4,7 @@ import discord import uvicorn -from api.main import app, init_api +from api.main import app, init_api, init_background_submission_manager from cogs.admin_cog import AdminCog from cogs.leaderboard_cog import LeaderboardCog from cogs.misc_cog import BotManagerCog @@ -15,6 +15,7 @@ from libkernelbot import consts from libkernelbot.backend import KernelBackend +from libkernelbot.background_submission_manager import BackgroundSubmissionManager from libkernelbot.launchers import GitHubLauncher, ModalLauncher from libkernelbot.utils import setup_logging @@ -216,6 +217,9 @@ async def start_bot_and_api(debug_mode: bool): bot_instance = ClusterBot(debug_mode=debug_mode) init_api(bot_instance.backend) + m = init_background_submission_manager(BackgroundSubmissionManager(bot_instance.backend)) + # Start manager queue BEFORE serving requests + await m.start() config = uvicorn.Config( app, @@ -225,10 +229,14 @@ async def start_bot_and_api(debug_mode: bool): limit_concurrency=10, ) server = uvicorn.Server(config) - - # we need this as discord and fastapi both run on the same event loop - await asyncio.gather(bot_instance.start_bot(token), server.serve()) - + try: + await asyncio.gather( + bot_instance.start_bot(token), + server.serve(), + ) + finally: + # graceful shutdown + await m.stop() def on_unhandled_exception(loop, context): logger.exception("Unhandled exception: %s", context["message"], exc_info=context["exception"]) diff --git a/src/libkernelbot/backend.py b/src/libkernelbot/backend.py index e61edb21..4cee7b8d 100644 --- a/src/libkernelbot/backend.py +++ b/src/libkernelbot/backend.py @@ -3,7 +3,7 @@ from datetime import datetime from types import SimpleNamespace from typing import Optional -import json + from libkernelbot.consts import GPU, GPU_TO_SM, SubmissionMode, get_gpu_by_name from libkernelbot.launchers import Launcher from libkernelbot.leaderboard_db import LeaderboardDB @@ -14,7 +14,7 @@ make_short_report, ) from libkernelbot.run_eval import FullResult -from libkernelbot.submission import ProcessedSubmissionRequest, compute_score, prepare_submission +from libkernelbot.submission import ProcessedSubmissionRequest, compute_score from libkernelbot.task import LeaderboardTask, build_task_config from libkernelbot.utils import setup_logging @@ -230,42 +230,3 @@ async def handle_submission( def _get_arch(self, gpu_type: GPU): return GPU_TO_SM[gpu_type.name] - - - async def submit_with_status( - self, - req: ProcessedSubmissionRequest, - mode: SubmissionMode, - reporter: MultiProgressReporter, - sub_id: int, - ): - - succeeded = False - try: - with self.db as db: - db.upsert_submission_status(sub_id, "prepare", None) - req = prepare_submission(req, self) - if not req.gpus or len(req.gpus) != 1: - raise ValueError("Invalid GPU type") - - with self.db as db: - db.upsert_submission_status(sub_id, "running", None) - sub_id2, results = await self.submit_full( - req, mode, reporter, pre_sub_id=sub_id - ) - with self.db as db: - db.upsert_submission_status(sub_id, "succeeded", None) - except Exception as e: - info = json.dumps({"error": str(e)}, ensure_ascii=False) - try: - with self.db as db: - db.upsert_submission_status(sub_id, "failed", info) - except Exception: - logger.exception("Failed to write failed status for submission %s", sub_id) - logger.exception("Detached run %s failed", sub_id) - finally: - with self.db as db: - if not succeeded: - db.upsert_submission_status(sub_id, "failed", None) - else: - db.upsert_submission_status(sub_id, "succeeded", None) diff --git a/src/libkernelbot/background_submission_manager.py b/src/libkernelbot/background_submission_manager.py new file mode 100644 index 00000000..bb40edef --- /dev/null +++ b/src/libkernelbot/background_submission_manager.py @@ -0,0 +1,243 @@ +import asyncio +import contextlib +import datetime as dt +import logging +from dataclasses import dataclass + +from kernelbot.api.api_utils import MultiProgressReporterAPI +from libkernelbot.backend import KernelBackend +from libkernelbot.consts import SubmissionMode +from libkernelbot.submission import ProcessedSubmissionRequest +from libkernelbot.utils import setup_logging + +logger = setup_logging(__name__) + + +@dataclass +class JobItem: + job_id: int + sub_id: int + req: ProcessedSubmissionRequest + mode: SubmissionMode + + +# Periodicaly update the last heartbeat time for the submission job in submission_job_status table +HEARTBEAT_SEC = 15 # heartbeat interval 15s +# HARD_TIMEOUT_SEC [3hours]:if a submission is not completed within the hard timeout, it will be marked as failed in submission_job_status table +HARD_TIMEOUT_SEC = 60 * 60 * 3 # hard timeout 3 hours + + +class BackgroundSubmissionManager: + """ + This class manages submission in the backeground. It is responsible for + submitting jobs to the backend, monitoring their progress, and updating the + submissoin status in the database. + + It is also responsible for managing the workers, starting and stopping them + as needed. By default, the api can maximum support 24 submission processes, + + Scale up: scale up up to max_workers based on the queue size and the + number of running workers + Scale down: each worker scale down automatically after hitting idle_seconds(hot) + if there is no job in the queue. + """ + + def __init__( + self, + backend: KernelBackend, + min_workers: int = 2, + max_workers: int = 20, + idle_seconds: int = 120, + ): + self.backend = backend + self.queue: asyncio.Queue[JobItem] = asyncio.Queue() + self._workers: list[asyncio.Task] = [] # workers currently running + self._live_tasks: set[asyncio.Task] = set() # tasks currently processing + self.idle_seconds = idle_seconds + # state variables + + self._state_lock = asyncio.Lock() + self._accepting: bool = False + self.min_workers = min_workers + self.max_workers = max_workers + + async def start(self): + logger.info("starting background submission manager") + async with self._state_lock: + self._accepting = True + need = max(0, self.min_workers - len(self._workers)) + for _ in range(need): + t = asyncio.create_task(self._worker_loop(), name="bg-worker") + async with self._state_lock: + self._workers.append(t) + + async def stop(self): + logger.info("stopping background submission manager...") + async with self._state_lock: + self._accepting = False + workers = list(self._workers) + self._workers.clear() + for t in workers: + t.cancel() + for t in workers: + with contextlib.suppress(asyncio.CancelledError): + await t + logger.info("...stopped all background submission manager") + + async def enqueue( + self, req: ProcessedSubmissionRequest, mode: SubmissionMode, sub_id: int + ) -> tuple[int, int]: + async with self._state_lock: + if not self._accepting: + raise RuntimeError( + "Manager is not accepting new jobs, potentially shutting down" + ) + logger.info("enqueueing submission %s", sub_id) + now = dt.datetime.now(dt.timezone.utc) + with self.backend.db as db: + job_id = db.upsert_submission_job_status( + sub_id, + status="pending", + last_heartbeat=now, + ) + await self.queue.put(JobItem(job_id=job_id, sub_id=sub_id, req=req, mode=mode)) + # if we have no workers and it does not hit maximum, start one + await self._autoscale_up() + return job_id, sub_id + + async def _worker_loop(self): + try: + while True: + try: + item = await asyncio.wait_for( + self.queue.get(), timeout=self.idle_seconds + ) + logger.info( + "worker %r got job %s", id(asyncio.current_task()), item.sub_id + ) + except asyncio.TimeoutError: + async with self._state_lock: + me = asyncio.current_task() + if ( + len(self._workers) > self.min_workers + and me in self._workers + ): + try: + self._workers.remove(me) + logger.info( + "scale down: worker %r exiting; workers=%d", + me.get_name() + if hasattr(me, "get_name") + else id(me), + len(self._workers), + ) + except ValueError: + pass + return # scale down: exit + + continue + t = asyncio.create_task(self._run_one(item), name=f"job-{item.sub_id}") + async with self._state_lock: + self._live_tasks.add(t) + try: + await t # wait job to finish + finally: + async with self._state_lock: + self._live_tasks.discard(t) + self.queue.task_done() + except asyncio.CancelledError: + return + + async def _task_done_async(self, tt: asyncio.Task, item: JobItem): + async with self._state_lock: + self._live_tasks.discard(tt) + self.queue.task_done() + await self._autoscale_up() + + async def _run_one(self, item: JobItem): + sub_id = item.sub_id + now = dt.datetime.now(dt.timezone.utc) + + logger.info("start processing %s", sub_id) + with self.backend.db as db: + db.upsert_submission_job_status( + sub_id, status="running", last_heartbeat=now + ) + + # heartbeat loop continuously update the last heartbeat time for the submission status + stop_heartbeat = asyncio.Event() + + async def heartbeat(): + while not stop_heartbeat.is_set(): + await asyncio.sleep(HEARTBEAT_SEC) + ts = dt.datetime.now(dt.timezone.utc) + try: + with self.backend.db as db: + db.update_heartbeat_if_active(sub_id, ts) + except Exception: + pass + + hb_task = asyncio.create_task(heartbeat(), name=f"hb-{sub_id}") + try: + reporter = MultiProgressReporterAPI() + await asyncio.wait_for( + self.backend.submit_full(item.req, item.mode, reporter, sub_id), + timeout=HARD_TIMEOUT_SEC, + ) + ts = dt.datetime.now(dt.timezone.utc) + logger.info("run submission %s succeeded", sub_id) + with self.backend.db as db: + db.upsert_submission_job_status( + sub_id, status="succeeded", last_heartbeat=ts + ) + except asyncio.TimeoutError: + ts = dt.datetime.now(dt.timezone.utc) + with self.backend.db as db: + db.upsert_submission_job_status( + sub_id, + status="timed_out", + last_heartbeat=ts, + error="hard timeout reached", + ) + except Exception as e: + ts = dt.datetime.now(dt.timezone.utc) + logger.error("run submission %s failed", sub_id, exc_info=True) + try: + with self.backend.db as db: + db.upsert_submission_job_status( + sub_id, status="failed", last_heartbeat=ts, error=str(e) + ) + except Exception: + logger.error("Failed to write failed status for submission %s", sub_id) + finally: + stop_heartbeat.set() + hb_task.cancel() + with contextlib.suppress(asyncio.CancelledError): + await hb_task + + async def _autoscale_up(self): + async with self._state_lock: + running = len(self._live_tasks) + workers = len(self._workers) + qsize = self.queue.qsize() + + desired = min(self.max_workers, max(self.min_workers, running + qsize)) + need = desired - workers + to_add = max(0, need) + logger.info( + "autoscale: max_workers: %d," + "busy worker=%d," + "available workers %s," + "jobs_in_quue:%d," + "add=%d", + self.max_workers, + running, + workers, + qsize, + to_add, + ) + + for _ in range(to_add): + logging.info("scale up: starting a new worker") + t = asyncio.create_task(self._worker_loop(), name="bg-worker") + self._workers.append(t) diff --git a/src/libkernelbot/launchers/modal.py b/src/libkernelbot/launchers/modal.py index 26adfe33..6ebc92ca 100644 --- a/src/libkernelbot/launchers/modal.py +++ b/src/libkernelbot/launchers/modal.py @@ -4,7 +4,7 @@ from libkernelbot.consts import GPU, ModalGPU from libkernelbot.report import RunProgressReporter -from libkernelbot.run_eval import FullResult +from libkernelbot.run_eval import FullResult, SystemInfo from libkernelbot.utils import setup_logging from .launcher import Launcher @@ -28,6 +28,16 @@ async def run_submission( logger.info(f"Starting Modal run using {func_name}") + + #elaine tesitng + await asyncio.sleep(10) + return FullResult( + success=True, + error="", + system= SystemInfo(), + runs={}, + ) + await status.push("⏳ Waiting for Modal run to finish...") result = await loop.run_in_executor( diff --git a/src/libkernelbot/leaderboard_db.py b/src/libkernelbot/leaderboard_db.py index d8c9af78..9434845d 100644 --- a/src/libkernelbot/leaderboard_db.py +++ b/src/libkernelbot/leaderboard_db.py @@ -272,7 +272,11 @@ def validate_identity( (identifier,), ) row = self.cursor.fetchone() - return {"user_id": row[0], "user_name": row[1], "id_type":id_type.value} if row else None + return { + "user_id": row[0], + "user_name": row[1], + "id_type":id_type.value + } if row else None except psycopg2.Error as e: self.connection.rollback() logger.exception("Error validating %s %s", human_label, identifier, exc_info=e) @@ -382,27 +386,55 @@ def mark_submission_done( self.connection.rollback() # Ensure rollback if error occurs raise KernelBotError("Error while finalizing submission") from e - def upsert_submission_status( - self, submission_id: int, status: str, info: Optional[str] = None - ) -> None: + def update_heartbeat_if_active(self, sub_id: int, ts: datetime.datetime) -> None: try: self.cursor.execute( """ - INSERT INTO leaderboard.submission_status (submission_id, status, info) - VALUES (%s, %s, %s) - ON CONFLICT (submission_id) - DO UPDATE SET - status = EXCLUDED.status, - info = EXCLUDED.info, - updated_at = now() + UPDATE leaderboard.submission_job_status + SET last_heartbeat = %s, + updated_at = %s + WHERE submission_id = %s + AND status IN ('pending','running') + """, + (ts, ts, sub_id), + ) + self.connection.commit() + except psycopg2.Error as e: + self.connection.rollback() + logger.error("Failed to upsert submission job status. sub_id: '%s'", sub_id, exc_info=e) + raise KernelBotError("Error updating job status") from e + + + def upsert_submission_job_status( + self, + sub_id: int, + status: str | None = None, + error: str | None = None, + last_heartbeat: datetime.datetime | None = None, + ) -> int: + try: + self.cursor.execute( + """ + INSERT INTO leaderboard.submission_job_status AS s + (submission_id, status, error, last_heartbeat) + VALUES + (%s, %s, %s, %s) + ON CONFLICT (submission_id) DO UPDATE + SET + status = COALESCE(EXCLUDED.status, s.status), + error = COALESCE(EXCLUDED.error, s.error), + last_heartbeat = COALESCE(EXCLUDED.last_heartbeat, s.last_heartbeat) + RETURNING submission_id; """, - (submission_id, status, info), + (sub_id, status, error, last_heartbeat), ) + job_id = self.cursor.fetchone()[0] self.connection.commit() + return int(job_id) except psycopg2.Error as e: self.connection.rollback() - logger.exception("Error upserting submission status for %s", submission_id, exc_info=e) - raise KernelBotError("Error updating submission status") from e + logger.error("Failed to upsert submission job status. sub_id: '%s'", sub_id, exc_info=e) + raise KernelBotError("Error updating job status") from e def create_submission_run( self, diff --git a/src/migrations/20250822_01_UtXzl-website-submission.py b/src/migrations/20250822_01_UtXzl-website-submission.py index fe8bfaef..9f27f52a 100644 --- a/src/migrations/20250822_01_UtXzl-website-submission.py +++ b/src/migrations/20250822_01_UtXzl-website-submission.py @@ -6,17 +6,24 @@ __depends__ = {'20250728_01_Q3jso-fix-code-table'} +# noqa: C901 steps = [ step( "ALTER TABLE leaderboard.user_info " "ADD COLUMN IF NOT EXISTS web_auth_id VARCHAR(255) DEFAULT NULL;" ), step(""" - CREATE TABLE IF NOT EXISTS leaderboard.submission_status ( - id SERIAL PRIMARY KEY, - submission_id INTEGER NOT NULL REFERENCES leaderboard.submission(id), - status VARCHAR(255) DEFAULT NULL, - info TEXT DEFAULT NULL - ) - """), + CREATE TABLE IF NOT EXISTS leaderboard.submission_job_status ( + id SERIAL PRIMARY KEY, + submission_id INTEGER NOT NULL + REFERENCES leaderboard.submission(id) + ON DELETE CASCADE, + status VARCHAR(255) DEFAULT NULL, -- pending | running | succeeded | failed | timed_out + error TEXT DEFAULT NULL, -- error details if failed + created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(), -- creation timestamp + last_heartbeat TIMESTAMPTZ DEFAULT NULL, -- updated periodically by worker + CONSTRAINT uq_submission_job_status_submission_id + UNIQUE (submission_id) -- one-to-one with submission + ); + """), ] diff --git a/unit-tests/test_background_submission_manager.py b/unit-tests/test_background_submission_manager.py new file mode 100644 index 00000000..d29b5186 --- /dev/null +++ b/unit-tests/test_background_submission_manager.py @@ -0,0 +1,148 @@ +import asyncio +import datetime +from unittest import mock + +import pytest + +from libkernelbot.background_submission_manager import BackgroundSubmissionManager +from libkernelbot.consts import SubmissionMode +from libkernelbot.submission import ProcessedSubmissionRequest + + +@pytest.fixture +def mock_backend(): + backend = mock.Mock() + backend.accepts_jobs = True + + # Mock database context manager + db_context = mock.Mock() + backend.db = db_context + db_context.__enter__ = mock.Mock(return_value=db_context) + db_context.__exit__ = mock.Mock(return_value=None) + + # Default mock responses + mock_task = mock.Mock() + db_context.get_leaderboard.return_value = { + "task": mock_task, + "secret_seed": 12345, + "deadline": datetime.datetime.now() + datetime.timedelta(days=1), + "name": "test_board", + } + db_context.get_leaderboard_gpu_types.return_value = ["A100", "V100"] + + return backend + + +def get_req(i: int) -> ProcessedSubmissionRequest: + return ProcessedSubmissionRequest( + leaderboard="lb", + task="dummy_task", + secret_seed=12345, + task_gpus=["A100"], + file_name=f"f{i}.py", + code="print('hi')", + user_id=1, + user_name="tester", + gpus=None, + ) + + +@pytest.mark.asyncio +async def test_enqueue_and_run_job(mock_backend): + # mock upsert/update + db_context = mock_backend.db + db_context.upsert_submission_job_status = mock.Mock( + side_effect=lambda *a, **k: a[0] + ) # 返回 sub_id 当 job_id + db_context.update_heartbeat_if_active = mock.Mock() + + # mock submit_full + async def fake_submit_full(req, mode, reporter, sub_id): + await asyncio.sleep(0.01) # simulate a long-running job + return None, None + + mock_backend.submit_full = fake_submit_full + + manager = BackgroundSubmissionManager( + mock_backend, min_workers=1, max_workers=2, idle_seconds=0.1 + ) + await manager.start() + + # create a fake submission request + job_id, sub_id = await manager.enqueue(get_req(1), SubmissionMode.TEST, sub_id=42) + assert job_id == 42 + + # wait for the queue is clear + await manager.queue.join() + await asyncio.sleep(0.05) + + # check db status + assert ( + mock.call(42, status="pending", last_heartbeat=mock.ANY) + in db_context.upsert_submission_job_status.call_args_list + ) + assert ( + mock.call(42, status="running", last_heartbeat=mock.ANY) + in db_context.upsert_submission_job_status.call_args_list + ) + assert ( + mock.call(42, status="succeeded", last_heartbeat=mock.ANY) + in db_context.upsert_submission_job_status.call_args_list + ) + + await manager.stop() + + +@pytest.mark.asyncio +async def test_stop_rejects_new_jobs(mock_backend): + db_context = mock_backend.db + db_context.upsert_submission_job_status = mock.Mock(return_value=1) + db_context.update_heartbeat_if_active = mock.Mock() + mock_backend.submit_full = mock.AsyncMock() + + manager = BackgroundSubmissionManager( + mock_backend, min_workers=1, max_workers=1, idle_seconds=0.1 + ) + await manager.start() + await manager.stop() + + req = get_req(1) + with pytest.raises(RuntimeError): + await manager.enqueue(req, SubmissionMode.TEST, 99) + + +@pytest.mark.asyncio +async def test_scale_up_and_down(mock_backend): + db_context = mock_backend.db + db_context.upsert_submission_job_status = mock.Mock( + side_effect=lambda *a, **k: a[0] + ) + db_context.update_heartbeat_if_active = mock.Mock() + + async def fake_submit_full(req, mode, reporter, sub_id): + await asyncio.sleep(0.05) + return None, None + + mock_backend.submit_full = fake_submit_full + + manager = BackgroundSubmissionManager( + mock_backend, min_workers=1, max_workers=3, idle_seconds=0.2 + ) + await manager.start() + + # send multiple request to scale up + for i in range(6): + await manager.enqueue( + get_req(i), + SubmissionMode.TEST, + sub_id=i + 1, + ) + + await manager.queue.join() + + # idle timeout + await asyncio.sleep(manager.idle_seconds + 0.1) + + async with manager._state_lock: + assert len(manager._workers) == manager.min_workers + await manager.stop() From 7b912e68aef8951f3d688937caecc0bbd9df5826 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 15:12:35 -0700 Subject: [PATCH 06/28] echo variables --- data.sql | 9194 ----------------- src/discord_cluster_manager.egg-info/PKG-INFO | 408 - .../SOURCES.txt | 59 - .../dependency_links.txt | 1 - .../requires.txt | 22 - .../top_level.txt | 4 - 6 files changed, 9688 deletions(-) delete mode 100644 data.sql delete mode 100644 src/discord_cluster_manager.egg-info/PKG-INFO delete mode 100644 src/discord_cluster_manager.egg-info/SOURCES.txt delete mode 100644 src/discord_cluster_manager.egg-info/dependency_links.txt delete mode 100644 src/discord_cluster_manager.egg-info/requires.txt delete mode 100644 src/discord_cluster_manager.egg-info/top_level.txt diff --git a/data.sql b/data.sql deleted file mode 100644 index 76a4633c..00000000 --- a/data.sql +++ /dev/null @@ -1,9194 +0,0 @@ --- --- PostgreSQL database dump --- - --- Dumped from database version 16.8 (Ubuntu 16.8-1.pgdg22.04+1) --- Dumped by pg_dump version 16.8 (Ubuntu 16.8-1.pgdg22.04+1) - -SET statement_timeout = 0; -SET lock_timeout = 0; -SET idle_in_transaction_session_timeout = 0; -SET client_encoding = 'UTF8'; -SET standard_conforming_strings = on; -SELECT pg_catalog.set_config('search_path', '', false); -SET check_function_bodies = false; -SET xmloption = content; -SET client_min_messages = warning; -SET row_security = off; - --- --- Name: leaderboard; Type: SCHEMA; Schema: -; Owner: - --- - -CREATE SCHEMA leaderboard; - - -SET default_tablespace = ''; - -SET default_table_access_method = heap; - --- --- Name: code_files; Type: TABLE; Schema: leaderboard; Owner: - --- - -CREATE TABLE leaderboard.code_files ( - id integer NOT NULL, - code text NOT NULL, - hash text GENERATED ALWAYS AS (encode(sha256((code)::bytea), 'hex'::text)) STORED -); - - --- --- Name: code_files_id_seq; Type: SEQUENCE; Schema: leaderboard; Owner: - --- - -CREATE SEQUENCE leaderboard.code_files_id_seq - AS integer - START WITH 1 - INCREMENT BY 1 - NO MINVALUE - NO MAXVALUE - CACHE 1; - - --- --- Name: code_files_id_seq; Type: SEQUENCE OWNED BY; Schema: leaderboard; Owner: - --- - -ALTER SEQUENCE leaderboard.code_files_id_seq OWNED BY leaderboard.code_files.id; - - --- --- Name: gpu_type; Type: TABLE; Schema: leaderboard; Owner: - --- - -CREATE TABLE leaderboard.gpu_type ( - leaderboard_id integer NOT NULL, - gpu_type text NOT NULL -); - - --- --- Name: leaderboard; Type: TABLE; Schema: leaderboard; Owner: - --- - -CREATE TABLE leaderboard.leaderboard ( - id integer NOT NULL, - name text NOT NULL, - deadline timestamp with time zone NOT NULL, - task jsonb NOT NULL, - creator_id bigint DEFAULT '-1'::integer NOT NULL, - forum_id bigint NOT NULL, - secret_seed bigint DEFAULT floor((random() * ('2147483648'::bigint)::double precision)) NOT NULL, - description text NOT NULL -); - - --- --- Name: leaderboard_id_seq; Type: SEQUENCE; Schema: leaderboard; Owner: - --- - -CREATE SEQUENCE leaderboard.leaderboard_id_seq - AS integer - START WITH 1 - INCREMENT BY 1 - NO MINVALUE - NO MAXVALUE - CACHE 1; - - --- --- Name: leaderboard_id_seq; Type: SEQUENCE OWNED BY; Schema: leaderboard; Owner: - --- - -ALTER SEQUENCE leaderboard.leaderboard_id_seq OWNED BY leaderboard.leaderboard.id; - - --- --- Name: runs; Type: TABLE; Schema: leaderboard; Owner: - --- - -CREATE TABLE leaderboard.runs ( - id integer NOT NULL, - submission_id integer NOT NULL, - start_time timestamp with time zone NOT NULL, - end_time timestamp with time zone NOT NULL, - mode text NOT NULL, - secret boolean NOT NULL, - runner text NOT NULL, - score numeric, - passed boolean NOT NULL, - compilation jsonb, - meta jsonb, - result jsonb, - system_info jsonb NOT NULL -); - - --- --- Name: runs_id_seq; Type: SEQUENCE; Schema: leaderboard; Owner: - --- - -CREATE SEQUENCE leaderboard.runs_id_seq - AS integer - START WITH 1 - INCREMENT BY 1 - NO MINVALUE - NO MAXVALUE - CACHE 1; - - --- --- Name: runs_id_seq; Type: SEQUENCE OWNED BY; Schema: leaderboard; Owner: - --- - -ALTER SEQUENCE leaderboard.runs_id_seq OWNED BY leaderboard.runs.id; - - --- --- Name: submission; Type: TABLE; Schema: leaderboard; Owner: - --- - -CREATE TABLE leaderboard.submission ( - id integer NOT NULL, - leaderboard_id integer NOT NULL, - file_name text NOT NULL, - user_id text NOT NULL, - code_id integer NOT NULL, - submission_time timestamp with time zone NOT NULL, - done boolean DEFAULT false -); - - --- --- Name: submission_id_seq; Type: SEQUENCE; Schema: leaderboard; Owner: - --- - -CREATE SEQUENCE leaderboard.submission_id_seq - AS integer - START WITH 1 - INCREMENT BY 1 - NO MINVALUE - NO MAXVALUE - CACHE 1; - - --- --- Name: submission_id_seq; Type: SEQUENCE OWNED BY; Schema: leaderboard; Owner: - --- - -ALTER SEQUENCE leaderboard.submission_id_seq OWNED BY leaderboard.submission.id; - - --- --- Name: user_info; Type: TABLE; Schema: leaderboard; Owner: - --- - -CREATE TABLE leaderboard.user_info ( - id text NOT NULL, - user_name text -); - - --- --- Name: code_files id; Type: DEFAULT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.code_files ALTER COLUMN id SET DEFAULT nextval('leaderboard.code_files_id_seq'::regclass); - - --- --- Name: leaderboard id; Type: DEFAULT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.leaderboard ALTER COLUMN id SET DEFAULT nextval('leaderboard.leaderboard_id_seq'::regclass); - - --- --- Name: runs id; Type: DEFAULT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.runs ALTER COLUMN id SET DEFAULT nextval('leaderboard.runs_id_seq'::regclass); - - --- --- Name: submission id; Type: DEFAULT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.submission ALTER COLUMN id SET DEFAULT nextval('leaderboard.submission_id_seq'::regclass); - - --- --- Data for Name: code_files; Type: TABLE DATA; Schema: leaderboard; Owner: - --- - -COPY leaderboard.code_files (id, code) FROM stdin; -13 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -14 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -24 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -25 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -39 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -40 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -41 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -211 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -53 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -56 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -83 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -102 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -101 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1608 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -105 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -125 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -126 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -127 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -147 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -159 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -905 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -146 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1459 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -153 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -161 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -907 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -172 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -184 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -806 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -290 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -807 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1274 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -929 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -372 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -373 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1275 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -392 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1276 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1278 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -423 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1361 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -429 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -435 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -442 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -445 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1362 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1363 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1364 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1365 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1366 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1367 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1368 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1369 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1370 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -507 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -572 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -513 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -521 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -524 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -526 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -534 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -535 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1371 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -578 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1372 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1373 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1374 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1375 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1376 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -709 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -716 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -717 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1377 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1378 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -743 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -744 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1379 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1390 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1391 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1393 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -793 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -794 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -890 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -891 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -820 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -821 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1553 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1554 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1555 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1556 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1601 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1049 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1078 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1135 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1117 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1143 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1144 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1146 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1149 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1161 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1192 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1602 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1603 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1604 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1605 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1606 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1609 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1610 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1611 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1612 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1613 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1614 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1615 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1616 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1617 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -22 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1270 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1277 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1299 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1300 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1301 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1322 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1321 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1324 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1353 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1354 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1392 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1410 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1422 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1411 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -298 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -299 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -300 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -301 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -23 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1539 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1543 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -26 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1588 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1607 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -302 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -303 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -29 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -30 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1661 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1663 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -304 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -305 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -31 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -32 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -33 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -15 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -306 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -16 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -291 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -292 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -293 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -294 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -279 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -295 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -296 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -307 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -353 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -354 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -355 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -356 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -357 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -358 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -379 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -380 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -381 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -382 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -383 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -384 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -385 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -386 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -387 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -388 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -389 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -390 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -391 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -72 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -970 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -393 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -5 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -394 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -395 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -396 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -397 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -400 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -401 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -402 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -17 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -18 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -19 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -20 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -21 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -34 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -35 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -36 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -37 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -38 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -46 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -54 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -55 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -58 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -59 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -60 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -403 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -404 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -405 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -406 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -407 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -408 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -409 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -410 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -411 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -412 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -413 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -414 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -308 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -415 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -417 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -418 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -421 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -425 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -309 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -426 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -427 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -431 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -433 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -432 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -310 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -437 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -438 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -439 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -440 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -61 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -62 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -63 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -64 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -65 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -71 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -66 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -67 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -68 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -69 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -70 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1644 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -962 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -447 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -448 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -449 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -451 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -452 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -453 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -454 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -455 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -456 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1645 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -457 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -458 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -459 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -460 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -461 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -462 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -463 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -464 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -465 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -466 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -467 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -468 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -469 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -470 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -471 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -472 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -473 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -474 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -475 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -476 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -477 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -478 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -479 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -480 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -482 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -483 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -484 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -485 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -486 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -487 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -488 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -490 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -489 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -491 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -492 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -493 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -495 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -497 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -499 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -500 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -976 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -536 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -537 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -538 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -539 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -540 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -541 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -542 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -543 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -544 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -545 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -546 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -547 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -548 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -549 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -550 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -551 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -552 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -553 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -554 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -555 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -556 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -557 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -558 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -559 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -560 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -561 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -562 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -563 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -564 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -565 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -566 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -567 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -568 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -569 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -570 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -963 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -571 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -573 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -574 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -575 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -576 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -577 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -245 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -579 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -580 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -581 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -582 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -583 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -584 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -585 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -586 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -587 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -588 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -589 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -590 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -591 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -592 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -594 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -595 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -596 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -597 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -598 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -599 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -600 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -601 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -603 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -604 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -605 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -606 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -607 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -608 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -609 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -611 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -610 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -613 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -612 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -614 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -615 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -616 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -617 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -618 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -619 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -620 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -621 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -622 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -623 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -624 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -625 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -626 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -627 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -628 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -629 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -630 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -636 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -631 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -632 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -633 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -634 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -635 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -637 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -638 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -639 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -640 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -641 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -642 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -643 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -644 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -645 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -646 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -648 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -647 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -649 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -650 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -651 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -652 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -653 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -654 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -656 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -655 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -657 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -658 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -659 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -660 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -661 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -662 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -663 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -664 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -665 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -666 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -668 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -667 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -669 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -670 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -671 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -672 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -673 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -674 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -675 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -676 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -677 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -11 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -73 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -74 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -75 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -76 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -678 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -679 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -680 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -681 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -682 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -683 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -684 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -685 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -686 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -687 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -688 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -689 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -691 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -690 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -692 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -693 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -694 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -696 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -695 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -697 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -698 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -700 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -964 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -971 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -972 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -699 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -701 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -702 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -703 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -704 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -705 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -722 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -706 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -707 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -708 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -710 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -723 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -711 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -712 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -714 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -718 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -721 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -724 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -725 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -726 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -727 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -728 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -729 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -730 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -731 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -732 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -733 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -734 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -735 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -736 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -737 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -738 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -77 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -78 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -79 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -80 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -82 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -81 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -6 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -85 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -84 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -86 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -87 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -89 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -90 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -92 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -93 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -95 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -96 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -97 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -98 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -100 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -745 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -746 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -747 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -748 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -749 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -750 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -751 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -752 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -753 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -754 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -755 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -756 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -757 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -758 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -759 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -760 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -761 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -762 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -965 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -763 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -764 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -765 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -766 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -767 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -768 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -769 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -770 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -771 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -772 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -773 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -774 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -775 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -130 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -776 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -777 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -778 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -103 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -106 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -107 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -108 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -109 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -110 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -111 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -114 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -115 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -132 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -116 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -117 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -119 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -120 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -121 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -122 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -123 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -124 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -129 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -131 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -134 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -954 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -968 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -779 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -780 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -781 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -783 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -784 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -785 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -786 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -805 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -787 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -788 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -803 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -802 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -148 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -150 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -151 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -152 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -154 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -156 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -157 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -158 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -804 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -808 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -809 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -810 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -811 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -812 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -813 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -814 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -815 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -816 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -817 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -818 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -819 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -822 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -826 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -827 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -828 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -829 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -830 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -831 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -832 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -833 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -834 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -835 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -836 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -837 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -838 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -839 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -840 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -841 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -842 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -843 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -844 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -845 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -846 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -847 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -848 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -849 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -850 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -851 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -852 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -854 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -853 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -855 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -857 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -856 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -859 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -858 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -860 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -861 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -862 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -863 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -864 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -160 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -782 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -865 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -866 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -867 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -868 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -869 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -870 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -871 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -872 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -873 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -874 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -875 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -876 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -877 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -878 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -879 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -880 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -881 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -882 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -883 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -966 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -884 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -885 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -886 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -887 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -888 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -889 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -892 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -893 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -894 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -895 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -896 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -897 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -898 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -899 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -900 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -901 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -902 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -903 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -904 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -906 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -179 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -908 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -909 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -910 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -911 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -912 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -913 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -914 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -915 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -916 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -917 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -918 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -919 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -920 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -921 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -922 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -923 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -924 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -164 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -165 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -166 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -167 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -168 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -169 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -925 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -926 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -927 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -928 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -930 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -931 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -932 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -934 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -935 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -936 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -937 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -938 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -939 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -940 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -941 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -942 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -943 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -944 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -945 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -946 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -947 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -948 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -949 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -950 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -951 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -952 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -953 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -955 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -956 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -957 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -974 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -958 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -959 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -960 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -961 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -967 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -973 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -975 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -978 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -979 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -980 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -991 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -992 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -993 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -994 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -995 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -996 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -997 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -998 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -999 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1000 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1001 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1002 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1003 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1004 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1005 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1006 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1007 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1008 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1009 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1010 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1011 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1012 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1013 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1014 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1015 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1016 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -170 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -171 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -173 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -174 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -176 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -177 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -178 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1017 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1018 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1019 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1020 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1021 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1022 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1023 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1024 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1025 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1027 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1028 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1029 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1030 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1031 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1032 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1033 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1034 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1035 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1036 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1037 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1038 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1039 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1040 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1041 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1042 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1043 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1044 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1045 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1046 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1047 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1048 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1050 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1051 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1052 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1053 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1054 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1055 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1056 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1057 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1058 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1059 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1060 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1061 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1062 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1079 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1080 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1081 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1082 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1083 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1084 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1085 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -181 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -182 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -185 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -186 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -187 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -188 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -190 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -191 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -192 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -193 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -194 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -195 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -196 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1086 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1087 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1088 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1089 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1090 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1091 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1092 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1093 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1094 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1095 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1096 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1101 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1102 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1103 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1104 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1105 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1106 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1107 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1108 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1109 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1110 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1111 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1112 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1113 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1114 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1115 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1116 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1118 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1119 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1120 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1121 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1124 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1125 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1126 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1127 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1128 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1129 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -197 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1130 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1131 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1132 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1133 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1134 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1136 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1140 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1141 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1151 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1152 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1153 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1154 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1155 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1156 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1157 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1158 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1159 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1162 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1163 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1166 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1167 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1168 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1169 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1170 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1171 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1172 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1173 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1174 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1175 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1176 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -213 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1177 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1178 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1179 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1180 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1181 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1182 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1183 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1184 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1185 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1186 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1187 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1188 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1189 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1190 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1191 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1193 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1194 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1195 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1196 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1197 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1198 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1199 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1200 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1201 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1202 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -225 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1203 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -198 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -199 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1267 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1268 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1269 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1271 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1272 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1279 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1280 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1281 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1282 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1283 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1284 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1285 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1286 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1287 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1288 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1289 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1290 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1291 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1292 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1293 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1294 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1295 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1296 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1297 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1298 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1323 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1355 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1356 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1357 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1358 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1359 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1360 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -244 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1380 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1381 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1383 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1384 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1385 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1386 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1387 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1388 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1389 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1394 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1395 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1396 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1397 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -200 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -201 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -202 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -203 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -204 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -205 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -206 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -207 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -208 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -209 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -210 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -212 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1412 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1413 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1414 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1415 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1416 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1417 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1418 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -216 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1419 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1420 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1421 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1423 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -217 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1424 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1453 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1454 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1455 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -218 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1456 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1457 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1458 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -331 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1273 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1460 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1461 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1462 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1463 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -219 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1464 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1465 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1466 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1467 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -220 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1468 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1469 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1470 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1471 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -221 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1472 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1473 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1474 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1475 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -222 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1476 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1477 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1478 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1479 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -223 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1480 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1481 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1482 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -214 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -215 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -224 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1483 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1487 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1488 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1489 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1490 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1491 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1497 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1498 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1545 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1546 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1499 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1500 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1501 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1502 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1547 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1548 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1503 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1504 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1505 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1506 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1549 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1550 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1507 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1509 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1510 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1511 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -226 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1512 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1513 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1514 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1515 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -227 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1516 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1517 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1518 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1519 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -228 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1520 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1521 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1522 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1523 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -229 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1524 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1525 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1526 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1527 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -230 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1528 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1529 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1530 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1531 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -231 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1532 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1533 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1534 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1535 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -232 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1536 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1537 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1538 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1540 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1541 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1542 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1544 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -233 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -234 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -235 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -236 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -237 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -238 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -239 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -240 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -241 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -242 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -243 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1551 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1552 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1557 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1558 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1559 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1560 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1561 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1562 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1563 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1564 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1565 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1566 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -10 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1567 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1568 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1569 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1570 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1571 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1572 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1573 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1574 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1575 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1576 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1577 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1578 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -246 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -247 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -248 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -249 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -250 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -251 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -252 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -253 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -254 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -255 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -256 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -257 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -969 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -977 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -258 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -259 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -260 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1579 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1580 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1581 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1582 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1583 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1584 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1585 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1586 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1587 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1589 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1590 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1591 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1592 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1593 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1594 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1595 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1596 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1597 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1598 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1599 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1600 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -311 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -312 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -313 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -314 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -315 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -316 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -317 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -318 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -319 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -320 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -321 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -322 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -323 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -324 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -325 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -326 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -327 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -328 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -329 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -330 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1618 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1619 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1620 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1621 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -332 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -333 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -334 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -335 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -336 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -337 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -338 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -339 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -340 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -341 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1622 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1623 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1624 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1625 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1626 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1627 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1628 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1629 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1630 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1631 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1632 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1633 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1634 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1646 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1647 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1648 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1649 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1650 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1651 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1652 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1653 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1654 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1655 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1656 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -359 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1657 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1658 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1659 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1660 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1664 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1665 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1667 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1669 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -343 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1670 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1671 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1672 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1673 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -344 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1674 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1675 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1676 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1677 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -345 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1678 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1679 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1680 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1681 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -346 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1682 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1683 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1684 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1685 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -342 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -347 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -348 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -349 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -350 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -351 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -352 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1686 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1687 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1635 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1636 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1637 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1642 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1638 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -261 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -262 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -263 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -264 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -265 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -266 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -267 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -268 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -269 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -270 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -271 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -272 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -273 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -274 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -275 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -276 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -277 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -278 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -280 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -281 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -282 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -283 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -284 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -285 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -286 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -287 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -288 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -289 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -297 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1643 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -360 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -361 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -362 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -363 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -364 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -365 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -366 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -367 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -368 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -369 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -370 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -371 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -376 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -377 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -378 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -593 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1639 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1640 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1641 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -\. - - --- --- Data for Name: gpu_type; Type: TABLE DATA; Schema: leaderboard; Owner: - --- - -COPY leaderboard.gpu_type (leaderboard_id, gpu_type) FROM stdin; -339 H100 -339 A100 -339 T4 -339 L4 -340 H100 -340 A100 -340 T4 -340 L4 -341 H100 -341 A100 -341 T4 -341 L4 -342 H100 -342 A100 -342 T4 -342 L4 -343 H100 -343 A100 -343 T4 -343 L4 -\. - - --- --- Data for Name: leaderboard; Type: TABLE DATA; Schema: leaderboard; Owner: - --- - -COPY leaderboard.leaderboard (id, name, deadline, task, creator_id, forum_id, secret_seed, description) FROM stdin; -339 conv2d 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "from typing import TypedDict, TypeVar, Tuple\\nimport torch\\n\\ninput_t = TypeVar(\\"input_t\\", bound=Tuple[torch.Tensor, torch.Tensor])\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor)\\n\\n\\nclass TestSpec(TypedDict):\\n size: int\\n kernelsize: int\\n channels: int\\n batch: int\\n seed: int ", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "from utils import make_match_reference\\nimport torch\\nimport torch.nn.functional as F\\nfrom task import input_t, output_t\\n\\nclass DisableCuDNNTF32:\\n def __init__(self):\\n self.allow_tf32 = torch.backends.cudnn.allow_tf32\\n self.deterministic = torch.backends.cudnn.deterministic\\n pass\\n\\n def __enter__(self):\\n torch.backends.cudnn.allow_tf32 = False\\n torch.backends.cudnn.deterministic = True\\n return self\\n\\n def __exit__(self, exc_type, exc_value, traceback):\\n torch.backends.cudnn.allow_tf32 = self.allow_tf32\\n torch.backends.cudnn.deterministic = self.deterministic\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n \\"\\"\\"\\n Reference implementation of 2D convolution using PyTorch.\\n Args:\\n data: Tuple of (input tensor, kernel tensor)\\n Returns:\\n Output tensor after convolution\\n \\"\\"\\"\\n with DisableCuDNNTF32():\\n input_tensor, kernel = data\\n return F.conv2d(\\n input_tensor, \\n kernel,\\n\\n # No padding and no striding\\n # TODO: Can revisit this in future problems\\n stride=1,\\n padding=0\\n )\\n\\n\\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\\n \\"\\"\\"\\n Generates random input and kernel tensors.\\n Returns:\\n Tuple of (input tensor, kernel tensor)\\n \\"\\"\\"\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n \\n # Generate input tensor: [batch, in_channels, height, width]\\n input_tensor = torch.randn(\\n batch, channels, size, size,\\n device='cuda', \\n dtype=torch.float32, \\n generator=gen\\n ).contiguous()\\n \\n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\\n # Here we use same number of output channels as input channels for simplicity\\n kernel = torch.randn(\\n channels, channels, kernelsize, kernelsize,\\n device='cuda',\\n dtype=torch.float32,\\n generator=gen\\n ).contiguous()\\n \\n return (input_tensor, kernel)\\n\\n\\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)", "submission.py": "@SUBMISSION@"}, "tests": [{"seed": 4242, "size": 32, "batch": 1, "channels": 16, "kernelsize": 4}, {"seed": 5236, "size": 32, "batch": 2, "channels": 16, "kernelsize": 4}, {"seed": 1001, "size": 64, "batch": 1, "channels": 32, "kernelsize": 4}, {"seed": 5531, "size": 64, "batch": 2, "channels": 32, "kernelsize": 8}, {"seed": 9173, "size": 128, "batch": 1, "channels": 64, "kernelsize": 8}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"seed": 54352, "size": 128, "batch": 4, "channels": 64, "kernelsize": 8}, {"seed": 93246, "size": 128, "batch": 4, "channels": 64, "kernelsize": 16}, {"seed": 6256, "size": 256, "batch": 2, "channels": 128, "kernelsize": 16}, {"seed": 8841, "size": 256, "batch": 2, "channels": 128, "kernelsize": 16}, {"seed": 6252, "size": 256, "batch": 1, "channels": 128, "kernelsize": 32}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279714277527582 1828004782 Implement a 2D convolution kernel that matches the reference implementation.\nThe kernel should perform 2D convolution with the given specifications\nWe will benchmark different sizes, kernel sizes, channels and batch sizes but they will all \nbe even numbers with the exception of batch size which can sometimes be 1\nWe assume no padding and striding and instead vary the size of the input and kernel,\nnumber of channels, and batch size.\n\nInput: Tuple of (input_tensor, kernel)\n - input_tensor: 4D tensor of shape (batch, channels, height, width) with arbitrary values\n - kernel: 4D tensor of shape (channels, channels, kernelsize, kernelsize) with arbitrary values\nOutput: 4D tensor of shape (batch, channels, height-kernelsize+1, width-kernelsize+1) with convolved values\n -340 grayscale 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "from typing import TypedDict, TypeVar\\nimport torch\\n\\ninput_t = TypeVar(\\"input_t\\", bound=torch.Tensor) # Input will be (H, W, 3) RGB tensor\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor) # Output will be (H, W) grayscale tensor\\n\\nclass TestSpec(TypedDict):\\n size: int # Size of the square image (H=W)\\n seed: int ", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "from utils import make_match_reference\\nimport torch\\nfrom task import input_t, output_t\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n \\"\\"\\"\\n Reference implementation of RGB to grayscale conversion using PyTorch.\\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\\n \\n Args:\\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\\n Returns:\\n Grayscale tensor of shape (H, W) with values in [0, 1]\\n \\"\\"\\"\\n # Standard RGB to Grayscale coefficients\\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \\n device=data.device, \\n dtype=data.dtype)\\n return torch.sum(data * weights, dim=-1)\\n\\n\\ndef generate_input(size: int, seed: int) -> input_t:\\n \\"\\"\\"\\n Generates random RGB image tensor of specified size.\\n Returns:\\n Tensor of shape (size, size, 3) with values in [0, 1]\\n \\"\\"\\"\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n return torch.rand(size, size, 3, \\n device='cuda', \\n dtype=torch.float32, \\n generator=gen).contiguous()\\n\\n\\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\\n", "submission.py": "@SUBMISSION@"}, "tests": [{"seed": 1001, "size": 128}, {"seed": 5531, "size": 256}, {"seed": 9173, "size": 512}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"seed": 54352, "size": 512}, {"seed": 93246, "size": 1024}, {"seed": 6256, "size": 2048}, {"seed": 8841, "size": 4096}, {"seed": 6252, "size": 8192}, {"seed": 54352, "size": 16384}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279718383878165 1067465556 Implement an RGB to grayscale conversion kernel that matches the reference implementation.\nThe kernel should convert square RGB images with even sizes to grayscale using the standard coefficients:\nY = 0.2989 R + 0.5870 G + 0.1140 B\n\nInput: RGB tensor of shape (H, W, 3) with values in [0, 1]\nOutput: Grayscale tensor of shape (H, W) with values in [0, 1]\n -341 histogram 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "from typing import TypedDict, TypeVar\\nimport torch\\n\\ninput_t = TypeVar(\\"input_t\\", bound=torch.Tensor)\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor)\\n\\nclass TestSpec(TypedDict):\\n size: int\\n seed: int\\n contention: int\\n\\n", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "from utils import verbose_allequal\\nimport torch\\nfrom task import input_t, output_t\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n \\"\\"\\"\\n Reference implementation of histogram using PyTorch.\\n Args:\\n data: tensor of shape (size,)\\n Returns:\\n Tensor containing bin counts\\n \\"\\"\\"\\n # Count values in each bin\\n return torch.bincount(data, minlength=256)\\n\\n\\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\\n \\"\\"\\"\\n Generates random input tensor for histogram.\\n\\n Args:\\n size: Size of the input tensor (must be multiple of 16)\\n contention: float in [0, 100], specifying the percentage of identical values\\n seed: Random seed\\n Returns:\\n The input tensor with values in [0, 255]\\n \\"\\"\\"\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n \\n # Generate integer values between 0 and 256\\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\\n\\n # make one value appear quite often, increasing the chance for atomic contention\\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\\n data[evil_loc] = evil_value\\n\\n return data.contiguous()\\n\\n\\ndef check_implementation(data, output):\\n expected = ref_kernel(data)\\n reasons = verbose_allequal(output, expected)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n", "submission.py": "@SUBMISSION@"}, "tests": [{"seed": 9991, "size": 5120, "contention": 10}, {"seed": 2105, "size": 7840, "contention": 10}, {"seed": 9999, "size": 30080, "contention": 10}, {"seed": 4254, "size": 30080, "contention": 90}, {"seed": 1212, "size": 100000, "contention": 10}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"seed": 6252, "size": 1310720, "contention": 10}, {"seed": 8841, "size": 2621440, "contention": 10}, {"seed": 3411, "size": 2621440, "contention": 40}, {"seed": 8753, "size": 2621440, "contention": 90}, {"seed": 6252, "size": 5242880, "contention": 10}, {"seed": 8841, "size": 10485760, "contention": 10}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279720996929577 186241933 Implement a histogram kernel that counts the number of elements falling into each bin across the specified range.\nThe minimum and maximum values of the range are fixed to 0 and 100 respectively.\nAll sizes are multiples of 16 and the number of bins is set to the size of the input tensor divided by 16.\n\nInput:\n - data: a tensor of shape (size,)\n -342 matmul 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "import torch\\nfrom typing import TypeVar, TypedDict\\n\\ninput_t = TypeVar(\\"input_t\\", bound=tuple[torch.Tensor, torch.Tensor])\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor)\\n\\nclass TestSpec(TypedDict):\\n m: int\\n n: int\\n k: int\\n seed: int\\n", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "import torch\\nfrom task import input_t, output_t\\nfrom utils import make_match_reference\\n\\n\\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\\n a.uniform_(0, 1, generator=gen)\\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\\n b.uniform_(0, 1, generator=gen)\\n return (a, b)\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n a, b = data\\n return a @ b\\n\\n\\ncheck_implementation = make_match_reference(ref_kernel)\\n", "submission.py": "@SUBMISSION@"}, "tests": [{"k": 64, "m": 64, "n": 64, "seed": 53124}, {"k": 128, "m": 128, "n": 128, "seed": 3321}, {"k": 256, "m": 256, "n": 256, "seed": 1200}, {"k": 32, "m": 32, "n": 512, "seed": 32523}, {"k": 64, "m": 64, "n": 1024, "seed": 4327}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"k": 128, "m": 128, "n": 128, "seed": 43214}, {"k": 256, "m": 256, "n": 256, "seed": 423011}, {"k": 512, "m": 512, "n": 512, "seed": 123456}, {"k": 1024, "m": 1024, "n": 1024, "seed": 1029}, {"k": 2048, "m": 2048, "n": 2048, "seed": 75342}, {"k": 1024, "m": 1024, "n": 1536, "seed": 321}, {"k": 2048, "m": 2048, "n": 3072, "seed": 32412}, {"k": 4096, "m": 4096, "n": 5120, "seed": 123456}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279724021026939 818466997 Implement a custom matmul function that matches the reference implementation.\nThe function should handle a tuple of input tensors and apply matmul\nThe shapes of all outer and inner dimensions of tensors are multiples of 16\n -343 prefixsum 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "from typing import TypedDict, TypeVar\\nimport torch\\n\\ninput_t = TypeVar(\\"input_t\\", bound=torch.Tensor)\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor)\\n\\nclass TestSpec(TypedDict):\\n size: int\\n seed: int ", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "from utils import match_reference\\nimport torch\\nfrom task import input_t, output_t\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n \\"\\"\\"\\n Reference implementation of inclusive prefix sum using PyTorch.\\n Args:\\n data: Input tensor to compute prefix sum on\\n Returns:\\n Tensor containing the inclusive prefix sum\\n \\"\\"\\"\\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\\n\\n\\ndef generate_input(size: int, seed: int) -> input_t:\\n \\"\\"\\"\\n Generates random input tensor.\\n Returns:\\n Tensor to compute prefix sum on\\n \\"\\"\\"\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\\n\\n\\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\\n# The tolerance is scaled by the square root of the input size\\ndef check_implementation(data: input_t, output: output_t) -> str:\\n # Then get the size for scaling the tolerance\\n n = data.numel()\\n \\n scale_factor = n ** 0.5 # Square root of input size\\n rtol = 1e-5 * scale_factor\\n atol = 1e-5 * scale_factor\\n\\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\\n", "submission.py": "@SUBMISSION@"}, "tests": [{"seed": 4242, "size": 1023}, {"seed": 5236, "size": 1024}, {"seed": 1001, "size": 1025}, {"seed": 5531, "size": 2048}, {"seed": 9173, "size": 4096}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"seed": 12345, "size": 262144}, {"seed": 67890, "size": 524288}, {"seed": 13579, "size": 1048576}, {"seed": 24680, "size": 2097152}, {"seed": 35791, "size": 4194304}, {"seed": 46802, "size": 8388608}, {"seed": 57913, "size": 16777216}, {"seed": 68024, "size": 33554432}, {"seed": 79135, "size": 67108864}, {"seed": 80246, "size": 134217728}, {"seed": 91357, "size": 268435456}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279728202874890 2028323019 Implement an inclusive prefix sum (scan) kernel that matches the reference implementation.\nThe kernel should compute the cumulative sum of all elements up to each position.\nBecause of numerical instability, the tolerance is scaled by the square root of the input size.\n\nInput:\n- `data`: A 1D tensor of size `n`\nOutput:\n- `output`: A 1D tensor of size `n`\n -\. - - --- --- Data for Name: runs; Type: TABLE DATA; Schema: leaderboard; Owner: - --- - -COPY leaderboard.runs (id, submission_id, start_time, end_time, mode, secret, runner, score, passed, compilation, meta, result, system_info) FROM stdin; -124 19 2025-02-23 10:35:45.16001-08 2025-02-23 09:11:28.790124-08 test f H100 \N t \N \N \N {} -125 19 2025-02-23 10:03:43.960257-08 2025-02-23 10:50:24.236204-08 benchmark f H100 \N t \N \N \N {} -126 19 2025-02-23 09:41:46.565827-08 2025-02-23 09:15:24.849129-08 leaderboard f H100 0.006118796 t \N \N \N {} -204 36 2025-02-23 12:17:55.397587-08 2025-02-23 12:19:38.507867-08 benchmark f A100 \N t \N \N \N {} -946 278 2025-02-24 14:44:53.850686-08 2025-02-24 14:00:11.854228-08 test f L4 \N t \N \N \N {} -205 36 2025-02-23 12:02:10.362107-08 2025-02-23 11:12:04.114552-08 benchmark f L4 \N t \N \N \N {} -206 36 2025-02-23 11:00:03.586181-08 2025-02-23 11:24:17.467616-08 benchmark f T4 \N t \N \N \N {} -207 37 2025-02-23 11:17:23.516624-08 2025-02-23 11:31:53.132068-08 test t H100 \N t \N \N \N {} -5225 1609 2025-03-06 07:56:36.142637-08 2025-03-06 06:19:05.979588-08 benchmark f T4 \N f \N \N \N {} -208 37 2025-02-23 11:26:57.890844-08 2025-02-23 11:52:24.509388-08 benchmark t H100 \N t \N \N \N {} -209 37 2025-02-23 12:35:01.552626-08 2025-02-23 11:29:30.650285-08 leaderboard t H100 0.012353844333333334 t \N \N \N {} -210 37 2025-02-23 11:15:04.963959-08 2025-02-23 11:05:51.028191-08 test f A100 \N t \N \N \N {} -3564 1210 2025-02-28 11:17:29.229928-08 2025-02-28 10:53:05.57324-08 leaderboard t A100 0.0030784606666666666 t \N \N \N {} -211 37 2025-02-23 12:02:46.792172-08 2025-02-23 11:06:28.086792-08 benchmark f A100 \N t \N \N \N {} -212 37 2025-02-23 12:10:16.775337-08 2025-02-23 11:11:59.763695-08 leaderboard f A100 0.018901413333333332 t \N \N \N {} -213 37 2025-02-23 11:16:17.316289-08 2025-02-23 11:02:40.657232-08 test f L4 \N t \N \N \N {} -214 37 2025-02-23 11:03:49.41155-08 2025-02-23 11:59:30.243303-08 benchmark f L4 \N t \N \N \N {} -215 37 2025-02-23 12:39:40.437807-08 2025-02-23 11:20:57.604522-08 leaderboard f L4 0.018757093833333332 t \N \N \N {} -217 37 2025-02-23 12:34:49.874051-08 2025-02-23 11:12:01.835801-08 benchmark t T4 \N t \N \N \N {} -218 37 2025-02-23 12:40:43.031117-08 2025-02-23 11:29:51.197423-08 leaderboard t T4 0.03179523833333333 t \N \N \N {} -219 37 2025-02-23 11:14:46.019525-08 2025-02-23 11:54:59.698254-08 test t A100 \N t \N \N \N {} -221 37 2025-02-23 11:25:33.405411-08 2025-02-23 12:18:02.508921-08 leaderboard t A100 0.018848922333333334 t \N \N \N {} -222 37 2025-02-23 11:58:16.947847-08 2025-02-23 12:21:51.3522-08 test t L4 \N t \N \N \N {} -223 37 2025-02-23 10:58:25.479183-08 2025-02-23 11:47:34.593766-08 benchmark t L4 \N t \N \N \N {} -224 37 2025-02-23 10:49:41.805569-08 2025-02-23 11:56:17.24445-08 leaderboard t L4 0.01939468366666667 t \N \N \N {} -226 37 2025-02-23 12:22:30.275578-08 2025-02-23 11:48:36.047752-08 benchmark f T4 \N t \N \N \N {} -227 37 2025-02-23 12:40:05.515776-08 2025-02-23 10:58:56.313103-08 leaderboard f T4 0.030903404 t \N \N \N {} -228 37 2025-02-23 11:04:15.184419-08 2025-02-23 11:00:17.817738-08 test f H100 \N t \N \N \N {} -229 37 2025-02-23 12:35:53.114693-08 2025-02-23 10:57:25.119918-08 benchmark f H100 \N t \N \N \N {} -230 37 2025-02-23 11:23:12.875657-08 2025-02-23 10:51:41.667717-08 leaderboard f H100 0.012353275333333334 t \N \N \N {} -231 38 2025-02-23 11:50:25.46655-08 2025-02-23 11:45:34.668208-08 test f T4 \N f \N \N \N {} -232 39 2025-02-23 12:32:41.932731-08 2025-02-23 10:59:56.084677-08 test f T4 \N f \N \N \N {} -259 54 2025-02-23 13:17:30.33764-08 2025-02-23 13:46:28.669109-08 test t T4 \N t \N \N \N {} -233 40 2025-02-23 11:29:02.58055-08 2025-02-23 12:36:20.107203-08 test f T4 \N f \N \N \N {} -234 41 2025-02-23 11:36:32.996867-08 2025-02-23 12:51:08.480576-08 test f T4 \N f \N \N \N {} -240 47 2025-02-23 11:50:55.177428-08 2025-02-23 12:30:28.807619-08 test f T4 \N f \N \N \N {} -241 48 2025-02-23 13:23:28.109642-08 2025-02-23 11:57:04.295155-08 test f T4 \N f \N \N \N {} -242 49 2025-02-23 11:53:10.397813-08 2025-02-23 12:03:03.783486-08 test f T4 \N f \N \N \N {} -1276 427 2025-02-24 22:41:37.072571-08 2025-02-24 23:30:11.670458-08 test t H100 \N t \N \N \N {} -246 53 2025-02-23 13:05:55.652535-08 2025-02-23 13:14:14.362137-08 benchmark f T4 \N t \N \N \N {} -254 54 2025-02-23 11:58:10.69175-08 2025-02-23 12:34:56.201415-08 benchmark f L4 \N t \N \N \N {} -255 54 2025-02-23 12:05:59.80503-08 2025-02-23 12:05:35.639007-08 leaderboard f L4 0.01764701466666667 t \N \N \N {} -256 54 2025-02-23 13:16:11.769925-08 2025-02-23 13:35:56.018887-08 test f A100 \N t \N \N \N {} -257 54 2025-02-23 12:06:54.552569-08 2025-02-23 12:27:58.339221-08 benchmark f A100 \N t \N \N \N {} -258 54 2025-02-23 11:52:48.189654-08 2025-02-23 13:04:58.541644-08 leaderboard f A100 0.0032710223333333333 t \N \N \N {} -260 54 2025-02-23 13:00:18.36019-08 2025-02-23 12:08:18.086439-08 benchmark t T4 \N t \N \N \N {} -261 54 2025-02-23 12:48:01.153003-08 2025-02-23 13:08:30.274831-08 leaderboard t T4 0.017266412 t \N \N \N {} -262 54 2025-02-23 12:11:01.579205-08 2025-02-23 11:58:34.491578-08 test t H100 \N t \N \N \N {} -263 54 2025-02-23 13:16:28.788531-08 2025-02-23 13:00:17.556954-08 benchmark t H100 \N t \N \N \N {} -264 54 2025-02-23 12:04:25.422389-08 2025-02-23 13:13:19.248136-08 leaderboard t H100 0.001458645875 t \N \N \N {} -265 54 2025-02-23 12:31:12.419543-08 2025-02-23 13:09:45.606598-08 test f T4 \N t \N \N \N {} -266 54 2025-02-23 12:40:46.985253-08 2025-02-23 13:34:24.451784-08 benchmark f T4 \N t \N \N \N {} -267 54 2025-02-23 13:25:58.537497-08 2025-02-23 12:54:43.007543-08 leaderboard f T4 0.01723258033333333 t \N \N \N {} -269 54 2025-02-23 12:20:20.316186-08 2025-02-23 13:13:12.18701-08 benchmark t L4 \N t \N \N \N {} -270 54 2025-02-23 13:11:14.571589-08 2025-02-23 12:58:35.784449-08 leaderboard t L4 0.017926125272727272 t \N \N \N {} -271 54 2025-02-23 12:33:04.814186-08 2025-02-23 12:38:40.126485-08 test f H100 \N t \N \N \N {} -272 54 2025-02-23 13:50:03.91953-08 2025-02-23 13:25:15.447661-08 benchmark f H100 \N t \N \N \N {} -273 54 2025-02-23 13:10:29.552929-08 2025-02-23 13:26:17.441886-08 leaderboard f H100 0.0010422076999999999 t \N \N \N {} -274 55 2025-02-23 13:01:35.147956-08 2025-02-23 13:02:44.097619-08 test f T4 \N t \N \N \N {} -275 56 2025-02-23 12:44:20.029345-08 2025-02-23 14:21:48.402951-08 test f T4 \N f \N \N \N {} -276 57 2025-02-23 12:54:37.579254-08 2025-02-23 13:41:28.569725-08 test f T4 \N t \N \N \N {} -314 77 2025-02-23 14:22:31.876974-08 2025-02-23 13:58:03.675484-08 test t H100 \N t \N \N \N {} -345 91 2025-02-23 18:28:50.540756-08 2025-02-23 18:30:32.69747-08 test f H100 \N f \N \N \N {} -277 58 2025-02-23 14:27:11.52301-08 2025-02-23 12:39:45.802969-08 benchmark f H100 \N t \N \N \N {} -278 59 2025-02-23 14:36:38.592486-08 2025-02-23 14:35:07.207052-08 test f T4 \N f \N \N \N {} -279 60 2025-02-23 13:55:19.127155-08 2025-02-23 14:12:12.400517-08 benchmark f T4 \N f \N \N \N {} -280 61 2025-02-23 13:18:07.578575-08 2025-02-23 14:55:51.403557-08 test f T4 \N f \N \N \N {} -320 78 2025-02-23 14:11:00.190481-08 2025-02-23 15:16:48.880961-08 test f H100 \N t \N \N \N {} -321 78 2025-02-23 14:03:20.036503-08 2025-02-23 14:23:37.916867-08 benchmark f H100 \N t \N \N \N {} -322 78 2025-02-23 15:36:20.056643-08 2025-02-23 15:34:57.100941-08 leaderboard f H100 0.001428631625 t \N \N \N {} -324 79 2025-02-23 15:02:01.218849-08 2025-02-23 15:47:08.188349-08 benchmark f H100 \N t \N \N \N {} -325 79 2025-02-23 14:41:07.330032-08 2025-02-23 15:38:15.89614-08 leaderboard f H100 0.0014191957142857144 t \N \N \N {} -326 79 2025-02-23 14:41:13.012481-08 2025-02-23 15:16:33.618694-08 test t H100 \N t \N \N \N {} -328 79 2025-02-23 14:27:28.406633-08 2025-02-23 15:30:01.543516-08 leaderboard t H100 0.0014211721666666666 t \N \N \N {} -329 80 2025-02-23 15:07:57.673532-08 2025-02-23 14:03:31.284187-08 test t H100 \N t \N \N \N {} -330 80 2025-02-23 13:57:07.60172-08 2025-02-23 13:55:16.031154-08 benchmark t H100 \N t \N \N \N {} -331 80 2025-02-23 14:45:52.647508-08 2025-02-23 14:51:00.631073-08 leaderboard t H100 0.0014186101666666668 t \N \N \N {} -333 80 2025-02-23 15:21:15.153944-08 2025-02-23 14:32:10.166808-08 benchmark f H100 \N t \N \N \N {} -334 80 2025-02-23 15:11:41.57439-08 2025-02-23 14:59:52.063767-08 leaderboard f H100 0.0014430232 t \N \N \N {} -4353 1425 2025-03-01 19:49:30.705637-08 2025-03-01 19:52:46.274107-08 leaderboard t A100 0.003362815 t \N \N \N {} -341 87 2025-02-23 18:32:19.084216-08 2025-02-23 18:24:30.655121-08 test f L4 \N f \N \N \N {} -342 88 2025-02-23 18:14:30.40463-08 2025-02-23 17:59:25.451214-08 test f H100 \N f \N \N \N {} -343 89 2025-02-23 17:38:34.140327-08 2025-02-23 17:53:30.915891-08 test f H100 \N f \N \N \N {} -344 90 2025-02-23 18:21:47.199663-08 2025-02-23 17:59:54.220257-08 test f H100 \N f \N \N \N {} -409 115 2025-02-23 19:35:25.852763-08 2025-02-23 18:57:39.125632-08 test f T4 \N t \N \N \N {} -575 162 2025-02-24 04:19:29.964217-08 2025-02-24 03:34:20.053499-08 benchmark t L4 \N t \N \N \N {} -346 92 2025-02-23 17:54:16.477409-08 2025-02-23 18:50:22.606457-08 test f H100 \N f \N \N \N {} -347 93 2025-02-23 18:25:21.117234-08 2025-02-23 18:07:32.76191-08 test t H100 \N t \N \N \N {} -348 93 2025-02-23 18:44:24.03074-08 2025-02-23 17:50:35.187274-08 benchmark t H100 \N t \N \N \N {} -349 93 2025-02-23 18:52:30.803751-08 2025-02-23 18:08:51.653673-08 leaderboard t H100 0.011944822666666665 t \N \N \N {} -350 93 2025-02-23 17:33:45.219692-08 2025-02-23 18:09:19.103073-08 test f H100 \N t \N \N \N {} -127 19 2025-02-23 09:52:43.386488-08 2025-02-23 09:04:44.918935-08 test t H100 \N t \N \N \N {} -128 19 2025-02-23 10:19:49.22057-08 2025-02-23 10:18:08.314956-08 benchmark t H100 \N t \N \N \N {} -129 19 2025-02-23 09:19:50.98444-08 2025-02-23 09:12:23.909584-08 leaderboard t H100 0.006109189 t \N \N \N {} -351 93 2025-02-23 18:02:58.08203-08 2025-02-23 19:27:48.608126-08 benchmark f H100 \N t \N \N \N {} -352 93 2025-02-23 17:35:41.478368-08 2025-02-23 17:43:26.596456-08 leaderboard f H100 0.0121398512 t \N \N \N {} -353 94 2025-02-23 18:01:28.888692-08 2025-02-23 18:25:10.085679-08 test t H100 \N t \N \N \N {} -355 94 2025-02-23 19:24:29.747081-08 2025-02-23 17:41:44.084312-08 leaderboard t H100 0.011895692333333334 t \N \N \N {} -356 94 2025-02-23 18:29:05.574286-08 2025-02-23 19:30:40.100513-08 test f H100 \N t \N \N \N {} -357 94 2025-02-23 19:00:06.086828-08 2025-02-23 19:27:29.302545-08 benchmark f H100 \N t \N \N \N {} -360 96 2025-02-23 18:52:27.921108-08 2025-02-23 19:32:10.278467-08 benchmark f H100 \N t \N \N \N {} -362 97 2025-02-23 18:43:11.382309-08 2025-02-23 18:32:53.162246-08 benchmark t H100 \N t \N \N \N {} -363 97 2025-02-23 18:08:22.457997-08 2025-02-23 18:01:35.677657-08 leaderboard t H100 0.0015710386666666668 t \N \N \N {} -364 97 2025-02-23 18:00:30.642191-08 2025-02-23 19:31:22.809425-08 test f H100 \N t \N \N \N {} -365 97 2025-02-23 19:12:44.951064-08 2025-02-23 17:52:24.96366-08 benchmark f H100 \N t \N \N \N {} -366 97 2025-02-23 18:34:04.98843-08 2025-02-23 19:32:44.684641-08 leaderboard f H100 0.0015707563333333333 t \N \N \N {} -370 99 2025-02-23 19:47:48.997469-08 2025-02-23 19:43:46.393992-08 leaderboard t H100 0.0014419603333333333 t \N \N \N {} -419 123 2025-02-23 20:03:41.74647-08 2025-02-23 19:48:21.795119-08 test f L4 \N t \N \N \N {} -372 99 2025-02-23 19:38:48.275786-08 2025-02-23 18:15:04.56295-08 benchmark f H100 \N t \N \N \N {} -373 99 2025-02-23 19:12:46.215077-08 2025-02-23 18:59:54.371176-08 leaderboard f H100 0.001431599 t \N \N \N {} -374 100 2025-02-23 19:55:52.569939-08 2025-02-23 19:27:50.142717-08 benchmark f H100 \N t \N \N \N {} -375 101 2025-02-23 18:47:52.05672-08 2025-02-23 18:49:29.177973-08 benchmark f H100 \N f \N \N \N {} -376 103 2025-02-23 19:50:51.240681-08 2025-02-23 19:03:51.348191-08 benchmark f T4 \N t \N \N \N {} -402 107 2025-02-23 19:56:20.721626-08 2025-02-23 19:44:53.872926-08 test f H100 \N f \N \N \N {} -381 104 2025-02-23 18:11:03.43025-08 2025-02-23 18:08:57.029995-08 test t A100 \N t \N \N \N {} -382 104 2025-02-23 18:46:23.802647-08 2025-02-23 18:27:06.148219-08 benchmark t A100 \N t \N \N \N {} -383 104 2025-02-23 20:01:50.376892-08 2025-02-23 19:53:06.069857-08 leaderboard t A100 0.0017416949090909091 t \N \N \N {} -384 104 2025-02-23 18:19:47.990746-08 2025-02-23 19:28:16.772556-08 test f A100 \N t \N \N \N {} -386 104 2025-02-23 18:12:18.644326-08 2025-02-23 19:34:19.590964-08 leaderboard f A100 0.001762813 t \N \N \N {} -387 104 2025-02-23 19:49:03.622017-08 2025-02-23 18:55:44.844842-08 test t H100 \N t \N \N \N {} -388 104 2025-02-23 19:24:20.239368-08 2025-02-23 19:08:05.533919-08 benchmark t H100 \N t \N \N \N {} -389 104 2025-02-23 20:02:50.775118-08 2025-02-23 19:19:38.596389-08 leaderboard t H100 0.001051997 t \N \N \N {} -390 104 2025-02-23 19:55:47.925265-08 2025-02-23 18:30:53.620534-08 test f T4 \N t \N \N \N {} -391 104 2025-02-23 19:19:44.079247-08 2025-02-23 19:10:46.577196-08 benchmark f T4 \N t \N \N \N {} -392 104 2025-02-23 19:52:00.493107-08 2025-02-23 19:03:38.169538-08 leaderboard f T4 0.009289433666666666 t \N \N \N {} -393 104 2025-02-23 18:24:57.464605-08 2025-02-23 20:02:29.61335-08 test f L4 \N t \N \N \N {} -582 167 2025-02-24 03:48:55.014484-08 2025-02-24 05:19:01.867475-08 test f H100 \N f \N \N \N {} -3574 1220 2025-02-28 13:45:47.445988-08 2025-02-28 13:55:19.067931-08 benchmark f A100 \N t \N \N \N {} -605 172 2025-02-24 05:12:35.084465-08 2025-02-24 03:53:41.553176-08 leaderboard f T4 0.017926890833333334 t \N \N \N {} -607 172 2025-02-24 05:35:11.303477-08 2025-02-24 04:35:10.682311-08 benchmark f L4 \N t \N \N \N {} -608 172 2025-02-24 05:14:02.764614-08 2025-02-24 05:32:57.132512-08 leaderboard f L4 0.017603741333333332 t \N \N \N {} -616 174 2025-02-24 04:08:35.184962-08 2025-02-24 04:11:19.105749-08 benchmark f A100 \N t \N \N \N {} -617 174 2025-02-24 04:45:50.307802-08 2025-02-24 04:49:07.034774-08 leaderboard f A100 0.003163628 t \N \N \N {} -618 174 2025-02-24 05:05:10.997982-08 2025-02-24 04:21:07.467177-08 test t H100 \N t \N \N \N {} -619 174 2025-02-24 04:49:07.394184-08 2025-02-24 05:36:35.84385-08 benchmark t H100 \N t \N \N \N {} -620 174 2025-02-24 03:56:43.871806-08 2025-02-24 05:06:06.267042-08 leaderboard t H100 0.0014537886666666667 t \N \N \N {} -621 174 2025-02-24 04:11:26.341666-08 2025-02-24 05:44:14.091621-08 test t A100 \N t \N \N \N {} -622 174 2025-02-24 05:03:36.072136-08 2025-02-24 04:51:58.063144-08 benchmark t A100 \N t \N \N \N {} -623 174 2025-02-24 04:20:01.163633-08 2025-02-24 03:57:19.043219-08 leaderboard t A100 0.0031645186666666666 t \N \N \N {} -690 217 2025-02-24 07:33:26.518857-08 2025-02-24 07:07:15.672567-08 test f A100 \N f \N \N \N {} -630 174 2025-02-24 04:46:32.413079-08 2025-02-24 05:37:46.353976-08 test f T4 \N t \N \N \N {} -631 174 2025-02-24 04:08:35.774251-08 2025-02-24 04:13:23.290244-08 benchmark f T4 \N t \N \N \N {} -632 174 2025-02-24 05:26:47.831146-08 2025-02-24 05:29:05.035813-08 leaderboard f T4 0.02047257833333333 t \N \N \N {} -3277 1144 2025-02-28 05:40:20.632509-08 2025-02-28 06:23:09.782086-08 leaderboard f A100 0.0030818813333333334 t \N \N \N {} -680 211 2025-02-24 06:16:43.935048-08 2025-02-24 05:24:09.037312-08 benchmark t H100 \N t \N \N \N {} -681 211 2025-02-24 06:20:50.131308-08 2025-02-24 06:19:33.50419-08 leaderboard t H100 0.00343199925 t \N \N \N {} -682 211 2025-02-24 06:04:20.742817-08 2025-02-24 06:51:37.437888-08 test f H100 \N t \N \N \N {} -725 231 2025-02-24 08:33:34.394553-08 2025-02-24 07:52:30.592827-08 test f T4 \N t \N \N \N {} -683 211 2025-02-24 05:48:41.277846-08 2025-02-24 06:20:29.451233-08 benchmark f H100 \N t \N \N \N {} -684 211 2025-02-24 05:40:28.316963-08 2025-02-24 05:24:29.585484-08 leaderboard f H100 0.0034547503333333335 t \N \N \N {} -685 212 2025-02-24 07:16:11.487533-08 2025-02-24 05:49:31.110402-08 benchmark f H100 \N t \N \N \N {} -707 225 2025-02-24 07:34:13.86609-08 2025-02-24 09:25:38.673749-08 leaderboard t A100 0.010140606949999999 t \N \N \N {} -713 229 2025-02-24 08:36:51.707477-08 2025-02-24 09:19:35.205825-08 leaderboard t A100 0.004042308666666667 t \N \N \N {} -715 229 2025-02-24 08:59:14.990632-08 2025-02-24 08:40:06.19623-08 benchmark f A100 \N t \N \N \N {} -1002 289 2025-02-24 16:17:52.085682-08 2025-02-24 15:49:52.549542-08 leaderboard t T4 0.017548657333333332 t \N \N \N {} -1004 289 2025-02-24 15:49:21.702779-08 2025-02-24 15:56:44.290148-08 benchmark f H100 \N t \N \N \N {} -1005 289 2025-02-24 16:30:57.115003-08 2025-02-24 16:53:11.026432-08 leaderboard f H100 0.0014277393999999998 t \N \N \N {} -1006 289 2025-02-24 17:08:54.259849-08 2025-02-24 16:01:18.083591-08 test f T4 \N t \N \N \N {} -1012 289 2025-02-24 15:50:44.700014-08 2025-02-24 16:13:26.646399-08 test t L4 \N t \N \N \N {} -2411 812 2025-02-26 11:55:00.140651-08 2025-02-26 12:25:31.245914-08 test t T4 \N f \N \N \N {} -1474 491 2025-02-25 04:38:47.881649-08 2025-02-25 06:09:17.695829-08 test f A100 \N t \N \N \N {} -1477 492 2025-02-25 05:11:27.321383-08 2025-02-25 05:35:34.773378-08 test t H100 \N t \N \N \N {} -1507 504 2025-02-25 07:20:39.230369-08 2025-02-25 07:49:03.830451-08 test f A100 \N t \N \N \N {} -1018 290 2025-02-24 17:13:55.431038-08 2025-02-24 16:41:27.422869-08 test f A100 \N f \N \N \N {} -1019 290 2025-02-24 16:59:40.514483-08 2025-02-24 15:54:21.408908-08 test t H100 \N f \N \N \N {} -1020 290 2025-02-24 16:35:12.120375-08 2025-02-24 17:02:25.401972-08 test t A100 \N f \N \N \N {} -1329 443 2025-02-25 01:04:39.585597-08 2025-02-25 02:46:51.917328-08 benchmark f A100 \N t \N \N \N {} -1330 443 2025-02-25 01:21:21.17285-08 2025-02-25 02:34:21.089915-08 leaderboard f A100 0.0031880928 t \N \N \N {} -1032 291 2025-02-24 17:02:48.537924-08 2025-02-24 16:42:54.273152-08 test t H100 \N t \N \N \N {} -1033 291 2025-02-24 17:18:28.789252-08 2025-02-24 17:23:51.01374-08 benchmark t H100 \N t \N \N \N {} -6109 1847 2025-03-11 00:39:56.642025-07 2025-03-11 00:31:13.424537-07 benchmark f A100 \N f \N \N \N {} -1034 291 2025-02-24 17:27:21.544186-08 2025-02-24 16:17:02.165474-08 leaderboard t H100 0.0014878653333333333 t \N \N \N {} -1035 291 2025-02-24 16:34:37.159795-08 2025-02-24 16:06:45.714048-08 test t L4 \N t \N \N \N {} -1047 291 2025-02-24 17:36:54.22773-08 2025-02-24 17:03:45.859127-08 test f L4 \N t \N \N \N {} -1480 492 2025-02-25 06:19:11.982018-08 2025-02-25 06:17:28.318413-08 test f H100 \N t \N \N \N {} -1036 291 2025-02-24 17:54:34.398457-08 2025-02-24 16:41:28.568693-08 benchmark t L4 \N t \N \N \N {} -1037 291 2025-02-24 16:38:12.362104-08 2025-02-24 16:19:38.183305-08 leaderboard t L4 0.017247674666666667 t \N \N \N {} -1038 291 2025-02-24 16:24:02.713537-08 2025-02-24 16:41:50.09925-08 test f H100 \N t \N \N \N {} -1039 291 2025-02-24 17:42:26.593225-08 2025-02-24 17:17:44.711243-08 benchmark f H100 \N t \N \N \N {} -1040 291 2025-02-24 17:17:51.955383-08 2025-02-24 17:24:28.626898-08 leaderboard f H100 0.001511652 t \N \N \N {} -1379 462 2025-02-25 04:30:12.6402-08 2025-02-25 03:17:09.15611-08 benchmark f H100 \N t \N \N \N {} -1042 291 2025-02-24 17:37:57.142995-08 2025-02-24 17:18:33.682684-08 benchmark t T4 \N t \N \N \N {} -1043 291 2025-02-24 17:05:51.220243-08 2025-02-24 17:10:08.13623-08 leaderboard t T4 0.017371874333333332 t \N \N \N {} -1044 291 2025-02-24 17:06:11.858432-08 2025-02-24 16:33:50.751909-08 test f T4 \N t \N \N \N {} -1045 291 2025-02-24 16:16:44.982001-08 2025-02-24 16:31:49.425344-08 benchmark f T4 \N t \N \N \N {} -1046 291 2025-02-24 17:12:14.548228-08 2025-02-24 16:41:40.82905-08 leaderboard f T4 0.017367958 t \N \N \N {} -1380 463 2025-02-25 03:45:25.261332-08 2025-02-25 04:20:54.913035-08 benchmark f H100 \N t \N \N \N {} -2380 802 2025-02-26 12:30:03.429377-08 2025-02-26 11:34:34.394213-08 benchmark f A100 \N f \N \N \N {} -1048 291 2025-02-24 16:19:24.170092-08 2025-02-24 17:06:26.862153-08 benchmark f L4 \N t \N \N \N {} -1049 291 2025-02-24 15:56:27.762751-08 2025-02-24 17:05:19.405224-08 leaderboard f L4 0.017399107333333334 t \N \N \N {} -1050 292 2025-02-24 17:52:15.055926-08 2025-02-24 17:44:34.698152-08 test f A100 \N t \N \N \N {} -1051 292 2025-02-24 16:22:47.842013-08 2025-02-24 17:43:41.94016-08 benchmark f A100 \N t \N \N \N {} -1052 292 2025-02-24 17:08:31.329041-08 2025-02-24 16:04:23.906272-08 leaderboard f A100 0.0031465863333333334 t \N \N \N {} -1381 464 2025-02-25 04:30:12.200923-08 2025-02-25 04:46:17.323875-08 test f H100 \N t \N \N \N {} -1510 504 2025-02-25 07:50:24.939578-08 2025-02-25 07:14:47.363201-08 test t A100 \N t \N \N \N {} -1053 292 2025-02-24 17:46:58.491665-08 2025-02-24 17:43:14.530104-08 test t A100 \N t \N \N \N {} -1054 292 2025-02-24 16:28:07.677678-08 2025-02-24 17:17:05.064075-08 benchmark t A100 \N t \N \N \N {} -1055 292 2025-02-24 17:20:28.611387-08 2025-02-24 16:25:56.736329-08 leaderboard t A100 0.003155266 t \N \N \N {} -1056 292 2025-02-24 16:22:44.412346-08 2025-02-24 16:35:31.252286-08 test t L4 \N t \N \N \N {} -1057 292 2025-02-24 17:51:06.9878-08 2025-02-24 16:57:56.1698-08 benchmark t L4 \N t \N \N \N {} -1384 464 2025-02-25 04:01:57.357806-08 2025-02-25 04:32:07.486918-08 test t H100 \N t \N \N \N {} -1485 495 2025-02-25 07:02:18.828551-08 2025-02-25 05:31:13.315806-08 test t A100 \N f \N \N \N {} -1058 292 2025-02-24 16:12:14.175483-08 2025-02-24 16:19:24.281562-08 leaderboard t L4 0.017181366666666666 t \N \N \N {} -1059 292 2025-02-24 16:45:08.313765-08 2025-02-24 16:24:12.371964-08 test f H100 \N t \N \N \N {} -1060 292 2025-02-24 16:57:23.927277-08 2025-02-24 17:45:53.131411-08 benchmark f H100 \N t \N \N \N {} -1387 465 2025-02-25 03:17:52.9791-08 2025-02-25 04:50:46.975041-08 test t H100 \N t \N \N \N {} -1063 292 2025-02-24 17:43:12.741558-08 2025-02-24 16:54:56.492661-08 benchmark t T4 \N t \N \N \N {} -1064 292 2025-02-24 16:36:30.649022-08 2025-02-24 16:31:15.555557-08 leaderboard t T4 0.016662345666666665 t \N \N \N {} -6110 1848 2025-03-11 02:24:40.439817-07 2025-03-11 01:49:27.022226-07 benchmark f A100 \N f \N \N \N {} -1065 292 2025-02-24 17:01:31.487312-08 2025-02-24 16:08:17.982794-08 test f T4 \N t \N \N \N {} -1066 292 2025-02-24 17:34:24.686206-08 2025-02-24 16:15:54.00558-08 benchmark f T4 \N t \N \N \N {} -1067 292 2025-02-24 16:25:45.909582-08 2025-02-24 17:35:16.325804-08 leaderboard f T4 0.016692271666666664 t \N \N \N {} -1089 300 2025-02-24 17:15:39.013548-08 2025-02-24 18:07:57.788624-08 test f A100 \N t \N \N \N {} -1390 465 2025-02-25 04:07:56.363414-08 2025-02-25 03:08:55.772199-08 test f H100 \N t \N \N \N {} -1069 292 2025-02-24 16:33:24.005735-08 2025-02-24 17:00:37.977793-08 benchmark f L4 \N t \N \N \N {} -1070 292 2025-02-24 16:11:38.035638-08 2025-02-24 16:35:28.298125-08 leaderboard f L4 0.01725526833333333 t \N \N \N {} -1072 292 2025-02-24 17:49:21.420511-08 2025-02-24 17:39:52.392614-08 benchmark t H100 \N t \N \N \N {} -1073 292 2025-02-24 17:39:03.403534-08 2025-02-24 16:41:35.838976-08 leaderboard t H100 0.00143393225 t \N \N \N {} -1074 293 2025-02-24 16:28:54.883242-08 2025-02-24 17:35:23.368271-08 test f H100 \N t \N \N \N {} -1075 294 2025-02-24 17:12:03.460856-08 2025-02-24 17:11:25.091501-08 benchmark f H100 \N t \N \N \N {} -1076 295 2025-02-24 17:45:00.760378-08 2025-02-24 18:03:30.527064-08 test f H100 \N t \N \N \N {} -3899 1300 2025-03-01 04:59:30.357371-08 2025-03-01 05:19:06.000963-08 benchmark f H100 \N t \N \N \N {} -1083 297 2025-02-24 18:12:12.906661-08 2025-02-24 16:31:51.879538-08 test f A100 \N t \N \N \N {} -1087 300 2025-02-24 17:00:42.01574-08 2025-02-24 17:08:59.120585-08 benchmark t A100 \N t \N \N \N {} -1088 300 2025-02-24 16:54:53.189438-08 2025-02-24 17:03:28.128475-08 leaderboard t A100 0.014111941 t \N \N \N {} -1090 300 2025-02-24 18:18:13.575515-08 2025-02-24 17:44:59.217622-08 benchmark f A100 \N t \N \N \N {} -1101 304 2025-02-24 17:40:45.55189-08 2025-02-24 17:01:05.496887-08 test t H100 \N t \N \N \N {} -1102 304 2025-02-24 18:50:24.116927-08 2025-02-24 18:27:17.991007-08 benchmark t H100 \N t \N \N \N {} -1103 304 2025-02-24 18:56:45.352747-08 2025-02-24 17:39:36.014601-08 leaderboard t H100 0.0036164646666666665 t \N \N \N {} -1104 304 2025-02-24 17:14:31.119701-08 2025-02-24 18:21:08.729398-08 test f T4 \N t \N \N \N {} -1108 304 2025-02-24 17:14:00.044903-08 2025-02-24 17:35:39.490324-08 benchmark t T4 \N t \N \N \N {} -1109 304 2025-02-24 18:01:06.49365-08 2025-02-24 17:46:19.984475-08 leaderboard t T4 0.02827727433333333 t \N \N \N {} -1110 304 2025-02-24 18:55:51.538374-08 2025-02-24 18:30:10.634801-08 test f H100 \N t \N \N \N {} -1112 304 2025-02-24 17:35:18.104482-08 2025-02-24 17:19:54.790912-08 leaderboard f H100 0.003638923 t \N \N \N {} -1115 304 2025-02-24 17:17:06.648489-08 2025-02-24 18:55:20.002642-08 leaderboard t L4 0.027880181666666667 t \N \N \N {} -1116 304 2025-02-24 18:01:36.171802-08 2025-02-24 17:39:40.210678-08 test f L4 \N t \N \N \N {} -1117 304 2025-02-24 17:16:05.293543-08 2025-02-24 17:09:24.002204-08 benchmark f L4 \N t \N \N \N {} -1149 322 2025-02-24 20:09:50.646883-08 2025-02-24 20:09:27.027041-08 benchmark f A100 \N t \N \N \N {} -1150 323 2025-02-24 19:44:17.381273-08 2025-02-24 18:40:51.316233-08 test f H100 \N f \N \N \N {} -1513 505 2025-02-25 06:40:15.410176-08 2025-02-25 07:37:48.715184-08 test f A100 \N t \N \N \N {} -1151 324 2025-02-24 18:48:04.557654-08 2025-02-24 19:42:55.295313-08 benchmark f A100 \N t \N \N \N {} -1152 325 2025-02-24 19:59:18.76177-08 2025-02-24 18:45:50.840944-08 benchmark f A100 \N f \N \N \N {} -1153 326 2025-02-24 18:52:24.053553-08 2025-02-24 19:29:33.646058-08 benchmark f A100 \N t \N \N \N {} -1154 327 2025-02-24 20:15:12.831932-08 2025-02-24 18:46:28.80301-08 benchmark f A100 \N t \N \N \N {} -1165 333 2025-02-24 19:59:10.567094-08 2025-02-24 19:43:21.331396-08 benchmark f A100 \N f \N \N \N {} -1156 329 2025-02-24 20:00:37.855323-08 2025-02-24 19:49:02.655715-08 test f H100 \N t \N \N \N {} -1157 329 2025-02-24 19:16:48.278346-08 2025-02-24 18:35:28.873095-08 benchmark f H100 \N t \N \N \N {} -1158 329 2025-02-24 19:19:35.133313-08 2025-02-24 19:39:48.264665-08 leaderboard f H100 0.0015907006666666668 t \N \N \N {} -1159 329 2025-02-24 20:06:20.184176-08 2025-02-24 19:17:10.273748-08 test t H100 \N t \N \N \N {} -1395 467 2025-02-25 04:01:56.308441-08 2025-02-25 03:40:45.970012-08 test f A100 \N f \N \N \N {} -1162 330 2025-02-24 20:15:53.048254-08 2025-02-24 19:40:58.672546-08 benchmark f A100 \N t \N \N \N {} -1163 331 2025-02-24 19:39:33.765253-08 2025-02-24 20:18:32.015656-08 benchmark f A100 \N f \N \N \N {} -1164 332 2025-02-24 19:57:11.008009-08 2025-02-24 19:45:28.206315-08 test f A100 \N f \N \N \N {} -1396 468 2025-02-25 03:56:27.738846-08 2025-02-25 04:21:51.589822-08 test f A100 \N t \N \N \N {} -1170 338 2025-02-24 20:00:52.228565-08 2025-02-24 19:06:41.353851-08 benchmark f A100 \N f \N \N \N {} -1185 353 2025-02-24 20:23:22.920373-08 2025-02-24 20:31:23.388252-08 benchmark f A100 \N f \N \N \N {} -1172 340 2025-02-24 20:30:32.647454-08 2025-02-24 19:20:53.458575-08 benchmark f A100 \N t \N \N \N {} -1173 341 2025-02-24 19:18:26.427276-08 2025-02-24 20:25:12.965728-08 benchmark f A100 \N t \N \N \N {} -1174 342 2025-02-24 19:16:50.848989-08 2025-02-24 20:26:36.909914-08 benchmark f A100 \N f \N \N \N {} -1399 468 2025-02-25 03:54:55.679208-08 2025-02-25 04:15:06.87232-08 test t A100 \N t \N \N \N {} -1516 505 2025-02-25 07:18:07.594758-08 2025-02-25 07:03:51.966033-08 test t A100 \N t \N \N \N {} -1178 346 2025-02-24 20:46:35.583472-08 2025-02-24 19:47:02.016449-08 benchmark f A100 \N f \N \N \N {} -1179 347 2025-02-24 20:01:38.196152-08 2025-02-24 19:54:49.601721-08 benchmark f A100 \N f \N \N \N {} -1402 469 2025-02-25 03:40:45.982253-08 2025-02-25 03:31:06.961948-08 test f A100 \N t \N \N \N {} -1182 350 2025-02-24 19:20:16.154751-08 2025-02-24 20:48:25.71245-08 benchmark f A100 \N t \N \N \N {} -1183 351 2025-02-24 20:45:34.464741-08 2025-02-24 20:18:14.635844-08 benchmark f H100 \N t \N \N \N {} -1184 352 2025-02-24 20:01:05.338551-08 2025-02-24 20:52:19.857137-08 test f A100 \N f \N \N \N {} -1405 469 2025-02-25 04:58:27.346875-08 2025-02-25 04:51:26.433509-08 test t A100 \N t \N \N \N {} -1519 506 2025-02-25 06:47:13.929749-08 2025-02-25 06:42:22.498667-08 test t H100 \N t \N \N \N {} -1188 358 2025-02-24 19:37:31.122358-08 2025-02-24 20:18:57.150066-08 benchmark f A100 \N f \N \N \N {} -1189 360 2025-02-24 21:06:56.386392-08 2025-02-24 19:27:17.523239-08 benchmark f A100 \N f \N \N \N {} -1190 363 2025-02-24 20:45:31.277208-08 2025-02-24 20:01:17.753967-08 benchmark f A100 \N f \N \N \N {} -1408 470 2025-02-25 03:59:58.320768-08 2025-02-25 04:28:37.828048-08 test t A100 \N t \N \N \N {} -1193 365 2025-02-24 19:43:43.570899-08 2025-02-24 19:39:23.906226-08 benchmark f A100 \N t \N \N \N {} -6140 1882 2025-03-11 10:55:44.95308-07 2025-03-11 11:05:57.417109-07 test t T4 \N f \N \N \N {} -1194 365 2025-02-24 20:08:47.222036-08 2025-02-24 19:30:56.207701-08 leaderboard f A100 0.01920636041666667 t \N \N \N {} -1195 365 2025-02-24 19:51:58.749971-08 2025-02-24 19:45:07.726733-08 test t A100 \N t \N \N \N {} -1196 365 2025-02-24 19:38:48.837266-08 2025-02-24 19:34:14.850955-08 benchmark t A100 \N t \N \N \N {} -1197 365 2025-02-24 21:18:06.454034-08 2025-02-24 20:41:46.182401-08 leaderboard t A100 0.023418701 t \N \N \N {} -1411 470 2025-02-25 03:23:48.300357-08 2025-02-25 04:14:39.867996-08 test f A100 \N t \N \N \N {} -1201 369 2025-02-24 19:57:06.809954-08 2025-02-24 19:38:21.922123-08 benchmark f A100 \N f \N \N \N {} -1202 370 2025-02-24 20:42:28.895887-08 2025-02-24 20:44:35.166507-08 benchmark f A100 \N f \N \N \N {} -1205 373 2025-02-24 20:31:46.327908-08 2025-02-24 19:52:32.554037-08 benchmark f A100 \N f \N \N \N {} -1251 412 2025-02-24 21:58:13.257224-08 2025-02-24 20:59:40.847628-08 benchmark f A100 \N t \N \N \N {} -1252 413 2025-02-24 21:02:53.363826-08 2025-02-24 21:32:32.418367-08 test f A100 \N t \N \N \N {} -1253 413 2025-02-24 21:45:44.88428-08 2025-02-24 22:03:57.186711-08 benchmark f A100 \N t \N \N \N {} -1254 413 2025-02-24 22:46:22.934333-08 2025-02-24 22:52:04.198254-08 leaderboard f A100 0.0008381501 t \N \N \N {} -1261 416 2025-02-24 22:58:19.495213-08 2025-02-24 21:04:11.500619-08 benchmark f H100 \N t \N \N \N {} -1262 416 2025-02-24 21:17:50.838915-08 2025-02-24 22:04:53.900724-08 leaderboard f H100 0.00182382483 t \N \N \N {} -1267 418 2025-02-24 23:14:01.289913-08 2025-02-24 23:08:07.113865-08 test f H100 \N f \N \N \N {} -1268 419 2025-02-24 22:02:19.904283-08 2025-02-24 22:45:33.136654-08 test f H100 \N f \N \N \N {} -1269 420 2025-02-24 22:11:27.988828-08 2025-02-24 22:20:38.794633-08 test f L4 \N f \N \N \N {} -1270 421 2025-02-24 22:44:39.054132-08 2025-02-24 23:23:37.763687-08 test f H100 \N f \N \N \N {} -1285 431 2025-02-25 00:40:59.712906-08 2025-02-25 00:18:36.054996-08 benchmark f A100 \N t \N \N \N {} -1286 432 2025-02-25 00:31:05.092152-08 2025-02-25 00:20:51.202659-08 test f A100 \N f \N \N \N {} -1287 432 2025-02-25 00:49:38.550792-08 2025-02-25 00:46:23.249092-08 test t A100 \N f \N \N \N {} -1288 433 2025-02-25 00:12:52.435038-08 2025-02-25 00:43:33.913224-08 test t A100 \N f \N \N \N {} -1289 433 2025-02-25 00:41:52.056466-08 2025-02-25 01:46:10.51578-08 test f A100 \N f \N \N \N {} -1290 434 2025-02-25 02:07:55.489435-08 2025-02-25 00:38:51.721901-08 test f A100 \N f \N \N \N {} -1300 437 2025-02-25 01:42:35.344955-08 2025-02-25 00:18:58.076587-08 test f A100 \N t \N \N \N {} -1301 438 2025-02-25 00:52:08.957799-08 2025-02-25 01:01:30.640546-08 test f A100 \N t \N \N \N {} -1306 438 2025-02-25 02:17:28.708296-08 2025-02-25 00:43:42.436098-08 leaderboard t A100 0.00319022725 t \N \N \N {} -1307 439 2025-02-25 00:35:26.857477-08 2025-02-25 00:40:59.59712-08 test t A100 \N t \N \N \N {} -1312 439 2025-02-25 02:25:43.593928-08 2025-02-25 00:33:11.310668-08 leaderboard f A100 0.018465732104166667 t \N \N \N {} -1313 440 2025-02-25 00:40:22.163117-08 2025-02-25 02:26:51.813453-08 test t A100 \N f \N \N \N {} -1345 447 2025-02-25 01:34:33.910168-08 2025-02-25 03:08:17.172342-08 test f A100 \N t \N \N \N {} -1314 440 2025-02-25 01:49:18.063405-08 2025-02-25 01:27:01.624065-08 test f A100 \N f \N \N \N {} -1315 441 2025-02-25 02:02:15.063829-08 2025-02-25 01:35:47.494284-08 test t A100 \N t \N \N \N {} -1316 441 2025-02-25 01:37:10.608604-08 2025-02-25 01:46:29.023721-08 benchmark t A100 \N f \N \N \N {} -1335 445 2025-02-25 02:43:54.759243-08 2025-02-25 01:44:21.434113-08 leaderboard f A100 0.0030836436666666664 t \N \N \N {} -1336 445 2025-02-25 02:54:21.021597-08 2025-02-25 01:51:35.634885-08 test t A100 \N t \N \N \N {} -1346 448 2025-02-25 02:33:47.252483-08 2025-02-25 02:35:35.888545-08 test f H100 \N t \N \N \N {} -1434 476 2025-02-25 04:00:22.852373-08 2025-02-25 04:04:45.593426-08 test t A100 \N f \N \N \N {} -1339 446 2025-02-25 02:36:06.477421-08 2025-02-25 03:07:01.814655-08 test f H100 \N t \N \N \N {} -1340 446 2025-02-25 01:30:46.736236-08 2025-02-25 01:35:56.032794-08 benchmark f H100 \N t \N \N \N {} -1341 446 2025-02-25 02:50:19.546464-08 2025-02-25 03:05:51.854793-08 leaderboard f H100 0.001413967 t \N \N \N {} -1342 446 2025-02-25 01:57:52.59555-08 2025-02-25 03:11:12.62416-08 test t H100 \N t \N \N \N {} -1343 446 2025-02-25 02:54:39.59271-08 2025-02-25 01:59:29.726735-08 benchmark t H100 \N t \N \N \N {} -1344 446 2025-02-25 02:39:25.860644-08 2025-02-25 02:13:36.717111-08 leaderboard t H100 0.001406711 t \N \N \N {} -1347 448 2025-02-25 02:43:41.717721-08 2025-02-25 03:26:33.446347-08 benchmark f H100 \N t \N \N \N {} -1348 448 2025-02-25 02:24:25.941676-08 2025-02-25 01:44:32.522029-08 leaderboard f H100 0.0014466804444444445 t \N \N \N {} -1349 448 2025-02-25 02:46:47.800926-08 2025-02-25 02:27:20.46965-08 test t H100 \N t \N \N \N {} -1350 448 2025-02-25 03:06:45.439062-08 2025-02-25 02:01:51.335675-08 benchmark t H100 \N t \N \N \N {} -1351 448 2025-02-25 03:30:09.49455-08 2025-02-25 03:12:41.990012-08 leaderboard t H100 0.0014756132222222221 t \N \N \N {} -1352 449 2025-02-25 03:33:12.39421-08 2025-02-25 02:23:52.923798-08 benchmark f H100 \N f \N \N \N {} -1435 476 2025-02-25 04:55:28.037485-08 2025-02-25 03:57:08.314649-08 test f A100 \N f \N \N \N {} -1555 513 2025-02-25 07:41:24.90562-08 2025-02-25 07:02:07.563164-08 test t A100 \N t \N \N \N {} -2909 1016 2025-02-27 00:36:25.1576-08 2025-02-27 00:50:15.121869-08 benchmark f L4 \N t \N \N \N {} -2910 1016 2025-02-27 00:33:26.148508-08 2025-02-27 01:43:46.236307-08 leaderboard f L4 0.017180370666666667 t \N \N \N {} -3041 1088 2025-02-27 14:56:34.384661-08 2025-02-27 15:37:21.179849-08 benchmark f A100 \N t \N \N \N {} -3369 1163 2025-02-28 10:09:00.6989-08 2025-02-28 09:38:40.385253-08 leaderboard t T4 0.01692768366666667 t \N \N \N {} -1400 468 2025-02-25 04:45:59.134192-08 2025-02-25 03:14:40.329073-08 benchmark t A100 \N t \N \N \N {} -1401 468 2025-02-25 03:36:58.192612-08 2025-02-25 04:35:55.042696-08 leaderboard t A100 0.003100401 t \N \N \N {} -2919 1023 2025-02-27 02:46:56.557824-08 2025-02-27 03:30:51.397045-08 test t A100 \N t \N \N \N {} -2920 1023 2025-02-27 02:54:26.545327-08 2025-02-27 03:57:32.864122-08 benchmark t A100 \N t \N \N \N {} -2921 1023 2025-02-27 04:31:43.865249-08 2025-02-27 03:08:56.104014-08 leaderboard t A100 0.00009803284313725489 t \N \N \N {} -3120 1110 2025-02-28 01:35:44.444997-08 2025-02-28 03:00:13.974579-08 leaderboard t A100 0.00310022 t \N \N \N {} -3559 1210 2025-02-28 11:35:52.32336-08 2025-02-28 10:56:38.662439-08 test f A100 \N t \N \N \N {} -1403 469 2025-02-25 04:31:28.4595-08 2025-02-25 03:33:06.99222-08 benchmark f A100 \N t \N \N \N {} -2381 803 2025-02-26 11:33:33.487376-08 2025-02-26 12:57:20.418702-08 benchmark f A100 \N f \N \N \N {} -2427 833 2025-02-26 13:42:17.786461-08 2025-02-26 12:30:47.6523-08 benchmark f H100 \N t \N \N \N {} -3009 1074 2025-02-27 13:15:17.144176-08 2025-02-27 12:29:52.339984-08 leaderboard t A100 0.008153932666666667 t \N \N \N {} -2952 1039 2025-02-27 09:30:20.903906-08 2025-02-27 09:13:37.115048-08 benchmark f A100 \N t \N \N \N {} -3010 1074 2025-02-27 13:59:33.48938-08 2025-02-27 13:27:19.116278-08 test f A100 \N t \N \N \N {} -3011 1074 2025-02-27 14:02:47.636628-08 2025-02-27 13:32:31.614474-08 benchmark f A100 \N t \N \N \N {} -3012 1074 2025-02-27 13:15:20.480402-08 2025-02-27 12:20:44.401517-08 leaderboard f A100 0.008157396666666667 t \N \N \N {} -3085 1104 2025-02-28 01:59:18.600676-08 2025-02-28 02:22:56.347954-08 test f A100 \N t \N \N \N {} -3094 1106 2025-02-28 02:25:58.750131-08 2025-02-28 03:06:28.944884-08 test t A100 \N t \N \N \N {} -3100 1107 2025-02-28 01:26:20.879799-08 2025-02-28 02:25:12.920428-08 test f A100 \N t \N \N \N {} -1478 492 2025-02-25 04:23:20.449898-08 2025-02-25 04:58:19.421573-08 benchmark t H100 \N t \N \N \N {} -1479 492 2025-02-25 04:51:19.751745-08 2025-02-25 05:39:17.407689-08 leaderboard t H100 0.0014122803333333333 t \N \N \N {} -2953 1040 2025-02-27 09:36:18.815464-08 2025-02-27 08:40:35.471157-08 test f A100 \N t \N \N \N {} -2954 1040 2025-02-27 08:26:37.101243-08 2025-02-27 09:01:30.532817-08 benchmark f A100 \N t \N \N \N {} -2955 1040 2025-02-27 09:41:18.891429-08 2025-02-27 08:04:00.37954-08 leaderboard f A100 0.003193513 t \N \N \N {} -3013 1075 2025-02-27 13:11:51.024355-08 2025-02-27 13:17:55.679639-08 test f A100 \N f \N \N \N {} -3103 1107 2025-02-28 03:07:10.741712-08 2025-02-28 02:19:44.946765-08 test t A100 \N t \N \N \N {} -3225 1127 2025-02-28 05:28:19.551054-08 2025-02-28 05:56:14.735613-08 leaderboard f A100 0.0030895356666666663 t \N \N \N {} -1481 492 2025-02-25 04:54:31.873992-08 2025-02-25 04:22:13.298402-08 benchmark f H100 \N t \N \N \N {} -1482 492 2025-02-25 06:14:51.357942-08 2025-02-25 06:18:57.491972-08 leaderboard f H100 0.001430011 t \N \N \N {} -1524 506 2025-02-25 06:33:04.067626-08 2025-02-25 07:23:14.188914-08 leaderboard f H100 0.0014538050909090909 t \N \N \N {} -1525 507 2025-02-25 07:34:25.667422-08 2025-02-25 06:18:53.214067-08 test t A100 \N f \N \N \N {} -1526 507 2025-02-25 08:04:00.305064-08 2025-02-25 07:53:47.234207-08 test f A100 \N f \N \N \N {} -1527 508 2025-02-25 06:51:53.729029-08 2025-02-25 06:49:15.739902-08 benchmark f A100 \N f \N \N \N {} -1528 509 2025-02-25 07:57:51.169726-08 2025-02-25 08:03:52.639663-08 benchmark f A100 \N t \N \N \N {} -3160 1117 2025-02-28 01:57:36.630356-08 2025-02-28 02:15:23.444492-08 test t A100 \N t \N \N \N {} -1531 512 2025-02-25 07:27:21.471591-08 2025-02-25 07:32:23.967466-08 test t A100 \N t \N \N \N {} -130 20 2025-02-23 09:47:46.962051-08 2025-02-23 10:49:22.323496-08 test f H100 \N t \N \N \N {} -1532 512 2025-02-25 07:07:12.686135-08 2025-02-25 06:51:42.064245-08 benchmark t A100 \N t \N \N \N {} -1533 512 2025-02-25 07:35:12.160809-08 2025-02-25 06:23:12.621053-08 leaderboard t A100 0.0033605953333333337 t \N \N \N {} -1534 512 2025-02-25 07:40:57.831066-08 2025-02-25 07:21:40.9266-08 test f A100 \N t \N \N \N {} -1535 512 2025-02-25 07:49:49.262012-08 2025-02-25 07:10:31.438503-08 benchmark f A100 \N t \N \N \N {} -1536 512 2025-02-25 06:32:25.921122-08 2025-02-25 07:25:06.894207-08 leaderboard f A100 0.0033895406666666667 t \N \N \N {} -1537 512 2025-02-25 06:24:52.920212-08 2025-02-25 06:36:53.942491-08 test t H100 \N t \N \N \N {} -1538 512 2025-02-25 06:30:33.771888-08 2025-02-25 07:19:58.296436-08 benchmark t H100 \N t \N \N \N {} -1558 513 2025-02-25 07:20:20.352133-08 2025-02-25 07:48:40.674871-08 test f A100 \N t \N \N \N {} -3263 1142 2025-02-28 06:26:36.269643-08 2025-02-28 06:34:10.748084-08 test f A100 \N t \N \N \N {} -2020 694 2025-02-25 14:25:32.099879-08 2025-02-25 14:05:01.976545-08 test f H100 \N f \N \N \N {} -1576 516 2025-02-25 07:10:56.831342-08 2025-02-25 07:01:47.567348-08 test f A100 \N t \N \N \N {} -1577 516 2025-02-25 08:23:01.443137-08 2025-02-25 07:25:44.603998-08 benchmark f A100 \N t \N \N \N {} -1578 516 2025-02-25 08:13:13.657148-08 2025-02-25 07:31:55.453583-08 leaderboard f A100 0.0031859834 t \N \N \N {} -1580 518 2025-02-25 08:07:50.629405-08 2025-02-25 07:17:48.243079-08 benchmark f T4 \N t \N \N \N {} -1582 519 2025-02-25 07:03:06.655132-08 2025-02-25 07:54:35.221308-08 benchmark t H100 \N t \N \N \N {} -1583 519 2025-02-25 08:20:50.0298-08 2025-02-25 08:34:10.546647-08 leaderboard t H100 0.0014114776666666667 t \N \N \N {} -1584 519 2025-02-25 08:01:59.762518-08 2025-02-25 08:40:03.53953-08 test f H100 \N t \N \N \N {} -1585 519 2025-02-25 06:48:35.700356-08 2025-02-25 07:22:01.937122-08 benchmark f H100 \N t \N \N \N {} -1586 519 2025-02-25 07:48:14.082024-08 2025-02-25 07:23:02.722501-08 leaderboard f H100 0.0014109543333333332 t \N \N \N {} -1587 520 2025-02-25 06:50:50.074767-08 2025-02-25 06:52:07.567587-08 test f A100 \N t \N \N \N {} -1588 521 2025-02-25 07:04:03.01671-08 2025-02-25 08:36:16.326265-08 benchmark f A100 \N t \N \N \N {} -1603 532 2025-02-25 08:01:48.392638-08 2025-02-25 07:55:14.350257-08 test t A100 \N t \N \N \N {} -1604 532 2025-02-25 08:04:01.52357-08 2025-02-25 08:48:53.380739-08 benchmark t A100 \N t \N \N \N {} -1605 532 2025-02-25 08:07:29.187473-08 2025-02-25 07:30:38.019107-08 leaderboard t A100 0.0030833873333333335 t \N \N \N {} -1606 533 2025-02-25 07:13:57.228578-08 2025-02-25 09:02:45.282064-08 test t H100 \N t \N \N \N {} -1607 533 2025-02-25 08:46:17.241842-08 2025-02-25 08:46:34.390308-08 benchmark t H100 \N t \N \N \N {} -1608 533 2025-02-25 07:15:43.847706-08 2025-02-25 07:17:56.713485-08 leaderboard t H100 0.0014808887368421052 t \N \N \N {} -1614 536 2025-02-25 07:40:05.156643-08 2025-02-25 08:50:38.71485-08 benchmark f H100 \N t \N \N \N {} -1615 537 2025-02-25 08:20:34.71729-08 2025-02-25 08:00:07.524271-08 test f A100 \N t \N \N \N {} -1626 539 2025-02-25 08:31:18.319762-08 2025-02-25 07:36:37.562316-08 benchmark t H100 \N t \N \N \N {} -1627 539 2025-02-25 07:45:22.622109-08 2025-02-25 07:26:16.545192-08 leaderboard t H100 0.0014020573333333333 t \N \N \N {} -1628 540 2025-02-25 08:39:12.579698-08 2025-02-25 08:21:54.313518-08 test f H100 \N t \N \N \N {} -1629 540 2025-02-25 09:06:18.861595-08 2025-02-25 09:06:08.76981-08 benchmark f H100 \N t \N \N \N {} -1630 540 2025-02-25 07:42:40.881925-08 2025-02-25 09:24:44.220023-08 leaderboard f H100 0.0014151046666666667 t \N \N \N {} -1631 540 2025-02-25 09:11:16.420021-08 2025-02-25 08:59:13.13714-08 test t H100 \N t \N \N \N {} -1683 557 2025-02-25 08:58:37.853947-08 2025-02-25 09:17:12.880606-08 test f A100 \N t \N \N \N {} -1642 543 2025-02-25 09:29:08.780743-08 2025-02-25 07:49:54.770844-08 benchmark t A100 \N t \N \N \N {} -1643 543 2025-02-25 08:08:43.291503-08 2025-02-25 07:45:38.670153-08 leaderboard t A100 0.003091805 t \N \N \N {} -1644 543 2025-02-25 08:16:39.851047-08 2025-02-25 09:04:53.563147-08 test f A100 \N t \N \N \N {} -1645 543 2025-02-25 08:33:33.890389-08 2025-02-25 09:25:02.242877-08 benchmark f A100 \N t \N \N \N {} -1647 544 2025-02-25 08:10:30.369306-08 2025-02-25 07:43:45.870981-08 test f A100 \N f \N \N \N {} -1648 544 2025-02-25 07:53:54.16523-08 2025-02-25 08:33:04.35208-08 test t A100 \N f \N \N \N {} -1649 544 2025-02-25 08:33:21.506269-08 2025-02-25 08:07:38.323301-08 test t T4 \N f \N \N \N {} -1650 544 2025-02-25 09:15:52.74522-08 2025-02-25 08:12:39.641754-08 test f T4 \N f \N \N \N {} -1651 545 2025-02-25 08:25:31.259943-08 2025-02-25 08:02:07.523474-08 test f A100 \N t \N \N \N {} -1976 679 2025-02-25 12:53:34.216219-08 2025-02-25 13:15:06.928515-08 test t T4 \N t \N \N \N {} -1710 571 2025-02-25 09:19:18.026341-08 2025-02-25 09:52:30.405634-08 test t T4 \N f \N \N \N {} -1711 571 2025-02-25 08:33:43.295723-08 2025-02-25 10:06:35.053909-08 test f T4 \N f \N \N \N {} -1726 579 2025-02-25 10:12:23.270121-08 2025-02-25 09:22:50.331776-08 test f A100 \N f \N \N \N {} -1734 585 2025-02-25 08:29:31.038561-08 2025-02-25 08:26:58.367928-08 test f A100 \N f \N \N \N {} -1735 583 2025-02-25 08:35:16.843418-08 2025-02-25 09:05:45.380553-08 test t H100 \N t \N \N \N {} -1736 583 2025-02-25 10:11:22.418499-08 2025-02-25 09:25:54.155768-08 benchmark t H100 \N t \N \N \N {} -1737 583 2025-02-25 08:51:06.601652-08 2025-02-25 09:10:23.575069-08 leaderboard t H100 0.0000772660625 t \N \N \N {} -2810 981 2025-02-26 23:47:22.315813-08 2025-02-26 23:04:15.576922-08 leaderboard f H100 0.00149895725 t \N \N \N {} -3817 1285 2025-03-01 03:36:02.152194-08 2025-03-01 03:19:46.959566-08 test f A100 \N t \N \N \N {} -4806 1487 2025-03-02 14:19:46.759466-08 2025-03-02 12:30:42.83975-08 test f T4 \N t \N \N \N {} -1784 613 2025-02-25 11:14:58.236656-08 2025-02-25 10:52:55.040195-08 test f A100 \N t \N \N \N {} -1785 614 2025-02-25 10:45:46.882457-08 2025-02-25 10:39:52.677212-08 benchmark f H100 \N t \N \N \N {} -2355 791 2025-02-26 08:55:33.742418-08 2025-02-26 07:47:49.701265-08 test f H100 \N f \N \N \N {} -1786 615 2025-02-25 10:56:29.193673-08 2025-02-25 09:37:14.897702-08 benchmark f H100 \N f \N \N \N {} -1787 616 2025-02-25 10:39:54.716283-08 2025-02-25 10:38:23.837561-08 test f A100 \N t \N \N \N {} -1788 616 2025-02-25 10:53:22.333552-08 2025-02-25 10:30:34.088241-08 benchmark f A100 \N t \N \N \N {} -1789 616 2025-02-25 11:11:25.986403-08 2025-02-25 10:31:45.03736-08 leaderboard f A100 0.000205736 t \N \N \N {} -1790 616 2025-02-25 09:41:53.090321-08 2025-02-25 10:12:39.131059-08 test t A100 \N t \N \N \N {} -1796 616 2025-02-25 09:49:54.963655-08 2025-02-25 11:04:32.101396-08 test t T4 \N t \N \N \N {} -2775 966 2025-02-26 20:12:59.070745-08 2025-02-26 19:39:08.158654-08 leaderboard f A100 0.014324531 t \N \N \N {} -1792 616 2025-02-25 10:01:38.707534-08 2025-02-25 11:11:50.587762-08 leaderboard t A100 0.00017068230769230768 t \N \N \N {} -1793 616 2025-02-25 10:03:00.704862-08 2025-02-25 09:45:03.774932-08 test f H100 \N t \N \N \N {} -1794 616 2025-02-25 10:53:30.74314-08 2025-02-25 11:05:56.056638-08 benchmark f H100 \N t \N \N \N {} -1795 616 2025-02-25 11:17:45.875715-08 2025-02-25 10:39:10.578077-08 leaderboard f H100 0.00012224712903225807 t \N \N \N {} -2356 791 2025-02-26 07:29:55.883929-08 2025-02-26 08:47:28.665781-08 test t H100 \N f \N \N \N {} -1798 616 2025-02-25 09:55:41.682206-08 2025-02-25 10:56:48.765016-08 leaderboard t T4 0.000549977 t \N \N \N {} -1799 616 2025-02-25 09:35:29.329871-08 2025-02-25 10:19:26.68501-08 test f T4 \N t \N \N \N {} -1800 616 2025-02-25 10:37:31.462732-08 2025-02-25 10:39:06.329009-08 benchmark f T4 \N t \N \N \N {} -1801 616 2025-02-25 10:49:46.717275-08 2025-02-25 09:34:27.810838-08 leaderboard f T4 0.0007055456666666667 t \N \N \N {} -2357 792 2025-02-26 09:14:17.578297-08 2025-02-26 08:39:13.532029-08 test t H100 \N f \N \N \N {} -1803 616 2025-02-25 10:02:17.879749-08 2025-02-25 11:05:44.711116-08 benchmark t H100 \N t \N \N \N {} -1804 616 2025-02-25 11:14:14.091418-08 2025-02-25 10:00:08.773034-08 leaderboard t H100 0.00012245449019607842 t \N \N \N {} -1805 616 2025-02-25 10:57:07.382054-08 2025-02-25 10:21:18.841312-08 test f L4 \N t \N \N \N {} -1806 616 2025-02-25 11:25:11.990406-08 2025-02-25 10:09:51.776687-08 benchmark f L4 \N t \N \N \N {} -2358 792 2025-02-26 08:54:16.016762-08 2025-02-26 08:39:38.92267-08 test f H100 \N f \N \N \N {} -1810 616 2025-02-25 11:00:52.381329-08 2025-02-25 11:23:28.247671-08 leaderboard t L4 0.00021845146153846154 t \N \N \N {} -1811 617 2025-02-25 10:55:46.819723-08 2025-02-25 10:55:23.275903-08 benchmark f H100 \N f \N \N \N {} -1812 618 2025-02-25 10:21:57.018191-08 2025-02-25 10:55:18.464595-08 benchmark f A100 \N t \N \N \N {} -2370 797 2025-02-26 10:53:12.943723-08 2025-02-26 09:33:24.539346-08 test f A100 \N f \N \N \N {} -1816 620 2025-02-25 10:05:33.623324-08 2025-02-25 10:33:30.515341-08 leaderboard t A100 0.00309566 t \N \N \N {} -1817 620 2025-02-25 11:21:18.896922-08 2025-02-25 10:03:28.563169-08 test f A100 \N t \N \N \N {} -1818 620 2025-02-25 11:29:14.645796-08 2025-02-25 09:47:07.54021-08 benchmark f A100 \N t \N \N \N {} -1819 620 2025-02-25 10:07:21.891596-08 2025-02-25 11:09:15.984274-08 leaderboard f A100 0.0030797906666666665 t \N \N \N {} -2415 817 2025-02-26 12:03:48.353023-08 2025-02-26 12:27:23.72571-08 benchmark f H100 \N f \N \N \N {} -1821 621 2025-02-25 09:54:58.090582-08 2025-02-25 11:30:23.907082-08 benchmark f A100 \N t \N \N \N {} -138 23 2025-02-23 11:54:58.067969-08 2025-02-23 12:08:04.435299-08 test f A100 \N t \N \N \N {} -147 23 2025-02-23 11:38:07.488367-08 2025-02-23 11:53:30.778579-08 test t T4 \N t \N \N \N {} -156 23 2025-02-23 10:30:22.436806-08 2025-02-23 10:14:59.268017-08 test f L4 \N t \N \N \N {} -1353 450 2025-02-25 02:01:19.511091-08 2025-02-25 01:50:23.554828-08 benchmark f H100 \N f \N \N \N {} -1822 621 2025-02-25 10:17:37.410428-08 2025-02-25 10:36:03.237286-08 leaderboard f A100 0.00011739522222222221 t \N \N \N {} -4809 1487 2025-03-02 13:45:28.37586-08 2025-03-02 12:40:42.351849-08 benchmark t L4 \N f \N \N \N {} -2226 725 2025-02-25 14:54:19.929968-08 2025-02-25 15:47:46.340338-08 benchmark f A100 \N t \N \N \N {} -2228 727 2025-02-25 20:35:49.008461-08 2025-02-25 21:27:48.607545-08 test f H100 \N f \N \N \N {} -2230 729 2025-02-25 21:17:44.501223-08 2025-02-25 20:34:01.289359-08 test f H100 \N f \N \N \N {} -2234 733 2025-02-25 21:21:37.259904-08 2025-02-25 22:02:04.25938-08 benchmark f H100 \N t \N \N \N {} -2235 734 2025-02-25 22:33:25.794507-08 2025-02-25 22:37:59.655817-08 test f H100 \N f \N \N \N {} -4810 1487 2025-03-02 14:06:07.69494-08 2025-03-02 14:05:29.275996-08 test f L4 \N t \N \N \N {} -2237 736 2025-02-25 23:19:21.988232-08 2025-02-25 23:45:39.636835-08 benchmark f A100 \N t \N \N \N {} -2238 737 2025-02-25 23:00:41.307289-08 2025-02-25 23:14:23.811188-08 benchmark f A100 \N t \N \N \N {} -2239 738 2025-02-25 23:48:44.965108-08 2025-02-25 23:42:33.619927-08 benchmark f A100 \N f \N \N \N {} -2240 739 2025-02-25 23:19:53.648595-08 2025-02-25 23:40:49.676196-08 benchmark f A100 \N f \N \N \N {} -2272 766 2025-02-26 04:44:38.752744-08 2025-02-26 03:37:57.159249-08 test f A100 \N t \N \N \N {} -2244 743 2025-02-26 00:35:45.969202-08 2025-02-26 00:32:30.806442-08 benchmark f A100 \N f \N \N \N {} -3587 1229 2025-02-28 14:32:10.95012-08 2025-02-28 14:44:22.261204-08 test f H100 \N t \N \N \N {} -2246 745 2025-02-26 00:37:33.931264-08 2025-02-26 00:40:26.964338-08 test f H100 \N f \N \N \N {} -2247 746 2025-02-26 01:55:19.252796-08 2025-02-26 00:36:07.797743-08 test f H100 \N f \N \N \N {} -2248 747 2025-02-26 01:15:55.844133-08 2025-02-26 01:53:31.828353-08 test f H100 \N f \N \N \N {} -2250 749 2025-02-26 00:25:17.403977-08 2025-02-26 00:24:56.829005-08 benchmark f H100 \N t \N \N \N {} -2256 755 2025-02-26 02:22:45.950588-08 2025-02-26 02:07:54.180517-08 benchmark f H100 \N f \N \N \N {} -2257 756 2025-02-26 03:00:14.310747-08 2025-02-26 02:46:46.937635-08 benchmark f H100 \N t \N \N \N {} -2258 757 2025-02-26 01:10:17.629744-08 2025-02-26 01:41:31.08645-08 benchmark f H100 \N t \N \N \N {} -2263 762 2025-02-26 02:18:34.242484-08 2025-02-26 02:55:39.021981-08 test t H100 \N t \N \N \N {} -2291 770 2025-02-26 04:07:08.486179-08 2025-02-26 03:34:41.389378-08 test f A100 \N t \N \N \N {} -2264 762 2025-02-26 02:58:11.327721-08 2025-02-26 03:46:33.348626-08 benchmark t H100 \N t \N \N \N {} -2266 762 2025-02-26 02:37:45.731387-08 2025-02-26 03:44:38.02645-08 test f H100 \N t \N \N \N {} -2267 762 2025-02-26 03:39:25.066238-08 2025-02-26 02:14:19.098169-08 benchmark f H100 \N t \N \N \N {} -2268 762 2025-02-26 02:23:03.127111-08 2025-02-26 02:36:35.552634-08 leaderboard f H100 0.0014381206666666667 t \N \N \N {} -2269 763 2025-02-26 02:45:57.978469-08 2025-02-26 02:49:10.932866-08 benchmark f H100 \N f \N \N \N {} -2270 765 2025-02-26 04:02:02.067979-08 2025-02-26 03:15:12.820422-08 test f A100 \N f \N \N \N {} -2271 765 2025-02-26 03:46:07.247621-08 2025-02-26 03:33:23.013529-08 test t A100 \N f \N \N \N {} -2273 766 2025-02-26 04:26:50.535168-08 2025-02-26 04:37:20.190686-08 benchmark f A100 \N t \N \N \N {} -2276 766 2025-02-26 03:57:01.450513-08 2025-02-26 04:19:48.460438-08 benchmark t A100 \N t \N \N \N {} -2277 766 2025-02-26 04:23:34.32179-08 2025-02-26 04:37:21.779651-08 leaderboard t A100 0.0008035725 t \N \N \N {} -2278 767 2025-02-26 04:59:10.716734-08 2025-02-26 04:56:31.615231-08 test f A100 \N t \N \N \N {} -2279 767 2025-02-26 04:04:53.620256-08 2025-02-26 03:40:43.285038-08 benchmark f A100 \N t \N \N \N {} -2811 981 2025-02-26 23:20:55.588139-08 2025-02-26 23:02:56.11656-08 test t H100 \N t \N \N \N {} -3820 1285 2025-03-01 02:46:38.245389-08 2025-03-01 02:36:19.519197-08 test t A100 \N t \N \N \N {} -2283 767 2025-02-26 03:48:47.510918-08 2025-02-26 04:12:24.590357-08 leaderboard t A100 0.0008112885 t \N \N \N {} -2285 769 2025-02-26 03:32:55.537334-08 2025-02-26 04:57:18.17064-08 test t A100 \N t \N \N \N {} -2286 769 2025-02-26 03:54:10.03714-08 2025-02-26 03:53:44.323045-08 benchmark t A100 \N t \N \N \N {} -2287 769 2025-02-26 04:13:17.84753-08 2025-02-26 04:35:13.277401-08 leaderboard t A100 0.0008150356 t \N \N \N {} -2288 769 2025-02-26 03:53:13.370089-08 2025-02-26 03:34:52.714707-08 test f A100 \N t \N \N \N {} -2289 769 2025-02-26 04:13:58.330006-08 2025-02-26 03:53:06.926282-08 benchmark f A100 \N t \N \N \N {} -2290 769 2025-02-26 04:11:02.675153-08 2025-02-26 04:13:02.665399-08 leaderboard f A100 0.0008101136666666666 t \N \N \N {} -2292 770 2025-02-26 03:47:42.434385-08 2025-02-26 03:50:45.275328-08 benchmark f A100 \N t \N \N \N {} -2293 770 2025-02-26 03:53:00.59453-08 2025-02-26 04:00:41.911017-08 leaderboard f A100 0.0008105805 t \N \N \N {} -2294 770 2025-02-26 04:01:53.351812-08 2025-02-26 03:50:07.775814-08 test t A100 \N t \N \N \N {} -2295 770 2025-02-26 03:11:56.96532-08 2025-02-26 04:01:17.104616-08 benchmark t A100 \N t \N \N \N {} -2296 770 2025-02-26 03:30:49.241367-08 2025-02-26 04:03:05.950494-08 leaderboard t A100 0.00080491525 t \N \N \N {} -2297 771 2025-02-26 04:33:09.624984-08 2025-02-26 04:02:12.069329-08 benchmark f A100 \N t \N \N \N {} -2298 772 2025-02-26 04:59:38.93896-08 2025-02-26 04:38:25.02308-08 benchmark f A100 \N t \N \N \N {} -2299 773 2025-02-26 04:20:34.328694-08 2025-02-26 03:54:56.809911-08 benchmark f A100 \N t \N \N \N {} -2304 774 2025-02-26 03:57:56.984318-08 2025-02-26 04:38:02.103282-08 benchmark t A100 \N t \N \N \N {} -2305 774 2025-02-26 03:44:38.635658-08 2025-02-26 05:02:38.874989-08 leaderboard t A100 0.00311144 t \N \N \N {} -2306 775 2025-02-26 04:12:31.181055-08 2025-02-26 04:59:52.882918-08 benchmark f A100 \N t \N \N \N {} -2307 776 2025-02-26 04:20:56.019237-08 2025-02-26 03:52:11.190759-08 benchmark f A100 \N t \N \N \N {} -2329 784 2025-02-26 07:42:05.262849-08 2025-02-26 07:36:19.042812-08 test f A100 \N f \N \N \N {} -2311 778 2025-02-26 03:30:09.435566-08 2025-02-26 04:06:02.956045-08 leaderboard t A100 0.003092609 t \N \N \N {} -2312 778 2025-02-26 05:23:38.077187-08 2025-02-26 03:53:55.424873-08 test f A100 \N t \N \N \N {} -2326 783 2025-02-26 08:05:37.792029-08 2025-02-26 08:12:28.335942-08 test f A100 \N t \N \N \N {} -2327 783 2025-02-26 08:39:42.131724-08 2025-02-26 08:09:32.991225-08 benchmark f A100 \N t \N \N \N {} -2328 783 2025-02-26 08:30:37.407938-08 2025-02-26 07:35:49.357033-08 leaderboard f A100 0.003227151 t \N \N \N {} -2331 785 2025-02-26 07:27:47.482468-08 2025-02-26 08:17:53.94964-08 test f A100 \N t \N \N \N {} -2332 785 2025-02-26 07:37:11.087311-08 2025-02-26 08:15:07.866294-08 benchmark f A100 \N t \N \N \N {} -2333 785 2025-02-26 07:59:24.368247-08 2025-02-26 08:20:21.480809-08 leaderboard f A100 0.004367405666666667 t \N \N \N {} -2337 786 2025-02-26 08:22:04.365539-08 2025-02-26 08:24:47.375813-08 test f A100 \N f \N \N \N {} -2338 786 2025-02-26 08:35:45.345849-08 2025-02-26 08:14:30.033798-08 test t A100 \N f \N \N \N {} -2339 787 2025-02-26 07:22:18.968697-08 2025-02-26 08:22:46.178651-08 test t A100 \N f \N \N \N {} -2346 789 2025-02-26 07:08:50.231065-08 2025-02-26 08:49:16.966065-08 test f A100 \N t \N \N \N {} -3844 1289 2025-03-01 02:58:23.301247-08 2025-03-01 03:47:22.156604-08 test t A100 \N t \N \N \N {} -2344 789 2025-02-26 08:30:43.202837-08 2025-02-26 08:53:02.953547-08 benchmark t A100 \N t \N \N \N {} -2345 789 2025-02-26 08:54:31.507554-08 2025-02-26 08:18:29.611102-08 leaderboard t A100 0.00322034475 t \N \N \N {} -2349 790 2025-02-26 07:27:15.296498-08 2025-02-26 07:45:31.770943-08 test t H100 \N t \N \N \N {} -2350 790 2025-02-26 07:27:09.22196-08 2025-02-26 08:40:58.669769-08 benchmark t H100 \N t \N \N \N {} -2351 790 2025-02-26 07:38:50.506814-08 2025-02-26 07:39:57.059049-08 leaderboard t H100 0.0015428555 t \N \N \N {} -2352 790 2025-02-26 07:14:10.99536-08 2025-02-26 08:11:26.301088-08 test f H100 \N t \N \N \N {} -2353 790 2025-02-26 07:55:02.506428-08 2025-02-26 08:30:47.285542-08 benchmark f H100 \N t \N \N \N {} -2354 790 2025-02-26 08:58:21.825582-08 2025-02-26 08:36:23.311282-08 leaderboard f H100 0.00153994575 t \N \N \N {} -2362 794 2025-02-26 09:16:23.731727-08 2025-02-26 09:45:35.701403-08 test f A100 \N f \N \N \N {} -2363 794 2025-02-26 10:05:30.642869-08 2025-02-26 10:06:11.654779-08 test f H100 \N f \N \N \N {} -2364 794 2025-02-26 08:54:28.852181-08 2025-02-26 10:36:05.947891-08 test f L4 \N f \N \N \N {} -2369 796 2025-02-26 09:04:59.633587-08 2025-02-26 08:58:58.997794-08 test f H100 \N f \N \N \N {} -3269 1143 2025-02-28 05:49:04.471481-08 2025-02-28 05:33:52.950669-08 test t A100 \N t \N \N \N {} -3272 1143 2025-02-28 07:24:05.395179-08 2025-02-28 06:21:30.549804-08 test f A100 \N t \N \N \N {} -2389 806 2025-02-26 11:59:47.526877-08 2025-02-26 13:27:08.118395-08 benchmark f H100 \N t \N \N \N {} -131 21 2025-02-23 10:54:12.413903-08 2025-02-23 11:03:24.456859-08 benchmark f H100 \N t \N \N \N {} -132 22 2025-02-23 11:18:52.406962-08 2025-02-23 11:21:40.34417-08 test f H100 \N t \N \N \N {} -133 22 2025-02-23 11:10:07.454357-08 2025-02-23 11:27:41.266157-08 benchmark f H100 \N t \N \N \N {} -134 22 2025-02-23 11:11:08.332204-08 2025-02-23 09:56:47.514659-08 leaderboard f H100 0.007636646 t \N \N \N {} -135 22 2025-02-23 11:15:29.967609-08 2025-02-23 10:36:22.679826-08 test t H100 \N t \N \N \N {} -136 22 2025-02-23 10:30:49.187464-08 2025-02-23 10:54:58.140967-08 benchmark t H100 \N t \N \N \N {} -137 22 2025-02-23 10:01:17.414414-08 2025-02-23 10:57:17.950859-08 leaderboard t H100 0.007676687 t \N \N \N {} -6139 1882 2025-03-11 10:43:46.751248-07 2025-03-11 11:39:34.207773-07 test f T4 \N f \N \N \N {} -139 23 2025-02-23 11:00:36.681319-08 2025-02-23 11:20:38.884145-08 benchmark f A100 \N t \N \N \N {} -140 23 2025-02-23 11:47:46.303484-08 2025-02-23 10:27:15.204326-08 leaderboard f A100 0.010159300357142857 t \N \N \N {} -141 23 2025-02-23 11:55:55.792415-08 2025-02-23 11:05:18.699548-08 test t A100 \N t \N \N \N {} -142 23 2025-02-23 10:45:14.3074-08 2025-02-23 10:59:33.198898-08 benchmark t A100 \N t \N \N \N {} -143 23 2025-02-23 11:17:34.462052-08 2025-02-23 10:26:24.612757-08 leaderboard t A100 0.010135129449999999 t \N \N \N {} -144 23 2025-02-23 10:09:53.746-08 2025-02-23 11:09:01.710035-08 test f H100 \N t \N \N \N {} -145 23 2025-02-23 11:23:51.944416-08 2025-02-23 11:35:42.525714-08 benchmark f H100 \N t \N \N \N {} -146 23 2025-02-23 11:15:27.676586-08 2025-02-23 12:03:10.988718-08 leaderboard f H100 0.006125231666666667 t \N \N \N {} -148 23 2025-02-23 11:13:16.041704-08 2025-02-23 11:50:47.350172-08 benchmark t T4 \N t \N \N \N {} -149 23 2025-02-23 11:28:20.433165-08 2025-02-23 10:28:06.298908-08 leaderboard t T4 0.048027397 t \N \N \N {} -150 23 2025-02-23 11:18:42.410803-08 2025-02-23 10:31:01.372052-08 test t H100 \N t \N \N \N {} -151 23 2025-02-23 11:58:30.811996-08 2025-02-23 11:21:35.60515-08 benchmark t H100 \N t \N \N \N {} -152 23 2025-02-23 11:11:00.271834-08 2025-02-23 11:00:53.563642-08 leaderboard t H100 0.006109236666666667 t \N \N \N {} -153 23 2025-02-23 10:26:41.850092-08 2025-02-23 10:39:34.799916-08 test f T4 \N t \N \N \N {} -2390 807 2025-02-26 12:40:07.882315-08 2025-02-26 13:29:00.67918-08 benchmark f A100 \N t \N \N \N {} -2391 808 2025-02-26 12:44:03.203064-08 2025-02-26 12:20:13.654261-08 benchmark f H100 \N t \N \N \N {} -2392 809 2025-02-26 11:46:24.444642-08 2025-02-26 13:37:16.547993-08 benchmark f H100 \N t \N \N \N {} -2393 811 2025-02-26 11:51:23.557392-08 2025-02-26 12:37:24.914401-08 benchmark f H100 \N f \N \N \N {} -4811 1487 2025-03-02 13:46:45.410776-08 2025-03-02 13:08:51.056877-08 benchmark f L4 \N f \N \N \N {} -3847 1290 2025-03-01 04:03:32.270689-08 2025-03-01 02:32:19.138733-08 test f A100 \N t \N \N \N {} -2394 810 2025-02-26 13:25:37.482593-08 2025-02-26 12:59:08.470332-08 benchmark f H100 \N f \N \N \N {} -2395 812 2025-02-26 13:33:28.597174-08 2025-02-26 12:57:21.570896-08 test f H100 \N t \N \N \N {} -2396 812 2025-02-26 12:34:20.532251-08 2025-02-26 12:31:27.053945-08 benchmark f H100 \N t \N \N \N {} -2397 812 2025-02-26 12:19:54.717591-08 2025-02-26 12:10:14.47041-08 leaderboard f H100 0.0032161647200000002 t \N \N \N {} -2398 812 2025-02-26 13:18:48.216215-08 2025-02-26 11:52:47.39111-08 test f A100 \N t \N \N \N {} -2401 812 2025-02-26 12:08:17.632371-08 2025-02-26 12:08:36.634538-08 test t A100 \N t \N \N \N {} -2402 812 2025-02-26 13:29:27.61967-08 2025-02-26 13:25:55.587367-08 benchmark t A100 \N t \N \N \N {} -2403 812 2025-02-26 13:21:36.142662-08 2025-02-26 12:50:20.677716-08 leaderboard t A100 0.00698412748 t \N \N \N {} -2404 812 2025-02-26 13:13:30.239955-08 2025-02-26 12:39:13.840154-08 test f L4 \N f \N \N \N {} -2405 812 2025-02-26 12:25:57.063676-08 2025-02-26 12:24:58.010138-08 test t H100 \N t \N \N \N {} -2410 812 2025-02-26 12:20:36.785307-08 2025-02-26 12:48:06.635821-08 test f T4 \N f \N \N \N {} -2435 839 2025-02-26 14:18:21.87914-08 2025-02-26 14:18:15.815334-08 benchmark t H100 \N t \N \N \N {} -2436 839 2025-02-26 14:10:30.590633-08 2025-02-26 14:11:00.415518-08 leaderboard t H100 0.0014024583333333332 t \N \N \N {} -2441 845 2025-02-26 14:15:04.422652-08 2025-02-26 14:13:19.625716-08 test t L4 \N t \N \N \N {} -2447 852 2025-02-26 14:16:39.200008-08 2025-02-26 13:25:34.941047-08 benchmark f A100 \N t \N \N \N {} -2442 845 2025-02-26 13:50:40.358584-08 2025-02-26 13:07:51.38911-08 benchmark t L4 \N t \N \N \N {} -2451 856 2025-02-26 12:43:06.254876-08 2025-02-26 13:32:18.217747-08 benchmark f A100 \N t \N \N \N {} -2452 858 2025-02-26 13:00:26.844507-08 2025-02-26 14:38:31.646993-08 test f T4 \N f \N \N \N {} -2453 859 2025-02-26 14:36:40.012139-08 2025-02-26 13:10:56.467322-08 benchmark f H100 \N t \N \N \N {} -2455 860 2025-02-26 13:23:16.833722-08 2025-02-26 14:41:34.259251-08 benchmark f A100 \N t \N \N \N {} -2462 864 2025-02-26 13:11:46.57133-08 2025-02-26 14:39:28.851851-08 test f T4 \N f \N \N \N {} -2463 865 2025-02-26 13:11:01.636695-08 2025-02-26 14:32:14.927023-08 test f A100 \N t \N \N \N {} -2464 865 2025-02-26 13:30:02.44975-08 2025-02-26 14:19:35.875255-08 benchmark f A100 \N t \N \N \N {} -2465 865 2025-02-26 13:09:06.850344-08 2025-02-26 14:04:10.639691-08 leaderboard f A100 0.0068088155999999995 t \N \N \N {} -2466 865 2025-02-26 14:08:58.695846-08 2025-02-26 12:57:46.38922-08 test t A100 \N t \N \N \N {} -3385 1166 2025-02-28 10:20:27.28906-08 2025-02-28 10:10:11.161724-08 leaderboard t L4 0.017129881333333333 t \N \N \N {} -2481 870 2025-02-26 13:59:09.266137-08 2025-02-26 13:07:02.894219-08 leaderboard f H100 0.001470785 t \N \N \N {} -2820 983 2025-02-26 23:04:13.026351-08 2025-02-26 23:16:23.959588-08 test f T4 \N t \N \N \N {} -2508 872 2025-02-26 13:21:09.133466-08 2025-02-26 13:53:51.09567-08 benchmark f A100 \N t \N \N \N {} -2509 872 2025-02-26 14:26:03.179438-08 2025-02-26 13:48:36.711846-08 leaderboard f A100 0.003223094 t \N \N \N {} -3286 1147 2025-02-28 07:23:54.538307-08 2025-02-28 06:54:57.696014-08 benchmark f L4 \N t \N \N \N {} -2511 872 2025-02-26 13:29:54.390732-08 2025-02-26 14:56:13.502248-08 benchmark f H100 \N t \N \N \N {} -2512 872 2025-02-26 14:00:24.384-08 2025-02-26 14:52:47.576219-08 leaderboard f H100 0.0014829895714285714 t \N \N \N {} -2513 872 2025-02-26 14:08:21.032655-08 2025-02-26 14:48:45.337774-08 test f T4 \N t \N \N \N {} -2514 872 2025-02-26 13:43:03.545363-08 2025-02-26 13:27:16.423676-08 benchmark f T4 \N t \N \N \N {} -2515 872 2025-02-26 14:53:04.043695-08 2025-02-26 14:31:15.017571-08 leaderboard f T4 0.016977648666666668 t \N \N \N {} -3297 1148 2025-02-28 05:56:14.732913-08 2025-02-28 06:35:38.237057-08 benchmark t T4 \N t \N \N \N {} -2516 872 2025-02-26 13:59:35.326154-08 2025-02-26 14:28:54.006791-08 test t T4 \N t \N \N \N {} -2517 872 2025-02-26 14:10:22.48064-08 2025-02-26 13:05:44.349918-08 benchmark t T4 \N t \N \N \N {} -2518 872 2025-02-26 13:38:47.072409-08 2025-02-26 13:18:53.541371-08 leaderboard t T4 0.016951106 t \N \N \N {} -2519 872 2025-02-26 14:41:30.759683-08 2025-02-26 13:51:40.998977-08 test f L4 \N t \N \N \N {} -2520 872 2025-02-26 13:15:09.29921-08 2025-02-26 13:07:54.264512-08 benchmark f L4 \N t \N \N \N {} -3181 1120 2025-02-28 02:42:01.210008-08 2025-02-28 02:43:08.325982-08 test t A100 \N t \N \N \N {} -154 23 2025-02-23 11:57:05.139267-08 2025-02-23 11:09:15.68083-08 benchmark f T4 \N t \N \N \N {} -180 27 2025-02-23 10:28:14.91215-08 2025-02-23 11:48:21.706378-08 test t T4 \N t \N \N \N {} -2521 872 2025-02-26 14:43:07.037109-08 2025-02-26 14:45:02.826917-08 leaderboard f L4 0.017294490333333332 t \N \N \N {} -2522 872 2025-02-26 14:50:44.169022-08 2025-02-26 14:42:28.113826-08 test t L4 \N t \N \N \N {} -2523 872 2025-02-26 13:56:52.894596-08 2025-02-26 13:28:48.739095-08 benchmark t L4 \N t \N \N \N {} -2527 875 2025-02-26 14:23:38.227767-08 2025-02-26 13:30:18.365385-08 test t L4 \N t \N \N \N {} -3589 1229 2025-02-28 13:21:19.11789-08 2025-02-28 14:31:58.395368-08 leaderboard f H100 0.0011892495 t \N \N \N {} -2532 875 2025-02-26 14:59:13.072405-08 2025-02-26 13:27:06.641704-08 leaderboard f H100 0.001465262 t \N \N \N {} -2533 875 2025-02-26 14:47:12.530795-08 2025-02-26 13:13:21.249202-08 test f A100 \N t \N \N \N {} -2534 875 2025-02-26 13:48:48.551741-08 2025-02-26 13:28:31.146854-08 benchmark f A100 \N t \N \N \N {} -2535 875 2025-02-26 14:39:27.031045-08 2025-02-26 13:25:27.404117-08 leaderboard f A100 0.0032578983333333335 t \N \N \N {} -3312 1150 2025-02-28 07:23:23.622707-08 2025-02-28 06:09:54.030373-08 benchmark f H100 \N t \N \N \N {} -2537 875 2025-02-26 14:32:13.549304-08 2025-02-26 14:39:07.302029-08 benchmark t A100 \N t \N \N \N {} -2548 875 2025-02-26 14:56:04.001163-08 2025-02-26 13:42:23.904365-08 test f L4 \N t \N \N \N {} -2549 875 2025-02-26 14:18:16.659538-08 2025-02-26 13:23:18.958744-08 benchmark f L4 \N t \N \N \N {} -2550 875 2025-02-26 14:22:09.462839-08 2025-02-26 14:54:20.343262-08 leaderboard f L4 0.01742031 t \N \N \N {} -2551 877 2025-02-26 13:57:57.39587-08 2025-02-26 13:12:36.270032-08 test f L4 \N t \N \N \N {} -2573 877 2025-02-26 13:16:45.52631-08 2025-02-26 14:52:12.270412-08 test t T4 \N t \N \N \N {} -3399 1172 2025-02-28 10:30:05.610744-08 2025-02-28 09:16:34.458793-08 test f L4 \N f \N \N \N {} -2553 877 2025-02-26 13:10:29.354581-08 2025-02-26 14:37:38.257685-08 leaderboard f L4 0.01741081066666667 t \N \N \N {} -2554 877 2025-02-26 13:58:10.825604-08 2025-02-26 14:00:37.261867-08 test t L4 \N t \N \N \N {} -2555 877 2025-02-26 13:48:17.730063-08 2025-02-26 14:50:07.298178-08 benchmark t L4 \N t \N \N \N {} -2556 877 2025-02-26 13:50:16.824546-08 2025-02-26 14:44:02.236307-08 leaderboard t L4 0.017350969 t \N \N \N {} -3353 1157 2025-02-28 07:00:17.816299-08 2025-02-28 08:05:40.861-08 test t A100 \N t \N \N \N {} -2559 877 2025-02-26 14:12:21.70114-08 2025-02-26 14:49:53.732127-08 benchmark f A100 \N t \N \N \N {} -2560 877 2025-02-26 13:13:01.61476-08 2025-02-26 14:52:53.675129-08 leaderboard f A100 0.00324053 t \N \N \N {} -2561 877 2025-02-26 14:17:12.652893-08 2025-02-26 14:33:51.585787-08 test f H100 \N t \N \N \N {} -3356 1157 2025-02-28 07:54:22.893197-08 2025-02-28 07:34:43.645784-08 test f A100 \N t \N \N \N {} -2563 877 2025-02-26 13:56:06.528311-08 2025-02-26 15:06:14.803986-08 leaderboard f H100 0.001457541 t \N \N \N {} -2564 877 2025-02-26 14:47:01.068968-08 2025-02-26 14:04:16.625491-08 test t H100 \N t \N \N \N {} -2565 877 2025-02-26 13:58:40.178869-08 2025-02-26 15:00:37.290202-08 benchmark t H100 \N t \N \N \N {} -2566 877 2025-02-26 14:22:11.994956-08 2025-02-26 14:57:41.541904-08 leaderboard t H100 0.00144549375 t \N \N \N {} -3359 1158 2025-02-28 06:54:49.380912-08 2025-02-28 07:18:43.60823-08 test f T4 \N f \N \N \N {} -3859 1292 2025-03-01 03:27:40.255676-08 2025-03-01 02:36:03.541477-08 test f A100 \N t \N \N \N {} -2568 877 2025-02-26 14:12:07.205591-08 2025-02-26 13:51:47.845088-08 benchmark t A100 \N t \N \N \N {} -2569 877 2025-02-26 13:57:24.028157-08 2025-02-26 14:47:07.033256-08 leaderboard t A100 0.0032292653333333334 t \N \N \N {} -2570 877 2025-02-26 14:16:41.930867-08 2025-02-26 13:09:12.487972-08 test f T4 \N t \N \N \N {} -2571 877 2025-02-26 14:36:17.713292-08 2025-02-26 14:26:39.900745-08 benchmark f T4 \N t \N \N \N {} -2572 877 2025-02-26 15:02:18.460758-08 2025-02-26 14:58:57.369637-08 leaderboard f T4 0.01811966666666667 t \N \N \N {} -3406 1174 2025-02-28 09:14:03.047294-08 2025-02-28 09:16:54.531135-08 leaderboard t L4 0.017114292333333333 t \N \N \N {} -2575 877 2025-02-26 13:12:49.74724-08 2025-02-26 14:37:18.913817-08 leaderboard t T4 0.01813283333333333 t \N \N \N {} -2576 878 2025-02-26 13:16:27.296503-08 2025-02-26 14:27:29.207894-08 benchmark f T4 \N f \N \N \N {} -2577 879 2025-02-26 14:34:25.671098-08 2025-02-26 13:45:30.958405-08 benchmark f T4 \N t \N \N \N {} -2578 881 2025-02-26 13:39:45.596644-08 2025-02-26 14:01:02.933021-08 test f T4 \N f \N \N \N {} -2579 881 2025-02-26 14:04:26.768148-08 2025-02-26 13:45:26.952914-08 test f L4 \N f \N \N \N {} -3408 1175 2025-02-28 10:29:09.88226-08 2025-02-28 09:14:06.803313-08 test f L4 \N f \N \N \N {} -3862 1292 2025-03-01 03:25:13.486827-08 2025-03-01 02:53:16.099437-08 test t A100 \N t \N \N \N {} -2581 882 2025-02-26 13:33:10.645023-08 2025-02-26 13:55:42.912415-08 test f L4 \N t \N \N \N {} -2582 883 2025-02-26 14:19:49.686171-08 2025-02-26 14:38:27.107218-08 benchmark f L4 \N t \N \N \N {} -2583 883 2025-02-26 13:51:19.440545-08 2025-02-26 14:09:42.760292-08 benchmark f H100 \N t \N \N \N {} -2584 883 2025-02-26 14:39:09.643525-08 2025-02-26 13:31:21.985198-08 benchmark f A100 \N t \N \N \N {} -3418 1178 2025-02-28 10:35:09.967262-08 2025-02-28 11:06:54.18143-08 test f L4 \N f \N \N \N {} -3865 1293 2025-03-01 02:35:22.489765-08 2025-03-01 04:14:08.608387-08 test f A100 \N t \N \N \N {} -2587 884 2025-02-26 13:22:21.621679-08 2025-02-26 13:44:27.093032-08 benchmark f A100 \N t \N \N \N {} -2588 884 2025-02-26 14:52:31.919133-08 2025-02-26 13:38:05.904058-08 leaderboard f A100 0.003262141 t \N \N \N {} -2589 885 2025-02-26 13:31:31.720937-08 2025-02-26 13:36:09.135751-08 test f H100 \N t \N \N \N {} -2607 884 2025-02-26 15:03:02.758478-08 2025-02-26 14:21:28.592486-08 test t T4 \N t \N \N \N {} -3419 1178 2025-02-28 09:14:52.41289-08 2025-02-28 09:52:57.968186-08 test t L4 \N f \N \N \N {} -2592 884 2025-02-26 14:47:15.785668-08 2025-02-26 14:38:49.447224-08 test f L4 \N t \N \N \N {} -2062 700 2025-02-25 13:08:14.95001-08 2025-02-25 15:03:46.466768-08 test f A100 \N f \N \N \N {} -4223 1394 2025-03-01 14:57:35.411891-08 2025-03-01 15:35:59.507097-08 benchmark t A100 \N t \N \N \N {} -4247 1402 2025-03-01 17:04:55.972494-08 2025-03-01 17:47:24.376431-08 test t H100 \N t \N \N \N {} -4228 1395 2025-03-01 17:18:07.852776-08 2025-03-01 15:48:31.55626-08 benchmark f A100 \N t \N \N \N {} -4229 1396 2025-03-01 16:29:41.366025-08 2025-03-01 17:24:30.840608-08 test f H100 \N t \N \N \N {} -4717 1477 2025-03-02 08:12:18.55485-08 2025-03-02 06:22:18.597758-08 test f A100 \N t \N \N \N {} -4231 1398 2025-03-01 17:25:09.232586-08 2025-03-01 17:18:31.325671-08 test t A100 \N t \N \N \N {} -4232 1398 2025-03-01 16:28:07.719269-08 2025-03-01 15:58:31.010676-08 benchmark t A100 \N t \N \N \N {} -4233 1398 2025-03-01 15:43:44.691938-08 2025-03-01 17:19:39.809228-08 leaderboard t A100 0.0031178923333333333 t \N \N \N {} -4234 1398 2025-03-01 16:39:23.354655-08 2025-03-01 15:51:38.701412-08 test f H100 \N t \N \N \N {} -4253 1403 2025-03-01 17:26:34.106395-08 2025-03-01 17:42:47.155658-08 test t H100 \N t \N \N \N {} -4236 1398 2025-03-01 16:06:23.791952-08 2025-03-01 17:26:44.856408-08 leaderboard f H100 0.0014146966666666668 t \N \N \N {} -4237 1398 2025-03-01 15:36:12.31163-08 2025-03-01 15:33:41.234566-08 test t H100 \N t \N \N \N {} -4238 1398 2025-03-01 16:56:17.454711-08 2025-03-01 17:19:38.557978-08 benchmark t H100 \N t \N \N \N {} -4239 1398 2025-03-01 16:47:15.049127-08 2025-03-01 16:11:47.093143-08 leaderboard t H100 0.0014016626666666667 t \N \N \N {} -4378 1432 2025-03-01 22:51:39.742514-08 2025-03-01 22:23:37.282462-08 benchmark f T4 \N t \N \N \N {} -4242 1399 2025-03-01 16:22:07.45004-08 2025-03-01 16:01:06.543886-08 benchmark f A100 \N f \N \N \N {} -4243 1400 2025-03-01 17:07:07.130708-08 2025-03-01 16:24:06.325488-08 benchmark f H100 \N t \N \N \N {} -4244 1400 2025-03-01 17:29:39.977791-08 2025-03-01 17:02:08.939787-08 benchmark f A100 \N f \N \N \N {} -4346 1425 2025-03-01 18:21:27.980051-08 2025-03-01 19:51:56.286091-08 benchmark t L4 \N t \N \N \N {} -4347 1425 2025-03-01 18:37:42.52065-08 2025-03-01 19:22:48.263204-08 leaderboard t L4 0.01815886933333333 t \N \N \N {} -4348 1425 2025-03-01 18:57:46.285303-08 2025-03-01 20:03:56.603557-08 test f A100 \N t \N \N \N {} -4349 1425 2025-03-01 19:34:06.836256-08 2025-03-01 19:17:45.64278-08 benchmark f A100 \N t \N \N \N {} -4350 1425 2025-03-01 19:38:27.662128-08 2025-03-01 18:55:37.052703-08 leaderboard f A100 0.0033818667999999997 t \N \N \N {} -4352 1425 2025-03-01 18:49:23.803275-08 2025-03-01 19:23:11.101795-08 benchmark t A100 \N t \N \N \N {} -4357 1425 2025-03-01 19:41:41.606451-08 2025-03-01 18:23:09.857404-08 test t H100 \N t \N \N \N {} -4358 1425 2025-03-01 20:09:08.19207-08 2025-03-01 19:19:21.809996-08 benchmark t H100 \N t \N \N \N {} -4359 1425 2025-03-01 19:59:58.903988-08 2025-03-01 19:30:42.092627-08 leaderboard t H100 0.0015028730833333333 t \N \N \N {} -4361 1425 2025-03-01 18:31:13.881994-08 2025-03-01 18:26:52.74436-08 benchmark f T4 \N t \N \N \N {} -4362 1425 2025-03-01 20:12:38.573176-08 2025-03-01 19:33:01.456733-08 leaderboard f T4 0.01936384925 t \N \N \N {} -4363 1425 2025-03-01 19:42:14.310793-08 2025-03-01 18:41:20.723626-08 test f L4 \N t \N \N \N {} -4389 1440 2025-03-02 00:03:48.502169-08 2025-03-01 23:51:41.439127-08 test f A100 \N f \N \N \N {} -4390 1440 2025-03-02 00:04:14.366803-08 2025-03-01 22:49:12.463788-08 test f L4 \N f \N \N \N {} -4391 1440 2025-03-02 00:19:06.804988-08 2025-03-01 22:28:37.519544-08 test t T4 \N f \N \N \N {} -155 23 2025-02-23 11:57:27.550237-08 2025-02-23 11:56:29.734882-08 leaderboard f T4 0.048176454 t \N \N \N {} -157 23 2025-02-23 11:12:21.468647-08 2025-02-23 10:43:21.975416-08 benchmark f L4 \N t \N \N \N {} -4392 1440 2025-03-01 22:48:12.01349-08 2025-03-01 23:58:44.155125-08 test f T4 \N f \N \N \N {} -4877 1500 2025-03-03 10:04:01.91414-08 2025-03-03 08:30:53.107105-08 test f A100 \N t \N \N \N {} -4394 1440 2025-03-01 22:21:22.693848-08 2025-03-01 22:51:41.663873-08 test t H100 \N f \N \N \N {} -4395 1441 2025-03-01 23:58:46.89894-08 2025-03-01 23:53:44.591271-08 test f H100 \N f \N \N \N {} -4396 1441 2025-03-01 22:28:23.815345-08 2025-03-01 23:22:03.532582-08 test t L4 \N f \N \N \N {} -4397 1441 2025-03-01 23:53:44.616031-08 2025-03-01 23:38:33.596759-08 test t A100 \N f \N \N \N {} -4884 1505 2025-03-03 12:56:34.606368-08 2025-03-03 12:53:18.783025-08 test t A100 \N f \N \N \N {} -4399 1441 2025-03-01 23:03:04.456378-08 2025-03-02 00:06:04.885856-08 test f T4 \N f \N \N \N {} -4400 1441 2025-03-01 23:24:40.888023-08 2025-03-01 22:59:35.615921-08 test t H100 \N f \N \N \N {} -4401 1441 2025-03-02 00:02:25.303815-08 2025-03-01 22:57:40.957396-08 test t T4 \N f \N \N \N {} -4402 1441 2025-03-01 22:31:27.972284-08 2025-03-02 00:14:26.267738-08 test f L4 \N f \N \N \N {} -5107 1565 2025-03-04 18:00:00.576218-08 2025-03-04 18:32:07.019701-08 test f A100 \N t \N \N \N {} -4411 1443 2025-03-01 23:33:39.745676-08 2025-03-02 00:11:02.528027-08 test f L4 \N t \N \N \N {} -4412 1443 2025-03-02 01:02:06.237344-08 2025-03-01 23:53:38.020406-08 benchmark f L4 \N t \N \N \N {} -4413 1443 2025-03-02 00:45:38.730106-08 2025-03-02 00:36:30.242811-08 leaderboard f L4 0.009132959666666666 t \N \N \N {} -4418 1443 2025-03-01 23:15:31.653046-08 2025-03-02 00:20:24.722124-08 benchmark t A100 \N t \N \N \N {} -4419 1443 2025-03-01 23:11:05.373713-08 2025-03-02 00:24:01.197231-08 leaderboard t A100 0.002639421 t \N \N \N {} -4420 1443 2025-03-02 00:51:28.235424-08 2025-03-01 23:57:03.643851-08 test f A100 \N t \N \N \N {} -4421 1443 2025-03-02 00:14:40.806085-08 2025-03-02 00:04:05.289386-08 benchmark f A100 \N t \N \N \N {} -4422 1443 2025-03-01 23:14:33.6022-08 2025-03-02 00:33:33.808058-08 leaderboard f A100 0.0022384285652173913 t \N \N \N {} -4426 1443 2025-03-02 00:44:05.280092-08 2025-03-02 00:15:14.019635-08 test t H100 \N t \N \N \N {} -4427 1443 2025-03-01 23:59:15.284401-08 2025-03-02 00:58:48.422971-08 benchmark t H100 \N t \N \N \N {} -4428 1443 2025-03-02 00:11:17.701257-08 2025-03-01 23:26:04.825333-08 leaderboard t H100 0.00150569175 t \N \N \N {} -4429 1443 2025-03-01 23:25:46.014191-08 2025-03-02 00:05:37.324677-08 test f T4 \N t \N \N \N {} -2776 965 2025-02-26 21:06:31.43475-08 2025-02-26 19:53:09.101962-08 test t A100 \N t \N \N \N {} -4482 1445 2025-03-01 23:50:49.419996-08 2025-03-02 00:57:24.575893-08 leaderboard t L4 0.009311355333333333 t \N \N \N {} -4483 1446 2025-03-01 23:44:24.497614-08 2025-03-02 00:35:56.595882-08 test f A100 \N t \N \N \N {} -4484 1447 2025-03-01 23:27:17.550566-08 2025-03-02 01:12:31.419414-08 test f H100 \N t \N \N \N {} -4485 1447 2025-03-02 00:26:50.566555-08 2025-03-02 00:25:59.636108-08 benchmark f H100 \N f \N \N \N {} -4487 1447 2025-03-01 23:39:57.57287-08 2025-03-02 00:18:06.18322-08 benchmark t A100 \N f \N \N \N {} -2045 697 2025-02-25 13:02:11.887058-08 2025-02-25 13:35:15.729017-08 test f T4 \N f \N \N \N {} -2047 698 2025-02-25 14:51:14.379308-08 2025-02-25 14:49:23.452588-08 test f T4 \N t \N \N \N {} -2791 974 2025-02-26 20:28:12.007924-08 2025-02-26 21:26:15.641402-08 leaderboard t A100 0.003154194 t \N \N \N {} -3509 1201 2025-02-28 11:18:27.367438-08 2025-02-28 11:48:52.303198-08 test f A100 \N t \N \N \N {} -4498 1447 2025-03-01 23:16:26.107227-08 2025-03-02 00:47:04.910932-08 test t L4 \N t \N \N \N {} -4499 1447 2025-03-02 00:01:54.884825-08 2025-03-01 23:38:49.704891-08 benchmark t L4 \N f \N \N \N {} -4891 1507 2025-03-03 12:40:00.041984-08 2025-03-03 13:14:35.944127-08 test t A100 \N t \N \N \N {} -4504 1448 2025-03-01 23:24:02.182135-08 2025-03-01 23:40:44.693368-08 benchmark t A100 \N t \N \N \N {} -4505 1448 2025-03-02 00:40:33.995417-08 2025-03-02 00:56:55.060033-08 leaderboard t A100 0.00524246372 t \N \N \N {} -4506 1449 2025-03-02 00:45:08.313002-08 2025-03-02 00:16:16.737716-08 test t H100 \N t \N \N \N {} -4507 1449 2025-03-01 23:29:42.540346-08 2025-03-01 23:39:28.57237-08 benchmark t H100 \N t \N \N \N {} -4508 1449 2025-03-02 01:19:24.4957-08 2025-03-01 23:51:56.634988-08 leaderboard t H100 0.0033840856666666665 t \N \N \N {} -4509 1449 2025-03-02 00:12:06.024757-08 2025-03-02 01:10:54.396657-08 test f H100 \N t \N \N \N {} -4510 1449 2025-03-02 01:18:17.542284-08 2025-03-02 00:19:32.525502-08 benchmark f H100 \N t \N \N \N {} -4511 1449 2025-03-01 23:57:24.368485-08 2025-03-02 00:13:37.996217-08 leaderboard f H100 0.00341148875 t \N \N \N {} -4512 1450 2025-03-02 00:45:11.616534-08 2025-03-02 00:48:49.228002-08 test t H100 \N t \N \N \N {} -4513 1450 2025-03-01 23:40:22.279655-08 2025-03-02 00:00:22.363114-08 benchmark t H100 \N t \N \N \N {} -4899 1508 2025-03-03 13:37:04.374969-08 2025-03-03 13:09:34.935444-08 leaderboard t H100 0.007641404666666667 t \N \N \N {} -4517 1450 2025-03-02 00:03:53.293716-08 2025-03-02 00:25:33.705236-08 leaderboard t L4 0.009105309 t \N \N \N {} -4518 1450 2025-03-02 00:49:38.190529-08 2025-03-02 00:22:20.570554-08 test f A100 \N t \N \N \N {} -4519 1450 2025-03-02 00:11:10.810461-08 2025-03-02 01:03:13.770329-08 benchmark f A100 \N t \N \N \N {} -4520 1450 2025-03-02 01:08:07.36635-08 2025-03-02 00:17:23.477428-08 leaderboard f A100 0.0018727816666666668 t \N \N \N {} -4524 1450 2025-03-02 01:17:14.573469-08 2025-03-02 00:17:34.147369-08 test t T4 \N t \N \N \N {} -4525 1450 2025-03-02 00:43:27.056954-08 2025-03-01 23:43:04.43667-08 benchmark t T4 \N t \N \N \N {} -4526 1450 2025-03-02 00:53:05.519328-08 2025-03-02 00:06:06.503136-08 leaderboard t T4 0.009121147333333334 t \N \N \N {} -4527 1450 2025-03-01 23:54:26.363976-08 2025-03-02 01:19:17.019076-08 test t A100 \N t \N \N \N {} -4528 1450 2025-03-02 01:18:02.912247-08 2025-03-01 23:49:49.929737-08 benchmark t A100 \N t \N \N \N {} -4529 1450 2025-03-01 23:35:24.197844-08 2025-03-02 01:11:23.44255-08 leaderboard t A100 0.0017849657777777778 t \N \N \N {} -4530 1450 2025-03-02 00:54:36.280862-08 2025-03-01 23:35:47.462129-08 test f T4 \N t \N \N \N {} -4531 1450 2025-03-02 01:00:08.84832-08 2025-03-02 00:06:08.090762-08 benchmark f T4 \N t \N \N \N {} -4532 1450 2025-03-01 23:55:16.426583-08 2025-03-02 00:04:54.110158-08 leaderboard f T4 0.009106582333333333 t \N \N \N {} -4533 1450 2025-03-02 01:11:28.694794-08 2025-03-02 00:06:59.909067-08 test f L4 \N t \N \N \N {} -4534 1450 2025-03-02 00:11:02.404367-08 2025-03-02 01:17:42.734039-08 benchmark f L4 \N t \N \N \N {} -6713 2081 2025-03-15 02:46:39.538367-07 2025-03-15 03:50:32.068412-07 test t A100 \N t \N \N \N {} -4537 1451 2025-03-02 00:52:04.139941-08 2025-03-02 00:11:49.100971-08 benchmark t H100 \N t \N \N \N {} -4538 1451 2025-03-02 00:51:58.06123-08 2025-03-02 00:02:39.242368-08 leaderboard t H100 0.001079664 t \N \N \N {} -4539 1451 2025-03-02 00:06:02.121158-08 2025-03-02 00:00:09.264632-08 test f H100 \N t \N \N \N {} -4540 1451 2025-03-02 00:23:19.306421-08 2025-03-02 00:51:05.732006-08 benchmark f H100 \N t \N \N \N {} -4541 1451 2025-03-01 23:45:53.396285-08 2025-03-02 01:03:28.077189-08 leaderboard f H100 0.0010786196666666668 t \N \N \N {} -4626 1459 2025-03-02 06:58:07.444981-08 2025-03-02 06:44:02.224251-08 test t A100 \N t \N \N \N {} -4627 1459 2025-03-02 06:21:17.548115-08 2025-03-02 06:15:21.551657-08 benchmark t A100 \N t \N \N \N {} -4628 1459 2025-03-02 07:19:36.161701-08 2025-03-02 06:24:21.742016-08 leaderboard t A100 0.0030925023333333336 t \N \N \N {} -4629 1459 2025-03-02 05:52:24.785964-08 2025-03-02 05:52:16.34119-08 test f A100 \N t \N \N \N {} -4630 1459 2025-03-02 06:37:13.167125-08 2025-03-02 05:59:45.399577-08 benchmark f A100 \N t \N \N \N {} -4631 1459 2025-03-02 05:50:19.68331-08 2025-03-02 05:29:11.389842-08 leaderboard f A100 0.003070814 t \N \N \N {} -4635 1460 2025-03-02 07:14:27.918333-08 2025-03-02 05:44:05.344296-08 test f A100 \N t \N \N \N {} -4636 1460 2025-03-02 06:43:52.831925-08 2025-03-02 07:13:44.23175-08 benchmark f A100 \N t \N \N \N {} -4637 1460 2025-03-02 06:16:46.167909-08 2025-03-02 06:00:38.936259-08 leaderboard f A100 0.0030692296666666665 t \N \N \N {} -4638 1461 2025-03-02 06:03:46.169582-08 2025-03-02 06:31:43.401529-08 test t A100 \N t \N \N \N {} -4639 1461 2025-03-02 06:35:00.966361-08 2025-03-02 05:55:09.655918-08 benchmark t A100 \N t \N \N \N {} -4640 1461 2025-03-02 05:53:37.416513-08 2025-03-02 06:54:43.324146-08 leaderboard t A100 0.003078079 t \N \N \N {} -4644 1462 2025-03-02 06:49:33.296679-08 2025-03-02 06:48:19.278536-08 test f A100 \N t \N \N \N {} -4645 1462 2025-03-02 05:51:40.609401-08 2025-03-02 05:54:32.467256-08 benchmark f A100 \N t \N \N \N {} -4646 1462 2025-03-02 07:10:09.012521-08 2025-03-02 06:39:52.753547-08 leaderboard f A100 0.0030936686666666667 t \N \N \N {} -4647 1462 2025-03-02 06:21:07.074149-08 2025-03-02 06:19:05.973852-08 test t A100 \N t \N \N \N {} -4648 1462 2025-03-02 06:22:56.946678-08 2025-03-02 06:32:18.964173-08 benchmark t A100 \N t \N \N \N {} -4649 1462 2025-03-02 06:57:59.888395-08 2025-03-02 06:30:20.934248-08 leaderboard t A100 0.0030776453333333335 t \N \N \N {} -4652 1463 2025-03-02 06:00:11.005396-08 2025-03-02 05:45:35.797748-08 leaderboard t A100 0.0030704546666666666 t \N \N \N {} -4654 1463 2025-03-02 07:23:29.318291-08 2025-03-02 06:15:05.549905-08 benchmark f A100 \N t \N \N \N {} -158 23 2025-02-23 10:46:27.961353-08 2025-02-23 10:54:07.890706-08 leaderboard f L4 0.043783646333333336 t \N \N \N {} -159 23 2025-02-23 11:24:25.855339-08 2025-02-23 11:24:36.858525-08 test t L4 \N t \N \N \N {} -160 23 2025-02-23 11:23:10.687318-08 2025-02-23 10:51:27.096721-08 benchmark t L4 \N t \N \N \N {} -161 23 2025-02-23 10:17:17.903963-08 2025-02-23 11:20:35.401154-08 leaderboard t L4 0.04378879433333334 t \N \N \N {} -162 24 2025-02-23 11:04:17.242792-08 2025-02-23 12:05:18.328255-08 test f T4 \N f \N \N \N {} -163 25 2025-02-23 11:29:26.781421-08 2025-02-23 11:38:33.754711-08 test f H100 \N t \N \N \N {} -164 25 2025-02-23 11:46:38.661863-08 2025-02-23 11:39:19.931063-08 test f A100 \N t \N \N \N {} -165 25 2025-02-23 11:44:46.673883-08 2025-02-23 12:23:21.640415-08 test f T4 \N t \N \N \N {} -4655 1463 2025-03-02 05:44:44.87667-08 2025-03-02 06:39:01.814288-08 leaderboard f A100 0.003075862 t \N \N \N {} -4656 1464 2025-03-02 06:24:06.602618-08 2025-03-02 06:25:40.742865-08 test t A100 \N t \N \N \N {} -4657 1464 2025-03-02 06:25:44.512665-08 2025-03-02 06:07:00.018787-08 benchmark t A100 \N t \N \N \N {} -4658 1464 2025-03-02 07:24:38.102391-08 2025-03-02 07:03:30.28672-08 leaderboard t A100 0.0030908703333333335 t \N \N \N {} -4662 1465 2025-03-02 06:45:59.862943-08 2025-03-02 06:27:15.188637-08 test f A100 \N t \N \N \N {} -4663 1465 2025-03-02 06:12:13.396238-08 2025-03-02 06:13:12.261244-08 benchmark f A100 \N t \N \N \N {} -4664 1465 2025-03-02 05:44:57.969997-08 2025-03-02 07:15:25.843464-08 leaderboard f A100 0.0031012606666666665 t \N \N \N {} -4665 1465 2025-03-02 07:22:35.937837-08 2025-03-02 07:15:34.451037-08 test t A100 \N t \N \N \N {} -4666 1465 2025-03-02 05:41:53.096577-08 2025-03-02 06:59:11.932007-08 benchmark t A100 \N t \N \N \N {} -4667 1465 2025-03-02 07:22:50.191152-08 2025-03-02 06:20:27.38764-08 leaderboard t A100 0.0031609141666666665 t \N \N \N {} -4671 1466 2025-03-02 06:48:49.912925-08 2025-03-02 06:36:05.088369-08 test f A100 \N t \N \N \N {} -4672 1466 2025-03-02 07:00:40.126712-08 2025-03-02 07:05:41.314194-08 benchmark f A100 \N t \N \N \N {} -4673 1466 2025-03-02 07:03:50.192296-08 2025-03-02 06:21:41.476403-08 leaderboard f A100 0.0030736896666666664 t \N \N \N {} -4674 1467 2025-03-02 07:34:10.787696-08 2025-03-02 07:29:26.411023-08 test t A100 \N t \N \N \N {} -4675 1467 2025-03-02 06:56:49.136222-08 2025-03-02 06:41:21.315023-08 benchmark t A100 \N t \N \N \N {} -4807 1487 2025-03-02 12:30:33.450976-08 2025-03-02 13:35:13.770124-08 benchmark f T4 \N f \N \N \N {} -4693 1470 2025-03-02 06:12:06.283994-08 2025-03-02 06:51:42.391905-08 benchmark t A100 \N t \N \N \N {} -4694 1470 2025-03-02 06:33:53.917995-08 2025-03-02 07:33:01.914128-08 leaderboard t A100 0.004008321333333334 t \N \N \N {} -4698 1471 2025-03-02 07:29:03.787601-08 2025-03-02 06:41:20.283599-08 test f A100 \N t \N \N \N {} -4699 1471 2025-03-02 05:48:12.197045-08 2025-03-02 06:12:43.42567-08 benchmark f A100 \N t \N \N \N {} -4700 1471 2025-03-02 06:56:34.879652-08 2025-03-02 07:44:48.551731-08 leaderboard f A100 0.0031009833333333335 t \N \N \N {} -4701 1471 2025-03-02 06:30:38.128029-08 2025-03-02 07:27:12.350551-08 test t A100 \N t \N \N \N {} -4702 1471 2025-03-02 07:44:43.71905-08 2025-03-02 07:20:57.056431-08 benchmark t A100 \N t \N \N \N {} -4703 1471 2025-03-02 05:52:40.876763-08 2025-03-02 06:18:11.15908-08 leaderboard t A100 0.003077995 t \N \N \N {} -4708 1476 2025-03-02 07:04:38.833526-08 2025-03-02 06:22:07.053844-08 test t A100 \N t \N \N \N {} -4711 1476 2025-03-02 06:32:53.010168-08 2025-03-02 08:13:38.448364-08 test f A100 \N t \N \N \N {} -4712 1476 2025-03-02 07:07:03.075398-08 2025-03-02 06:39:50.920146-08 benchmark f A100 \N t \N \N \N {} -4713 1476 2025-03-02 07:29:16.863809-08 2025-03-02 08:09:32.549873-08 leaderboard f A100 0.003081995 t \N \N \N {} -4714 1477 2025-03-02 08:12:43.521171-08 2025-03-02 07:24:21.691154-08 test t A100 \N t \N \N \N {} -4715 1477 2025-03-02 08:12:18.641473-08 2025-03-02 08:16:06.720104-08 benchmark t A100 \N t \N \N \N {} -4716 1477 2025-03-02 07:47:17.510849-08 2025-03-02 06:38:36.379617-08 leaderboard t A100 0.0030823416666666665 t \N \N \N {} -4720 1478 2025-03-02 06:43:15.887395-08 2025-03-02 07:01:09.324629-08 test f A100 \N t \N \N \N {} -4721 1478 2025-03-02 06:19:24.519466-08 2025-03-02 07:02:12.8161-08 benchmark f A100 \N t \N \N \N {} -4722 1478 2025-03-02 07:24:47.923184-08 2025-03-02 06:22:30.821228-08 leaderboard f A100 0.0030851883333333336 t \N \N \N {} -4723 1478 2025-03-02 06:21:50.565071-08 2025-03-02 06:38:29.276371-08 test t A100 \N t \N \N \N {} -4724 1478 2025-03-02 07:55:40.137891-08 2025-03-02 08:07:32.643622-08 benchmark t A100 \N t \N \N \N {} -4725 1478 2025-03-02 08:04:19.082753-08 2025-03-02 06:39:54.506806-08 leaderboard t A100 0.0030785193333333336 t \N \N \N {} -4728 1479 2025-03-02 08:14:46.908587-08 2025-03-02 07:03:37.792816-08 leaderboard f A100 0.0030867813333333334 t \N \N \N {} -4729 1479 2025-03-02 06:45:56.767886-08 2025-03-02 08:16:10.271116-08 test t A100 \N t \N \N \N {} -4730 1479 2025-03-02 07:53:43.730007-08 2025-03-02 06:49:41.785807-08 benchmark t A100 \N t \N \N \N {} -4731 1479 2025-03-02 07:11:52.419771-08 2025-03-02 07:23:15.061576-08 leaderboard t A100 0.0031140623333333335 t \N \N \N {} -4732 1480 2025-03-02 07:09:43.554747-08 2025-03-02 06:58:05.009739-08 test t A100 \N t \N \N \N {} -4733 1480 2025-03-02 07:52:32.541906-08 2025-03-02 07:38:29.663474-08 benchmark t A100 \N t \N \N \N {} -4734 1480 2025-03-02 07:44:10.008433-08 2025-03-02 08:17:23.134924-08 leaderboard t A100 0.0030920546666666666 t \N \N \N {} -4738 1481 2025-03-02 07:04:34.701596-08 2025-03-02 07:21:13.788761-08 test f A100 \N t \N \N \N {} -4739 1481 2025-03-02 06:49:36.00208-08 2025-03-02 07:31:10.469625-08 benchmark f A100 \N t \N \N \N {} -4740 1481 2025-03-02 07:47:10.568157-08 2025-03-02 08:09:45.89037-08 leaderboard f A100 0.003085828 t \N \N \N {} -4741 1481 2025-03-02 06:48:53.469551-08 2025-03-02 07:03:55.679567-08 test t A100 \N t \N \N \N {} -4742 1481 2025-03-02 06:31:13.795781-08 2025-03-02 06:49:28.737931-08 benchmark t A100 \N t \N \N \N {} -4743 1481 2025-03-02 08:15:11.812141-08 2025-03-02 06:37:00.374019-08 leaderboard t A100 0.0030922536666666664 t \N \N \N {} -4744 1482 2025-03-02 07:43:03.723058-08 2025-03-02 07:35:15.071326-08 test f T4 \N f \N \N \N {} -4745 1483 2025-03-02 07:31:56.372241-08 2025-03-02 08:05:39.164579-08 test f T4 \N t \N \N \N {} -4746 1484 2025-03-02 08:35:02.123221-08 2025-03-02 07:56:53.596064-08 test f T4 \N t \N \N \N {} -4747 1484 2025-03-02 07:06:16.362073-08 2025-03-02 07:58:54.271757-08 benchmark f T4 \N t \N \N \N {} -4748 1484 2025-03-02 07:48:44.592479-08 2025-03-02 06:54:14.32109-08 leaderboard f T4 0.07661455133333332 t \N \N \N {} -4749 1484 2025-03-02 08:43:32.528981-08 2025-03-02 07:50:46.63591-08 test t T4 \N t \N \N \N {} -4750 1484 2025-03-02 07:57:18.393313-08 2025-03-02 07:47:01.612778-08 benchmark t T4 \N t \N \N \N {} -4751 1484 2025-03-02 07:42:07.868143-08 2025-03-02 08:02:04.953244-08 leaderboard t T4 0.07684545833333332 t \N \N \N {} -6735 2086 2025-03-15 04:33:46.257775-07 2025-03-15 04:41:06.722991-07 test t A100 \N t \N \N \N {} -4755 1485 2025-03-02 11:37:55.155684-08 2025-03-02 12:30:52.129299-08 test t A100 \N t \N \N \N {} -4756 1485 2025-03-02 11:54:47.977319-08 2025-03-02 11:49:51.955954-08 benchmark t A100 \N t \N \N \N {} -4757 1485 2025-03-02 12:35:43.476034-08 2025-03-02 13:11:14.518517-08 leaderboard t A100 0.0017045449166666667 t \N \N \N {} -4758 1485 2025-03-02 12:19:44.247676-08 2025-03-02 12:41:13.158847-08 test f H100 \N t \N \N \N {} -4762 1485 2025-03-02 13:26:36.984272-08 2025-03-02 13:09:50.663429-08 benchmark t H100 \N t \N \N \N {} -4763 1485 2025-03-02 11:57:05.793115-08 2025-03-02 12:51:08.493618-08 leaderboard t H100 0.0010300953333333333 t \N \N \N {} -4765 1485 2025-03-02 13:18:29.547704-08 2025-03-02 11:53:12.069821-08 benchmark f T4 \N t \N \N \N {} -4766 1485 2025-03-02 12:34:18.620178-08 2025-03-02 12:01:46.474428-08 leaderboard f T4 0.013093859 t \N \N \N {} -4770 1485 2025-03-02 11:28:06.060656-08 2025-03-02 12:33:39.575643-08 test t L4 \N t \N \N \N {} -4771 1485 2025-03-02 12:38:32.300929-08 2025-03-02 12:35:21.086611-08 benchmark t L4 \N t \N \N \N {} -4772 1485 2025-03-02 11:38:38.993577-08 2025-03-02 12:03:06.955576-08 leaderboard t L4 0.009067796666666666 t \N \N \N {} -4782 1486 2025-03-02 13:23:24.636755-08 2025-03-02 13:02:48.120735-08 leaderboard t L4 0.01330954 t \N \N \N {} -4783 1486 2025-03-02 11:52:32.464719-08 2025-03-02 11:51:42.704104-08 test t T4 \N t \N \N \N {} -4784 1486 2025-03-02 12:54:34.061003-08 2025-03-02 13:23:05.121934-08 benchmark t T4 \N f \N \N \N {} -4785 1486 2025-03-02 13:03:39.801457-08 2025-03-02 13:50:21.033648-08 test t H100 \N t \N \N \N {} -4786 1486 2025-03-02 13:02:54.20138-08 2025-03-02 13:03:49.524039-08 benchmark t H100 \N t \N \N \N {} -4787 1486 2025-03-02 13:37:42.944284-08 2025-03-02 13:04:38.530285-08 leaderboard t H100 0.014144686666666666 t \N \N \N {} -166 25 2025-02-23 11:29:44.547374-08 2025-02-23 11:45:47.371586-08 test f L4 \N t \N \N \N {} -6741 2087 2025-03-15 03:27:43.798435-07 2025-03-15 04:38:01.97182-07 test t A100 \N t \N \N \N {} -4789 1486 2025-03-02 13:07:40.863559-08 2025-03-02 12:56:43.445817-08 benchmark f T4 \N f \N \N \N {} -4790 1486 2025-03-02 11:54:48.604048-08 2025-03-02 13:06:33.970638-08 test f H100 \N t \N \N \N {} -4791 1486 2025-03-02 13:12:20.061089-08 2025-03-02 13:06:35.664868-08 benchmark f H100 \N t \N \N \N {} -4792 1486 2025-03-02 12:46:36.012816-08 2025-03-02 13:51:06.73015-08 leaderboard f H100 0.014137576333333334 t \N \N \N {} -4793 1486 2025-03-02 11:56:43.712458-08 2025-03-02 12:12:52.126902-08 test f L4 \N t \N \N \N {} -4794 1486 2025-03-02 12:57:19.285267-08 2025-03-02 13:40:00.434892-08 benchmark f L4 \N t \N \N \N {} -6744 2087 2025-03-15 03:03:55.336213-07 2025-03-15 04:21:27.642744-07 test f A100 \N t \N \N \N {} -4796 1487 2025-03-02 13:56:03.952211-08 2025-03-02 14:17:06.867401-08 test t H100 \N t \N \N \N {} -2058 700 2025-02-25 13:35:50.559263-08 2025-02-25 14:57:49.798923-08 test t A100 \N f \N \N \N {} -4944 1522 2025-03-03 14:24:33.347813-08 2025-03-03 14:26:01.708268-08 test t H100 \N t \N \N \N {} -4946 1522 2025-03-03 15:27:11.77608-08 2025-03-03 14:42:48.21655-08 leaderboard t H100 0.00110717921 t \N \N \N {} -4948 1524 2025-03-03 14:06:57.97848-08 2025-03-03 15:53:35.131501-08 test f A100 \N t \N \N \N {} -4949 1524 2025-03-03 14:35:09.882957-08 2025-03-03 14:20:11.280882-08 benchmark f A100 \N t \N \N \N {} -6769 2097 2025-03-15 04:27:49.490385-07 2025-03-15 03:27:02.099035-07 test f A100 \N t \N \N \N {} -4957 1524 2025-03-03 14:23:59.058716-08 2025-03-03 15:25:20.637415-08 test f H100 \N t \N \N \N {} -4958 1524 2025-03-03 14:03:34.405543-08 2025-03-03 14:18:35.717577-08 benchmark f H100 \N t \N \N \N {} -4959 1524 2025-03-03 14:09:29.703995-08 2025-03-03 15:30:42.011483-08 leaderboard f H100 0.00140554825 t \N \N \N {} -5255 1613 2025-03-06 07:12:38.56695-08 2025-03-06 08:33:42.642582-08 leaderboard f L4 0.01713484 t \N \N \N {} -4981 1533 2025-03-03 23:32:30.171269-08 2025-03-03 22:37:21.705286-08 benchmark f L4 \N f \N \N \N {} -5018 1541 2025-03-04 09:09:22.632315-08 2025-03-04 09:22:15.724438-08 test f T4 \N f \N \N \N {} -4982 1533 2025-03-03 22:41:27.291617-08 2025-03-03 22:26:36.666409-08 test t L4 \N t \N \N \N {} -4983 1533 2025-03-03 22:10:42.942356-08 2025-03-03 23:52:58.998599-08 benchmark t L4 \N f \N \N \N {} -4984 1534 2025-03-03 22:26:12.301319-08 2025-03-03 22:26:15.332411-08 test t A100 \N t \N \N \N {} -4985 1534 2025-03-03 22:36:43.072753-08 2025-03-03 23:30:03.866041-08 benchmark t A100 \N t \N \N \N {} -4986 1534 2025-03-03 22:14:27.365325-08 2025-03-03 23:53:50.401219-08 leaderboard t A100 0.001491775 t \N \N \N {} -4997 1534 2025-03-03 23:13:33.43142-08 2025-03-03 22:46:25.592457-08 benchmark t H100 \N t \N \N \N {} -4998 1534 2025-03-03 23:34:45.888365-08 2025-03-03 22:22:19.090656-08 leaderboard t H100 0.001050694 t \N \N \N {} -4999 1534 2025-03-03 23:33:14.778803-08 2025-03-03 23:11:24.883939-08 test t T4 \N t \N \N \N {} -5004 1534 2025-03-03 22:52:44.48133-08 2025-03-03 22:20:58.315866-08 leaderboard f T4 0.013578895 t \N \N \N {} -5005 1534 2025-03-03 22:45:45.446291-08 2025-03-03 23:14:25.105517-08 test t L4 \N t \N \N \N {} -5006 1534 2025-03-03 23:46:05.132215-08 2025-03-03 23:00:38.449509-08 benchmark t L4 \N t \N \N \N {} -5007 1534 2025-03-03 23:25:45.105749-08 2025-03-03 22:52:16.954981-08 leaderboard t L4 0.00906405 t \N \N \N {} -5022 1545 2025-03-04 14:58:30.960919-08 2025-03-04 14:52:33.273284-08 test t A100 \N t \N \N \N {} -5023 1545 2025-03-04 14:51:10.938222-08 2025-03-04 15:28:30.503521-08 benchmark t A100 \N t \N \N \N {} -5074 1551 2025-03-04 16:47:22.476681-08 2025-03-04 17:10:56.60229-08 test f H100 \N t \N \N \N {} -5024 1545 2025-03-04 13:51:12.047062-08 2025-03-04 14:35:53.536936-08 leaderboard t A100 0.00004836930555555555 t \N \N \N {} -5025 1545 2025-03-04 14:21:17.803519-08 2025-03-04 15:31:14.61315-08 test f A100 \N t \N \N \N {} -5026 1545 2025-03-04 13:52:48.051537-08 2025-03-04 15:31:03.104837-08 benchmark f A100 \N t \N \N \N {} -5028 1545 2025-03-04 15:34:09.122729-08 2025-03-04 14:42:01.686509-08 test t H100 \N t \N \N \N {} -5034 1545 2025-03-04 14:29:25.07288-08 2025-03-04 14:33:25.56239-08 test t T4 \N t \N \N \N {} -5029 1545 2025-03-04 15:35:11.114668-08 2025-03-04 14:50:01.633693-08 benchmark t H100 \N t \N \N \N {} -5030 1545 2025-03-04 14:15:23.548104-08 2025-03-04 14:34:05.166616-08 leaderboard t H100 0.00004719919 t \N \N \N {} -5031 1545 2025-03-04 13:53:27.796904-08 2025-03-04 13:50:34.783512-08 test f H100 \N t \N \N \N {} -5157 1571 2025-03-04 23:08:49.976878-08 2025-03-04 22:42:28.36004-08 benchmark f A100 \N t \N \N \N {} -5158 1571 2025-03-04 22:35:15.21-08 2025-03-04 22:34:20.752683-08 leaderboard f A100 0.0014883393333333334 t \N \N \N {} -5159 1571 2025-03-04 23:12:18.343342-08 2025-03-04 23:12:10.298764-08 test f H100 \N t \N \N \N {} -5160 1571 2025-03-04 22:52:31.849847-08 2025-03-04 22:15:55.305164-08 benchmark f H100 \N t \N \N \N {} -5161 1571 2025-03-04 21:54:25.033079-08 2025-03-04 23:19:13.810944-08 leaderboard f H100 0.0010518306666666667 t \N \N \N {} -5162 1571 2025-03-04 23:26:27.704888-08 2025-03-04 21:45:35.062949-08 test t H100 \N t \N \N \N {} -2066 702 2025-02-25 14:01:35.67269-08 2025-02-25 14:13:03.658618-08 test f H100 \N t \N \N \N {} -2422 827 2025-02-26 13:49:48.149073-08 2025-02-26 13:42:33.751386-08 benchmark f H100 \N f \N \N \N {} -5622 1716 2025-03-09 10:46:42.607602-07 2025-03-09 09:07:56.188107-07 benchmark t T4 \N f \N \N \N {} -5623 1716 2025-03-09 09:31:22.907917-07 2025-03-09 09:56:01.467636-07 test f T4 \N t \N \N \N {} -5624 1716 2025-03-09 10:16:02.064243-07 2025-03-09 10:32:48.331759-07 benchmark f T4 \N f \N \N \N {} -5625 1717 2025-03-09 10:13:52.307292-07 2025-03-09 09:42:55.90134-07 test f T4 \N t \N \N \N {} -5626 1717 2025-03-09 10:41:28.462205-07 2025-03-09 10:30:33.37398-07 benchmark f T4 \N t \N \N \N {} -5627 1717 2025-03-09 09:58:14.43763-07 2025-03-09 09:29:59.555675-07 leaderboard f T4 0.01689148266666667 t \N \N \N {} -5628 1717 2025-03-09 08:54:15.401227-07 2025-03-09 09:01:20.665735-07 test t T4 \N t \N \N \N {} -5633 1718 2025-03-09 10:16:16.764055-07 2025-03-09 08:57:06.957134-07 leaderboard t T4 0.02135399933333333 t \N \N \N {} -5660 1723 2025-03-09 10:03:33.814323-07 2025-03-09 10:04:32.576403-07 leaderboard f T4 0.016605525666666666 t \N \N \N {} -5661 1723 2025-03-09 10:47:11.568642-07 2025-03-09 09:35:57.762614-07 test t T4 \N t \N \N \N {} -5662 1723 2025-03-09 10:00:24.122137-07 2025-03-09 09:14:18.616382-07 benchmark t T4 \N t \N \N \N {} -5683 1726 2025-03-09 09:19:42.062291-07 2025-03-09 09:47:18.455329-07 benchmark f L4 \N t \N \N \N {} -5684 1726 2025-03-09 09:35:37.887021-07 2025-03-09 10:36:27.46401-07 leaderboard f L4 0.017259138 t \N \N \N {} -5685 1727 2025-03-09 10:09:36.558925-07 2025-03-09 09:36:26.692942-07 test f L4 \N t \N \N \N {} -5692 1728 2025-03-09 09:54:55.125187-07 2025-03-09 09:30:43.343027-07 benchmark f A100 \N t \N \N \N {} -5693 1728 2025-03-09 10:44:16.533423-07 2025-03-09 10:12:57.412213-07 leaderboard f A100 0.0026361306666666663 t \N \N \N {} -5694 1729 2025-03-09 10:18:27.24795-07 2025-03-09 11:00:29.021145-07 test t H100 \N t \N \N \N {} -5780 1750 2025-03-09 22:31:41.177478-07 2025-03-09 22:32:40.296083-07 leaderboard t H100 0.0015017373333333333 t \N \N \N {} -5783 1751 2025-03-09 22:33:46.42896-07 2025-03-09 22:29:10.948791-07 leaderboard t L4 0.017186112666666666 t \N \N \N {} -5789 1752 2025-03-09 22:09:13.460918-07 2025-03-09 22:30:06.841849-07 leaderboard f T4 0.016641214666666668 t \N \N \N {} -5791 1752 2025-03-09 21:25:43.431271-07 2025-03-09 22:03:39.205218-07 benchmark t T4 \N t \N \N \N {} -5792 1752 2025-03-09 22:06:34.639731-07 2025-03-09 22:03:04.943777-07 leaderboard t T4 0.016605053666666664 t \N \N \N {} -5890 1783 2025-03-10 11:42:15.635873-07 2025-03-10 12:18:31.181049-07 test f T4 \N f \N \N \N {} -5891 1784 2025-03-10 12:42:17.820815-07 2025-03-10 12:42:44.878659-07 test f T4 \N f \N \N \N {} -5892 1784 2025-03-10 13:03:34.982484-07 2025-03-10 11:10:33.398792-07 test t T4 \N f \N \N \N {} -6112 1851 2025-03-11 02:01:12.667219-07 2025-03-11 01:10:58.721364-07 test f A100 \N f \N \N \N {} -5899 1788 2025-03-10 12:51:42.965452-07 2025-03-10 11:53:10.996306-07 test t T4 \N f \N \N \N {} -5900 1788 2025-03-10 11:22:13.20187-07 2025-03-10 11:32:35.572673-07 test f T4 \N f \N \N \N {} -5901 1789 2025-03-10 13:02:58.074539-07 2025-03-10 13:17:21.023123-07 test f T4 \N f \N \N \N {} -5902 1789 2025-03-10 12:01:52.164022-07 2025-03-10 11:27:33.462883-07 test t T4 \N f \N \N \N {} -5903 1790 2025-03-10 11:26:13.302143-07 2025-03-10 12:50:52.591159-07 test t T4 \N f \N \N \N {} -5915 1796 2025-03-10 12:15:59.801508-07 2025-03-10 13:02:17.771902-07 test t T4 \N f \N \N \N {} -5916 1796 2025-03-10 12:57:44.974492-07 2025-03-10 12:53:59.035234-07 test f T4 \N f \N \N \N {} -5944 1809 2025-03-10 13:50:04.063532-07 2025-03-10 14:02:36.910964-07 test t A100 \N t \N \N \N {} -5917 1797 2025-03-10 12:47:21.898424-07 2025-03-10 12:35:53.990658-07 test f T4 \N f \N \N \N {} -5918 1797 2025-03-10 11:54:01.992015-07 2025-03-10 13:25:49.093367-07 test t T4 \N f \N \N \N {} -5919 1798 2025-03-10 12:02:54.007464-07 2025-03-10 12:00:35.023294-07 test f T4 \N f \N \N \N {} -5920 1798 2025-03-10 11:53:48.460819-07 2025-03-10 12:54:41.600572-07 test t T4 \N f \N \N \N {} -5921 1799 2025-03-10 12:01:32.892869-07 2025-03-10 12:01:20.064834-07 test f T4 \N f \N \N \N {} -5945 1809 2025-03-10 13:30:52.742-07 2025-03-10 13:35:00.53151-07 benchmark t A100 \N t \N \N \N {} -2067 702 2025-02-25 13:08:25.421143-08 2025-02-25 13:34:01.386083-08 benchmark f H100 \N t \N \N \N {} -2068 702 2025-02-25 13:42:25.465302-08 2025-02-25 13:31:29.0514-08 leaderboard f H100 0.001931559125 t \N \N \N {} -167 26 2025-02-23 12:21:33.030404-08 2025-02-23 11:41:55.949378-08 benchmark f A100 \N t \N \N \N {} -168 26 2025-02-23 11:54:48.8754-08 2025-02-23 11:19:18.094078-08 benchmark f H100 \N t \N \N \N {} -169 26 2025-02-23 11:53:04.673811-08 2025-02-23 11:52:00.921431-08 benchmark f T4 \N t \N \N \N {} -170 26 2025-02-23 10:45:00.538357-08 2025-02-23 11:33:31.309144-08 benchmark f L4 \N t \N \N \N {} -5969 1818 2025-03-10 13:34:50.62569-07 2025-03-10 13:50:19.381297-07 leaderboard f T4 0.007938620666666667 t \N \N \N {} -5988 1821 2025-03-10 14:19:10.235543-07 2025-03-10 13:00:17.011132-07 test t L4 \N t \N \N \N {} -5972 1818 2025-03-10 14:20:22.971921-07 2025-03-10 13:57:46.364461-07 leaderboard t T4 0.006741959333333333 t \N \N \N {} -5973 1819 2025-03-10 14:09:36.845256-07 2025-03-10 12:47:23.183159-07 test f A100 \N t \N \N \N {} -5974 1819 2025-03-10 13:27:31.914549-07 2025-03-10 13:38:50.356656-07 benchmark f A100 \N t \N \N \N {} -5975 1819 2025-03-10 13:22:24.682951-07 2025-03-10 12:52:47.777634-07 leaderboard f A100 0.00066186475 t \N \N \N {} -5976 1820 2025-03-10 13:05:57.916285-07 2025-03-10 14:10:04.245483-07 test f H100 \N t \N \N \N {} -5977 1820 2025-03-10 13:06:16.389801-07 2025-03-10 13:56:50.012127-07 benchmark f H100 \N t \N \N \N {} -5980 1820 2025-03-10 14:27:30.646317-07 2025-03-10 14:22:26.85464-07 benchmark t H100 \N t \N \N \N {} -5981 1820 2025-03-10 13:34:45.928055-07 2025-03-10 14:10:03.141068-07 leaderboard t H100 0.00025382670689655173 t \N \N \N {} -5982 1821 2025-03-10 13:18:05.58891-07 2025-03-10 12:57:07.627811-07 test f L4 \N t \N \N \N {} -5983 1821 2025-03-10 13:10:21.68803-07 2025-03-10 12:59:33.456792-07 benchmark f L4 \N t \N \N \N {} -5984 1821 2025-03-10 14:27:34.743682-07 2025-03-10 13:17:42.837991-07 leaderboard f L4 0.0023442443333333337 t \N \N \N {} -5985 1819 2025-03-10 14:00:17.259921-07 2025-03-10 13:54:42.382815-07 test t A100 \N t \N \N \N {} -5986 1819 2025-03-10 13:40:20.642276-07 2025-03-10 13:23:25.385771-07 benchmark t A100 \N t \N \N \N {} -5991 1822 2025-03-10 13:01:51.312859-07 2025-03-10 13:23:25.247008-07 test f A100 \N t \N \N \N {} -5992 1822 2025-03-10 13:52:54.966043-07 2025-03-10 13:40:58.589221-07 benchmark f A100 \N t \N \N \N {} -5993 1822 2025-03-10 13:20:20.347858-07 2025-03-10 13:01:28.199599-07 leaderboard f A100 0.011864730666666667 t \N \N \N {} -5994 1822 2025-03-10 14:40:44.674945-07 2025-03-10 13:28:53.37983-07 test t A100 \N t \N \N \N {} -5995 1822 2025-03-10 14:21:29.232684-07 2025-03-10 13:52:16.353634-07 benchmark t A100 \N t \N \N \N {} -5996 1822 2025-03-10 13:03:56.315257-07 2025-03-10 13:23:43.023609-07 leaderboard t A100 0.011399361 t \N \N \N {} -6003 1824 2025-03-10 14:26:38.384984-07 2025-03-10 14:09:13.727909-07 test f L4 \N t \N \N \N {} -6004 1824 2025-03-10 13:12:15.653503-07 2025-03-10 14:52:55.190975-07 benchmark f L4 \N t \N \N \N {} -6005 1824 2025-03-10 13:13:37.254193-07 2025-03-10 13:18:36.888686-07 leaderboard f L4 0.07897275766666667 t \N \N \N {} -6011 1825 2025-03-10 13:09:55.890405-07 2025-03-10 13:48:11.961778-07 leaderboard t T4 0.07677698866666667 t \N \N \N {} -6013 1825 2025-03-10 14:28:08.622984-07 2025-03-10 14:46:31.402484-07 benchmark f T4 \N t \N \N \N {} -6014 1825 2025-03-10 14:21:16.246646-07 2025-03-10 13:28:59.68858-07 leaderboard f T4 0.07667353866666667 t \N \N \N {} -6095 1844 2025-03-10 22:07:21.414-07 2025-03-10 23:17:28.15254-07 benchmark t A100 \N t \N \N \N {} -6107 1846 2025-03-10 22:00:08.835784-07 2025-03-10 22:00:11.368406-07 benchmark f L4 \N t \N \N \N {} -6108 1846 2025-03-10 22:23:46.695176-07 2025-03-10 23:57:02.247288-07 leaderboard f L4 0.3110419582857143 t \N \N \N {} -6655 2057 2025-03-14 12:30:26.103998-07 2025-03-14 12:52:39.669941-07 benchmark f A100 \N t \N \N \N {} -6661 2058 2025-03-14 13:23:30.755804-07 2025-03-14 13:50:09.852519-07 leaderboard f A100 0.002474787 t \N \N \N {} -6672 2064 2025-03-14 15:30:52.385494-07 2025-03-14 14:26:29.449376-07 test t T4 \N f \N \N \N {} -6673 2064 2025-03-14 15:46:19.901898-07 2025-03-14 14:34:56.062065-07 test f T4 \N f \N \N \N {} -6693 2074 2025-03-14 16:21:36.880226-07 2025-03-14 17:05:48.053303-07 test f T4 \N f \N \N \N {} -6889 2142 2025-03-16 10:09:32.257151-07 2025-03-16 11:37:21.891364-07 test t H100 \N t \N \N \N {} -6694 2075 2025-03-14 16:49:43.41812-07 2025-03-14 16:35:30.091773-07 test f T4 \N f \N \N \N {} -6890 2142 2025-03-16 10:05:39.67375-07 2025-03-16 11:46:49.850371-07 benchmark t H100 \N f \N \N \N {} -6708 2080 2025-03-15 03:13:35.894439-07 2025-03-15 04:06:56.714414-07 benchmark f A100 \N t \N \N \N {} -6709 2080 2025-03-15 03:36:07.318078-07 2025-03-15 04:02:50.2109-07 leaderboard f A100 0.0025194633333333336 t \N \N \N {} -6711 2081 2025-03-15 03:52:50.095799-07 2025-03-15 03:57:17.540486-07 benchmark f A100 \N t \N \N \N {} -6721 2082 2025-03-15 02:47:16.092609-07 2025-03-15 02:57:12.920931-07 leaderboard f A100 0.0025854276666666667 t \N \N \N {} -6804 2105 2025-03-15 04:42:04.98069-07 2025-03-15 03:45:38.84867-07 benchmark f A100 \N t \N \N \N {} -6806 2107 2025-03-15 04:57:27.096967-07 2025-03-15 05:03:18.110884-07 benchmark f A100 \N t \N \N \N {} -6807 2108 2025-03-15 03:40:00.092892-07 2025-03-15 05:03:01.447944-07 benchmark f A100 \N t \N \N \N {} -6808 2109 2025-03-15 05:29:02.178375-07 2025-03-15 04:43:10.854188-07 benchmark f A100 \N t \N \N \N {} -6891 2142 2025-03-16 10:03:19.487989-07 2025-03-16 10:15:21.78931-07 test f H100 \N t \N \N \N {} -6816 2117 2025-03-15 09:06:01.891683-07 2025-03-15 09:51:02.921922-07 benchmark f A100 \N t \N \N \N {} -6893 2143 2025-03-16 12:26:21.451192-07 2025-03-16 11:55:44.286131-07 test f T4 \N f \N \N \N {} -6823 2120 2025-03-15 10:10:54.860558-07 2025-03-15 09:57:15.216128-07 benchmark t A100 \N t \N \N \N {} -6868 2128 2025-03-15 13:36:39.60935-07 2025-03-15 12:30:47.242903-07 test f T4 \N f \N \N \N {} -6869 2129 2025-03-15 12:55:04.568241-07 2025-03-15 13:27:08.036947-07 test t T4 \N f \N \N \N {} -6870 2129 2025-03-15 13:43:39.360608-07 2025-03-15 13:40:42.286936-07 test f T4 \N f \N \N \N {} -6871 2130 2025-03-15 12:56:13.688421-07 2025-03-15 13:31:59.222715-07 test t T4 \N f \N \N \N {} -6872 2130 2025-03-15 13:37:07.488274-07 2025-03-15 14:00:52.365959-07 test f T4 \N f \N \N \N {} -6874 2131 2025-03-15 14:10:20.038407-07 2025-03-15 13:06:30.274573-07 test t T4 \N f \N \N \N {} -6875 2132 2025-03-15 13:03:08.713745-07 2025-03-15 14:16:20.509728-07 test f T4 \N f \N \N \N {} -6876 2132 2025-03-15 12:46:33.250206-07 2025-03-15 14:16:03.803892-07 test t T4 \N f \N \N \N {} -6877 2133 2025-03-15 14:13:55.89837-07 2025-03-15 14:32:53.847522-07 test f T4 \N f \N \N \N {} -6878 2133 2025-03-15 13:49:17.874309-07 2025-03-15 13:42:01.322803-07 test t T4 \N f \N \N \N {} -6884 2137 2025-03-16 09:35:46.366813-07 2025-03-16 09:58:28.560263-07 test f H100 \N f \N \N \N {} -6941 2161 2025-03-16 15:54:46.742477-07 2025-03-16 16:55:32.003397-07 leaderboard t T4 0.00036617938461538463 t \N \N \N {} -6942 2161 2025-03-16 15:48:02.479891-07 2025-03-16 15:54:40.057512-07 test f T4 \N t \N \N \N {} -6943 2161 2025-03-16 16:14:50.39459-07 2025-03-16 16:50:14.655957-07 benchmark f T4 \N t \N \N \N {} -6945 2162 2025-03-16 15:42:59.335687-07 2025-03-16 16:32:27.860049-07 test t T4 \N t \N \N \N {} -6946 2162 2025-03-16 15:21:34.523769-07 2025-03-16 15:44:43.801402-07 benchmark t T4 \N t \N \N \N {} -6947 2162 2025-03-16 15:24:00.324625-07 2025-03-16 16:09:43.79608-07 leaderboard t T4 0.00028399790999999997 t \N \N \N {} -6948 2162 2025-03-16 15:07:29.788194-07 2025-03-16 16:45:42.575251-07 test f T4 \N t \N \N \N {} -6949 2162 2025-03-16 16:58:35.480846-07 2025-03-16 15:40:08.469048-07 benchmark f T4 \N t \N \N \N {} -2083 702 2025-02-25 13:37:08.890367-08 2025-02-25 14:31:19.469976-08 leaderboard f T4 0.019992128 t \N \N \N {} -2829 984 2025-02-26 23:44:55.909365-08 2025-02-26 23:59:03.36721-08 test f L4 \N t \N \N \N {} -2843 988 2025-02-27 00:16:23.988573-08 2025-02-26 23:57:40.2744-08 leaderboard f H100 0.00153285175 t \N \N \N {} -171 27 2025-02-23 10:45:11.479148-08 2025-02-23 11:31:42.340162-08 test t A100 \N t \N \N \N {} -172 27 2025-02-23 11:43:09.385926-08 2025-02-23 11:39:14.096637-08 benchmark t A100 \N t \N \N \N {} -220 37 2025-02-23 11:24:37.144954-08 2025-02-23 12:36:11.010727-08 benchmark t A100 \N t \N \N \N {} -173 27 2025-02-23 12:14:48.113644-08 2025-02-23 10:35:05.48636-08 leaderboard t A100 0.014219568666666666 t \N \N \N {} -174 27 2025-02-23 11:52:06.861806-08 2025-02-23 12:06:16.599311-08 test f A100 \N t \N \N \N {} -175 27 2025-02-23 11:14:31.938617-08 2025-02-23 10:50:54.199169-08 benchmark f A100 \N t \N \N \N {} -176 27 2025-02-23 10:55:35.824906-08 2025-02-23 11:40:46.262363-08 leaderboard f A100 0.014177354333333335 t \N \N \N {} -177 27 2025-02-23 10:54:29.927447-08 2025-02-23 11:20:37.982222-08 test f H100 \N t \N \N \N {} -178 27 2025-02-23 11:15:34.972209-08 2025-02-23 12:22:50.553085-08 benchmark f H100 \N t \N \N \N {} -179 27 2025-02-23 11:27:38.137008-08 2025-02-23 12:17:23.904583-08 leaderboard f H100 0.00628814 t \N \N \N {} -181 27 2025-02-23 12:09:44.028933-08 2025-02-23 11:26:46.80693-08 benchmark t T4 \N t \N \N \N {} -182 27 2025-02-23 11:56:25.478243-08 2025-02-23 10:36:01.655206-08 leaderboard t T4 0.07672247533333333 t \N \N \N {} -183 27 2025-02-23 11:51:05.559443-08 2025-02-23 10:39:36.577419-08 test f L4 \N t \N \N \N {} -184 27 2025-02-23 11:41:30.376423-08 2025-02-23 10:37:16.305101-08 benchmark f L4 \N t \N \N \N {} -185 27 2025-02-23 10:35:57.229029-08 2025-02-23 10:39:52.072211-08 leaderboard f L4 0.07915553666666666 t \N \N \N {} -186 27 2025-02-23 11:10:53.767744-08 2025-02-23 10:48:50.231998-08 test f T4 \N t \N \N \N {} -187 27 2025-02-23 12:02:00.098937-08 2025-02-23 11:08:01.038877-08 benchmark f T4 \N t \N \N \N {} -188 27 2025-02-23 11:11:25.926517-08 2025-02-23 10:30:09.448745-08 leaderboard f T4 0.076629145 t \N \N \N {} -189 27 2025-02-23 11:53:45.306343-08 2025-02-23 12:16:41.267247-08 test t L4 \N t \N \N \N {} -190 27 2025-02-23 11:28:09.310531-08 2025-02-23 10:48:48.140068-08 benchmark t L4 \N t \N \N \N {} -191 27 2025-02-23 12:08:23.202643-08 2025-02-23 11:17:51.689748-08 leaderboard t L4 0.07890172333333333 t \N \N \N {} -192 27 2025-02-23 10:44:11.726542-08 2025-02-23 11:02:53.940073-08 test t H100 \N t \N \N \N {} -193 27 2025-02-23 12:19:32.856515-08 2025-02-23 10:47:43.576992-08 benchmark t H100 \N t \N \N \N {} -194 27 2025-02-23 12:21:29.202301-08 2025-02-23 11:46:31.676892-08 leaderboard t H100 0.006306255 t \N \N \N {} -195 28 2025-02-23 11:13:19.67724-08 2025-02-23 10:39:48.64665-08 test f T4 \N f \N \N \N {} -196 29 2025-02-23 10:49:56.579251-08 2025-02-23 11:05:27.758434-08 test f T4 \N f \N \N \N {} -197 30 2025-02-23 12:27:41.798772-08 2025-02-23 12:02:37.751314-08 test f T4 \N f \N \N \N {} -198 31 2025-02-23 11:37:04.951166-08 2025-02-23 12:07:49.407209-08 test f T4 \N f \N \N \N {} -199 32 2025-02-23 11:58:09.474874-08 2025-02-23 10:43:38.089712-08 test f T4 \N t \N \N \N {} -200 33 2025-02-23 11:22:39.540982-08 2025-02-23 11:42:22.336345-08 benchmark f T4 \N t \N \N \N {} -201 34 2025-02-23 12:04:23.683096-08 2025-02-23 10:52:18.627224-08 test f T4 \N t \N \N \N {} -1622 539 2025-02-25 07:52:47.641192-08 2025-02-25 08:52:28.657841-08 test f H100 \N t \N \N \N {} -202 35 2025-02-23 12:24:22.817189-08 2025-02-23 10:54:19.203754-08 benchmark f T4 \N t \N \N \N {} -216 37 2025-02-23 11:14:38.746659-08 2025-02-23 11:03:54.206188-08 test t T4 \N t \N \N \N {} -243 50 2025-02-23 13:18:19.125258-08 2025-02-23 13:13:38.565546-08 test f T4 \N f \N \N \N {} -244 51 2025-02-23 13:01:35.786926-08 2025-02-23 12:24:04.575677-08 test f T4 \N f \N \N \N {} -245 52 2025-02-23 12:24:37.291631-08 2025-02-23 12:23:14.530709-08 test f T4 \N t \N \N \N {} -268 54 2025-02-23 12:25:21.411502-08 2025-02-23 12:05:19.559567-08 test t L4 \N t \N \N \N {} -247 53 2025-02-23 12:25:27.998153-08 2025-02-23 12:00:47.080967-08 benchmark f A100 \N t \N \N \N {} -248 53 2025-02-23 12:39:45.238062-08 2025-02-23 12:53:50.087404-08 benchmark f L4 \N t \N \N \N {} -249 53 2025-02-23 13:45:33.218295-08 2025-02-23 12:33:06.247895-08 benchmark f H100 \N t \N \N \N {} -250 54 2025-02-23 13:11:45.380271-08 2025-02-23 12:54:03.319507-08 test t A100 \N t \N \N \N {} -251 54 2025-02-23 12:41:48.180775-08 2025-02-23 12:18:18.458891-08 benchmark t A100 \N t \N \N \N {} -252 54 2025-02-23 13:47:01.02295-08 2025-02-23 13:12:03.617869-08 leaderboard t A100 0.00318012725 t \N \N \N {} -253 54 2025-02-23 12:36:35.151783-08 2025-02-23 12:27:40.807586-08 test f L4 \N t \N \N \N {} -3565 1211 2025-02-28 12:01:14.621003-08 2025-02-28 12:23:14.001408-08 test f H100 \N t \N \N \N {} -291 67 2025-02-23 13:52:22.554731-08 2025-02-23 15:04:28.302779-08 benchmark f H100 \N t \N \N \N {} -292 68 2025-02-23 14:13:32.52352-08 2025-02-23 13:39:23.211145-08 test f H100 \N t \N \N \N {} -294 70 2025-02-23 13:48:10.257644-08 2025-02-23 15:04:21.69425-08 test f H100 \N t \N \N \N {} -295 70 2025-02-23 13:45:01.008267-08 2025-02-23 15:19:04.053139-08 benchmark f H100 \N t \N \N \N {} -296 70 2025-02-23 14:08:32.500158-08 2025-02-23 13:34:17.476377-08 leaderboard f H100 0.0014198665 t \N \N \N {} -297 70 2025-02-23 14:22:14.947889-08 2025-02-23 14:43:43.399906-08 test t H100 \N t \N \N \N {} -395 104 2025-02-23 19:37:48.159657-08 2025-02-23 19:42:22.47971-08 leaderboard f L4 0.009219998333333333 t \N \N \N {} -396 104 2025-02-23 19:29:57.556657-08 2025-02-23 19:14:44.962181-08 test t T4 \N t \N \N \N {} -397 104 2025-02-23 19:06:51.535467-08 2025-02-23 19:09:13.584955-08 benchmark t T4 \N t \N \N \N {} -398 104 2025-02-23 19:08:03.666634-08 2025-02-23 18:31:14.289034-08 leaderboard t T4 0.009337150333333334 t \N \N \N {} -399 104 2025-02-23 18:20:58.004675-08 2025-02-23 18:12:39.887533-08 test t L4 \N t \N \N \N {} -400 104 2025-02-23 18:37:28.598432-08 2025-02-23 19:04:08.192125-08 benchmark t L4 \N t \N \N \N {} -401 104 2025-02-23 18:23:08.135641-08 2025-02-23 19:25:04.431404-08 leaderboard t L4 0.009286899666666666 t \N \N \N {} -440 137 2025-02-23 19:19:33.088698-08 2025-02-23 19:28:34.03025-08 test t A100 \N t \N \N \N {} -405 110 2025-02-23 19:36:40.687466-08 2025-02-23 19:24:36.001868-08 test f H100 \N f \N \N \N {} -406 111 2025-02-23 19:22:44.96918-08 2025-02-23 18:57:09.743491-08 test f L4 \N f \N \N \N {} -407 113 2025-02-23 18:49:58.235949-08 2025-02-23 19:26:22.885286-08 test f L4 \N f \N \N \N {} -408 112 2025-02-23 19:16:17.884334-08 2025-02-23 20:17:51.435513-08 test f H100 \N f \N \N \N {} -4354 1425 2025-03-01 20:13:46.966029-08 2025-03-01 18:52:04.589122-08 test t T4 \N t \N \N \N {} -465 144 2025-02-24 00:55:32.36629-08 2025-02-24 01:02:07.010747-08 leaderboard f A100 0.0031325836666666663 t \N \N \N {} -466 145 2025-02-24 00:32:22.702838-08 2025-02-24 01:20:22.072236-08 test f L4 \N t \N \N \N {} -467 145 2025-02-24 00:52:22.990735-08 2025-02-24 00:23:20.150431-08 benchmark f L4 \N t \N \N \N {} -468 145 2025-02-24 01:17:15.371174-08 2025-02-24 00:43:01.83229-08 leaderboard f L4 0.01690829933333333 t \N \N \N {} -472 145 2025-02-24 00:20:39.930346-08 2025-02-24 01:30:42.571148-08 test t L4 \N t \N \N \N {} -473 145 2025-02-24 01:15:01.028642-08 2025-02-24 01:45:10.923025-08 benchmark t L4 \N t \N \N \N {} -474 145 2025-02-24 01:13:01.276238-08 2025-02-24 02:03:18.528002-08 leaderboard t L4 0.016441613666666667 t \N \N \N {} -475 145 2025-02-24 00:23:20.873525-08 2025-02-24 00:29:22.877398-08 test t A100 \N t \N \N \N {} -476 145 2025-02-24 01:36:08.741349-08 2025-02-24 01:17:03.910006-08 benchmark t A100 \N t \N \N \N {} -477 145 2025-02-24 01:59:25.837709-08 2025-02-24 01:17:11.163833-08 leaderboard t A100 0.00310126 t \N \N \N {} -478 145 2025-02-24 02:01:13.502168-08 2025-02-24 01:09:26.58412-08 test t H100 \N t \N \N \N {} -298 70 2025-02-23 13:31:54.273224-08 2025-02-23 14:34:44.74164-08 benchmark t H100 \N t \N \N \N {} -299 70 2025-02-23 13:44:07.386708-08 2025-02-23 15:06:34.650489-08 leaderboard t H100 0.001028769 t \N \N \N {} -323 79 2025-02-23 15:26:33.721086-08 2025-02-23 15:20:07.296257-08 test f H100 \N t \N \N \N {} -486 145 2025-02-24 01:01:38.175295-08 2025-02-24 01:58:42.065024-08 test t T4 \N t \N \N \N {} -769 244 2025-02-24 11:02:11.786679-08 2025-02-24 09:19:00.797899-08 test f A100 \N t \N \N \N {} -479 145 2025-02-24 00:22:39.260481-08 2025-02-24 00:31:46.970049-08 benchmark t H100 \N t \N \N \N {} -480 145 2025-02-24 01:01:44.442094-08 2025-02-24 00:11:35.7708-08 leaderboard t H100 0.00142161375 t \N \N \N {} -481 145 2025-02-24 00:37:45.034628-08 2025-02-24 00:06:19.291315-08 test f H100 \N t \N \N \N {} -482 145 2025-02-24 01:23:58.17948-08 2025-02-24 00:43:49.157244-08 benchmark f H100 \N t \N \N \N {} -483 145 2025-02-24 00:09:29.066055-08 2025-02-24 00:27:00.430461-08 leaderboard f H100 0.00140814175 t \N \N \N {} -484 145 2025-02-24 02:01:37.341833-08 2025-02-24 01:22:53.107149-08 test f T4 \N t \N \N \N {} -541 157 2025-02-24 03:23:47.844322-08 2025-02-24 03:43:51.69252-08 test f A100 \N t \N \N \N {} -558 159 2025-02-24 03:52:44.512606-08 2025-02-24 05:07:17.070819-08 test f H100 \N t \N \N \N {} -542 157 2025-02-24 04:17:48.694412-08 2025-02-24 04:12:41.545476-08 benchmark f A100 \N t \N \N \N {} -549 157 2025-02-24 04:20:20.945717-08 2025-02-24 03:23:10.699715-08 leaderboard t T4 0.023143011666666668 t \N \N \N {} -1491 498 2025-02-25 07:26:44.389059-08 2025-02-25 06:14:45.396909-08 test f A100 \N f \N \N \N {} -550 157 2025-02-24 04:41:06.56919-08 2025-02-24 03:16:56.08599-08 test t A100 \N t \N \N \N {} -551 157 2025-02-24 04:37:58.696168-08 2025-02-24 03:37:13.752989-08 benchmark t A100 \N t \N \N \N {} -552 157 2025-02-24 03:31:21.654659-08 2025-02-24 04:02:58.352237-08 leaderboard t A100 0.006896261545454546 t \N \N \N {} -553 157 2025-02-24 05:03:09.664072-08 2025-02-24 05:13:14.483324-08 test t L4 \N f \N \N \N {} -554 157 2025-02-24 04:29:42.313161-08 2025-02-24 05:11:38.142222-08 test f L4 \N t \N \N \N {} -555 157 2025-02-24 04:36:10.687082-08 2025-02-24 03:31:24.036485-08 benchmark f L4 \N t \N \N \N {} -556 157 2025-02-24 03:38:18.277868-08 2025-02-24 03:25:50.851837-08 leaderboard f L4 0.018886963333333333 t \N \N \N {} -557 158 2025-02-24 04:36:41.191894-08 2025-02-24 04:18:45.993597-08 test f H100 \N t \N \N \N {} -1492 498 2025-02-25 06:34:51.735413-08 2025-02-25 05:41:39.626728-08 test t A100 \N f \N \N \N {} -559 160 2025-02-24 04:47:33.735469-08 2025-02-24 03:48:26.300529-08 test f H100 \N t \N \N \N {} -572 162 2025-02-24 05:00:12.670271-08 2025-02-24 05:05:35.448889-08 leaderboard f T4 0.018292196666666666 t \N \N \N {} -573 162 2025-02-24 03:26:01.433346-08 2025-02-24 03:56:51.262514-08 test f L4 \N f \N \N \N {} -574 162 2025-02-24 04:51:39.961542-08 2025-02-24 04:59:26.105146-08 test t L4 \N t \N \N \N {} -5259 1613 2025-03-06 06:50:43.258546-08 2025-03-06 08:19:11.165198-08 test f T4 \N t \N \N \N {} -600 172 2025-02-24 04:53:48.285554-08 2025-02-24 05:23:18.845114-08 test f A100 \N t \N \N \N {} -581 166 2025-02-24 03:35:47.72346-08 2025-02-24 03:38:46.586413-08 test f H100 \N f \N \N \N {} -606 172 2025-02-24 03:42:34.249272-08 2025-02-24 05:40:44.184189-08 test f L4 \N t \N \N \N {} -311 77 2025-02-23 14:12:30.101398-08 2025-02-23 15:24:57.946248-08 test f H100 \N t \N \N \N {} -312 77 2025-02-23 15:19:37.391023-08 2025-02-23 14:11:23.026317-08 benchmark f H100 \N t \N \N \N {} -313 77 2025-02-23 15:05:19.0137-08 2025-02-23 15:11:53.57026-08 leaderboard f H100 0.001450680857142857 t \N \N \N {} -315 77 2025-02-23 13:53:12.61876-08 2025-02-23 13:59:11.207858-08 benchmark t H100 \N t \N \N \N {} -316 77 2025-02-23 14:48:41.323897-08 2025-02-23 15:13:01.5905-08 leaderboard t H100 0.00145937425 t \N \N \N {} -317 78 2025-02-23 14:02:23.760311-08 2025-02-23 13:57:30.080076-08 test t H100 \N t \N \N \N {} -716 229 2025-02-24 08:35:17.736081-08 2025-02-24 07:54:05.005879-08 leaderboard f A100 0.004042775333333333 t \N \N \N {} -717 230 2025-02-24 09:22:18.875039-08 2025-02-24 08:00:26.274996-08 test f A100 \N t \N \N \N {} -1135 316 2025-02-24 20:02:44.708551-08 2025-02-24 18:21:09.94372-08 leaderboard t A100 0.011420428666666666 t \N \N \N {} -718 230 2025-02-24 08:33:59.269854-08 2025-02-24 07:51:14.292412-08 benchmark f A100 \N t \N \N \N {} -720 230 2025-02-24 09:24:41.117156-08 2025-02-24 08:14:51.47456-08 test t A100 \N t \N \N \N {} -721 230 2025-02-24 09:25:59.168682-08 2025-02-24 07:38:18.264898-08 benchmark t A100 \N t \N \N \N {} -722 230 2025-02-24 08:13:38.930592-08 2025-02-24 07:37:12.954266-08 leaderboard t A100 0.0032000705 t \N \N \N {} -723 231 2025-02-24 09:17:26.030333-08 2025-02-24 08:18:23.337179-08 test f H100 \N t \N \N \N {} -724 231 2025-02-24 09:02:18.510228-08 2025-02-24 08:27:34.118305-08 test f A100 \N t \N \N \N {} -1136 316 2025-02-24 18:52:30.044102-08 2025-02-24 19:36:46.191371-08 test f A100 \N t \N \N \N {} -726 231 2025-02-24 08:34:48.003809-08 2025-02-24 08:40:27.717239-08 test f L4 \N f \N \N \N {} -732 233 2025-02-24 09:29:37.727181-08 2025-02-24 08:02:29.392295-08 benchmark f A100 \N t \N \N \N {} -733 233 2025-02-24 08:20:40.863772-08 2025-02-24 07:58:20.853653-08 leaderboard f A100 0.0031231213333333336 t \N \N \N {} -734 233 2025-02-24 08:49:10.944512-08 2025-02-24 08:12:13.280323-08 test f H100 \N t \N \N \N {} -735 233 2025-02-24 08:30:07.553648-08 2025-02-24 08:58:56.261174-08 benchmark f H100 \N t \N \N \N {} -1354 451 2025-02-25 02:04:40.095144-08 2025-02-25 03:49:19.013675-08 benchmark f H100 \N t \N \N \N {} -736 233 2025-02-24 08:27:18.705589-08 2025-02-24 09:10:53.230123-08 leaderboard f H100 0.002061958 t \N \N \N {} -741 233 2025-02-24 08:13:06.765051-08 2025-02-24 07:57:46.536533-08 benchmark f T4 \N t \N \N \N {} -742 233 2025-02-24 09:28:14.411862-08 2025-02-24 07:55:13.882323-08 leaderboard f T4 0.004024178 t \N \N \N {} -743 233 2025-02-24 08:45:35.726194-08 2025-02-24 07:49:16.0139-08 test t H100 \N t \N \N \N {} -744 233 2025-02-24 08:30:17.598827-08 2025-02-24 08:45:38.138191-08 benchmark t H100 \N t \N \N \N {} -745 233 2025-02-24 08:29:13.376851-08 2025-02-24 08:41:38.50256-08 leaderboard t H100 0.0024627566666666667 t \N \N \N {} -318 78 2025-02-23 14:02:29.033294-08 2025-02-23 14:26:37.663496-08 benchmark t H100 \N t \N \N \N {} -319 78 2025-02-23 15:24:09.465856-08 2025-02-23 14:31:24.442508-08 leaderboard t H100 0.001455489 t \N \N \N {} -327 79 2025-02-23 15:44:13.226491-08 2025-02-23 13:56:26.731737-08 benchmark t H100 \N t \N \N \N {} -336 82 2025-02-23 17:25:08.638515-08 2025-02-23 17:57:21.345045-08 test f H100 \N f \N \N \N {} -337 83 2025-02-23 18:00:31.70052-08 2025-02-23 17:58:50.769159-08 test f H100 \N f \N \N \N {} -338 84 2025-02-23 18:06:57.258257-08 2025-02-23 16:51:18.239842-08 test f H100 \N f \N \N \N {} -339 85 2025-02-23 17:10:24.770217-08 2025-02-23 17:18:24.35571-08 test f H100 \N f \N \N \N {} -768 244 2025-02-24 10:02:14.922389-08 2025-02-24 10:56:52.559337-08 leaderboard t A100 0.0032697126666666665 t \N \N \N {} -340 86 2025-02-23 17:42:30.9851-08 2025-02-23 18:05:44.756059-08 test f H100 \N f \N \N \N {} -3566 1211 2025-02-28 12:19:44.674046-08 2025-02-28 11:20:06.595714-08 benchmark f H100 \N t \N \N \N {} -1356 453 2025-02-25 02:50:36.296171-08 2025-02-25 03:40:22.203999-08 benchmark f H100 \N t \N \N \N {} -777 250 2025-02-24 10:13:18.705553-08 2025-02-24 11:57:51.322708-08 test f A100 \N t \N \N \N {} -778 250 2025-02-24 11:03:04.113706-08 2025-02-24 11:50:05.562623-08 benchmark f A100 \N t \N \N \N {} -779 250 2025-02-24 11:49:40.71682-08 2025-02-24 10:20:18.789178-08 leaderboard f A100 0.0032043086666666666 t \N \N \N {} -780 250 2025-02-24 11:38:14.616988-08 2025-02-24 11:50:19.652707-08 test t A100 \N t \N \N \N {} -781 250 2025-02-24 11:06:57.166985-08 2025-02-24 11:37:32.323803-08 benchmark t A100 \N t \N \N \N {} -1358 456 2025-02-25 03:04:57.340562-08 2025-02-25 03:41:50.970915-08 benchmark f H100 \N t \N \N \N {} -782 250 2025-02-24 11:40:30.959556-08 2025-02-24 11:55:45.22426-08 leaderboard t A100 0.0031807153333333333 t \N \N \N {} -783 251 2025-02-24 10:28:34.15622-08 2025-02-24 10:26:21.513069-08 test t A100 \N t \N \N \N {} -784 251 2025-02-24 11:22:43.230478-08 2025-02-24 10:17:13.237357-08 benchmark t A100 \N t \N \N \N {} -785 251 2025-02-24 11:01:06.012176-08 2025-02-24 10:42:38.296715-08 leaderboard t A100 0.003201019 t \N \N \N {} -354 94 2025-02-23 18:07:11.918125-08 2025-02-23 18:49:09.9145-08 benchmark t H100 \N t \N \N \N {} -361 97 2025-02-23 19:03:55.153785-08 2025-02-23 19:25:28.656928-08 test t H100 \N t \N \N \N {} -367 98 2025-02-23 19:12:25.166912-08 2025-02-23 18:43:49.363313-08 benchmark f H100 \N t \N \N \N {} -368 99 2025-02-23 18:24:18.547946-08 2025-02-23 19:38:43.869117-08 test t H100 \N t \N \N \N {} -369 99 2025-02-23 19:55:02.527597-08 2025-02-23 19:45:12.210798-08 benchmark t H100 \N t \N \N \N {} -371 99 2025-02-23 19:05:59.158414-08 2025-02-23 18:08:37.209856-08 test f H100 \N t \N \N \N {} -377 102 2025-02-23 18:16:40.647606-08 2025-02-23 18:22:04.894771-08 benchmark f H100 \N t \N \N \N {} -378 104 2025-02-23 18:44:39.804161-08 2025-02-23 20:02:52.428781-08 test f H100 \N t \N \N \N {} -379 104 2025-02-23 18:34:28.483515-08 2025-02-23 19:39:00.450206-08 benchmark f H100 \N t \N \N \N {} -380 104 2025-02-23 19:35:24.292215-08 2025-02-23 20:07:53.402422-08 leaderboard f H100 0.0010334816666666667 t \N \N \N {} -385 104 2025-02-23 19:53:09.908295-08 2025-02-23 19:18:59.271408-08 benchmark f A100 \N t \N \N \N {} -394 104 2025-02-23 19:10:09.186446-08 2025-02-23 18:43:18.809569-08 benchmark f L4 \N t \N \N \N {} -403 108 2025-02-23 19:55:38.938073-08 2025-02-23 20:17:37.031218-08 test f H100 \N f \N \N \N {} -404 109 2025-02-23 19:08:16.246023-08 2025-02-23 19:56:30.430723-08 test f H100 \N f \N \N \N {} -410 114 2025-02-23 20:00:48.18605-08 2025-02-23 19:19:29.420657-08 test f H100 \N f \N \N \N {} -411 116 2025-02-23 19:34:58.202046-08 2025-02-23 20:01:11.73353-08 benchmark f T4 \N t \N \N \N {} -412 118 2025-02-23 20:37:38.267216-08 2025-02-23 19:02:18.39604-08 benchmark f T4 \N f \N \N \N {} -413 117 2025-02-23 19:24:46.341973-08 2025-02-23 18:57:45.646571-08 test f A100 \N f \N \N \N {} -414 117 2025-02-23 18:57:59.477452-08 2025-02-23 20:15:43.624804-08 test t A100 \N f \N \N \N {} -415 119 2025-02-23 18:57:34.156909-08 2025-02-23 18:54:25.73194-08 benchmark f T4 \N f \N \N \N {} -416 120 2025-02-23 20:05:08.814298-08 2025-02-23 18:59:37.20305-08 test f L4 \N t \N \N \N {} -418 122 2025-02-23 20:50:03.177865-08 2025-02-23 20:14:16.240173-08 test f L4 \N t \N \N \N {} -786 251 2025-02-24 10:26:17.480514-08 2025-02-24 10:27:14.231102-08 test t T4 \N t \N \N \N {} -792 251 2025-02-24 11:19:29.761457-08 2025-02-24 10:12:45.329053-08 test t H100 \N t \N \N \N {} -1444 484 2025-02-25 05:32:24.532424-08 2025-02-25 05:46:04.733398-08 test t A100 \N t \N \N \N {} -788 251 2025-02-24 10:59:47.100658-08 2025-02-24 11:38:00.698912-08 leaderboard t T4 0.017558684 t \N \N \N {} -789 251 2025-02-24 12:04:49.533693-08 2025-02-24 10:27:37.844537-08 test f A100 \N t \N \N \N {} -790 251 2025-02-24 12:04:40.357942-08 2025-02-24 10:51:07.619523-08 benchmark f A100 \N t \N \N \N {} -791 251 2025-02-24 11:22:21.198065-08 2025-02-24 11:52:00.418781-08 leaderboard f A100 0.003236336 t \N \N \N {} -811 253 2025-02-24 11:06:37.307391-08 2025-02-24 11:40:28.537535-08 test t A100 \N t \N \N \N {} -1447 484 2025-02-25 05:28:22.571964-08 2025-02-25 04:07:59.842135-08 test f A100 \N t \N \N \N {} -793 251 2025-02-24 11:52:18.452547-08 2025-02-24 11:37:31.847226-08 benchmark t H100 \N t \N \N \N {} -794 251 2025-02-24 10:55:56.967992-08 2025-02-24 12:02:37.363415-08 leaderboard t H100 0.001464676 t \N \N \N {} -795 251 2025-02-24 11:55:37.519662-08 2025-02-24 10:47:45.331086-08 test f H100 \N t \N \N \N {} -796 251 2025-02-24 10:51:49.810385-08 2025-02-24 10:25:51.454365-08 benchmark f H100 \N t \N \N \N {} -797 251 2025-02-24 11:15:30.778494-08 2025-02-24 11:00:25.052673-08 leaderboard f H100 0.0014206696 t \N \N \N {} -825 258 2025-02-24 13:00:16.497406-08 2025-02-24 13:34:23.466302-08 leaderboard t A100 0.0032076514 t \N \N \N {} -420 124 2025-02-23 20:02:27.111264-08 2025-02-23 20:04:53.203923-08 test f L4 \N t \N \N \N {} -422 126 2025-02-23 20:06:43.793931-08 2025-02-23 20:32:18.759967-08 test f L4 \N t \N \N \N {} -423 127 2025-02-23 20:24:10.490624-08 2025-02-23 20:17:20.346438-08 test f L4 \N f \N \N \N {} -425 129 2025-02-23 20:46:15.037395-08 2025-02-23 19:07:35.835051-08 test f L4 \N f \N \N \N {} -1450 485 2025-02-25 04:19:06.14904-08 2025-02-25 04:39:46.798027-08 test t A100 \N t \N \N \N {} -800 251 2025-02-24 10:59:21.599538-08 2025-02-24 10:25:36.051807-08 leaderboard f T4 0.01756872166666667 t \N \N \N {} -801 251 2025-02-24 10:42:44.855208-08 2025-02-24 10:38:35.745986-08 test f L4 \N t \N \N \N {} -802 251 2025-02-24 11:52:41.634738-08 2025-02-24 11:17:02.406538-08 benchmark f L4 \N t \N \N \N {} -826 258 2025-02-24 12:08:48.726753-08 2025-02-24 13:40:11.091506-08 test f A100 \N t \N \N \N {} -1453 485 2025-02-25 04:53:38.828316-08 2025-02-25 04:03:40.596895-08 test f A100 \N t \N \N \N {} -806 251 2025-02-24 10:12:06.628686-08 2025-02-24 10:32:34.248863-08 leaderboard t L4 0.017363646666666666 t \N \N \N {} -807 252 2025-02-24 10:34:25.534395-08 2025-02-24 12:10:21.1189-08 test f A100 \N f \N \N \N {} -808 252 2025-02-24 12:08:19.909711-08 2025-02-24 11:07:28.545277-08 test t A100 \N f \N \N \N {} -809 253 2025-02-24 10:56:51.671658-08 2025-02-24 12:19:25.430319-08 test f A100 \N t \N \N \N {} -810 253 2025-02-24 12:24:27.607242-08 2025-02-24 11:11:09.097006-08 benchmark f A100 \N f \N \N \N {} -821 256 2025-02-24 13:32:00.37212-08 2025-02-24 12:39:04.454955-08 test f A100 \N f \N \N \N {} -822 257 2025-02-24 11:59:03.654568-08 2025-02-24 12:25:11.023194-08 test f H100 \N t \N \N \N {} -823 258 2025-02-24 13:11:25.747263-08 2025-02-24 12:51:12.133715-08 test t A100 \N t \N \N \N {} -824 258 2025-02-24 12:23:35.99065-08 2025-02-24 12:54:48.794104-08 benchmark t A100 \N t \N \N \N {} -828 258 2025-02-24 12:28:03.710997-08 2025-02-24 12:02:38.320312-08 leaderboard f A100 0.00319396075 t \N \N \N {} -830 259 2025-02-24 13:38:53.547783-08 2025-02-24 12:05:16.740651-08 benchmark f H100 \N t \N \N \N {} -831 259 2025-02-24 12:19:41.162515-08 2025-02-24 13:02:34.045891-08 leaderboard f H100 0.0014907706363636365 t \N \N \N {} -832 259 2025-02-24 13:34:25.778466-08 2025-02-24 12:32:27.73762-08 test t H100 \N t \N \N \N {} -833 259 2025-02-24 11:57:09.771187-08 2025-02-24 13:17:24.239643-08 benchmark t H100 \N t \N \N \N {} -834 259 2025-02-24 12:53:22.94161-08 2025-02-24 13:02:51.615542-08 leaderboard t H100 0.0015141807894736843 t \N \N \N {} -837 261 2025-02-24 14:33:31.764087-08 2025-02-24 14:09:19.937583-08 benchmark f H100 \N t \N \N \N {} -838 261 2025-02-24 13:29:16.943379-08 2025-02-24 14:28:28.491199-08 benchmark f A100 \N t \N \N \N {} -965 286 2025-02-24 15:17:07.072445-08 2025-02-24 15:23:57.396796-08 test f A100 \N t \N \N \N {} -843 262 2025-02-24 14:18:36.348524-08 2025-02-24 14:09:28.031494-08 leaderboard f H100 0.00158875175 t \N \N \N {} -1456 486 2025-02-25 05:57:12.50863-08 2025-02-25 04:24:28.062012-08 test f A100 \N t \N \N \N {} -850 262 2025-02-24 13:53:27.308737-08 2025-02-24 14:16:34.938488-08 test f L4 \N t \N \N \N {} -851 262 2025-02-24 12:43:25.694004-08 2025-02-24 14:30:18.247948-08 benchmark f L4 \N t \N \N \N {} -852 262 2025-02-24 14:41:37.221242-08 2025-02-24 14:15:54.675901-08 leaderboard f L4 0.01720207333333333 t \N \N \N {} -853 262 2025-02-24 13:03:41.972489-08 2025-02-24 13:01:38.374088-08 test f A100 \N t \N \N \N {} -854 262 2025-02-24 14:31:55.855835-08 2025-02-24 13:01:31.443949-08 benchmark f A100 \N t \N \N \N {} -1359 457 2025-02-25 02:33:43.299256-08 2025-02-25 02:58:46.454768-08 benchmark f H100 \N t \N \N \N {} -860 262 2025-02-24 14:13:52.225617-08 2025-02-24 14:17:22.296848-08 test t H100 \N t \N \N \N {} -1360 458 2025-02-25 04:21:39.8674-08 2025-02-25 03:31:46.64471-08 benchmark f H100 \N t \N \N \N {} -866 264 2025-02-24 14:18:15.875273-08 2025-02-24 14:25:54.32018-08 test t A100 \N f \N \N \N {} -426 130 2025-02-23 19:07:19.532288-08 2025-02-23 19:27:05.035489-08 test f L4 \N f \N \N \N {} -427 131 2025-02-23 19:48:04.004883-08 2025-02-23 20:55:59.519835-08 test f L4 \N f \N \N \N {} -428 132 2025-02-23 19:43:42.154861-08 2025-02-23 19:58:51.977474-08 test f L4 \N t \N \N \N {} -430 134 2025-02-23 19:41:20.008317-08 2025-02-23 19:11:47.214628-08 test f L4 \N t \N \N \N {} -431 135 2025-02-23 20:41:38.742341-08 2025-02-23 20:10:24.488802-08 test t A100 \N t \N \N \N {} -867 264 2025-02-24 13:47:02.310423-08 2025-02-24 14:19:30.07954-08 test f A100 \N f \N \N \N {} -868 265 2025-02-24 13:25:41.120417-08 2025-02-24 13:19:04.569194-08 test f A100 \N f \N \N \N {} -869 265 2025-02-24 14:09:21.007191-08 2025-02-24 14:19:43.13391-08 test t A100 \N f \N \N \N {} -5676 1725 2025-03-09 09:53:25.9676-07 2025-03-09 10:54:50.998675-07 test t H100 \N t \N \N \N {} -1361 459 2025-02-25 02:30:56.124266-08 2025-02-25 02:51:08.772443-08 test t A100 \N t \N \N \N {} -871 266 2025-02-24 13:56:37.656623-08 2025-02-24 13:34:36.431618-08 test f H100 \N f \N \N \N {} -872 267 2025-02-24 13:27:30.522948-08 2025-02-24 14:25:22.159132-08 test f A100 \N t \N \N \N {} -873 267 2025-02-24 14:06:05.758615-08 2025-02-24 14:17:03.596666-08 benchmark f A100 \N t \N \N \N {} -874 267 2025-02-24 14:47:02.145718-08 2025-02-24 14:26:33.636732-08 leaderboard f A100 0.0032893633333333336 t \N \N \N {} -875 267 2025-02-24 14:02:32.569668-08 2025-02-24 13:04:10.687457-08 test t A100 \N t \N \N \N {} -876 267 2025-02-24 14:20:17.088913-08 2025-02-24 13:03:00.990794-08 benchmark t A100 \N t \N \N \N {} -877 267 2025-02-24 13:03:37.085609-08 2025-02-24 14:42:26.626312-08 leaderboard t A100 0.0032918306666666667 t \N \N \N {} -878 268 2025-02-24 13:32:17.727867-08 2025-02-24 14:42:15.893182-08 test f H100 \N t \N \N \N {} -879 268 2025-02-24 13:46:35.691848-08 2025-02-24 14:33:17.187051-08 benchmark f H100 \N t \N \N \N {} -880 268 2025-02-24 13:09:44.706773-08 2025-02-24 14:40:37.527146-08 leaderboard f H100 0.00148714025 t \N \N \N {} -881 268 2025-02-24 14:38:11.050645-08 2025-02-24 14:37:44.308056-08 test t H100 \N t \N \N \N {} -972 288 2025-02-24 16:59:51.753306-08 2025-02-24 16:32:43.265594-08 test t A100 \N t \N \N \N {} -889 270 2025-02-24 13:23:28.860312-08 2025-02-24 15:14:01.508532-08 benchmark f H100 \N t \N \N \N {} -890 270 2025-02-24 14:06:21.369663-08 2025-02-24 13:44:06.555384-08 leaderboard f H100 0.00633421 t \N \N \N {} -891 270 2025-02-24 15:11:19.3884-08 2025-02-24 14:22:05.663402-08 test t H100 \N t \N \N \N {} -892 270 2025-02-24 13:34:23.965761-08 2025-02-24 15:03:36.056155-08 benchmark t H100 \N t \N \N \N {} -897 271 2025-02-24 13:23:42.310773-08 2025-02-24 13:47:13.289788-08 benchmark f T4 \N t \N \N \N {} -432 135 2025-02-23 19:22:15.455814-08 2025-02-23 19:58:43.024263-08 benchmark t A100 \N t \N \N \N {} -433 135 2025-02-23 19:46:23.596065-08 2025-02-23 21:02:09.718637-08 leaderboard t A100 0.011416369 t \N \N \N {} -434 135 2025-02-23 20:11:32.421186-08 2025-02-23 20:54:46.274828-08 test f A100 \N t \N \N \N {} -435 135 2025-02-23 20:18:13.731355-08 2025-02-23 20:15:53.15599-08 benchmark f A100 \N t \N \N \N {} -436 135 2025-02-23 20:00:20.118277-08 2025-02-23 20:51:44.559411-08 leaderboard f A100 0.010131549619047619 t \N \N \N {} -437 137 2025-02-23 19:49:28.16914-08 2025-02-23 20:57:31.153582-08 test f A100 \N t \N \N \N {} -438 137 2025-02-23 20:09:57.38955-08 2025-02-23 20:26:38.729961-08 benchmark f A100 \N t \N \N \N {} -439 137 2025-02-23 19:51:53.206453-08 2025-02-23 20:31:39.322151-08 leaderboard f A100 0.010931205 t \N \N \N {} -441 137 2025-02-23 20:15:20.626379-08 2025-02-23 20:57:29.810715-08 benchmark t A100 \N t \N \N \N {} -442 137 2025-02-23 20:45:29.846555-08 2025-02-23 19:10:45.121007-08 leaderboard t A100 0.010102002266666667 t \N \N \N {} -576 162 2025-02-24 04:19:34.761817-08 2025-02-24 05:01:13.840464-08 leaderboard t L4 0.017179168666666668 t \N \N \N {} -577 163 2025-02-24 04:01:18.525271-08 2025-02-24 04:17:48.618121-08 test f T4 \N f \N \N \N {} -898 272 2025-02-24 14:03:42.781647-08 2025-02-24 13:55:34.397369-08 test t H100 \N t \N \N \N {} -899 272 2025-02-24 13:44:04.949972-08 2025-02-24 13:28:20.378369-08 benchmark t H100 \N t \N \N \N {} -900 272 2025-02-24 13:45:21.588357-08 2025-02-24 15:00:10.446354-08 leaderboard t H100 0.001380456 t \N \N \N {} -901 272 2025-02-24 14:59:05.351263-08 2025-02-24 15:02:57.541551-08 test f A100 \N t \N \N \N {} -3584 1228 2025-02-28 13:43:38.707851-08 2025-02-28 14:06:32.301701-08 test t A100 \N t \N \N \N {} -902 272 2025-02-24 13:41:50.302661-08 2025-02-24 14:48:59.498164-08 benchmark f A100 \N t \N \N \N {} -903 272 2025-02-24 13:24:49.213705-08 2025-02-24 13:38:04.997177-08 leaderboard f A100 0.0030942173333333334 t \N \N \N {} -904 272 2025-02-24 14:29:42.928168-08 2025-02-24 13:26:18.774967-08 test t A100 \N t \N \N \N {} -905 272 2025-02-24 14:12:54.933528-08 2025-02-24 14:48:10.033573-08 benchmark t A100 \N t \N \N \N {} -906 272 2025-02-24 14:06:49.466739-08 2025-02-24 14:26:14.363403-08 leaderboard t A100 0.0030895613333333334 t \N \N \N {} -907 272 2025-02-24 13:27:26.40339-08 2025-02-24 14:22:30.451832-08 test f T4 \N t \N \N \N {} -908 272 2025-02-24 13:50:22.416422-08 2025-02-24 14:23:23.865231-08 benchmark f T4 \N t \N \N \N {} -909 272 2025-02-24 15:02:51.400167-08 2025-02-24 14:45:31.55137-08 leaderboard f T4 0.016812591 t \N \N \N {} -910 272 2025-02-24 14:54:42.275644-08 2025-02-24 14:02:35.113798-08 test t T4 \N t \N \N \N {} -911 272 2025-02-24 14:14:55.576574-08 2025-02-24 13:55:39.639099-08 benchmark t T4 \N t \N \N \N {} -912 272 2025-02-24 14:30:30.596529-08 2025-02-24 15:11:06.343919-08 leaderboard t T4 0.016815438 t \N \N \N {} -913 272 2025-02-24 14:03:43.587782-08 2025-02-24 13:32:24.372553-08 test f H100 \N t \N \N \N {} -914 272 2025-02-24 13:58:45.530798-08 2025-02-24 14:37:34.053628-08 benchmark f H100 \N t \N \N \N {} -915 272 2025-02-24 13:44:39.285633-08 2025-02-24 14:28:08.305027-08 leaderboard f H100 0.0013929076666666668 t \N \N \N {} -916 273 2025-02-24 13:46:49.172996-08 2025-02-24 14:08:28.506582-08 test f H100 \N t \N \N \N {} -917 273 2025-02-24 13:44:56.476334-08 2025-02-24 14:40:36.885261-08 benchmark f H100 \N t \N \N \N {} -918 273 2025-02-24 14:57:37.29992-08 2025-02-24 14:13:39.653275-08 leaderboard f H100 0.00629756 t \N \N \N {} -919 272 2025-02-24 14:32:30.167492-08 2025-02-24 14:11:08.41029-08 test t L4 \N t \N \N \N {} -920 272 2025-02-24 13:47:24.679318-08 2025-02-24 13:20:10.828648-08 benchmark t L4 \N t \N \N \N {} -921 272 2025-02-24 15:14:24.435268-08 2025-02-24 15:12:42.536442-08 leaderboard t L4 0.016873888666666666 t \N \N \N {} -923 272 2025-02-24 14:50:59.333942-08 2025-02-24 14:12:16.270455-08 benchmark f L4 \N t \N \N \N {} -924 272 2025-02-24 14:47:47.523473-08 2025-02-24 13:44:27.360148-08 leaderboard f L4 0.016924354 t \N \N \N {} -925 273 2025-02-24 15:11:48.660708-08 2025-02-24 13:29:55.704446-08 test t H100 \N t \N \N \N {} -926 273 2025-02-24 15:15:14.766004-08 2025-02-24 13:19:47.409296-08 benchmark t H100 \N t \N \N \N {} -927 273 2025-02-24 14:10:49.544286-08 2025-02-24 14:57:42.082587-08 leaderboard t H100 0.004302405666666667 t \N \N \N {} -984 288 2025-02-24 15:26:18.349293-08 2025-02-24 15:44:04.650422-08 test f A100 \N t \N \N \N {} -928 274 2025-02-24 15:05:47.967562-08 2025-02-24 15:17:26.160717-08 test f H100 \N f \N \N \N {} -929 274 2025-02-24 15:04:47.728115-08 2025-02-24 14:57:41.957077-08 test t H100 \N f \N \N \N {} -3585 1228 2025-02-28 14:17:57.496054-08 2025-02-28 13:50:52.43144-08 benchmark t A100 \N t \N \N \N {} -935 278 2025-02-24 14:28:49.91519-08 2025-02-24 15:19:34.617051-08 benchmark t A100 \N t \N \N \N {} -936 278 2025-02-24 13:44:17.851045-08 2025-02-24 15:26:06.123862-08 leaderboard t A100 0.0031084026666666664 t \N \N \N {} -937 278 2025-02-24 15:01:10.720121-08 2025-02-24 14:26:25.903037-08 test f A100 \N t \N \N \N {} -990 288 2025-02-24 16:29:43.864283-08 2025-02-24 16:06:04.412307-08 test f L4 \N t \N \N \N {} -938 278 2025-02-24 15:22:13.9455-08 2025-02-24 15:34:30.906411-08 benchmark f A100 \N t \N \N \N {} -939 278 2025-02-24 15:34:14.43871-08 2025-02-24 14:09:55.104192-08 leaderboard f A100 0.003099416 t \N \N \N {} -449 138 2025-02-23 20:17:26.595429-08 2025-02-23 20:35:29.611944-08 benchmark f L4 \N t \N \N \N {} -451 139 2025-02-23 20:51:10.6817-08 2025-02-23 20:38:48.437704-08 test f A100 \N t \N \N \N {} -452 139 2025-02-23 19:49:38.747753-08 2025-02-23 19:40:42.834492-08 benchmark f A100 \N t \N \N \N {} -940 278 2025-02-24 15:09:41.101654-08 2025-02-24 13:40:05.003362-08 test f H100 \N t \N \N \N {} -941 278 2025-02-24 14:12:31.025675-08 2025-02-24 15:21:52.442215-08 benchmark f H100 \N t \N \N \N {} -942 278 2025-02-24 15:26:06.113726-08 2025-02-24 14:54:47.505262-08 leaderboard f H100 0.0013924253333333332 t \N \N \N {} -943 278 2025-02-24 13:41:17.222805-08 2025-02-24 14:40:55.819332-08 test t H100 \N t \N \N \N {} -944 278 2025-02-24 14:51:33.706683-08 2025-02-24 15:02:31.7023-08 benchmark t H100 \N t \N \N \N {} -945 278 2025-02-24 15:15:53.225485-08 2025-02-24 13:41:18.489963-08 leaderboard t H100 0.001391712 t \N \N \N {} -947 278 2025-02-24 13:47:07.517676-08 2025-02-24 14:35:18.503122-08 benchmark f L4 \N t \N \N \N {} -948 278 2025-02-24 15:32:37.786282-08 2025-02-24 14:47:49.400993-08 leaderboard f L4 0.016921183 t \N \N \N {} -950 278 2025-02-24 14:52:40.705932-08 2025-02-24 15:12:22.348896-08 benchmark t L4 \N t \N \N \N {} -951 278 2025-02-24 15:20:16.824802-08 2025-02-24 14:51:53.886502-08 leaderboard t L4 0.01689634233333333 t \N \N \N {} -952 278 2025-02-24 13:42:04.283901-08 2025-02-24 13:50:01.376078-08 test t T4 \N t \N \N \N {} -959 280 2025-02-24 14:37:57.453433-08 2025-02-24 13:55:35.067117-08 benchmark f A100 \N t \N \N \N {} -960 281 2025-02-24 15:29:16.351075-08 2025-02-24 15:15:34.238127-08 benchmark f A100 \N f \N \N \N {} -996 289 2025-02-24 17:05:53.711415-08 2025-02-24 16:12:17.469633-08 leaderboard f A100 0.0031874743333333336 t \N \N \N {} -961 282 2025-02-24 14:54:40.402838-08 2025-02-24 15:07:51.744951-08 benchmark f A100 \N t \N \N \N {} -1326 443 2025-02-25 01:31:19.548777-08 2025-02-25 02:21:57.556426-08 benchmark t A100 \N t \N \N \N {} -969 286 2025-02-24 14:27:36.98298-08 2025-02-24 14:24:18.873791-08 benchmark t A100 \N t \N \N \N {} -970 286 2025-02-24 15:45:02.969062-08 2025-02-24 15:20:58.131836-08 leaderboard t A100 0.003574085 t \N \N \N {} -971 287 2025-02-24 16:45:49.221907-08 2025-02-24 15:11:06.695278-08 benchmark f A100 \N t \N \N \N {} -1364 459 2025-02-25 03:07:07.507086-08 2025-02-25 03:28:33.611205-08 test f A100 \N t \N \N \N {} -973 288 2025-02-24 16:57:39.832425-08 2025-02-24 16:14:19.158825-08 benchmark t A100 \N t \N \N \N {} -974 288 2025-02-24 16:17:36.67277-08 2025-02-24 15:25:36.315446-08 leaderboard t A100 0.0035733863333333336 t \N \N \N {} -976 288 2025-02-24 16:12:37.835505-08 2025-02-24 16:32:25.940514-08 benchmark t H100 \N t \N \N \N {} -977 288 2025-02-24 16:02:16.632285-08 2025-02-24 15:30:08.973921-08 leaderboard t H100 0.0015792905 t \N \N \N {} -1367 460 2025-02-25 02:54:18.569745-08 2025-02-25 03:04:44.660402-08 test f H100 \N t \N \N \N {} -1462 487 2025-02-25 05:11:56.373844-08 2025-02-25 05:12:25.148355-08 test t H100 \N t \N \N \N {} -979 288 2025-02-24 16:39:20.43363-08 2025-02-24 16:31:03.690589-08 benchmark f H100 \N t \N \N \N {} -980 288 2025-02-24 15:39:09.650488-08 2025-02-24 15:08:31.848197-08 leaderboard f H100 0.001654801 t \N \N \N {} -981 288 2025-02-24 16:32:20.98845-08 2025-02-24 15:27:23.058332-08 test f T4 \N t \N \N \N {} -982 288 2025-02-24 15:11:31.029521-08 2025-02-24 15:54:36.042517-08 benchmark f T4 \N t \N \N \N {} -983 288 2025-02-24 15:57:27.675389-08 2025-02-24 16:16:35.300427-08 leaderboard f T4 0.01727558566666667 t \N \N \N {} -1370 460 2025-02-25 03:37:37.587036-08 2025-02-25 03:12:01.251999-08 test t H100 \N t \N \N \N {} -1465 487 2025-02-25 05:28:37.945849-08 2025-02-25 05:31:06.395877-08 test f H100 \N t \N \N \N {} -985 288 2025-02-24 16:00:46.319439-08 2025-02-24 16:11:47.492733-08 benchmark f A100 \N t \N \N \N {} -986 288 2025-02-24 16:16:37.007915-08 2025-02-24 16:47:35.551783-08 leaderboard f A100 0.0035907183333333333 t \N \N \N {} -987 288 2025-02-24 15:53:31.716837-08 2025-02-24 15:52:50.195056-08 test t T4 \N t \N \N \N {} -988 288 2025-02-24 15:24:51.892743-08 2025-02-24 15:37:05.023212-08 benchmark t T4 \N t \N \N \N {} -989 288 2025-02-24 15:27:07.594575-08 2025-02-24 16:56:10.768764-08 leaderboard t T4 0.017142831666666667 t \N \N \N {} -1373 461 2025-02-25 03:02:32.607966-08 2025-02-25 02:46:59.676745-08 test t H100 \N t \N \N \N {} -1469 489 2025-02-25 06:10:25.611874-08 2025-02-25 04:53:36.581953-08 test f A100 \N t \N \N \N {} -991 288 2025-02-24 16:15:35.403958-08 2025-02-24 15:42:05.66197-08 benchmark f L4 \N t \N \N \N {} -992 288 2025-02-24 15:44:25.515087-08 2025-02-24 16:58:38.135537-08 leaderboard f L4 0.017213298666666668 t \N \N \N {} -453 139 2025-02-23 20:25:09.33463-08 2025-02-23 21:00:42.630896-08 leaderboard f A100 2.3280243876666664 t \N \N \N {} -454 139 2025-02-23 19:54:51.244923-08 2025-02-23 20:51:32.367147-08 test t A100 \N t \N \N \N {} -455 139 2025-02-23 19:45:18.073547-08 2025-02-23 20:41:22.97686-08 benchmark t A100 \N t \N \N \N {} -456 139 2025-02-23 21:07:14.882985-08 2025-02-23 20:12:14.513014-08 leaderboard t A100 2.2617255652727275 t \N \N \N {} -458 142 2025-02-24 01:14:42.946921-08 2025-02-24 00:35:39.452004-08 test f A100 \N f \N \N \N {} -459 143 2025-02-24 01:11:00.689386-08 2025-02-24 00:41:21.530234-08 test f A100 \N t \N \N \N {} -460 144 2025-02-24 00:41:59.723288-08 2025-02-24 00:27:53.246315-08 test t A100 \N t \N \N \N {} -461 144 2025-02-24 00:21:52.786601-08 2025-02-24 01:33:07.398183-08 benchmark t A100 \N t \N \N \N {} -462 144 2025-02-24 01:54:02.801471-08 2025-02-24 01:19:41.256152-08 leaderboard t A100 0.003105596 t \N \N \N {} -463 144 2025-02-24 00:31:47.833871-08 2025-02-24 01:26:28.316452-08 test f A100 \N t \N \N \N {} -464 144 2025-02-24 01:15:21.059343-08 2025-02-24 01:40:04.362432-08 benchmark f A100 \N t \N \N \N {} -469 145 2025-02-24 00:21:16.048547-08 2025-02-24 01:12:36.574185-08 test f A100 \N t \N \N \N {} -470 145 2025-02-24 00:17:52.83432-08 2025-02-24 01:49:29.222442-08 benchmark f A100 \N t \N \N \N {} -471 145 2025-02-24 01:06:14.871957-08 2025-02-24 01:44:46.357832-08 leaderboard f A100 0.0030890706666666665 t \N \N \N {} -485 145 2025-02-24 01:20:54.849052-08 2025-02-24 01:08:55.25546-08 benchmark f T4 \N f \N \N \N {} -487 145 2025-02-24 00:17:54.793622-08 2025-02-24 00:31:22.4894-08 benchmark t T4 \N f \N \N \N {} -488 148 2025-02-24 03:09:35.205519-08 2025-02-24 03:27:23.971276-08 benchmark f A100 \N t \N \N \N {} -489 149 2025-02-24 03:30:55.449817-08 2025-02-24 03:37:56.567594-08 test f H100 \N t \N \N \N {} -490 150 2025-02-24 04:21:26.450468-08 2025-02-24 04:24:44.221828-08 test f H100 \N f \N \N \N {} -491 150 2025-02-24 04:05:31.466828-08 2025-02-24 03:04:56.25643-08 test t H100 \N t \N \N \N {} -993 288 2025-02-24 16:22:57.509895-08 2025-02-24 16:11:34.646111-08 test t L4 \N f \N \N \N {} -994 289 2025-02-24 16:42:29.124469-08 2025-02-24 16:15:50.980733-08 test f A100 \N t \N \N \N {} -995 289 2025-02-24 16:03:41.521804-08 2025-02-24 15:55:40.302003-08 benchmark f A100 \N t \N \N \N {} -997 289 2025-02-24 15:54:37.535241-08 2025-02-24 15:25:33.427509-08 test t A100 \N t \N \N \N {} -1327 443 2025-02-25 02:11:01.004727-08 2025-02-25 02:37:11.369093-08 leaderboard t A100 0.003228157 t \N \N \N {} -1328 443 2025-02-25 02:29:43.574743-08 2025-02-25 01:45:12.561001-08 test f A100 \N t \N \N \N {} -998 289 2025-02-24 15:30:32.784103-08 2025-02-24 15:14:53.659942-08 benchmark t A100 \N t \N \N \N {} -492 150 2025-02-24 03:09:36.145036-08 2025-02-24 03:37:47.928735-08 benchmark t H100 \N t \N \N \N {} -493 150 2025-02-24 03:50:14.789495-08 2025-02-24 04:18:31.651743-08 leaderboard t H100 0.00032365892307692306 t \N \N \N {} -494 151 2025-02-24 03:23:37.243425-08 2025-02-24 03:24:21.097123-08 test t H100 \N t \N \N \N {} -495 151 2025-02-24 04:50:10.450163-08 2025-02-24 03:28:44.586591-08 benchmark t H100 \N t \N \N \N {} -496 151 2025-02-24 04:56:39.642072-08 2025-02-24 04:24:35.925272-08 leaderboard t H100 0.00029347884745762714 t \N \N \N {} -497 151 2025-02-24 03:19:04.263395-08 2025-02-24 04:48:57.333637-08 test f H100 \N t \N \N \N {} -498 151 2025-02-24 04:44:55.465081-08 2025-02-24 04:38:58.27472-08 benchmark f H100 \N t \N \N \N {} -499 151 2025-02-24 04:22:14.523605-08 2025-02-24 03:36:00.958698-08 leaderboard f H100 0.0003125368620689655 t \N \N \N {} -5247 1613 2025-03-06 06:38:02.469655-08 2025-03-06 08:12:20.172654-08 test f H100 \N t \N \N \N {} -1322 442 2025-02-25 00:41:54.545147-08 2025-02-25 02:36:02.144792-08 test t T4 \N t \N \N \N {} -999 289 2025-02-24 15:25:33.457905-08 2025-02-24 16:27:05.872169-08 leaderboard t A100 0.0031989713333333337 t \N \N \N {} -1000 289 2025-02-24 16:48:53.415372-08 2025-02-24 16:21:48.670188-08 test t T4 \N t \N \N \N {} -1001 289 2025-02-24 17:09:32.511952-08 2025-02-24 15:33:31.453203-08 benchmark t T4 \N t \N \N \N {} -1376 461 2025-02-25 03:14:56.330738-08 2025-02-25 02:37:49.658574-08 test f H100 \N t \N \N \N {} -1471 491 2025-02-25 05:29:20.665807-08 2025-02-25 05:15:22.315477-08 test t A100 \N t \N \N \N {} -532 154 2025-02-24 04:48:16.131212-08 2025-02-24 04:44:31.007006-08 test f H100 \N t \N \N \N {} -3573 1218 2025-02-28 13:45:23.407437-08 2025-02-28 12:25:46.532879-08 test f H100 \N t \N \N \N {} -1118 304 2025-02-24 18:18:52.599968-08 2025-02-24 18:09:21.428872-08 leaderboard f L4 0.027837713666666666 t \N \N \N {} -2417 820 2025-02-26 12:21:06.882191-08 2025-02-26 13:15:22.606597-08 benchmark f H100 \N t \N \N \N {} -1123 306 2025-02-24 17:10:46.114629-08 2025-02-24 18:46:22.129619-08 benchmark f L4 \N t \N \N \N {} -1124 307 2025-02-24 17:18:47.489571-08 2025-02-24 17:33:10.036339-08 benchmark f H100 \N f \N \N \N {} -1129 312 2025-02-24 18:14:08.836383-08 2025-02-24 19:05:16.466962-08 test f T4 \N t \N \N \N {} -1130 313 2025-02-24 18:32:21.793683-08 2025-02-24 18:56:10.553395-08 test f T4 \N f \N \N \N {} -1131 314 2025-02-24 19:53:32.767284-08 2025-02-24 18:20:01.673708-08 test f A100 \N t \N \N \N {} -1132 315 2025-02-24 19:41:33.984587-08 2025-02-24 19:22:46.368176-08 test f A100 \N f \N \N \N {} -1133 316 2025-02-24 19:51:34.421459-08 2025-02-24 18:58:50.828608-08 test t A100 \N t \N \N \N {} -1134 316 2025-02-24 19:10:22.774186-08 2025-02-24 18:51:45.619893-08 benchmark t A100 \N t \N \N \N {} -1137 316 2025-02-24 19:55:11.126516-08 2025-02-24 18:17:41.336484-08 benchmark f A100 \N t \N \N \N {} -1138 316 2025-02-24 19:34:48.644475-08 2025-02-24 18:14:08.964849-08 leaderboard f A100 0.01013649895 t \N \N \N {} -1139 317 2025-02-24 19:01:55.707471-08 2025-02-24 19:26:25.81366-08 test f A100 \N f \N \N \N {} -1140 318 2025-02-24 18:34:52.552066-08 2025-02-24 19:48:38.716235-08 benchmark f A100 \N t \N \N \N {} -1141 319 2025-02-24 19:23:43.21952-08 2025-02-24 18:51:42.032684-08 test f A100 \N t \N \N \N {} -1142 320 2025-02-24 19:25:23.832847-08 2025-02-24 18:33:34.811732-08 benchmark f A100 \N t \N \N \N {} -1143 321 2025-02-24 20:03:32.364119-08 2025-02-24 18:41:04.100608-08 test f A100 \N t \N \N \N {} -1148 321 2025-02-24 19:35:02.480415-08 2025-02-24 19:46:33.415377-08 leaderboard t A100 0.008000332333333334 t \N \N \N {} -533 154 2025-02-24 04:30:12.737914-08 2025-02-24 03:41:45.719226-08 benchmark f H100 \N t \N \N \N {} -534 154 2025-02-24 04:26:39.743431-08 2025-02-24 03:49:38.668231-08 leaderboard f H100 0.00028956029310344826 t \N \N \N {} -1652 545 2025-02-25 09:28:59.147977-08 2025-02-25 08:22:34.616891-08 benchmark f A100 \N t \N \N \N {} -1653 545 2025-02-25 09:29:22.402709-08 2025-02-25 09:43:20.480827-08 leaderboard f A100 0.0031917851666666665 t \N \N \N {} -1654 545 2025-02-25 09:23:36.561531-08 2025-02-25 09:22:56.481202-08 test t A100 \N t \N \N \N {} -1655 545 2025-02-25 09:14:47.319988-08 2025-02-25 09:09:28.866828-08 benchmark t A100 \N t \N \N \N {} -1665 549 2025-02-25 09:27:34.175531-08 2025-02-25 08:31:10.590161-08 test t H100 \N t \N \N \N {} -2774 966 2025-02-26 20:38:42.929598-08 2025-02-26 19:33:54.323653-08 benchmark f A100 \N t \N \N \N {} -1671 550 2025-02-25 08:02:54.544859-08 2025-02-25 09:33:33.645968-08 test t H100 \N t \N \N \N {} -1672 550 2025-02-25 08:20:15.110251-08 2025-02-25 09:46:59.895856-08 benchmark t H100 \N t \N \N \N {} -1673 550 2025-02-25 08:16:05.976617-08 2025-02-25 08:56:29.376989-08 leaderboard t H100 0.0015150700833333332 t \N \N \N {} -1674 550 2025-02-25 09:23:16.872377-08 2025-02-25 09:42:17.075687-08 test f H100 \N t \N \N \N {} -1675 550 2025-02-25 08:00:39.040555-08 2025-02-25 08:14:08.930921-08 benchmark f H100 \N t \N \N \N {} -1677 551 2025-02-25 09:24:55.210117-08 2025-02-25 08:31:50.387183-08 benchmark f H100 \N t \N \N \N {} -4802 1487 2025-03-02 13:11:42.877171-08 2025-03-02 12:36:48.868582-08 test t A100 \N t \N \N \N {} -1704 565 2025-02-25 10:02:10.859525-08 2025-02-25 10:07:50.65938-08 test f A100 \N f \N \N \N {} -1705 566 2025-02-25 08:18:19.009138-08 2025-02-25 09:21:13.615277-08 test f A100 \N f \N \N \N {} -1706 566 2025-02-25 08:59:25.06759-08 2025-02-25 08:45:34.409254-08 test t A100 \N f \N \N \N {} -1708 570 2025-02-25 10:07:15.647675-08 2025-02-25 08:36:37.22994-08 test f T4 \N f \N \N \N {} -1709 570 2025-02-25 09:55:08.132128-08 2025-02-25 08:32:55.552216-08 test t T4 \N f \N \N \N {} -535 154 2025-02-24 04:07:21.29398-08 2025-02-24 04:13:20.791686-08 test t H100 \N t \N \N \N {} -536 154 2025-02-24 03:48:34.48216-08 2025-02-24 03:16:51.711753-08 benchmark t H100 \N t \N \N \N {} -537 154 2025-02-24 04:32:16.672746-08 2025-02-24 03:57:43.518406-08 leaderboard t H100 0.0002925063181818182 t \N \N \N {} -538 155 2025-02-24 04:35:32.646526-08 2025-02-24 05:04:46.599926-08 test f L4 \N f \N \N \N {} -539 155 2025-02-24 04:18:31.344638-08 2025-02-24 04:55:25.858103-08 test f T4 \N f \N \N \N {} -540 156 2025-02-24 03:43:29.839126-08 2025-02-24 03:21:47.390495-08 test f T4 \N t \N \N \N {} -543 157 2025-02-24 04:59:50.4166-08 2025-02-24 04:19:05.690038-08 leaderboard f A100 0.007824704333333333 t \N \N \N {} -544 157 2025-02-24 03:39:23.735651-08 2025-02-24 04:15:01.411276-08 test f T4 \N t \N \N \N {} -545 157 2025-02-24 05:04:10.421675-08 2025-02-24 04:14:54.241718-08 benchmark f T4 \N t \N \N \N {} -546 157 2025-02-24 04:57:33.857732-08 2025-02-24 04:22:04.003536-08 leaderboard f T4 0.022980689 t \N \N \N {} -547 157 2025-02-24 05:07:45.788613-08 2025-02-24 05:08:16.466658-08 test t T4 \N t \N \N \N {} -548 157 2025-02-24 03:17:36.228955-08 2025-02-24 05:13:27.73913-08 benchmark t T4 \N t \N \N \N {} -561 162 2025-02-24 05:06:05.251848-08 2025-02-24 03:48:29.825948-08 test f A100 \N t \N \N \N {} -562 162 2025-02-24 04:57:26.287277-08 2025-02-24 04:27:02.699784-08 benchmark f A100 \N t \N \N \N {} -563 162 2025-02-24 03:32:11.303899-08 2025-02-24 04:54:23.971009-08 leaderboard f A100 0.003460327 t \N \N \N {} -564 162 2025-02-24 04:54:00.853848-08 2025-02-24 04:59:22.66851-08 test t A100 \N t \N \N \N {} -565 162 2025-02-24 03:49:00.261076-08 2025-02-24 04:32:14.986087-08 benchmark t A100 \N t \N \N \N {} -566 162 2025-02-24 05:24:04.211942-08 2025-02-24 04:24:57.431418-08 leaderboard t A100 0.0034634703333333333 t \N \N \N {} -567 162 2025-02-24 04:05:26.770481-08 2025-02-24 03:41:41.45303-08 test t T4 \N t \N \N \N {} -1823 621 2025-02-25 09:37:22.310956-08 2025-02-25 10:04:30.945593-08 test f H100 \N t \N \N \N {} -1824 621 2025-02-25 09:48:47.475736-08 2025-02-25 10:36:25.122648-08 benchmark f H100 \N t \N \N \N {} -2416 818 2025-02-26 13:08:10.332573-08 2025-02-26 12:27:32.298697-08 benchmark f H100 \N f \N \N \N {} -2631 885 2025-02-26 14:51:45.736554-08 2025-02-26 14:47:20.105491-08 test f T4 \N t \N \N \N {} -1829 621 2025-02-25 09:36:01.751668-08 2025-02-25 10:06:26.482401-08 test t H100 \N t \N \N \N {} -1835 621 2025-02-25 10:55:06.644298-08 2025-02-25 10:16:25.436989-08 test t T4 \N t \N \N \N {} -1831 621 2025-02-25 09:36:43.309023-08 2025-02-25 09:39:20.008693-08 leaderboard t H100 0.00007977966666666668 t \N \N \N {} -1832 621 2025-02-25 09:37:12.560705-08 2025-02-25 11:23:33.885332-08 test f T4 \N t \N \N \N {} -1833 621 2025-02-25 10:54:16.770926-08 2025-02-25 10:13:23.913495-08 benchmark f T4 \N t \N \N \N {} -1834 621 2025-02-25 10:45:06.995356-08 2025-02-25 11:28:55.043321-08 leaderboard f T4 0.0003147605 t \N \N \N {} -1885 637 2025-02-25 11:14:01.886924-08 2025-02-25 10:33:34.875247-08 test t A100 \N t \N \N \N {} -3795 1280 2025-03-01 03:06:18.765637-08 2025-03-01 02:10:41.553886-08 test f A100 \N t \N \N \N {} -1836 621 2025-02-25 09:59:07.958405-08 2025-02-25 11:28:20.518166-08 benchmark t T4 \N t \N \N \N {} -1837 621 2025-02-25 10:14:38.896693-08 2025-02-25 10:18:05.012617-08 leaderboard t T4 0.00028145 t \N \N \N {} -568 162 2025-02-24 04:07:53.242047-08 2025-02-24 04:45:46.701262-08 benchmark t T4 \N t \N \N \N {} -569 162 2025-02-24 04:57:58.483951-08 2025-02-24 03:47:41.879786-08 leaderboard t T4 0.018000088 t \N \N \N {} -570 162 2025-02-24 04:07:02.728359-08 2025-02-24 03:30:28.466692-08 test f T4 \N t \N \N \N {} -571 162 2025-02-24 04:55:55.111211-08 2025-02-24 03:47:05.632536-08 benchmark f T4 \N t \N \N \N {} -578 164 2025-02-24 03:34:30.302743-08 2025-02-24 03:44:44.420625-08 test t H100 \N f \N \N \N {} -579 164 2025-02-24 04:40:03.835891-08 2025-02-24 05:14:49.678388-08 test f H100 \N f \N \N \N {} -580 165 2025-02-24 03:46:14.412089-08 2025-02-24 03:30:10.462639-08 test f T4 \N f \N \N \N {} -583 168 2025-02-24 03:40:59.436525-08 2025-02-24 04:13:33.24366-08 test f T4 \N f \N \N \N {} -584 169 2025-02-24 04:11:41.64701-08 2025-02-24 04:29:29.162765-08 test f T4 \N f \N \N \N {} -585 170 2025-02-24 03:50:54.355136-08 2025-02-24 04:32:48.353854-08 test f T4 \N f \N \N \N {} -609 172 2025-02-24 04:16:21.994773-08 2025-02-24 05:27:31.23999-08 test t L4 \N t \N \N \N {} -1839 621 2025-02-25 10:11:09.972425-08 2025-02-25 11:10:01.691566-08 benchmark f L4 \N t \N \N \N {} -1840 621 2025-02-25 10:32:35.014208-08 2025-02-25 10:14:06.170474-08 leaderboard f L4 0.00015338506896551724 t \N \N \N {} -1891 641 2025-02-25 12:30:16.797873-08 2025-02-25 12:50:15.869754-08 test t A100 \N t \N \N \N {} -3798 1280 2025-03-01 02:58:08.249313-08 2025-03-01 02:10:02.417766-08 test t A100 \N t \N \N \N {} -1844 622 2025-02-25 10:39:39.253611-08 2025-02-25 10:37:50.127615-08 benchmark f H100 \N t \N \N \N {} -1845 623 2025-02-25 11:18:57.219265-08 2025-02-25 10:30:49.939246-08 benchmark f H100 \N t \N \N \N {} -1851 626 2025-02-25 11:37:40.953324-08 2025-02-25 10:18:34.146973-08 test t H100 \N t \N \N \N {} -1911 658 2025-02-25 13:07:44.459666-08 2025-02-25 12:36:55.345563-08 test f L4 \N t \N \N \N {} -1854 627 2025-02-25 11:09:31.179796-08 2025-02-25 09:46:07.024596-08 test f H100 \N t \N \N \N {} -1855 627 2025-02-25 10:50:06.45193-08 2025-02-25 10:00:12.679191-08 benchmark f H100 \N t \N \N \N {} -1856 627 2025-02-25 11:18:03.678053-08 2025-02-25 09:50:41.832265-08 leaderboard f H100 0.001456563 t \N \N \N {} -1857 627 2025-02-25 10:15:45.419667-08 2025-02-25 09:46:39.110459-08 test t H100 \N t \N \N \N {} -1858 627 2025-02-25 11:37:33.070625-08 2025-02-25 10:23:48.04595-08 benchmark t H100 \N t \N \N \N {} -1859 627 2025-02-25 10:14:25.240727-08 2025-02-25 10:53:04.749149-08 leaderboard t H100 0.0014359026000000002 t \N \N \N {} -1860 628 2025-02-25 10:38:15.088855-08 2025-02-25 11:33:14.17158-08 benchmark f A100 \N t \N \N \N {} -1861 629 2025-02-25 11:24:33.838768-08 2025-02-25 11:45:59.052869-08 test f T4 \N t \N \N \N {} -1862 629 2025-02-25 10:18:02.87223-08 2025-02-25 10:01:30.670626-08 benchmark f T4 \N t \N \N \N {} -1863 629 2025-02-25 10:09:20.677026-08 2025-02-25 09:56:35.921709-08 leaderboard f T4 0.02022982983333333 t \N \N \N {} -2418 821 2025-02-26 12:32:06.906595-08 2025-02-26 12:30:33.821366-08 benchmark f H100 \N f \N \N \N {} -1872 635 2025-02-25 10:44:47.83768-08 2025-02-25 11:13:43.163043-08 leaderboard f A100 0.0031772633333333336 t \N \N \N {} -1873 635 2025-02-25 11:15:16.010215-08 2025-02-25 11:53:54.827853-08 test t A100 \N t \N \N \N {} -1875 635 2025-02-25 11:43:28.20069-08 2025-02-25 10:13:38.45603-08 leaderboard t A100 0.0032446378333333336 t \N \N \N {} -1877 636 2025-02-25 11:17:18.590101-08 2025-02-25 10:55:34.300914-08 benchmark f H100 \N t \N \N \N {} -1878 636 2025-02-25 11:59:53.786933-08 2025-02-25 11:34:35.925027-08 leaderboard f H100 0.0015205423333333333 t \N \N \N {} -588 172 2025-02-24 03:58:37.431798-08 2025-02-24 05:14:46.271612-08 test t A100 \N t \N \N \N {} -589 172 2025-02-24 05:31:54.505019-08 2025-02-24 04:23:39.720037-08 benchmark t A100 \N t \N \N \N {} -590 172 2025-02-24 03:42:59.187635-08 2025-02-24 04:26:15.997691-08 leaderboard t A100 0.003166644 t \N \N \N {} -591 172 2025-02-24 05:33:24.628818-08 2025-02-24 05:27:53.609663-08 test f H100 \N t \N \N \N {} -624 174 2025-02-24 04:00:03.344427-08 2025-02-24 04:30:10.212094-08 test f H100 \N t \N \N \N {} -592 172 2025-02-24 04:51:20.046359-08 2025-02-24 04:45:20.807066-08 benchmark f H100 \N t \N \N \N {} -1879 636 2025-02-25 10:21:35.338595-08 2025-02-25 10:41:36.288859-08 test t H100 \N t \N \N \N {} -1880 636 2025-02-25 11:47:02.581075-08 2025-02-25 11:37:54.178586-08 benchmark t H100 \N t \N \N \N {} -1881 636 2025-02-25 11:55:10.637717-08 2025-02-25 10:36:44.890626-08 leaderboard t H100 0.0014829484285714286 t \N \N \N {} -1886 637 2025-02-25 12:00:57.953939-08 2025-02-25 10:55:31.592722-08 benchmark t A100 \N t \N \N \N {} -1887 637 2025-02-25 11:56:19.406489-08 2025-02-25 11:35:39.598186-08 leaderboard t A100 0.003213315 t \N \N \N {} -1888 638 2025-02-25 12:00:43.449685-08 2025-02-25 12:07:45.096661-08 benchmark f A100 \N t \N \N \N {} -2420 823 2025-02-26 13:00:06.805549-08 2025-02-26 12:21:44.75151-08 benchmark f H100 \N t \N \N \N {} -1895 641 2025-02-25 12:54:01.264518-08 2025-02-25 11:46:30.101684-08 benchmark f A100 \N t \N \N \N {} -1896 641 2025-02-25 13:38:23.446635-08 2025-02-25 13:36:59.184489-08 leaderboard f A100 0.0030864853333333335 t \N \N \N {} -1897 642 2025-02-25 13:27:14.029692-08 2025-02-25 12:51:21.781335-08 test f T4 \N f \N \N \N {} -1912 658 2025-02-25 13:03:54.537257-08 2025-02-25 13:47:58.322026-08 benchmark f L4 \N t \N \N \N {} -1898 643 2025-02-25 13:22:18.087617-08 2025-02-25 11:54:21.931015-08 benchmark f A100 \N t \N \N \N {} -2421 825 2025-02-26 13:18:04.197511-08 2025-02-26 13:05:21.334106-08 benchmark f H100 \N t \N \N \N {} -1904 650 2025-02-25 12:31:28.402471-08 2025-02-25 13:39:04.170868-08 benchmark f A100 \N t \N \N \N {} -1905 651 2025-02-25 13:24:04.41766-08 2025-02-25 13:44:49.042952-08 benchmark f A100 \N t \N \N \N {} -1914 658 2025-02-25 13:39:12.203771-08 2025-02-25 12:35:51.134173-08 test t L4 \N t \N \N \N {} -1906 652 2025-02-25 13:04:03.79534-08 2025-02-25 12:26:24.162075-08 benchmark f A100 \N t \N \N \N {} -1907 654 2025-02-25 12:31:53.004451-08 2025-02-25 12:56:24.053648-08 test f T4 \N f \N \N \N {} -1908 653 2025-02-25 12:56:54.374943-08 2025-02-25 12:02:39.31621-08 benchmark f A100 \N t \N \N \N {} -1909 656 2025-02-25 13:47:06.574062-08 2025-02-25 13:06:17.287316-08 benchmark f A100 \N t \N \N \N {} -1910 657 2025-02-25 12:05:55.108287-08 2025-02-25 12:31:57.728641-08 benchmark f A100 \N t \N \N \N {} -3804 1281 2025-03-01 03:15:18.618566-08 2025-03-01 03:09:21.435376-08 test t A100 \N t \N \N \N {} -1915 658 2025-02-25 12:52:49.663863-08 2025-02-25 12:15:36.754739-08 benchmark t L4 \N t \N \N \N {} -1916 658 2025-02-25 13:46:15.680818-08 2025-02-25 12:14:55.778199-08 leaderboard t L4 0.017479398 t \N \N \N {} -593 172 2025-02-24 04:00:11.157726-08 2025-02-24 04:23:52.92541-08 leaderboard f H100 0.001457972 t \N \N \N {} -594 172 2025-02-24 04:24:57.48744-08 2025-02-24 05:39:56.243248-08 test t H100 \N t \N \N \N {} -595 172 2025-02-24 04:05:36.140858-08 2025-02-24 04:45:49.663541-08 benchmark t H100 \N t \N \N \N {} -596 172 2025-02-24 04:03:24.134887-08 2025-02-24 04:07:50.692444-08 leaderboard t H100 0.0014493626666666668 t \N \N \N {} -597 172 2025-02-24 04:22:04.734823-08 2025-02-24 05:20:24.419522-08 test t T4 \N t \N \N \N {} -598 172 2025-02-24 04:54:26.09647-08 2025-02-24 04:37:32.588527-08 benchmark t T4 \N t \N \N \N {} -599 172 2025-02-24 05:20:32.672091-08 2025-02-24 04:22:02.580439-08 leaderboard t T4 0.01753550875 t \N \N \N {} -601 172 2025-02-24 04:01:40.262757-08 2025-02-24 04:31:18.294476-08 benchmark f A100 \N t \N \N \N {} -602 172 2025-02-24 04:53:49.361583-08 2025-02-24 04:48:49.182764-08 leaderboard f A100 0.0031660506666666664 t \N \N \N {} -603 172 2025-02-24 03:52:06.072469-08 2025-02-24 03:59:33.848814-08 test f T4 \N t \N \N \N {} -604 172 2025-02-24 03:42:48.462511-08 2025-02-24 05:26:31.44727-08 benchmark f T4 \N t \N \N \N {} -610 172 2025-02-24 05:04:05.242805-08 2025-02-24 03:44:40.846459-08 benchmark t L4 \N t \N \N \N {} -611 172 2025-02-24 04:35:15.919214-08 2025-02-24 05:11:46.93162-08 leaderboard t L4 0.017569029 t \N \N \N {} -612 173 2025-02-24 05:24:50.95809-08 2025-02-24 04:11:33.537819-08 test f H100 \N f \N \N \N {} -613 173 2025-02-24 03:51:03.148107-08 2025-02-24 05:38:19.375618-08 test t H100 \N f \N \N \N {} -614 175 2025-02-24 04:40:13.642706-08 2025-02-24 05:40:34.3719-08 test f T4 \N f \N \N \N {} -615 174 2025-02-24 05:40:44.406358-08 2025-02-24 03:57:34.265608-08 test f A100 \N t \N \N \N {} -625 174 2025-02-24 05:28:15.27384-08 2025-02-24 04:58:32.636743-08 benchmark f H100 \N t \N \N \N {} -626 174 2025-02-24 04:27:19.875646-08 2025-02-24 05:38:11.378119-08 leaderboard f H100 0.00146166825 t \N \N \N {} -627 174 2025-02-24 04:57:35.414356-08 2025-02-24 05:31:36.9668-08 test t T4 \N t \N \N \N {} -628 174 2025-02-24 04:45:34.255845-08 2025-02-24 04:05:43.860604-08 benchmark t T4 \N t \N \N \N {} -629 174 2025-02-24 05:27:09.661991-08 2025-02-24 04:50:39.24896-08 leaderboard t T4 0.0208113558 t \N \N \N {} -1499 502 2025-02-25 06:47:02.590704-08 2025-02-25 06:27:19.767132-08 test f T4 \N t \N \N \N {} -633 174 2025-02-24 04:12:12.277241-08 2025-02-24 05:10:27.381931-08 test t L4 \N t \N \N \N {} -634 174 2025-02-24 05:00:36.484962-08 2025-02-24 04:04:08.518337-08 benchmark t L4 \N t \N \N \N {} -635 174 2025-02-24 04:34:32.791262-08 2025-02-24 04:57:05.719322-08 leaderboard t L4 0.017792222333333333 t \N \N \N {} -636 174 2025-02-24 04:08:01.422936-08 2025-02-24 05:26:59.018927-08 test f L4 \N t \N \N \N {} -637 174 2025-02-24 05:17:03.979995-08 2025-02-24 04:45:28.587227-08 benchmark f L4 \N t \N \N \N {} -638 174 2025-02-24 04:27:22.118036-08 2025-02-24 03:57:14.285261-08 leaderboard f L4 0.017829622 t \N \N \N {} -3276 1144 2025-02-28 06:55:15.001553-08 2025-02-28 05:46:00.750654-08 benchmark f A100 \N t \N \N \N {} -1961 670 2025-02-25 12:57:56.587732-08 2025-02-25 13:49:48.593819-08 benchmark f A100 \N t \N \N \N {} -642 177 2025-02-24 05:07:49.574927-08 2025-02-24 04:45:31.62306-08 test f T4 \N f \N \N \N {} -1962 671 2025-02-25 13:46:47.86222-08 2025-02-25 14:02:39.534938-08 benchmark f A100 \N t \N \N \N {} -1963 672 2025-02-25 14:00:04.67254-08 2025-02-25 13:25:13.842123-08 test t H100 \N t \N \N \N {} -1964 672 2025-02-25 12:36:59.057138-08 2025-02-25 14:02:22.120333-08 benchmark t H100 \N t \N \N \N {} -1965 672 2025-02-25 12:29:52.01167-08 2025-02-25 14:05:06.656703-08 leaderboard t H100 0.003437508 t \N \N \N {} -1966 672 2025-02-25 12:39:24.055726-08 2025-02-25 13:00:24.611271-08 test f H100 \N t \N \N \N {} -1967 672 2025-02-25 12:58:49.474139-08 2025-02-25 13:26:41.827259-08 benchmark f H100 \N t \N \N \N {} -1968 672 2025-02-25 13:26:33.936094-08 2025-02-25 12:40:21.065589-08 leaderboard f H100 0.003485398 t \N \N \N {} -2215 724 2025-02-25 14:37:54.567252-08 2025-02-25 14:43:57.444662-08 leaderboard t A100 0.010253208460000001 t \N \N \N {} -2216 724 2025-02-25 15:33:44.119406-08 2025-02-25 15:13:09.534917-08 test t H100 \N t \N \N \N {} -2253 752 2025-02-26 00:42:28.558196-08 2025-02-26 02:01:39.061078-08 benchmark f H100 \N f \N \N \N {} -2218 724 2025-02-25 15:32:48.937419-08 2025-02-25 15:25:26.740188-08 leaderboard t H100 0.00353085645 t \N \N \N {} -2219 724 2025-02-25 15:11:42.946931-08 2025-02-25 14:44:35.593924-08 test f A100 \N t \N \N \N {} -2220 724 2025-02-25 14:34:20.966564-08 2025-02-25 15:36:27.340882-08 benchmark f A100 \N t \N \N \N {} -2221 724 2025-02-25 15:58:30.291254-08 2025-02-25 14:42:07.237481-08 leaderboard f A100 0.0107430945 t \N \N \N {} -691 217 2025-02-24 07:04:53.233308-08 2025-02-24 08:10:24.296185-08 test t A100 \N f \N \N \N {} -5663 1723 2025-03-09 10:05:43.141436-07 2025-03-09 10:57:02.505632-07 leaderboard t T4 0.016583018 t \N \N \N {} -2313 778 2025-02-26 03:54:39.554659-08 2025-02-26 04:48:44.686597-08 benchmark f A100 \N t \N \N \N {} -2314 778 2025-02-26 03:35:24.258305-08 2025-02-26 04:57:01.237295-08 leaderboard f A100 0.0030904886666666665 t \N \N \N {} -2315 781 2025-02-26 08:10:42.313296-08 2025-02-26 08:01:03.24323-08 test t A100 \N t \N \N \N {} -2330 784 2025-02-26 08:48:24.701263-08 2025-02-26 06:56:52.366196-08 test t A100 \N f \N \N \N {} -2318 781 2025-02-26 08:17:57.941797-08 2025-02-26 07:48:29.179932-08 test f A100 \N t \N \N \N {} -2319 781 2025-02-26 08:04:49.559876-08 2025-02-26 07:59:49.9534-08 benchmark f A100 \N t \N \N \N {} -2320 781 2025-02-26 07:21:32.163845-08 2025-02-26 08:26:16.891069-08 leaderboard f A100 0.0031749516666666664 t \N \N \N {} -2321 782 2025-02-26 07:07:41.841142-08 2025-02-26 08:08:18.275768-08 test f T4 \N f \N \N \N {} -2322 782 2025-02-26 08:40:25.972436-08 2025-02-26 07:46:04.302799-08 test t T4 \N f \N \N \N {} -2323 783 2025-02-26 08:25:20.355357-08 2025-02-26 07:28:44.30517-08 test t A100 \N t \N \N \N {} -2334 785 2025-02-26 08:50:29.968504-08 2025-02-26 08:16:16.86245-08 test t A100 \N t \N \N \N {} -3266 1142 2025-02-28 05:40:58.845992-08 2025-02-28 06:20:23.84623-08 test t A100 \N t \N \N \N {} -692 218 2025-02-24 07:35:12.909281-08 2025-02-24 08:32:29.712533-08 test f T4 \N f \N \N \N {} -3856 1291 2025-03-01 03:58:25.966221-08 2025-03-01 02:58:47.903585-08 test f A100 \N t \N \N \N {} -2500 871 2025-02-26 14:21:21.727765-08 2025-02-26 14:48:13.334885-08 test f T4 \N t \N \N \N {} -2501 872 2025-02-26 14:53:44.720161-08 2025-02-26 14:09:15.911305-08 test t A100 \N t \N \N \N {} -2502 872 2025-02-26 14:29:17.86941-08 2025-02-26 14:42:23.807854-08 benchmark t A100 \N t \N \N \N {} -2504 872 2025-02-26 14:21:53.085089-08 2025-02-26 13:33:28.796563-08 test t H100 \N t \N \N \N {} -3396 1171 2025-02-28 08:52:34.344836-08 2025-02-28 10:12:06.257868-08 test t L4 \N f \N \N \N {} -663 194 2025-02-24 05:41:36.621215-08 2025-02-24 06:09:54.974777-08 test f T4 \N t \N \N \N {} -664 195 2025-02-24 06:08:45.709175-08 2025-02-24 05:08:22.138404-08 benchmark f H100 \N f \N \N \N {} -666 197 2025-02-24 06:56:56.061652-08 2025-02-24 06:22:23.208702-08 benchmark f H100 \N f \N \N \N {} -667 198 2025-02-24 06:34:23.314709-08 2025-02-24 05:20:01.543454-08 benchmark f H100 \N f \N \N \N {} -668 199 2025-02-24 04:59:42.957047-08 2025-02-24 05:30:49.743808-08 benchmark f H100 \N f \N \N \N {} -2598 885 2025-02-26 14:21:31.605809-08 2025-02-26 13:26:53.536131-08 test t L4 \N t \N \N \N {} -2599 885 2025-02-26 14:56:23.588162-08 2025-02-26 13:44:20.657543-08 benchmark t L4 \N t \N \N \N {} -2600 885 2025-02-26 14:30:27.342225-08 2025-02-26 14:48:35.398787-08 leaderboard t L4 0.017775642666666668 t \N \N \N {} -2601 884 2025-02-26 14:45:15.947305-08 2025-02-26 14:55:41.866073-08 test t H100 \N t \N \N \N {} -2613 884 2025-02-26 13:25:03.130497-08 2025-02-26 13:21:04.408924-08 test f T4 \N t \N \N \N {} -2604 884 2025-02-26 15:11:47.082796-08 2025-02-26 14:52:15.530642-08 test t A100 \N t \N \N \N {} -2605 884 2025-02-26 13:27:46.343561-08 2025-02-26 14:55:24.020372-08 benchmark t A100 \N t \N \N \N {} -2606 884 2025-02-26 14:44:05.568836-08 2025-02-26 13:52:58.061404-08 leaderboard t A100 0.0032559906666666666 t \N \N \N {} -2621 885 2025-02-26 14:23:14.309831-08 2025-02-26 13:26:20.529447-08 leaderboard t A100 0.00319686775 t \N \N \N {} -3421 1179 2025-02-28 10:14:03.171361-08 2025-02-28 10:16:52.20393-08 test t L4 \N f \N \N \N {} -2610 884 2025-02-26 15:13:57.604086-08 2025-02-26 13:38:33.967499-08 test f H100 \N t \N \N \N {} -2611 884 2025-02-26 14:21:20.575521-08 2025-02-26 14:28:32.061414-08 benchmark f H100 \N t \N \N \N {} -2612 884 2025-02-26 14:43:21.793286-08 2025-02-26 15:00:54.664103-08 leaderboard f H100 0.0014509983333333332 t \N \N \N {} -2622 885 2025-02-26 14:42:43.496715-08 2025-02-26 14:25:56.942011-08 test f L4 \N t \N \N \N {} -3428 1183 2025-02-28 10:08:45.167126-08 2025-02-28 11:18:19.555595-08 test t L4 \N f \N \N \N {} -2614 884 2025-02-26 15:08:00.153692-08 2025-02-26 14:14:23.980237-08 benchmark f T4 \N t \N \N \N {} -2615 884 2025-02-26 14:02:53.674937-08 2025-02-26 14:12:29.617332-08 leaderboard f T4 0.01843677733333333 t \N \N \N {} -2616 885 2025-02-26 13:36:00.637075-08 2025-02-26 13:52:46.766673-08 test t H100 \N t \N \N \N {} -2617 885 2025-02-26 14:50:02.881007-08 2025-02-26 15:06:57.135832-08 benchmark t H100 \N t \N \N \N {} -2618 885 2025-02-26 13:35:29.138108-08 2025-02-26 15:14:08.351625-08 leaderboard t H100 0.0014837716 t \N \N \N {} -2619 885 2025-02-26 13:51:02.414525-08 2025-02-26 14:11:16.314329-08 test t A100 \N t \N \N \N {} -2620 885 2025-02-26 14:21:00.02625-08 2025-02-26 13:34:27.254132-08 benchmark t A100 \N t \N \N \N {} -2625 885 2025-02-26 13:29:27.081756-08 2025-02-26 14:06:55.891402-08 test f A100 \N t \N \N \N {} -2626 885 2025-02-26 14:36:19.92372-08 2025-02-26 14:31:38.799097-08 benchmark f A100 \N t \N \N \N {} -2627 885 2025-02-26 13:30:35.621947-08 2025-02-26 14:27:23.062497-08 leaderboard f A100 0.003209451 t \N \N \N {} -2628 885 2025-02-26 14:10:08.722188-08 2025-02-26 15:18:05.698046-08 test t T4 \N t \N \N \N {} -2629 885 2025-02-26 14:59:49.529515-08 2025-02-26 13:34:26.486834-08 benchmark t T4 \N t \N \N \N {} -2630 885 2025-02-26 14:58:52.752015-08 2025-02-26 14:53:03.340521-08 leaderboard t T4 0.017220341666666666 t \N \N \N {} -2634 886 2025-02-26 14:08:14.352049-08 2025-02-26 13:46:49.236868-08 benchmark f H100 \N t \N \N \N {} -670 201 2025-02-24 05:45:11.842551-08 2025-02-24 06:28:30.253378-08 benchmark f H100 \N f \N \N \N {} -704 225 2025-02-24 08:56:12.740421-08 2025-02-24 08:34:20.349342-08 leaderboard f A100 0.011330630333333334 t \N \N \N {} -1439 480 2025-02-25 04:48:27.37121-08 2025-02-25 05:01:05.017307-08 test t A100 \N f \N \N \N {} -671 203 2025-02-24 06:24:20.549783-08 2025-02-24 05:24:34.368331-08 test f H100 \N f \N \N \N {} -673 205 2025-02-24 07:04:18.018562-08 2025-02-24 05:35:41.546773-08 test f T4 \N t \N \N \N {} -674 206 2025-02-24 06:06:45.212354-08 2025-02-24 07:06:51.567687-08 benchmark f T4 \N t \N \N \N {} -676 208 2025-02-24 06:06:40.542676-08 2025-02-24 06:41:12.566535-08 test f H100 \N t \N \N \N {} -705 225 2025-02-24 08:55:10.930728-08 2025-02-24 07:46:29.223577-08 test t A100 \N t \N \N \N {} -1502 502 2025-02-25 07:17:02.787-08 2025-02-25 07:28:38.183565-08 test t T4 \N t \N \N \N {} -677 209 2025-02-24 05:52:20.627547-08 2025-02-24 06:50:43.221253-08 benchmark f H100 \N t \N \N \N {} -678 210 2025-02-24 05:28:59.266404-08 2025-02-24 06:40:36.860883-08 benchmark f H100 \N t \N \N \N {} -679 211 2025-02-24 05:59:18.290632-08 2025-02-24 05:31:04.882898-08 test t H100 \N t \N \N \N {} -6187 1904 2025-03-11 16:30:03.874833-07 2025-03-11 16:23:04.032961-07 test f T4 \N f \N \N \N {} -2635 887 2025-02-26 14:23:20.516781-08 2025-02-26 13:30:42.901197-08 benchmark f H100 \N t \N \N \N {} -2636 888 2025-02-26 15:08:09.895226-08 2025-02-26 15:02:44.771912-08 benchmark f H100 \N t \N \N \N {} -2637 889 2025-02-26 14:20:26.49628-08 2025-02-26 14:31:54.196513-08 benchmark f H100 \N t \N \N \N {} -3429 1183 2025-02-28 10:30:24.045825-08 2025-02-28 09:58:43.334761-08 test f L4 \N f \N \N \N {} -2658 917 2025-02-26 16:04:22.640816-08 2025-02-26 15:50:33.550145-08 test t L4 \N t \N \N \N {} -2642 894 2025-02-26 15:22:35.57726-08 2025-02-26 15:19:16.529445-08 benchmark f H100 \N t \N \N \N {} -2643 895 2025-02-26 14:19:06.387984-08 2025-02-26 13:54:35.774098-08 benchmark f H100 \N t \N \N \N {} -2644 896 2025-02-26 14:32:56.05166-08 2025-02-26 15:21:02.107636-08 benchmark f H100 \N t \N \N \N {} -2645 897 2025-02-26 14:43:32.97254-08 2025-02-26 15:35:10.004299-08 benchmark f H100 \N t \N \N \N {} -3642 1244 2025-02-28 23:55:30.080534-08 2025-03-01 00:02:11.688916-08 test f A100 \N t \N \N \N {} -2648 900 2025-02-26 14:55:59.543208-08 2025-02-26 15:16:42.738329-08 benchmark f H100 \N f \N \N \N {} -2649 902 2025-02-26 15:19:37.018566-08 2025-02-26 14:33:09.313223-08 benchmark f H100 \N t \N \N \N {} -2650 905 2025-02-26 15:06:58.156118-08 2025-02-26 15:39:57.941001-08 benchmark f H100 \N t \N \N \N {} -3868 1293 2025-03-01 03:27:42.833831-08 2025-03-01 02:55:20.805453-08 test t A100 \N t \N \N \N {} -2653 910 2025-02-26 15:22:55.54094-08 2025-02-26 15:29:37.757077-08 benchmark f H100 \N f \N \N \N {} -2654 912 2025-02-26 15:40:15.529449-08 2025-02-26 14:30:04.45723-08 test f H100 \N t \N \N \N {} -2655 913 2025-02-26 15:41:19.632645-08 2025-02-26 15:25:04.114543-08 benchmark f H100 \N t \N \N \N {} -2656 914 2025-02-26 15:28:36.303476-08 2025-02-26 14:41:59.161801-08 benchmark f A100 \N t \N \N \N {} -686 213 2025-02-24 06:35:37.280408-08 2025-02-24 07:07:31.745156-08 benchmark f H100 \N t \N \N \N {} -687 214 2025-02-24 06:45:56.939468-08 2025-02-24 06:25:32.523917-08 benchmark f H100 \N t \N \N \N {} -693 219 2025-02-24 09:02:17.179137-08 2025-02-24 08:31:55.588104-08 test f T4 \N f \N \N \N {} -694 220 2025-02-24 07:57:20.923442-08 2025-02-24 08:06:53.334965-08 test f T4 \N f \N \N \N {} -6861 2127 2025-03-15 09:30:21.684959-07 2025-03-15 10:10:14.889721-07 test f A100 \N t \N \N \N {} -2657 915 2025-02-26 15:22:50.97253-08 2025-02-26 14:13:32.883512-08 benchmark f H100 \N f \N \N \N {} -2659 917 2025-02-26 15:59:10.277444-08 2025-02-26 16:06:48.272166-08 benchmark t L4 \N t \N \N \N {} -2660 917 2025-02-26 15:22:27.480576-08 2025-02-26 15:49:28.223669-08 leaderboard t L4 0.017139778 t \N \N \N {} -2661 917 2025-02-26 14:50:12.413294-08 2025-02-26 14:36:02.481211-08 test t A100 \N t \N \N \N {} -2662 917 2025-02-26 14:49:43.883464-08 2025-02-26 15:40:03.528298-08 benchmark t A100 \N t \N \N \N {} -695 221 2025-02-24 08:04:03.997077-08 2025-02-24 09:06:50.538681-08 test f T4 \N f \N \N \N {} -696 222 2025-02-24 08:59:10.271492-08 2025-02-24 07:29:33.102147-08 benchmark f A100 \N f \N \N \N {} -697 222 2025-02-24 08:07:36.901531-08 2025-02-24 07:47:31.363673-08 benchmark f T4 \N f \N \N \N {} -1440 480 2025-02-25 04:05:00.096466-08 2025-02-25 05:05:26.708798-08 test f A100 \N f \N \N \N {} -698 222 2025-02-24 08:43:36.056535-08 2025-02-24 08:16:52.237368-08 benchmark f L4 \N f \N \N \N {} -699 222 2025-02-24 08:22:16.816685-08 2025-02-24 08:37:10.387978-08 benchmark f H100 \N f \N \N \N {} -700 223 2025-02-24 09:06:11.706145-08 2025-02-24 09:03:29.685017-08 benchmark f A100 \N f \N \N \N {} -701 224 2025-02-24 08:10:04.943823-08 2025-02-24 08:28:43.870189-08 test f T4 \N f \N \N \N {} -702 225 2025-02-24 07:56:55.117396-08 2025-02-24 08:48:49.945346-08 test f A100 \N t \N \N \N {} -703 225 2025-02-24 07:41:19.688526-08 2025-02-24 07:41:37.470123-08 benchmark f A100 \N t \N \N \N {} -706 225 2025-02-24 08:37:11.189015-08 2025-02-24 09:05:40.321522-08 benchmark t A100 \N t \N \N \N {} -708 226 2025-02-24 09:25:07.198881-08 2025-02-24 07:35:03.936398-08 benchmark f A100 \N f \N \N \N {} -709 227 2025-02-24 08:24:29.897497-08 2025-02-24 09:20:50.161506-08 benchmark f A100 \N t \N \N \N {} -710 228 2025-02-24 08:50:24.79349-08 2025-02-24 08:55:39.391024-08 test f T4 \N t \N \N \N {} -711 229 2025-02-24 08:11:00.669556-08 2025-02-24 09:13:50.969935-08 test t A100 \N t \N \N \N {} -1111 304 2025-02-24 17:03:32.591659-08 2025-02-24 17:41:47.088082-08 benchmark f H100 \N t \N \N \N {} -712 229 2025-02-24 08:28:09.285744-08 2025-02-24 09:27:17.171225-08 benchmark t A100 \N t \N \N \N {} -2663 917 2025-02-26 15:02:41.87638-08 2025-02-26 15:04:14.826114-08 leaderboard t A100 0.002618883 t \N \N \N {} -3474 1194 2025-02-28 10:51:23.270197-08 2025-02-28 10:28:38.739112-08 benchmark f A100 \N f \N \N \N {} -714 229 2025-02-24 09:20:32.013179-08 2025-02-24 09:10:19.888175-08 test f A100 \N t \N \N \N {} -719 230 2025-02-24 07:43:26.148361-08 2025-02-24 09:27:29.034748-08 leaderboard f A100 0.003160102 t \N \N \N {} -728 233 2025-02-24 07:51:31.813355-08 2025-02-24 07:55:17.330164-08 test t A100 \N t \N \N \N {} -729 233 2025-02-24 07:58:04.52096-08 2025-02-24 09:14:58.005432-08 benchmark t A100 \N t \N \N \N {} -730 233 2025-02-24 09:01:40.583243-08 2025-02-24 08:17:02.267771-08 leaderboard t A100 0.0031330916666666665 t \N \N \N {} -1443 483 2025-02-25 04:08:48.170684-08 2025-02-25 04:18:24.374905-08 test f A100 \N f \N \N \N {} -731 233 2025-02-24 08:17:49.25495-08 2025-02-24 09:15:06.490991-08 test f A100 \N t \N \N \N {} -5675 1724 2025-03-09 09:09:34.316424-07 2025-03-09 09:56:58.297658-07 leaderboard t A100 0.002777401 t \N \N \N {} -2664 917 2025-02-26 15:33:18.523093-08 2025-02-26 14:23:26.27638-08 test f T4 \N t \N \N \N {} -2665 917 2025-02-26 14:41:08.119956-08 2025-02-26 14:40:45.367615-08 benchmark f T4 \N t \N \N \N {} -2666 917 2025-02-26 14:19:24.006278-08 2025-02-26 15:07:56.17656-08 leaderboard f T4 0.017432490666666668 t \N \N \N {} -2667 917 2025-02-26 14:50:03.208616-08 2025-02-26 14:15:31.2517-08 test f H100 \N t \N \N \N {} -2668 917 2025-02-26 15:41:58.425053-08 2025-02-26 14:23:19.930763-08 benchmark f H100 \N t \N \N \N {} -3184 1121 2025-02-28 04:45:28.941252-08 2025-02-28 03:32:18.191207-08 test f A100 \N t \N \N \N {} -2669 917 2025-02-26 15:11:08.928473-08 2025-02-26 15:58:37.434081-08 leaderboard f H100 0.0014553213333333332 t \N \N \N {} -2670 917 2025-02-26 15:28:35.839236-08 2025-02-26 15:52:24.038032-08 test f A100 \N t \N \N \N {} -2671 917 2025-02-26 14:52:23.197769-08 2025-02-26 15:34:23.428279-08 benchmark f A100 \N t \N \N \N {} -2672 917 2025-02-26 14:12:48.444376-08 2025-02-26 14:28:23.028924-08 leaderboard f A100 0.003219043 t \N \N \N {} -2673 917 2025-02-26 14:12:35.128126-08 2025-02-26 14:43:26.451849-08 test t T4 \N t \N \N \N {} -737 233 2025-02-24 09:16:03.275201-08 2025-02-24 09:11:08.635071-08 test t T4 \N t \N \N \N {} -738 233 2025-02-24 08:28:01.859065-08 2025-02-24 09:22:10.211632-08 benchmark t T4 \N t \N \N \N {} -739 233 2025-02-24 09:08:18.555299-08 2025-02-24 09:02:46.143397-08 leaderboard t T4 0.0036530893333333336 t \N \N \N {} -740 233 2025-02-24 08:49:10.05294-08 2025-02-24 08:07:07.637237-08 test f T4 \N t \N \N \N {} -776 249 2025-02-24 10:24:50.908208-08 2025-02-24 11:51:41.650348-08 test f A100 \N t \N \N \N {} -3898 1300 2025-03-01 04:41:21.824886-08 2025-03-01 04:35:22.271324-08 benchmark f A100 \N t \N \N \N {} -747 235 2025-02-24 09:17:44.247963-08 2025-02-24 09:28:51.71791-08 test f A100 \N f \N \N \N {} -748 235 2025-02-24 09:36:38.154313-08 2025-02-24 09:52:27.287797-08 test t A100 \N f \N \N \N {} -749 236 2025-02-24 09:03:52.762503-08 2025-02-24 09:18:03.476551-08 test f A100 \N t \N \N \N {} -750 237 2025-02-24 09:27:00.648875-08 2025-02-24 09:17:20.060425-08 test t A100 \N t \N \N \N {} -758 241 2025-02-24 10:35:45.406451-08 2025-02-24 10:49:56.940266-08 test f A100 \N f \N \N \N {} -2679 917 2025-02-26 15:58:31.502323-08 2025-02-26 15:05:12.236739-08 test f L4 \N t \N \N \N {} -3475 1196 2025-02-28 10:58:00.781332-08 2025-02-28 11:31:50.239552-08 test t A100 \N t \N \N \N {} -2675 917 2025-02-26 15:10:31.598044-08 2025-02-26 15:45:21.966872-08 leaderboard t T4 0.017423966 t \N \N \N {} -2676 917 2025-02-26 15:37:23.354452-08 2025-02-26 14:45:19.799848-08 test t H100 \N t \N \N \N {} -2677 917 2025-02-26 15:32:46.804737-08 2025-02-26 14:42:52.925066-08 benchmark t H100 \N t \N \N \N {} -2678 917 2025-02-26 15:26:19.888166-08 2025-02-26 15:42:16.647691-08 leaderboard t H100 0.0014749786666666668 t \N \N \N {} -3478 1196 2025-02-28 11:49:03.159248-08 2025-02-28 11:21:38.865185-08 test f A100 \N t \N \N \N {} -3871 1294 2025-03-01 03:25:39.575831-08 2025-03-01 03:47:44.417542-08 test f A100 \N t \N \N \N {} -2680 917 2025-02-26 14:12:15.287164-08 2025-02-26 14:56:49.444837-08 benchmark f L4 \N t \N \N \N {} -2681 917 2025-02-26 14:13:57.106393-08 2025-02-26 14:37:54.475639-08 leaderboard f L4 0.017202002666666667 t \N \N \N {} -2682 919 2025-02-26 15:07:06.306277-08 2025-02-26 14:43:54.993315-08 test f T4 \N t \N \N \N {} -2683 918 2025-02-26 14:37:18.145006-08 2025-02-26 14:58:27.554341-08 test f H100 \N f \N \N \N {} -2684 920 2025-02-26 15:32:10.569826-08 2025-02-26 15:21:11.603029-08 benchmark f H100 \N f \N \N \N {} -2690 925 2025-02-26 15:23:42.15559-08 2025-02-26 16:09:05.490772-08 leaderboard t A100 0.003207825 t \N \N \N {} -2685 921 2025-02-26 15:18:14.224991-08 2025-02-26 15:44:27.451168-08 benchmark f H100 \N f \N \N \N {} -2687 923 2025-02-26 15:03:01.412592-08 2025-02-26 15:06:11.143738-08 benchmark f H100 \N t \N \N \N {} -2688 925 2025-02-26 16:08:56.665897-08 2025-02-26 15:00:21.657629-08 test t A100 \N t \N \N \N {} -2689 925 2025-02-26 16:07:17.241061-08 2025-02-26 14:31:28.824618-08 benchmark t A100 \N t \N \N \N {} -751 237 2025-02-24 10:32:35.667519-08 2025-02-24 09:04:14.834137-08 benchmark t A100 \N t \N \N \N {} -752 237 2025-02-24 10:42:58.851055-08 2025-02-24 09:40:55.016006-08 leaderboard t A100 0.0033126033333333335 t \N \N \N {} -753 237 2025-02-24 10:22:09.7399-08 2025-02-24 10:48:42.661894-08 test f A100 \N t \N \N \N {} -754 237 2025-02-24 09:37:37.615117-08 2025-02-24 10:02:09.097882-08 benchmark f A100 \N t \N \N \N {} -755 237 2025-02-24 10:36:07.704848-08 2025-02-24 10:23:31.161901-08 leaderboard f A100 0.003277688 t \N \N \N {} -756 238 2025-02-24 09:28:50.167162-08 2025-02-24 09:49:32.625124-08 test f A100 \N f \N \N \N {} -759 242 2025-02-24 10:36:29.62006-08 2025-02-24 10:53:18.167093-08 test f A100 \N t \N \N \N {} -3485 1197 2025-02-28 11:18:37.224946-08 2025-02-28 11:49:48.964789-08 test f A100 \N t \N \N \N {} -3874 1294 2025-03-01 03:31:01.665765-08 2025-03-01 04:31:31.171928-08 test t A100 \N t \N \N \N {} -760 242 2025-02-24 09:47:29.285286-08 2025-02-24 10:30:48.947657-08 benchmark f A100 \N t \N \N \N {} -761 242 2025-02-24 10:47:46.257274-08 2025-02-24 10:32:35.015062-08 leaderboard f A100 0.0032635085 t \N \N \N {} -762 242 2025-02-24 10:33:54.353655-08 2025-02-24 09:20:51.991089-08 test t A100 \N t \N \N \N {} -763 242 2025-02-24 10:41:28.131991-08 2025-02-24 10:23:01.349186-08 benchmark t A100 \N t \N \N \N {} -764 242 2025-02-24 10:31:24.13368-08 2025-02-24 11:10:39.141474-08 leaderboard t A100 0.0032074475454545452 t \N \N \N {} -766 244 2025-02-24 11:12:28.205288-08 2025-02-24 10:55:23.216173-08 test t A100 \N t \N \N \N {} -767 244 2025-02-24 10:50:20.125343-08 2025-02-24 10:53:26.469742-08 benchmark t A100 \N t \N \N \N {} -2691 925 2025-02-26 15:03:38.341726-08 2025-02-26 14:39:04.512785-08 test f H100 \N t \N \N \N {} -770 244 2025-02-24 09:48:07.049405-08 2025-02-24 11:16:17.375008-08 benchmark f A100 \N t \N \N \N {} -771 244 2025-02-24 10:16:24.565263-08 2025-02-24 10:42:42.951293-08 leaderboard f A100 0.003206431 t \N \N \N {} -774 247 2025-02-24 11:24:50.833311-08 2025-02-24 11:11:00.480374-08 test f A100 \N t \N \N \N {} -775 248 2025-02-24 11:18:01.396675-08 2025-02-24 09:46:03.356509-08 benchmark f A100 \N t \N \N \N {} -1357 454 2025-02-25 03:22:20.667093-08 2025-02-25 02:18:17.785956-08 benchmark f H100 \N f \N \N \N {} -2692 925 2025-02-26 15:31:22.064733-08 2025-02-26 15:24:18.486092-08 benchmark f H100 \N t \N \N \N {} -2693 925 2025-02-26 15:40:09.437246-08 2025-02-26 14:33:37.515202-08 leaderboard f H100 0.0014418213333333332 t \N \N \N {} -2694 925 2025-02-26 14:21:30.999182-08 2025-02-26 14:59:14.162816-08 test f A100 \N t \N \N \N {} -2695 925 2025-02-26 14:49:05.851761-08 2025-02-26 16:02:19.46454-08 benchmark f A100 \N t \N \N \N {} -3187 1121 2025-02-28 04:13:45.106756-08 2025-02-28 05:15:10.243175-08 test t A100 \N t \N \N \N {} -2697 925 2025-02-26 15:49:51.58385-08 2025-02-26 15:15:43.400087-08 test t H100 \N t \N \N \N {} -2698 925 2025-02-26 15:20:13.529064-08 2025-02-26 14:54:28.716289-08 benchmark t H100 \N t \N \N \N {} -2699 925 2025-02-26 15:12:28.653251-08 2025-02-26 15:26:50.456366-08 leaderboard t H100 0.0014572976666666667 t \N \N \N {} -2700 925 2025-02-26 14:23:46.440832-08 2025-02-26 15:33:30.662407-08 test f T4 \N t \N \N \N {} -2706 925 2025-02-26 15:56:56.719925-08 2025-02-26 15:31:05.149568-08 test f L4 \N t \N \N \N {} -2701 925 2025-02-26 14:27:02.624231-08 2025-02-26 14:29:33.448522-08 benchmark f T4 \N t \N \N \N {} -2702 925 2025-02-26 15:37:17.423193-08 2025-02-26 14:15:41.379785-08 leaderboard f T4 0.01748257666666667 t \N \N \N {} -2703 925 2025-02-26 15:02:07.78409-08 2025-02-26 14:30:59.074601-08 test t T4 \N t \N \N \N {} -2704 925 2025-02-26 15:25:00.161914-08 2025-02-26 14:41:31.01698-08 benchmark t T4 \N t \N \N \N {} -2705 925 2025-02-26 15:45:54.49678-08 2025-02-26 15:14:52.211382-08 leaderboard t T4 0.01740308366666667 t \N \N \N {} -2712 926 2025-02-26 14:49:00.905432-08 2025-02-26 15:15:12.066984-08 test f H100 \N f \N \N \N {} -2707 925 2025-02-26 15:14:33.474068-08 2025-02-26 16:13:05.73025-08 benchmark f L4 \N t \N \N \N {} -2708 925 2025-02-26 14:20:21.316161-08 2025-02-26 14:20:47.541084-08 leaderboard f L4 0.017136907333333333 t \N \N \N {} -2709 925 2025-02-26 14:35:12.137581-08 2025-02-26 15:27:12.663189-08 test t L4 \N t \N \N \N {} -2710 925 2025-02-26 14:22:20.587234-08 2025-02-26 15:41:26.009938-08 benchmark t L4 \N t \N \N \N {} -2711 925 2025-02-26 14:28:34.108955-08 2025-02-26 15:21:09.620226-08 leaderboard t L4 0.017147484 t \N \N \N {} -2757 955 2025-02-26 19:44:57.978305-08 2025-02-26 20:19:00.046496-08 test f A100 \N f \N \N \N {} -3488 1197 2025-02-28 12:13:22.706712-08 2025-02-28 11:38:53.381675-08 test t A100 \N t \N \N \N {} -2715 927 2025-02-26 15:38:50.899973-08 2025-02-26 15:07:34.985733-08 leaderboard f H100 0.0008018275555555555 t \N \N \N {} -787 251 2025-02-24 11:51:27.707677-08 2025-02-24 11:37:57.611418-08 benchmark t T4 \N t \N \N \N {} -798 251 2025-02-24 10:21:21.926398-08 2025-02-24 11:59:58.050957-08 test f T4 \N t \N \N \N {} -799 251 2025-02-24 10:39:23.728982-08 2025-02-24 12:03:26.185356-08 benchmark f T4 \N t \N \N \N {} -803 251 2025-02-24 11:17:20.227202-08 2025-02-24 12:07:21.562687-08 leaderboard f L4 0.017377531 t \N \N \N {} -804 251 2025-02-24 10:36:19.381723-08 2025-02-24 11:38:04.632489-08 test t L4 \N t \N \N \N {} -805 251 2025-02-24 11:30:07.481337-08 2025-02-24 11:39:59.955081-08 benchmark t L4 \N t \N \N \N {} -812 253 2025-02-24 12:20:58.985139-08 2025-02-24 11:40:09.015087-08 benchmark t A100 \N f \N \N \N {} -814 255 2025-02-24 12:02:11.606734-08 2025-02-24 13:15:58.848433-08 test f H100 \N t \N \N \N {} -815 255 2025-02-24 12:02:01.586263-08 2025-02-24 12:14:04.067053-08 benchmark f H100 \N t \N \N \N {} -816 255 2025-02-24 12:48:59.000761-08 2025-02-24 12:42:09.155319-08 leaderboard f H100 0.006132886 t \N \N \N {} -817 255 2025-02-24 12:15:55.811254-08 2025-02-24 12:36:09.13928-08 test t H100 \N t \N \N \N {} -844 262 2025-02-24 13:35:47.457777-08 2025-02-24 13:57:51.505602-08 test t A100 \N t \N \N \N {} -2716 927 2025-02-26 15:52:42.380063-08 2025-02-26 14:28:51.260977-08 test t H100 \N t \N \N \N {} -2717 927 2025-02-26 16:02:37.652274-08 2025-02-26 16:05:08.637406-08 benchmark t H100 \N t \N \N \N {} -2780 968 2025-02-26 20:29:07.654188-08 2025-02-26 19:21:21.673508-08 test f A100 \N t \N \N \N {} -3491 1198 2025-02-28 12:21:58.688283-08 2025-02-28 12:05:53.605746-08 test f A100 \N t \N \N \N {} -2719 928 2025-02-26 16:32:16.602958-08 2025-02-26 16:19:31.629908-08 test f H100 \N f \N \N \N {} -2720 930 2025-02-26 15:23:05.898632-08 2025-02-26 15:07:22.998261-08 test f H100 \N f \N \N \N {} -2721 934 2025-02-26 17:22:57.87553-08 2025-02-26 15:53:13.995243-08 test f T4 \N t \N \N \N {} -2722 935 2025-02-26 16:04:13.430604-08 2025-02-26 15:30:59.454902-08 benchmark f H100 \N t \N \N \N {} -2723 936 2025-02-26 15:35:49.505504-08 2025-02-26 17:20:48.238503-08 benchmark f H100 \N t \N \N \N {} -2782 970 2025-02-26 21:07:51.669113-08 2025-02-26 20:49:24.139845-08 test f A100 \N f \N \N \N {} -2729 937 2025-02-26 17:32:52.663266-08 2025-02-26 16:47:18.507852-08 leaderboard t H100 0.0009355417142857144 t \N \N \N {} -2730 938 2025-02-26 16:48:14.702847-08 2025-02-26 16:57:10.702808-08 benchmark f H100 \N t \N \N \N {} -2731 939 2025-02-26 17:31:10.651035-08 2025-02-26 17:03:20.742136-08 test f H100 \N t \N \N \N {} -2732 939 2025-02-26 16:04:46.223011-08 2025-02-26 17:44:01.018405-08 benchmark f H100 \N t \N \N \N {} -2733 939 2025-02-26 16:09:00.549881-08 2025-02-26 16:26:03.875919-08 leaderboard f H100 0.00047589718181818185 t \N \N \N {} -2751 952 2025-02-26 18:36:20.359926-08 2025-02-26 18:56:33.112066-08 leaderboard t H100 0.00004619103 t \N \N \N {} -2752 952 2025-02-26 17:58:47.604278-08 2025-02-26 18:46:38.011565-08 test f H100 \N t \N \N \N {} -2759 957 2025-02-26 19:37:58.190081-08 2025-02-26 19:32:35.710825-08 test f A100 \N f \N \N \N {} -2760 958 2025-02-26 19:20:20.101499-08 2025-02-26 18:54:34.381861-08 test f A100 \N t \N \N \N {} -818 255 2025-02-24 12:36:47.623295-08 2025-02-24 11:44:27.318735-08 benchmark t H100 \N t \N \N \N {} -819 255 2025-02-24 12:45:56.514079-08 2025-02-24 13:03:45.699083-08 leaderboard t H100 0.006123367 t \N \N \N {} -820 256 2025-02-24 12:00:59.412291-08 2025-02-24 11:48:22.822008-08 test t A100 \N f \N \N \N {} -827 258 2025-02-24 12:14:03.313645-08 2025-02-24 13:12:29.40344-08 benchmark f A100 \N t \N \N \N {} -829 259 2025-02-24 13:27:44.987486-08 2025-02-24 12:15:45.854513-08 test f H100 \N t \N \N \N {} -835 260 2025-02-24 12:44:46.406235-08 2025-02-24 12:32:35.981113-08 test f H100 \N f \N \N \N {} -836 260 2025-02-24 11:57:43.314454-08 2025-02-24 13:37:10.081022-08 test t H100 \N f \N \N \N {} -839 261 2025-02-24 13:24:41.686947-08 2025-02-24 14:04:01.45224-08 benchmark f L4 \N t \N \N \N {} -840 261 2025-02-24 12:44:55.198063-08 2025-02-24 13:15:16.477866-08 benchmark f T4 \N t \N \N \N {} -841 262 2025-02-24 14:40:33.295376-08 2025-02-24 14:37:39.287063-08 test f H100 \N t \N \N \N {} -842 262 2025-02-24 13:32:24.520488-08 2025-02-24 12:51:42.430233-08 benchmark f H100 \N t \N \N \N {} -845 262 2025-02-24 13:16:57.772484-08 2025-02-24 13:43:01.738354-08 benchmark t A100 \N t \N \N \N {} -846 262 2025-02-24 14:40:12.521043-08 2025-02-24 12:41:50.695193-08 leaderboard t A100 0.0035759156666666666 t \N \N \N {} -847 262 2025-02-24 13:06:53.677846-08 2025-02-24 13:04:43.106693-08 test t T4 \N t \N \N \N {} -848 262 2025-02-24 13:21:20.33413-08 2025-02-24 14:07:43.524626-08 benchmark t T4 \N t \N \N \N {} -849 262 2025-02-24 13:08:55.082034-08 2025-02-24 14:11:39.783402-08 leaderboard t T4 0.017268438333333334 t \N \N \N {} -855 262 2025-02-24 13:00:14.369727-08 2025-02-24 12:58:55.821216-08 leaderboard f A100 0.0035754176666666663 t \N \N \N {} -856 263 2025-02-24 14:12:04.211732-08 2025-02-24 14:32:29.922479-08 benchmark f A100 \N f \N \N \N {} -857 262 2025-02-24 13:01:52.259649-08 2025-02-24 13:08:26.610715-08 test f T4 \N t \N \N \N {} -858 262 2025-02-24 13:09:26.79495-08 2025-02-24 14:17:18.323779-08 benchmark f T4 \N t \N \N \N {} -859 262 2025-02-24 14:15:52.323889-08 2025-02-24 14:32:11.723833-08 leaderboard f T4 0.017249578 t \N \N \N {} -1459 486 2025-02-25 04:17:57.329398-08 2025-02-25 04:19:00.137084-08 test t A100 \N t \N \N \N {} -861 262 2025-02-24 13:55:33.663644-08 2025-02-24 14:37:09.657969-08 benchmark t H100 \N t \N \N \N {} -862 262 2025-02-24 14:11:40.394727-08 2025-02-24 14:04:47.293746-08 leaderboard t H100 0.00157941125 t \N \N \N {} -863 262 2025-02-24 14:14:35.256564-08 2025-02-24 13:32:29.562338-08 test t L4 \N t \N \N \N {} -864 262 2025-02-24 12:48:02.755552-08 2025-02-24 14:13:00.945425-08 benchmark t L4 \N t \N \N \N {} -865 262 2025-02-24 12:46:48.53663-08 2025-02-24 14:29:57.105212-08 leaderboard t L4 0.017202579333333332 t \N \N \N {} -870 266 2025-02-24 14:10:29.80768-08 2025-02-24 13:31:05.267924-08 test t H100 \N f \N \N \N {} -882 268 2025-02-24 13:48:16.246206-08 2025-02-24 13:16:22.804481-08 benchmark t H100 \N t \N \N \N {} -883 268 2025-02-24 13:47:51.158916-08 2025-02-24 14:12:31.13551-08 leaderboard t H100 0.001499999 t \N \N \N {} -884 269 2025-02-24 13:36:44.335857-08 2025-02-24 13:23:48.204042-08 test f L4 \N t \N \N \N {} -885 269 2025-02-24 13:40:59.689967-08 2025-02-24 14:59:46.238789-08 test f A100 \N t \N \N \N {} -886 269 2025-02-24 14:18:48.147333-08 2025-02-24 14:54:09.931319-08 test f T4 \N t \N \N \N {} -887 269 2025-02-24 13:58:15.398454-08 2025-02-24 14:08:43.206835-08 test f H100 \N t \N \N \N {} -2761 959 2025-02-26 20:04:15.692556-08 2025-02-26 20:10:38.466984-08 test f A100 \N t \N \N \N {} -3506 1200 2025-02-28 12:29:44.776123-08 2025-02-28 11:41:55.023296-08 test t A100 \N t \N \N \N {} -2762 960 2025-02-26 19:50:11.129181-08 2025-02-26 19:57:28.353335-08 test f A100 \N t \N \N \N {} -888 270 2025-02-24 14:36:02.624025-08 2025-02-24 15:09:25.11226-08 test f H100 \N t \N \N \N {} -893 270 2025-02-24 13:25:28.447878-08 2025-02-24 14:26:32.820494-08 leaderboard t H100 0.006313673333333333 t \N \N \N {} -978 288 2025-02-24 16:16:39.684041-08 2025-02-24 16:53:33.866555-08 test f H100 \N t \N \N \N {} -894 271 2025-02-24 13:37:51.574913-08 2025-02-24 13:30:44.90995-08 benchmark f A100 \N t \N \N \N {} -895 271 2025-02-24 14:42:33.925165-08 2025-02-24 13:50:25.827947-08 benchmark f L4 \N t \N \N \N {} -896 271 2025-02-24 15:14:26.679127-08 2025-02-24 13:24:56.860101-08 benchmark f H100 \N t \N \N \N {} -2763 961 2025-02-26 20:33:39.926563-08 2025-02-26 18:53:59.629244-08 test f A100 \N f \N \N \N {} -3591 1229 2025-02-28 14:00:43.377485-08 2025-02-28 13:02:18.799382-08 benchmark t H100 \N t \N \N \N {} -922 272 2025-02-24 14:21:16.338815-08 2025-02-24 13:57:53.832712-08 test f L4 \N t \N \N \N {} -930 275 2025-02-24 14:06:04.090352-08 2025-02-24 15:32:28.313994-08 test t H100 \N f \N \N \N {} -931 275 2025-02-24 15:08:25.725574-08 2025-02-24 14:48:09.110921-08 test f H100 \N f \N \N \N {} -932 276 2025-02-24 15:08:07.755574-08 2025-02-24 14:00:19.773242-08 benchmark f H100 \N t \N \N \N {} -933 277 2025-02-24 15:11:54.355362-08 2025-02-24 14:30:44.412152-08 benchmark f H100 \N t \N \N \N {} -934 278 2025-02-24 15:11:08.217229-08 2025-02-24 13:56:46.532795-08 test t A100 \N t \N \N \N {} -2765 963 2025-02-26 21:05:46.567488-08 2025-02-26 21:01:24.814437-08 test f A100 \N t \N \N \N {} -949 278 2025-02-24 14:01:41.92456-08 2025-02-24 13:43:58.586813-08 test t L4 \N t \N \N \N {} -953 278 2025-02-24 15:30:55.085434-08 2025-02-24 15:03:27.528219-08 benchmark t T4 \N t \N \N \N {} -954 278 2025-02-24 14:13:18.813125-08 2025-02-24 14:25:32.061848-08 leaderboard t T4 0.01679078633333333 t \N \N \N {} -955 278 2025-02-24 13:55:20.752079-08 2025-02-24 14:29:46.038209-08 test f T4 \N t \N \N \N {} -956 278 2025-02-24 14:26:05.784131-08 2025-02-24 14:04:02.728948-08 benchmark f T4 \N t \N \N \N {} -957 278 2025-02-24 14:40:08.470773-08 2025-02-24 14:15:08.834032-08 leaderboard f T4 0.01680730033333333 t \N \N \N {} -958 279 2025-02-24 13:58:02.12755-08 2025-02-24 14:09:39.155243-08 test f A100 \N t \N \N \N {} -962 283 2025-02-24 15:58:39.196492-08 2025-02-24 14:11:06.156964-08 benchmark f A100 \N t \N \N \N {} -963 284 2025-02-24 14:28:10.002662-08 2025-02-24 14:33:20.990272-08 benchmark f A100 \N t \N \N \N {} -964 285 2025-02-24 14:50:05.118746-08 2025-02-24 16:06:00.21924-08 benchmark f A100 \N t \N \N \N {} -966 286 2025-02-24 15:11:06.589828-08 2025-02-24 15:54:57.512172-08 benchmark f A100 \N t \N \N \N {} -2766 964 2025-02-26 19:08:30.791267-08 2025-02-26 20:25:23.844568-08 test f A100 \N t \N \N \N {} -967 286 2025-02-24 15:50:38.06952-08 2025-02-24 14:35:41.812052-08 leaderboard f A100 0.003564924125 t \N \N \N {} -968 286 2025-02-24 15:34:26.225087-08 2025-02-24 14:31:55.790602-08 test t A100 \N t \N \N \N {} -975 288 2025-02-24 16:06:09.241772-08 2025-02-24 15:20:44.834631-08 test t H100 \N t \N \N \N {} -1003 289 2025-02-24 16:59:20.310533-08 2025-02-24 16:33:52.660533-08 test f H100 \N t \N \N \N {} -1007 289 2025-02-24 15:48:45.290987-08 2025-02-24 16:12:36.369625-08 benchmark f T4 \N t \N \N \N {} -1008 289 2025-02-24 16:43:13.03202-08 2025-02-24 16:09:32.189032-08 leaderboard f T4 0.017463506 t \N \N \N {} -1009 289 2025-02-24 15:26:10.010531-08 2025-02-24 15:31:30.134666-08 test f L4 \N t \N \N \N {} -1010 289 2025-02-24 15:34:17.620631-08 2025-02-24 15:20:56.610577-08 benchmark f L4 \N t \N \N \N {} -1011 289 2025-02-24 15:17:08.27862-08 2025-02-24 16:53:24.408023-08 leaderboard f L4 0.017437469 t \N \N \N {} -1013 289 2025-02-24 17:10:06.760302-08 2025-02-24 16:01:34.694517-08 benchmark t L4 \N t \N \N \N {} -1014 289 2025-02-24 17:02:49.759681-08 2025-02-24 16:46:54.792055-08 leaderboard t L4 0.017468976333333334 t \N \N \N {} -1015 289 2025-02-24 16:23:10.660698-08 2025-02-24 16:34:48.55816-08 test t H100 \N t \N \N \N {} -1016 289 2025-02-24 17:08:58.086549-08 2025-02-24 17:12:19.202409-08 benchmark t H100 \N t \N \N \N {} -1017 289 2025-02-24 15:29:25.320543-08 2025-02-24 16:41:10.791533-08 leaderboard t H100 0.001555808 t \N \N \N {} -1021 290 2025-02-24 17:27:27.359016-08 2025-02-24 17:10:34.608977-08 test f T4 \N f \N \N \N {} -1022 290 2025-02-24 16:04:38.081723-08 2025-02-24 16:01:10.893306-08 test t T4 \N f \N \N \N {} -1023 290 2025-02-24 17:30:53.275597-08 2025-02-24 16:15:31.909539-08 test f H100 \N f \N \N \N {} -1024 290 2025-02-24 16:19:28.643489-08 2025-02-24 17:41:16.119971-08 test t L4 \N f \N \N \N {} -2767 965 2025-02-26 20:43:06.138197-08 2025-02-26 20:38:46.941894-08 test f A100 \N t \N \N \N {} -1025 290 2025-02-24 15:50:33.00311-08 2025-02-24 17:22:52.823354-08 test f L4 \N f \N \N \N {} -1026 291 2025-02-24 16:00:27.990747-08 2025-02-24 17:05:43.520942-08 test t A100 \N t \N \N \N {} -1027 291 2025-02-24 17:29:02.565531-08 2025-02-24 17:17:14.052814-08 benchmark t A100 \N t \N \N \N {} -1028 291 2025-02-24 16:02:23.141852-08 2025-02-24 16:33:31.463874-08 leaderboard t A100 0.003320683 t \N \N \N {} -1029 291 2025-02-24 16:21:24.320655-08 2025-02-24 16:33:35.189391-08 test f A100 \N t \N \N \N {} -1030 291 2025-02-24 16:54:28.950428-08 2025-02-24 16:34:49.566872-08 benchmark f A100 \N t \N \N \N {} -1041 291 2025-02-24 16:51:50.87004-08 2025-02-24 16:52:44.22599-08 test t T4 \N t \N \N \N {} -1031 291 2025-02-24 16:57:17.908772-08 2025-02-24 17:21:40.225798-08 leaderboard f A100 0.0033077086666666667 t \N \N \N {} -1061 292 2025-02-24 16:14:47.786921-08 2025-02-24 16:02:43.643719-08 leaderboard f H100 0.0014229046666666668 t \N \N \N {} -1062 292 2025-02-24 16:05:24.343352-08 2025-02-24 16:26:39.742121-08 test t T4 \N t \N \N \N {} -1068 292 2025-02-24 16:14:27.218676-08 2025-02-24 16:12:18.471021-08 test f L4 \N t \N \N \N {} -1071 292 2025-02-24 16:21:46.18687-08 2025-02-24 17:29:33.25851-08 test t H100 \N t \N \N \N {} -1077 295 2025-02-24 17:38:05.121185-08 2025-02-24 17:38:57.063894-08 benchmark f H100 \N t \N \N \N {} -1078 295 2025-02-24 17:58:44.68097-08 2025-02-24 17:14:34.343302-08 leaderboard f H100 0.0010347983333333333 t \N \N \N {} -1079 295 2025-02-24 17:04:05.158398-08 2025-02-24 17:25:58.879946-08 test t H100 \N t \N \N \N {} -1080 295 2025-02-24 16:58:08.102388-08 2025-02-24 17:56:34.485958-08 benchmark t H100 \N t \N \N \N {} -1081 295 2025-02-24 16:55:29.797081-08 2025-02-24 17:47:07.608762-08 leaderboard t H100 0.001039122 t \N \N \N {} -1096 304 2025-02-24 18:56:04.543979-08 2025-02-24 17:04:43.557942-08 benchmark f A100 \N t \N \N \N {} -1082 296 2025-02-24 17:31:42.820566-08 2025-02-24 16:33:59.876329-08 benchmark f H100 \N f \N \N \N {} -1084 299 2025-02-24 17:39:48.424492-08 2025-02-24 17:07:52.030306-08 benchmark f A100 \N t \N \N \N {} -1085 298 2025-02-24 16:35:28.403851-08 2025-02-24 17:02:30.160259-08 benchmark f H100 \N f \N \N \N {} -1086 300 2025-02-24 17:14:06.318787-08 2025-02-24 16:50:30.934776-08 test t A100 \N t \N \N \N {} -1091 300 2025-02-24 17:29:22.731228-08 2025-02-24 17:12:13.670748-08 leaderboard f A100 0.014157763333333333 t \N \N \N {} -1092 301 2025-02-24 17:59:41.646434-08 2025-02-24 16:56:21.661999-08 benchmark f H100 \N f \N \N \N {} -1093 302 2025-02-24 18:21:23.246489-08 2025-02-24 17:07:12.364112-08 benchmark f A100 \N f \N \N \N {} -1094 303 2025-02-24 17:50:55.598954-08 2025-02-24 17:13:45.078131-08 benchmark f H100 \N t \N \N \N {} -1095 304 2025-02-24 17:38:46.646025-08 2025-02-24 17:11:44.810342-08 test f A100 \N t \N \N \N {} -1486 495 2025-02-25 05:26:36.575717-08 2025-02-25 05:35:53.585466-08 test f A100 \N f \N \N \N {} -1097 304 2025-02-24 17:03:14.22191-08 2025-02-24 17:48:13.256134-08 leaderboard f A100 0.006075353 t \N \N \N {} -3576 1223 2025-02-28 12:52:29.871321-08 2025-02-28 13:12:16.401319-08 test f H100 \N t \N \N \N {} -1098 304 2025-02-24 18:52:41.173136-08 2025-02-24 18:10:45.04049-08 test t A100 \N t \N \N \N {} -1099 304 2025-02-24 17:19:09.825043-08 2025-02-24 17:28:38.91665-08 benchmark t A100 \N t \N \N \N {} -1100 304 2025-02-24 18:23:50.783076-08 2025-02-24 18:13:43.170325-08 leaderboard t A100 0.006081086333333333 t \N \N \N {} -1105 304 2025-02-24 17:34:55.399719-08 2025-02-24 17:27:11.226139-08 benchmark f T4 \N t \N \N \N {} -1106 304 2025-02-24 18:28:11.076985-08 2025-02-24 18:13:11.8461-08 leaderboard f T4 0.028245293666666667 t \N \N \N {} -1107 304 2025-02-24 18:33:57.535639-08 2025-02-24 17:46:08.937512-08 test t T4 \N t \N \N \N {} -1113 304 2025-02-24 18:34:00.598847-08 2025-02-24 18:51:13.208664-08 test t L4 \N t \N \N \N {} -1114 304 2025-02-24 18:06:21.101459-08 2025-02-24 17:31:57.238174-08 benchmark t L4 \N t \N \N \N {} -1119 305 2025-02-24 17:42:57.925612-08 2025-02-24 17:15:14.683808-08 benchmark f H100 \N f \N \N \N {} -1120 306 2025-02-24 17:21:15.587258-08 2025-02-24 18:44:01.440783-08 benchmark f A100 \N t \N \N \N {} -1121 306 2025-02-24 18:36:34.865426-08 2025-02-24 17:49:17.471548-08 benchmark f H100 \N t \N \N \N {} -1122 306 2025-02-24 17:26:55.287166-08 2025-02-24 18:35:50.569086-08 benchmark f T4 \N t \N \N \N {} -1125 308 2025-02-24 18:34:58.15099-08 2025-02-24 17:56:55.545892-08 benchmark f H100 \N t \N \N \N {} -1126 309 2025-02-24 18:10:41.370592-08 2025-02-24 19:01:32.847335-08 test f T4 \N t \N \N \N {} -1127 310 2025-02-24 18:21:38.286944-08 2025-02-24 19:30:50.197993-08 test f T4 \N f \N \N \N {} -1128 311 2025-02-24 19:40:10.137721-08 2025-02-24 19:12:06.15593-08 test f T4 \N f \N \N \N {} -1144 321 2025-02-24 19:09:00.011949-08 2025-02-24 19:27:42.347167-08 benchmark f A100 \N t \N \N \N {} -1145 321 2025-02-24 18:28:50.942544-08 2025-02-24 20:07:48.608885-08 leaderboard f A100 0.008048783333333333 t \N \N \N {} -1146 321 2025-02-24 19:54:14.030406-08 2025-02-24 19:26:42.983807-08 test t A100 \N t \N \N \N {} -1147 321 2025-02-24 19:37:58.992398-08 2025-02-24 18:38:17.162254-08 benchmark t A100 \N t \N \N \N {} -1155 328 2025-02-24 18:59:30.528684-08 2025-02-24 18:51:38.131222-08 benchmark f A100 \N t \N \N \N {} -1160 329 2025-02-24 19:38:57.967787-08 2025-02-24 18:57:02.949354-08 benchmark t H100 \N t \N \N \N {} -1161 329 2025-02-24 19:14:33.290908-08 2025-02-24 20:08:23.310109-08 leaderboard t H100 0.0015865033333333333 t \N \N \N {} -1166 334 2025-02-24 19:50:26.32731-08 2025-02-24 20:31:43.298423-08 test f A100 \N f \N \N \N {} -1167 335 2025-02-24 19:52:47.832137-08 2025-02-24 18:40:52.747222-08 test f A100 \N f \N \N \N {} -1168 336 2025-02-24 18:58:04.317224-08 2025-02-24 20:40:01.428171-08 test f A100 \N t \N \N \N {} -1169 337 2025-02-24 19:33:06.587039-08 2025-02-24 20:20:18.024082-08 benchmark f A100 \N t \N \N \N {} -1171 339 2025-02-24 20:25:27.128083-08 2025-02-24 18:45:26.166042-08 benchmark f A100 \N t \N \N \N {} -1175 343 2025-02-24 20:01:36.447161-08 2025-02-24 20:17:57.794145-08 benchmark f A100 \N t \N \N \N {} -1176 344 2025-02-24 18:57:03.668266-08 2025-02-24 20:32:08.204872-08 benchmark f A100 \N t \N \N \N {} -1177 345 2025-02-24 19:19:48.575647-08 2025-02-24 20:35:58.402184-08 benchmark f A100 \N f \N \N \N {} -1180 348 2025-02-24 20:38:35.466753-08 2025-02-24 20:27:20.582819-08 benchmark f A100 \N t \N \N \N {} -1181 349 2025-02-24 19:50:45.13357-08 2025-02-24 19:35:45.516463-08 test f A100 \N f \N \N \N {} -1186 356 2025-02-24 20:44:38.220701-08 2025-02-24 20:35:27.553404-08 benchmark f A100 \N f \N \N \N {} -1187 357 2025-02-24 20:31:41.284738-08 2025-02-24 20:51:31.622096-08 benchmark f A100 \N t \N \N \N {} -1191 364 2025-02-24 19:38:43.168308-08 2025-02-24 20:03:40.713134-08 benchmark f A100 \N t \N \N \N {} -1192 365 2025-02-24 20:20:30.975678-08 2025-02-24 20:47:45.806082-08 test f A100 \N t \N \N \N {} -1198 366 2025-02-24 20:50:37.04848-08 2025-02-24 20:03:24.288635-08 benchmark f A100 \N f \N \N \N {} -1199 367 2025-02-24 20:01:53.464636-08 2025-02-24 20:50:55.06292-08 benchmark f A100 \N f \N \N \N {} -1200 368 2025-02-24 20:48:53.017372-08 2025-02-24 21:07:09.746977-08 benchmark f A100 \N f \N \N \N {} -1203 371 2025-02-24 21:03:40.316811-08 2025-02-24 20:06:59.770551-08 benchmark f A100 \N f \N \N \N {} -1204 372 2025-02-24 21:16:31.917525-08 2025-02-24 20:19:23.12487-08 benchmark f A100 \N f \N \N \N {} -1212 380 2025-02-24 21:30:39.643844-08 2025-02-24 21:22:48.195881-08 benchmark f A100 \N f \N \N \N {} -1213 381 2025-02-24 21:27:50.804873-08 2025-02-24 20:30:39.877648-08 benchmark f A100 \N f \N \N \N {} -1214 382 2025-02-24 21:26:22.567911-08 2025-02-24 20:42:27.609326-08 benchmark f A100 \N t \N \N \N {} -1215 383 2025-02-24 21:43:35.675505-08 2025-02-24 21:41:12.864918-08 benchmark f A100 \N f \N \N \N {} -1217 385 2025-02-24 20:39:07.414152-08 2025-02-24 21:16:56.728524-08 benchmark f A100 \N t \N \N \N {} -1218 386 2025-02-24 21:24:25.618173-08 2025-02-24 20:59:42.5791-08 benchmark f A100 \N f \N \N \N {} -1219 387 2025-02-24 20:37:38.559826-08 2025-02-24 20:50:09.030352-08 benchmark f A100 \N t \N \N \N {} -1220 388 2025-02-24 21:12:35.63089-08 2025-02-24 21:07:52.286646-08 test f H100 \N f \N \N \N {} -1221 390 2025-02-24 20:41:29.07709-08 2025-02-24 21:50:07.217387-08 test f A100 \N f \N \N \N {} -1222 391 2025-02-24 20:36:54.532658-08 2025-02-24 20:27:33.575008-08 test f A100 \N f \N \N \N {} -1223 391 2025-02-24 22:09:52.859803-08 2025-02-24 20:25:04.338196-08 test f L4 \N f \N \N \N {} -1224 391 2025-02-24 20:57:57.971901-08 2025-02-24 22:07:50.668858-08 test f T4 \N f \N \N \N {} -1225 391 2025-02-24 21:26:12.689911-08 2025-02-24 20:21:51.434441-08 test f H100 \N f \N \N \N {} -1226 392 2025-02-24 21:22:04.179078-08 2025-02-24 21:02:36.047543-08 test f A100 \N f \N \N \N {} -1227 393 2025-02-24 21:27:17.29266-08 2025-02-24 21:05:23.934691-08 test t A100 \N t \N \N \N {} -1228 393 2025-02-24 21:57:42.36025-08 2025-02-24 20:56:27.385816-08 benchmark t A100 \N t \N \N \N {} -1232 393 2025-02-24 21:19:55.551644-08 2025-02-24 20:43:59.005894-08 leaderboard f A100 0.0033388506666666667 t \N \N \N {} -1265 416 2025-02-24 21:37:21.368251-08 2025-02-24 21:44:53.806727-08 leaderboard t H100 0.0021056229500000002 t \N \N \N {} -1233 394 2025-02-24 20:48:53.683982-08 2025-02-24 21:10:47.005539-08 test f A100 \N f \N \N \N {} -1234 395 2025-02-24 21:42:25.587766-08 2025-02-24 21:13:54.206602-08 test f A100 \N f \N \N \N {} -1235 396 2025-02-24 21:31:47.507986-08 2025-02-24 21:41:14.621874-08 test f A100 \N f \N \N \N {} -1236 397 2025-02-24 20:53:51.866306-08 2025-02-24 21:41:52.555281-08 test f A100 \N f \N \N \N {} -1237 398 2025-02-24 22:13:49.271446-08 2025-02-24 21:54:00.876085-08 test f A100 \N f \N \N \N {} -1273 424 2025-02-24 23:15:56.010969-08 2025-02-24 22:22:18.360257-08 test f H100 \N f \N \N \N {} -1238 399 2025-02-24 21:20:05.240709-08 2025-02-24 22:10:17.303543-08 test f A100 \N f \N \N \N {} -1239 400 2025-02-24 21:02:33.153711-08 2025-02-24 22:14:52.737511-08 test f A100 \N f \N \N \N {} -1242 403 2025-02-24 21:44:20.600901-08 2025-02-24 22:19:00.048671-08 test f A100 \N t \N \N \N {} -1274 425 2025-02-24 22:32:28.342997-08 2025-02-24 22:46:18.153042-08 test f H100 \N f \N \N \N {} -1244 405 2025-02-24 21:15:42.934708-08 2025-02-24 22:00:36.897134-08 benchmark f A100 \N t \N \N \N {} -1245 406 2025-02-24 22:41:21.855098-08 2025-02-24 21:22:41.861309-08 test f A100 \N f \N \N \N {} -1246 407 2025-02-24 22:14:38.935217-08 2025-02-24 21:17:28.999889-08 test f A100 \N f \N \N \N {} -1247 408 2025-02-24 21:15:17.037056-08 2025-02-24 22:47:31.85541-08 test f A100 \N f \N \N \N {} -1255 413 2025-02-24 22:41:32.544432-08 2025-02-24 21:50:59.410312-08 test t A100 \N t \N \N \N {} -1275 426 2025-02-24 22:50:00.313014-08 2025-02-24 22:01:51.946596-08 test f H100 \N f \N \N \N {} -1256 413 2025-02-24 21:28:53.296618-08 2025-02-24 21:56:09.683656-08 benchmark t A100 \N t \N \N \N {} -1257 413 2025-02-24 22:52:41.725644-08 2025-02-24 21:02:44.177771-08 leaderboard t A100 0.00082829875 t \N \N \N {} -1258 414 2025-02-24 22:43:01.622934-08 2025-02-24 21:44:54.450946-08 test f H100 \N t \N \N \N {} -1259 415 2025-02-24 21:05:33.640351-08 2025-02-24 22:22:12.322858-08 benchmark f H100 \N t \N \N \N {} -1260 416 2025-02-24 22:50:18.115204-08 2025-02-24 22:10:37.88296-08 test f H100 \N t \N \N \N {} -1263 416 2025-02-24 22:41:58.320403-08 2025-02-24 22:46:15.121319-08 test t H100 \N t \N \N \N {} -1264 416 2025-02-24 21:33:11.644917-08 2025-02-24 22:43:20.018249-08 benchmark t H100 \N t \N \N \N {} -1266 417 2025-02-24 23:17:42.124344-08 2025-02-24 22:14:36.706679-08 test f L4 \N f \N \N \N {} -1271 422 2025-02-24 23:24:55.93824-08 2025-02-24 23:09:05.952447-08 test f H100 \N f \N \N \N {} -1272 423 2025-02-24 23:33:25.559896-08 2025-02-24 23:04:20.171899-08 test f H100 \N f \N \N \N {} -1277 427 2025-02-25 00:21:05.704041-08 2025-02-24 23:17:15.254253-08 benchmark t H100 \N t \N \N \N {} -1278 427 2025-02-24 23:05:44.473555-08 2025-02-24 22:36:18.623353-08 leaderboard t H100 0.00151173175 t \N \N \N {} -1279 427 2025-02-24 23:22:06.892269-08 2025-02-25 00:08:22.994675-08 test f H100 \N t \N \N \N {} -1280 427 2025-02-25 00:06:49.982745-08 2025-02-25 00:02:16.454215-08 benchmark f H100 \N t \N \N \N {} -1281 427 2025-02-24 23:30:10.509612-08 2025-02-24 22:24:37.733967-08 leaderboard f H100 0.0014917988095238094 t \N \N \N {} -1282 428 2025-02-25 00:29:11.621733-08 2025-02-25 00:25:31.895544-08 test f L4 \N t \N \N \N {} -1283 429 2025-02-25 00:23:40.437427-08 2025-02-25 00:28:38.668036-08 benchmark f L4 \N t \N \N \N {} -1420 472 2025-02-25 05:09:52.78126-08 2025-02-25 03:33:17.606456-08 test f A100 \N t \N \N \N {} -1284 430 2025-02-24 22:56:08.41611-08 2025-02-24 23:53:11.011713-08 benchmark f L4 \N t \N \N \N {} -1291 434 2025-02-25 00:38:27.016325-08 2025-02-25 01:33:09.270327-08 test t A100 \N f \N \N \N {} -1292 435 2025-02-25 01:13:37.578908-08 2025-02-25 02:04:12.790967-08 test t A100 \N f \N \N \N {} -1293 435 2025-02-25 00:34:17.674028-08 2025-02-25 01:20:00.978456-08 test f A100 \N f \N \N \N {} -1294 436 2025-02-25 00:52:16.868772-08 2025-02-25 01:46:34.105184-08 test t A100 \N t \N \N \N {} -1295 436 2025-02-25 00:34:18.664042-08 2025-02-25 01:29:38.828363-08 benchmark t A100 \N t \N \N \N {} -2768 965 2025-02-26 19:49:02.478379-08 2025-02-26 19:27:59.359226-08 benchmark f A100 \N t \N \N \N {} -2769 965 2025-02-26 20:58:22.764397-08 2025-02-26 20:38:26.615803-08 leaderboard f A100 0.014282038666666667 t \N \N \N {} -2770 966 2025-02-26 20:24:44.026742-08 2025-02-26 19:35:29.731923-08 test t A100 \N t \N \N \N {} -2771 966 2025-02-26 19:37:41.565742-08 2025-02-26 20:03:41.368376-08 benchmark t A100 \N t \N \N \N {} -2790 974 2025-02-26 20:10:51.878483-08 2025-02-26 21:28:32.546366-08 benchmark t A100 \N t \N \N \N {} -1296 436 2025-02-25 00:18:28.614962-08 2025-02-25 00:32:08.809058-08 leaderboard t A100 0.01866328503614458 t \N \N \N {} -1297 436 2025-02-25 00:51:06.440802-08 2025-02-25 00:45:31.265356-08 test f A100 \N t \N \N \N {} -1423 472 2025-02-25 04:26:56.092902-08 2025-02-25 04:03:45.687235-08 test t A100 \N t \N \N \N {} -1298 436 2025-02-25 00:31:34.02733-08 2025-02-25 01:09:08.022812-08 benchmark f A100 \N t \N \N \N {} -1299 436 2025-02-25 01:34:03.732205-08 2025-02-25 01:19:15.994949-08 leaderboard f A100 0.01933390226 t \N \N \N {} -1302 438 2025-02-25 02:13:44.700479-08 2025-02-25 01:53:56.844408-08 benchmark f A100 \N t \N \N \N {} -1303 438 2025-02-25 01:52:21.421101-08 2025-02-25 01:13:41.748765-08 leaderboard f A100 0.00318387075 t \N \N \N {} -1304 438 2025-02-25 00:58:34.99604-08 2025-02-25 00:26:07.558315-08 test t A100 \N t \N \N \N {} -1426 473 2025-02-25 03:33:23.323189-08 2025-02-25 04:45:59.064294-08 test t A100 \N t \N \N \N {} -1522 506 2025-02-25 06:29:30.73263-08 2025-02-25 06:29:47.306703-08 test f H100 \N t \N \N \N {} -1305 438 2025-02-25 01:45:16.792963-08 2025-02-25 00:41:59.854224-08 benchmark t A100 \N t \N \N \N {} -1308 439 2025-02-25 01:05:46.577398-08 2025-02-25 01:11:50.101033-08 benchmark t A100 \N t \N \N \N {} -1309 439 2025-02-25 00:31:25.37309-08 2025-02-25 01:33:59.391129-08 leaderboard t A100 0.01829094864864865 t \N \N \N {} -1429 473 2025-02-25 04:12:04.123883-08 2025-02-25 04:05:49.033486-08 test f A100 \N t \N \N \N {} -1310 439 2025-02-25 00:48:25.781713-08 2025-02-25 00:53:44.022446-08 test f A100 \N t \N \N \N {} -1311 439 2025-02-25 00:44:11.592056-08 2025-02-25 01:42:04.821849-08 benchmark f A100 \N t \N \N \N {} -3577 1224 2025-02-28 13:39:20.462449-08 2025-02-28 14:41:42.556499-08 benchmark f H100 \N t \N \N \N {} -2781 969 2025-02-26 19:55:03.2042-08 2025-02-26 20:10:38.143515-08 test f A100 \N f \N \N \N {} -2792 975 2025-02-26 20:50:18.58077-08 2025-02-26 21:14:29.36909-08 test t A100 \N t \N \N \N {} -2785 973 2025-02-26 19:58:45.213429-08 2025-02-26 20:46:29.95438-08 test f A100 \N t \N \N \N {} -2786 974 2025-02-26 21:50:24.917282-08 2025-02-26 21:26:01.992988-08 test f A100 \N t \N \N \N {} -1317 441 2025-02-25 01:27:39.741757-08 2025-02-25 01:07:56.270804-08 test f A100 \N t \N \N \N {} -1318 441 2025-02-25 00:45:28.035248-08 2025-02-25 01:01:14.020655-08 benchmark f A100 \N f \N \N \N {} -1319 442 2025-02-25 01:17:35.027985-08 2025-02-25 01:08:09.855491-08 test f T4 \N t \N \N \N {} -1320 442 2025-02-25 02:23:45.784619-08 2025-02-25 01:07:58.424072-08 benchmark f T4 \N t \N \N \N {} -1321 442 2025-02-25 02:07:32.64996-08 2025-02-25 01:07:33.995652-08 leaderboard f T4 0.017366781666666668 t \N \N \N {} -1323 442 2025-02-25 02:26:58.697335-08 2025-02-25 02:40:12.565833-08 benchmark t T4 \N t \N \N \N {} -1324 442 2025-02-25 02:12:07.116909-08 2025-02-25 00:49:02.771059-08 leaderboard t T4 0.017343184 t \N \N \N {} -1325 443 2025-02-25 02:37:32.638106-08 2025-02-25 02:42:15.65973-08 test t A100 \N t \N \N \N {} -1331 444 2025-02-25 01:34:34.727263-08 2025-02-25 01:39:09.005069-08 test f A100 \N f \N \N \N {} -1332 444 2025-02-25 02:59:13.676931-08 2025-02-25 02:28:54.180908-08 test t A100 \N f \N \N \N {} -1333 445 2025-02-25 01:51:47.936405-08 2025-02-25 03:05:33.196327-08 test f A100 \N t \N \N \N {} -1334 445 2025-02-25 02:55:01.288465-08 2025-02-25 02:42:58.024491-08 benchmark f A100 \N t \N \N \N {} -1337 445 2025-02-25 02:37:39.036581-08 2025-02-25 03:06:46.109288-08 benchmark t A100 \N t \N \N \N {} -1338 445 2025-02-25 02:27:46.062726-08 2025-02-25 02:38:14.514333-08 leaderboard t A100 0.0030933733333333335 t \N \N \N {} -2902 1013 2025-02-27 02:13:19.283137-08 2025-02-27 00:49:55.016565-08 benchmark f L4 \N t \N \N \N {} -2972 1054 2025-02-27 11:12:54.54238-08 2025-02-27 09:51:41.285788-08 benchmark f A100 \N t \N \N \N {} -3032 1085 2025-02-27 16:19:10.737007-08 2025-02-27 15:35:26.72855-08 benchmark t H100 \N t \N \N \N {} -3033 1085 2025-02-27 15:30:06.20078-08 2025-02-27 14:54:00.220725-08 leaderboard t H100 0.0025986981400000003 t \N \N \N {} -3190 1122 2025-02-28 05:22:46.19105-08 2025-02-28 05:19:12.334732-08 test f A100 \N t \N \N \N {} -2787 974 2025-02-26 21:02:26.857575-08 2025-02-26 20:41:35.440073-08 benchmark f A100 \N t \N \N \N {} -2788 974 2025-02-26 21:28:18.31903-08 2025-02-26 20:11:08.511332-08 leaderboard f A100 0.003155315 t \N \N \N {} -3512 1201 2025-02-28 12:09:29.633702-08 2025-02-28 11:30:22.549861-08 test t A100 \N t \N \N \N {} -2794 975 2025-02-26 21:18:52.089772-08 2025-02-26 20:24:31.866857-08 leaderboard t A100 0.003347336 t \N \N \N {} -2796 975 2025-02-26 21:50:59.541074-08 2025-02-26 21:19:54.451136-08 benchmark f A100 \N t \N \N \N {} -2797 975 2025-02-26 21:39:11.70514-08 2025-02-26 20:18:16.784568-08 leaderboard f A100 0.0033508456666666666 t \N \N \N {} -2798 976 2025-02-26 20:38:37.194925-08 2025-02-26 20:58:59.28349-08 test f A100 \N t \N \N \N {} -2799 976 2025-02-26 21:00:51.351138-08 2025-02-26 21:11:09.227246-08 benchmark f A100 \N t \N \N \N {} -2814 982 2025-02-26 23:11:35.068266-08 2025-02-26 22:05:12.788499-08 test t A100 \N t \N \N \N {} -2815 982 2025-02-26 22:41:38.849192-08 2025-02-26 22:55:52.272396-08 benchmark t A100 \N t \N \N \N {} -2816 982 2025-02-26 22:55:13.597102-08 2025-02-26 22:18:52.107493-08 leaderboard t A100 0.0031956527999999996 t \N \N \N {} -2817 982 2025-02-26 22:04:16.387504-08 2025-02-26 22:18:31.80599-08 test f A100 \N t \N \N \N {} -2818 982 2025-02-26 23:45:04.257267-08 2025-02-26 22:14:37.906328-08 benchmark f A100 \N t \N \N \N {} -2819 982 2025-02-26 22:55:44.283823-08 2025-02-26 23:06:19.863356-08 leaderboard f A100 0.00318407825 t \N \N \N {} -1362 459 2025-02-25 02:55:11.371716-08 2025-02-25 03:57:41.197189-08 benchmark t A100 \N t \N \N \N {} -1363 459 2025-02-25 04:23:27.373732-08 2025-02-25 03:20:58.157958-08 leaderboard t A100 0.0030870156666666666 t \N \N \N {} -2903 1014 2025-02-27 01:15:06.478798-08 2025-02-27 01:26:08.615896-08 benchmark f L4 \N t \N \N \N {} -3034 1086 2025-02-27 16:07:05.961453-08 2025-02-27 15:24:55.001093-08 test t A100 \N t \N \N \N {} -3035 1086 2025-02-27 14:56:09.755895-08 2025-02-27 14:44:03.736702-08 benchmark t A100 \N t \N \N \N {} -3036 1086 2025-02-27 15:11:18.717508-08 2025-02-27 16:25:37.497879-08 leaderboard t A100 0.00219666254 t \N \N \N {} -2822 983 2025-02-26 23:58:17.638886-08 2025-02-26 23:08:43.554386-08 leaderboard f T4 0.017258812666666668 t \N \N \N {} -2823 983 2025-02-26 23:08:02.482508-08 2025-02-26 22:46:14.565363-08 test t T4 \N t \N \N \N {} -2824 983 2025-02-26 22:41:21.612063-08 2025-02-26 23:37:03.076526-08 benchmark t T4 \N t \N \N \N {} -2825 983 2025-02-26 22:11:02.562982-08 2025-02-26 23:08:38.480756-08 leaderboard t T4 0.017232594333333334 t \N \N \N {} -2826 984 2025-02-26 23:55:23.356201-08 2025-02-26 22:34:06.41761-08 test t L4 \N t \N \N \N {} -2827 984 2025-02-26 23:39:16.573547-08 2025-02-26 23:45:06.294967-08 benchmark t L4 \N t \N \N \N {} -1365 459 2025-02-25 04:10:18.717828-08 2025-02-25 03:53:14.773432-08 benchmark f A100 \N t \N \N \N {} -1366 459 2025-02-25 02:36:43.695926-08 2025-02-25 03:37:20.520163-08 leaderboard f A100 0.003083136 t \N \N \N {} -2904 1015 2025-02-27 01:39:23.749307-08 2025-02-27 00:47:34.63079-08 benchmark f L4 \N t \N \N \N {} -3037 1086 2025-02-27 15:04:43.578785-08 2025-02-27 16:05:13.802056-08 test f A100 \N t \N \N \N {} -3038 1086 2025-02-27 15:52:40.863821-08 2025-02-27 15:55:36.239122-08 benchmark f A100 \N t \N \N \N {} -3039 1086 2025-02-27 14:57:57.258869-08 2025-02-27 16:22:35.716253-08 leaderboard f A100 0.006317708599999999 t \N \N \N {} -2828 984 2025-02-26 22:43:53.565232-08 2025-02-26 22:20:13.280092-08 leaderboard t L4 0.01789583566666667 t \N \N \N {} -2835 986 2025-02-26 23:41:34.087432-08 2025-02-26 22:22:07.716492-08 test f H100 \N f \N \N \N {} -2839 988 2025-02-26 23:14:11.180538-08 2025-02-26 22:55:38.097536-08 benchmark t H100 \N t \N \N \N {} -2840 988 2025-02-26 23:51:51.947336-08 2025-02-27 00:11:57.409637-08 leaderboard t H100 0.0014754095555555556 t \N \N \N {} -2841 988 2025-02-26 23:43:56.311146-08 2025-02-26 22:51:36.110909-08 test f H100 \N t \N \N \N {} -2842 988 2025-02-27 00:31:41.8135-08 2025-02-26 23:01:52.346478-08 benchmark f H100 \N t \N \N \N {} -1368 460 2025-02-25 03:56:45.320682-08 2025-02-25 04:12:23.36629-08 benchmark f H100 \N t \N \N \N {} -1369 460 2025-02-25 03:15:26.015945-08 2025-02-25 03:02:05.905441-08 leaderboard f H100 0.0014066026666666668 t \N \N \N {} -2905 1016 2025-02-27 01:18:12.382929-08 2025-02-27 01:17:06.286041-08 test t L4 \N t \N \N \N {} -2906 1016 2025-02-27 01:26:20.055734-08 2025-02-27 00:56:16.777841-08 benchmark t L4 \N t \N \N \N {} -2907 1016 2025-02-27 02:18:18.942716-08 2025-02-27 02:21:22.058687-08 leaderboard t L4 0.017129494 t \N \N \N {} -3040 1087 2025-02-27 16:44:40.535196-08 2025-02-27 15:03:57.635039-08 test f A100 \N t \N \N \N {} -3193 1122 2025-02-28 03:38:44.016859-08 2025-02-28 03:42:55.449712-08 test t A100 \N t \N \N \N {} -2847 989 2025-02-26 23:11:09.581049-08 2025-02-26 23:57:11.977062-08 test f H100 \N t \N \N \N {} -2848 989 2025-02-26 23:28:17.877389-08 2025-02-26 22:53:38.145262-08 benchmark f H100 \N t \N \N \N {} -1371 460 2025-02-25 03:58:04.611454-08 2025-02-25 02:31:49.105023-08 benchmark t H100 \N t \N \N \N {} -1372 460 2025-02-25 04:03:19.955243-08 2025-02-25 04:08:28.45914-08 leaderboard t H100 0.001423033 t \N \N \N {} -2908 1016 2025-02-27 01:21:48.281072-08 2025-02-27 00:41:29.117391-08 test f L4 \N t \N \N \N {} -1374 461 2025-02-25 04:09:34.446371-08 2025-02-25 02:35:46.24839-08 benchmark t H100 \N t \N \N \N {} -1375 461 2025-02-25 03:23:18.745022-08 2025-02-25 04:16:13.049744-08 leaderboard t H100 0.0014008253333333332 t \N \N \N {} -2911 1017 2025-02-27 03:00:45.796745-08 2025-02-27 01:43:35.233574-08 test f H100 \N f \N \N \N {} -3042 1089 2025-02-27 16:38:00.448626-08 2025-02-27 15:58:00.42158-08 benchmark f A100 \N t \N \N \N {} -3074 1102 2025-02-28 02:56:01.659272-08 2025-02-28 01:40:10.731732-08 benchmark t A100 \N t \N \N \N {} -3075 1102 2025-02-28 02:21:16.939778-08 2025-02-28 02:01:28.710259-08 leaderboard t A100 0.0030923946666666664 t \N \N \N {} -2849 989 2025-02-27 00:12:17.835592-08 2025-02-26 22:40:43.431694-08 leaderboard f H100 0.001638096 t \N \N \N {} -2850 990 2025-02-26 22:57:30.808633-08 2025-02-27 00:18:26.83744-08 test t H100 \N t \N \N \N {} -2851 990 2025-02-26 23:06:02.704806-08 2025-02-26 23:22:40.730823-08 benchmark t H100 \N t \N \N \N {} -2852 990 2025-02-26 23:16:39.034056-08 2025-02-27 00:31:18.681964-08 leaderboard t H100 0.001507587888888889 t \N \N \N {} -2855 990 2025-02-26 22:47:36.052869-08 2025-02-27 00:05:47.184279-08 leaderboard f H100 0.0015178355 t \N \N \N {} -2856 991 2025-02-26 23:22:25.154438-08 2025-02-27 00:25:57.062615-08 test f H100 \N f \N \N \N {} -1377 461 2025-02-25 03:11:23.466982-08 2025-02-25 03:14:58.304434-08 benchmark f H100 \N t \N \N \N {} -1378 461 2025-02-25 03:19:43.776884-08 2025-02-25 03:47:15.782517-08 leaderboard f H100 0.0014126543333333333 t \N \N \N {} -2912 1018 2025-02-27 02:15:28.857362-08 2025-02-27 03:05:57.617691-08 test f H100 \N t \N \N \N {} -3043 1090 2025-02-27 16:23:02.016071-08 2025-02-27 15:37:11.121549-08 benchmark f A100 \N t \N \N \N {} -3076 1103 2025-02-28 01:45:24.142839-08 2025-02-28 01:57:12.178482-08 test f A100 \N t \N \N \N {} -3119 1110 2025-02-28 03:01:10.825974-08 2025-02-28 02:12:58.251641-08 benchmark t A100 \N t \N \N \N {} -2857 991 2025-02-27 00:12:21.904324-08 2025-02-26 23:35:25.811072-08 test t H100 \N f \N \N \N {} -2858 992 2025-02-27 00:04:38.346043-08 2025-02-26 23:29:33.532762-08 test t H100 \N t \N \N \N {} -2859 992 2025-02-27 00:22:03.205134-08 2025-02-26 23:23:33.781116-08 benchmark t H100 \N t \N \N \N {} -2864 993 2025-02-26 23:55:03.486115-08 2025-02-26 23:18:13.205693-08 test f H100 \N t \N \N \N {} -2865 993 2025-02-27 00:45:02.104993-08 2025-02-26 23:57:45.704039-08 benchmark f H100 \N t \N \N \N {} -2866 993 2025-02-26 23:20:23.784092-08 2025-02-26 23:18:10.376928-08 leaderboard f H100 0.0015042338999999999 t \N \N \N {} -1382 464 2025-02-25 04:24:56.79314-08 2025-02-25 04:46:29.308114-08 benchmark f H100 \N t \N \N \N {} -1383 464 2025-02-25 03:22:15.683757-08 2025-02-25 03:47:50.3734-08 leaderboard f H100 0.0014120043333333333 t \N \N \N {} -2913 1019 2025-02-27 02:15:25.259011-08 2025-02-27 02:01:14.038287-08 test f H100 \N f \N \N \N {} -3044 1091 2025-02-27 16:34:45.373039-08 2025-02-27 17:04:56.053201-08 benchmark f A100 \N t \N \N \N {} -3077 1103 2025-02-28 02:26:24.054371-08 2025-02-28 02:57:27.049866-08 benchmark f A100 \N t \N \N \N {} -2867 993 2025-02-27 00:20:03.256964-08 2025-02-26 23:38:08.774778-08 test t H100 \N t \N \N \N {} -2868 993 2025-02-27 00:17:21.143055-08 2025-02-26 23:43:44.61358-08 benchmark t H100 \N t \N \N \N {} -2869 993 2025-02-27 00:41:17.765937-08 2025-02-27 00:38:24.740261-08 leaderboard t H100 0.0014829258999999998 t \N \N \N {} -2873 995 2025-02-26 23:14:08.542885-08 2025-02-27 00:24:07.258227-08 benchmark t H100 \N t \N \N \N {} -2874 995 2025-02-26 22:57:32.493875-08 2025-02-26 23:39:16.1763-08 leaderboard t H100 0.0014810648 t \N \N \N {} -2875 995 2025-02-26 23:55:02.996586-08 2025-02-27 00:01:45.681815-08 test f H100 \N t \N \N \N {} -2889 1000 2025-02-27 00:47:29.323693-08 2025-02-27 01:19:30.043071-08 benchmark f L4 \N t \N \N \N {} -2876 995 2025-02-27 00:48:27.410452-08 2025-02-26 23:02:52.737338-08 benchmark f H100 \N t \N \N \N {} -2877 995 2025-02-27 00:46:15.879218-08 2025-02-26 22:53:09.26022-08 leaderboard f H100 0.00151809525 t \N \N \N {} -2878 996 2025-02-26 23:32:33.325133-08 2025-02-27 00:20:52.668636-08 test t H100 \N f \N \N \N {} -2879 996 2025-02-26 23:32:09.509312-08 2025-02-27 00:16:13.600353-08 test f H100 \N f \N \N \N {} -2880 997 2025-02-26 23:10:26.947465-08 2025-02-27 00:59:43.704157-08 test f H100 \N f \N \N \N {} -2895 1006 2025-02-27 00:30:50.677826-08 2025-02-27 02:02:45.191772-08 benchmark f T4 \N t \N \N \N {} -2881 997 2025-02-26 23:16:25.148851-08 2025-02-26 23:15:22.787437-08 test t H100 \N f \N \N \N {} -1385 464 2025-02-25 03:54:35.416434-08 2025-02-25 03:14:13.277902-08 benchmark t H100 \N t \N \N \N {} -1386 464 2025-02-25 03:44:28.181743-08 2025-02-25 04:52:22.685863-08 leaderboard t H100 0.0013942456666666667 t \N \N \N {} -2914 1021 2025-02-27 04:12:21.245081-08 2025-02-27 02:35:09.897785-08 test f H100 \N t \N \N \N {} -3045 1092 2025-02-27 15:48:36.342402-08 2025-02-27 16:50:15.479403-08 benchmark f A100 \N t \N \N \N {} -2882 998 2025-02-26 23:31:45.732141-08 2025-02-26 23:28:03.798982-08 test f H100 \N t \N \N \N {} -2883 998 2025-02-27 00:46:38.657542-08 2025-02-27 00:13:36.156442-08 benchmark f H100 \N t \N \N \N {} -2884 998 2025-02-26 23:36:22.797455-08 2025-02-27 00:38:35.235535-08 leaderboard f H100 0.0014596549 t \N \N \N {} -2885 998 2025-02-27 00:25:48.975564-08 2025-02-27 00:17:37.217535-08 test t H100 \N t \N \N \N {} -2886 998 2025-02-27 01:13:36.817864-08 2025-02-27 00:29:21.912198-08 benchmark t H100 \N t \N \N \N {} -2887 998 2025-02-27 00:25:46.035637-08 2025-02-27 00:48:51.353021-08 leaderboard t H100 0.00146596 t \N \N \N {} -2888 999 2025-02-26 23:44:36.851934-08 2025-02-27 00:13:44.746988-08 test f H100 \N f \N \N \N {} -2891 1002 2025-02-27 00:08:35.698636-08 2025-02-27 00:37:38.529546-08 benchmark f L4 \N t \N \N \N {} -2892 1003 2025-02-27 01:04:08.050505-08 2025-02-27 01:56:08.100836-08 benchmark f T4 \N f \N \N \N {} -2893 1004 2025-02-27 01:20:48.626974-08 2025-02-27 02:00:18.32054-08 benchmark f T4 \N t \N \N \N {} -2894 1005 2025-02-27 00:48:26.399604-08 2025-02-27 01:32:13.188985-08 benchmark f T4 \N t \N \N \N {} -3515 1202 2025-02-28 10:44:20.111423-08 2025-02-28 10:41:52.613484-08 test f A100 \N t \N \N \N {} -3883 1296 2025-03-01 03:31:39.319577-08 2025-03-01 04:35:51.070787-08 test t A100 \N t \N \N \N {} -2896 1007 2025-02-27 00:30:01.989297-08 2025-02-27 01:26:11.483563-08 benchmark f L4 \N t \N \N \N {} -2897 1008 2025-02-27 00:19:27.820406-08 2025-02-27 01:13:08.554592-08 benchmark f L4 \N t \N \N \N {} -2898 1009 2025-02-27 00:14:37.058625-08 2025-02-27 00:33:25.198225-08 benchmark f T4 \N t \N \N \N {} -2899 1010 2025-02-27 01:32:50.152071-08 2025-02-27 01:26:35.416449-08 benchmark f L4 \N f \N \N \N {} -2900 1011 2025-02-27 00:40:17.675693-08 2025-02-27 00:34:54.012193-08 benchmark f L4 \N t \N \N \N {} -2901 1012 2025-02-27 00:57:52.617774-08 2025-02-27 01:00:23.764973-08 benchmark f L4 \N t \N \N \N {} -3031 1085 2025-02-27 15:24:29.601416-08 2025-02-27 14:51:34.912701-08 test t H100 \N t \N \N \N {} -6462 2006 2025-03-14 07:51:45.870673-07 2025-03-14 07:40:39.999265-07 benchmark f A100 \N t \N \N \N {} -3102 1107 2025-02-28 01:54:23.349256-08 2025-02-28 01:56:27.939879-08 leaderboard f A100 0.003092532 t \N \N \N {} -6142 1883 2025-03-11 11:28:02.967532-07 2025-03-11 11:35:29.392865-07 test t T4 \N f \N \N \N {} -6314 1954 2025-03-12 20:17:28.206705-07 2025-03-12 20:22:28.033335-07 test f A100 \N t \N \N \N {} -6315 1954 2025-03-12 19:44:04.442778-07 2025-03-12 20:05:34.313377-07 benchmark f A100 \N t \N \N \N {} -6894 2143 2025-03-16 12:07:38.644838-07 2025-03-16 12:03:19.501535-07 test f L4 \N f \N \N \N {} -1388 465 2025-02-25 03:44:13.489138-08 2025-02-25 04:58:52.178206-08 benchmark t H100 \N t \N \N \N {} -1389 465 2025-02-25 04:48:22.345502-08 2025-02-25 03:34:29.544054-08 leaderboard t H100 0.000999849 t \N \N \N {} -2915 1022 2025-02-27 03:08:40.368035-08 2025-02-27 03:55:02.355278-08 benchmark f A100 \N t \N \N \N {} -3046 1093 2025-02-27 16:57:16.382054-08 2025-02-27 16:07:28.917105-08 test t A100 \N t \N \N \N {} -6895 2144 2025-03-16 12:40:59.707177-07 2025-03-16 12:04:30.038571-07 benchmark f T4 \N f \N \N \N {} -3108 1108 2025-02-28 02:25:29.231129-08 2025-02-28 01:30:03.455293-08 leaderboard t A100 0.0030845013333333335 t \N \N \N {} -6144 1885 2025-03-11 11:27:23.29413-07 2025-03-11 12:06:00.249613-07 test f T4 \N f \N \N \N {} -6317 1954 2025-03-12 20:05:57.526603-07 2025-03-12 19:36:21.76166-07 test t A100 \N t \N \N \N {} -6318 1954 2025-03-12 20:14:29.660956-07 2025-03-12 20:07:14.51853-07 benchmark t A100 \N t \N \N \N {} -6319 1954 2025-03-12 20:17:32.676671-07 2025-03-12 19:52:30.024723-07 leaderboard t A100 0.0013705575900000001 t \N \N \N {} -6465 2008 2025-03-14 06:25:10.510564-07 2025-03-14 06:10:34.856104-07 benchmark f A100 \N t \N \N \N {} -6467 2008 2025-03-14 07:05:50.283776-07 2025-03-14 06:54:47.878347-07 test t A100 \N t \N \N \N {} -6468 2008 2025-03-14 07:57:23.655081-07 2025-03-14 07:57:46.860585-07 benchmark t A100 \N t \N \N \N {} -6321 1956 2025-03-13 09:39:59.949032-07 2025-03-13 10:36:07.030735-07 test t T4 \N f \N \N \N {} -6470 2009 2025-03-14 07:54:17.367584-07 2025-03-14 07:52:14.667761-07 test t A100 \N t \N \N \N {} -6471 2009 2025-03-14 08:25:39.938379-07 2025-03-14 06:36:09.564554-07 benchmark t A100 \N t \N \N \N {} -3122 1110 2025-02-28 03:18:32.981187-08 2025-02-28 02:42:29.642779-08 benchmark f A100 \N t \N \N \N {} -3123 1110 2025-02-28 02:50:51.08401-08 2025-02-28 01:46:09.197929-08 leaderboard f A100 0.003097124 t \N \N \N {} -6322 1956 2025-03-13 08:55:36.22372-07 2025-03-13 10:04:13.347907-07 test f T4 \N f \N \N \N {} -6473 2009 2025-03-14 07:46:08.399013-07 2025-03-14 08:25:12.694431-07 test f A100 \N t \N \N \N {} -3578 1225 2025-02-28 14:25:25.372721-08 2025-02-28 13:59:09.123935-08 benchmark f A100 \N t \N \N \N {} -3128 1111 2025-02-28 02:02:43.50836-08 2025-02-28 01:29:59.933956-08 benchmark t A100 \N t \N \N \N {} -3129 1111 2025-02-28 02:09:35.864061-08 2025-02-28 02:11:31.900287-08 leaderboard t A100 0.00320221 t \N \N \N {} -6150 1888 2025-03-11 12:00:16.100477-07 2025-03-11 12:59:35.590119-07 test f T4 \N f \N \N \N {} -6324 1957 2025-03-13 10:47:36.919965-07 2025-03-13 09:33:50.353862-07 test f T4 \N f \N \N \N {} -3132 1112 2025-02-28 02:00:15.175704-08 2025-02-28 01:42:45.822482-08 leaderboard f A100 0.0030739046666666664 t \N \N \N {} -6151 1888 2025-03-11 11:24:19.34183-07 2025-03-11 11:15:09.363246-07 test t T4 \N f \N \N \N {} -6325 1958 2025-03-13 10:54:39.858929-07 2025-03-13 10:57:53.21954-07 test t T4 \N f \N \N \N {} -6481 2010 2025-03-14 07:16:40.068566-07 2025-03-14 08:11:25.193743-07 leaderboard f A100 0.0026579623333333334 t \N \N \N {} -3134 1112 2025-02-28 02:21:04.795319-08 2025-02-28 03:19:20.667414-08 benchmark t A100 \N t \N \N \N {} -3135 1112 2025-02-28 02:49:56.527812-08 2025-02-28 02:10:59.95073-08 leaderboard t A100 0.0030922646666666667 t \N \N \N {} -6153 1889 2025-03-11 11:42:50.98288-07 2025-03-11 13:36:59.142943-07 benchmark t T4 \N f \N \N \N {} -1391 465 2025-02-25 04:55:41.868356-08 2025-02-25 03:36:33.891601-08 benchmark f H100 \N t \N \N \N {} -1392 465 2025-02-25 04:37:36.417233-08 2025-02-25 03:28:58.50135-08 leaderboard f H100 0.00140509 t \N \N \N {} -1393 466 2025-02-25 03:10:36.309883-08 2025-02-25 03:21:53.515918-08 test f H100 \N f \N \N \N {} -1394 466 2025-02-25 03:11:29.000771-08 2025-02-25 03:41:58.917775-08 test t H100 \N f \N \N \N {} -2916 1023 2025-02-27 02:46:36.620437-08 2025-02-27 02:58:33.660277-08 test f A100 \N t \N \N \N {} -3078 1103 2025-02-28 01:22:48.676241-08 2025-02-28 02:52:22.813395-08 leaderboard f A100 0.0031237426666666664 t \N \N \N {} -6155 1889 2025-03-11 12:10:23.846696-07 2025-03-11 12:13:59.236186-07 benchmark f T4 \N f \N \N \N {} -1397 468 2025-02-25 03:38:51.530876-08 2025-02-25 04:52:35.406014-08 benchmark f A100 \N t \N \N \N {} -1398 468 2025-02-25 04:38:38.385416-08 2025-02-25 03:58:06.257632-08 leaderboard f A100 0.0033990103333333336 t \N \N \N {} -2917 1023 2025-02-27 03:14:37.23347-08 2025-02-27 03:11:45.911539-08 benchmark f A100 \N t \N \N \N {} -2918 1023 2025-02-27 03:03:16.65023-08 2025-02-27 04:18:18.937857-08 leaderboard f A100 0.0001010261724137931 t \N \N \N {} -3079 1103 2025-02-28 01:31:17.940598-08 2025-02-28 02:41:55.766318-08 test t A100 \N t \N \N \N {} -6897 2146 2025-03-16 12:39:46.739273-07 2025-03-16 13:06:56.642031-07 benchmark f T4 \N f \N \N \N {} -3141 1113 2025-02-28 01:34:25.294505-08 2025-02-28 02:09:38.976633-08 leaderboard t A100 0.0030885226666666665 t \N \N \N {} -3579 1226 2025-02-28 13:23:19.822733-08 2025-02-28 13:38:01.202424-08 benchmark f H100 \N t \N \N \N {} -3144 1114 2025-02-28 03:32:16.197256-08 2025-02-28 02:47:43.563886-08 leaderboard t A100 0.003070266 t \N \N \N {} -6158 1890 2025-03-11 13:28:57.157083-07 2025-03-11 12:19:09.927667-07 test f T4 \N t \N \N \N {} -3147 1114 2025-02-28 03:28:26.003711-08 2025-02-28 01:44:07.837604-08 leaderboard f A100 0.0030685503333333333 t \N \N \N {} -6160 1891 2025-03-11 13:43:23.540375-07 2025-03-11 13:07:39.214045-07 test f T4 \N t \N \N \N {} -6484 2011 2025-03-14 07:02:10.29025-07 2025-03-14 08:11:39.923824-07 leaderboard f A100 0.0025160156666666667 t \N \N \N {} -3149 1115 2025-02-28 02:35:43.630546-08 2025-02-28 02:15:39.443589-08 benchmark f A100 \N t \N \N \N {} -3150 1115 2025-02-28 02:15:43.119849-08 2025-02-28 01:43:19.893985-08 leaderboard f A100 0.0030932896666666667 t \N \N \N {} -6161 1892 2025-03-11 11:57:49.455042-07 2025-03-11 12:21:21.948804-07 benchmark f T4 \N f \N \N \N {} -3580 1227 2025-02-28 13:22:39.155063-08 2025-02-28 13:51:24.355953-08 benchmark f H100 \N t \N \N \N {} -3152 1115 2025-02-28 01:56:27.157682-08 2025-02-28 01:41:45.62036-08 benchmark t A100 \N t \N \N \N {} -3153 1115 2025-02-28 03:24:29.539728-08 2025-02-28 03:03:11.575433-08 leaderboard t A100 0.0030942826666666666 t \N \N \N {} -6162 1893 2025-03-11 12:05:53.460089-07 2025-03-11 13:52:23.016257-07 benchmark f T4 \N f \N \N \N {} -3155 1116 2025-02-28 01:56:03.204295-08 2025-02-28 02:29:07.453242-08 benchmark f A100 \N t \N \N \N {} -3156 1116 2025-02-28 03:41:05.90631-08 2025-02-28 03:37:39.667047-08 leaderboard f A100 0.0030983496666666666 t \N \N \N {} -3581 1228 2025-02-28 14:07:30.971092-08 2025-02-28 14:44:25.55772-08 test f A100 \N t \N \N \N {} -6487 2011 2025-03-14 07:33:24.422282-07 2025-03-14 07:03:24.285193-07 leaderboard t A100 0.0024820293333333333 t \N \N \N {} -6674 2065 2025-03-14 15:48:12.867984-07 2025-03-14 15:10:31.440491-07 test t T4 \N f \N \N \N {} -1406 469 2025-02-25 04:38:40.125755-08 2025-02-25 04:37:21.18501-08 benchmark t A100 \N t \N \N \N {} -1407 469 2025-02-25 04:41:04.15737-08 2025-02-25 04:10:44.089578-08 leaderboard t A100 0.0030951176666666664 t \N \N \N {} -2925 1024 2025-02-27 04:23:16.250435-08 2025-02-27 03:24:58.288515-08 test t T4 \N t \N \N \N {} -1412 470 2025-02-25 03:40:12.003397-08 2025-02-25 04:12:17.525955-08 benchmark f A100 \N t \N \N \N {} -1413 470 2025-02-25 04:37:09.951863-08 2025-02-25 03:30:09.91909-08 leaderboard f A100 0.003099017 t \N \N \N {} -2931 1025 2025-02-27 03:01:05.861477-08 2025-02-27 04:00:58.846217-08 test t L4 \N t \N \N \N {} -2932 1025 2025-02-27 04:33:16.006317-08 2025-02-27 04:33:30.552743-08 benchmark t L4 \N t \N \N \N {} -2933 1025 2025-02-27 03:00:47.162532-08 2025-02-27 03:18:26.934968-08 leaderboard t L4 0.00011731689361702128 t \N \N \N {} -3127 1111 2025-02-28 03:03:45.476308-08 2025-02-28 01:34:30.667207-08 test t A100 \N t \N \N \N {} -1415 471 2025-02-25 05:07:03.031352-08 2025-02-25 04:44:07.11511-08 benchmark f A100 \N t \N \N \N {} -1416 471 2025-02-25 03:49:37.382368-08 2025-02-25 03:16:26.151128-08 leaderboard f A100 0.003083305 t \N \N \N {} -2934 1026 2025-02-27 03:29:31.212038-08 2025-02-27 03:36:48.365495-08 benchmark f A100 \N t \N \N \N {} -3049 1093 2025-02-27 15:47:45.331679-08 2025-02-27 16:54:04.404866-08 test f A100 \N t \N \N \N {} -3130 1112 2025-02-28 02:53:32.669829-08 2025-02-28 01:59:58.104291-08 test f A100 \N t \N \N \N {} -3158 1116 2025-02-28 03:00:27.935274-08 2025-02-28 03:06:58.848252-08 benchmark t A100 \N t \N \N \N {} -3582 1228 2025-02-28 14:47:56.576495-08 2025-02-28 13:38:26.115181-08 benchmark f A100 \N t \N \N \N {} -6334 1962 2025-03-13 12:17:44.907621-07 2025-03-13 10:47:39.798292-07 test f T4 \N f \N \N \N {} -6488 2012 2025-03-14 07:37:48.319936-07 2025-03-14 06:40:10.451158-07 test t A100 \N t \N \N \N {} -6905 2149 2025-03-16 13:54:55.728222-07 2025-03-16 12:29:31.998021-07 test f A100 \N f \N \N \N {} -3161 1117 2025-02-28 03:20:59.962421-08 2025-02-28 03:07:10.955256-08 benchmark t A100 \N t \N \N \N {} -6489 2012 2025-03-14 07:07:28.772602-07 2025-03-14 06:43:54.465366-07 benchmark t A100 \N t \N \N \N {} -6490 2012 2025-03-14 07:43:28.617109-07 2025-03-14 07:02:38.240011-07 leaderboard t A100 0.002517398 t \N \N \N {} -6912 2154 2025-03-16 16:29:02.680063-07 2025-03-16 16:07:12.630657-07 test t T4 \N f \N \N \N {} -3164 1117 2025-02-28 01:55:34.743977-08 2025-02-28 03:46:05.60909-08 benchmark f A100 \N t \N \N \N {} -3165 1117 2025-02-28 03:22:15.291884-08 2025-02-28 03:01:38.071188-08 leaderboard f A100 0.0031003043333333335 t \N \N \N {} -3166 1118 2025-02-28 02:36:20.544219-08 2025-02-28 02:03:38.914711-08 test t A100 \N t \N \N \N {} -3167 1118 2025-02-28 02:27:47.915164-08 2025-02-28 02:31:18.69465-08 benchmark t A100 \N t \N \N \N {} -3168 1118 2025-02-28 03:38:38.742518-08 2025-02-28 02:42:05.29698-08 leaderboard t A100 0.0031557713333333335 t \N \N \N {} -3467 1192 2025-02-28 11:34:31.331723-08 2025-02-28 10:29:39.69554-08 test f A100 \N f \N \N \N {} -1418 471 2025-02-25 04:36:59.718452-08 2025-02-25 03:23:10.279404-08 benchmark t A100 \N t \N \N \N {} -1419 471 2025-02-25 03:23:16.170077-08 2025-02-25 04:14:16.278504-08 leaderboard t A100 0.0030839833333333334 t \N \N \N {} -2935 1027 2025-02-27 04:13:09.20703-08 2025-02-27 03:53:36.446829-08 benchmark f A100 \N t \N \N \N {} -3133 1112 2025-02-28 01:44:54.216121-08 2025-02-28 02:38:03.906338-08 test t A100 \N t \N \N \N {} -3518 1202 2025-02-28 11:33:17.396025-08 2025-02-28 12:16:13.271104-08 test t A100 \N t \N \N \N {} -6913 2154 2025-03-16 16:38:39.962668-07 2025-03-16 14:53:33.732977-07 test f T4 \N f \N \N \N {} -3568 1211 2025-02-28 11:12:30.656088-08 2025-02-28 12:38:32.554868-08 test t H100 \N f \N \N \N {} -3171 1118 2025-02-28 03:57:16.999936-08 2025-02-28 02:52:16.718421-08 leaderboard f A100 0.003146471 t \N \N \N {} -3172 1119 2025-02-28 03:47:11.316659-08 2025-02-28 03:09:45.561459-08 test f A100 \N t \N \N \N {} -3173 1119 2025-02-28 02:39:13.039039-08 2025-02-28 02:10:32.463474-08 benchmark f A100 \N t \N \N \N {} -6914 2155 2025-03-16 15:06:19.793513-07 2025-03-16 16:22:51.137738-07 test t T4 \N f \N \N \N {} -3177 1119 2025-02-28 03:56:27.839518-08 2025-02-28 03:53:00.543375-08 leaderboard t A100 0.003148472 t \N \N \N {} -6166 1897 2025-03-11 12:32:48.919729-07 2025-03-11 13:04:35.751745-07 benchmark f T4 \N t \N \N \N {} -6336 1963 2025-03-13 11:44:12.666263-07 2025-03-13 11:35:54.4531-07 test t T4 \N f \N \N \N {} -6491 2012 2025-03-14 06:56:50.114221-07 2025-03-14 07:03:33.962589-07 test f A100 \N t \N \N \N {} -6915 2155 2025-03-16 16:05:45.981468-07 2025-03-16 16:53:11.056967-07 test f T4 \N f \N \N \N {} -3886 1296 2025-03-01 04:03:42.524707-08 2025-03-01 04:26:34.298074-08 test f A100 \N t \N \N \N {} -6167 1898 2025-03-11 14:02:34.088566-07 2025-03-11 13:21:01.318443-07 test f T4 \N t \N \N \N {} -6168 1898 2025-03-11 13:56:37.141151-07 2025-03-11 12:54:02.739765-07 benchmark f T4 \N t \N \N \N {} -6169 1898 2025-03-11 13:26:27.542536-07 2025-03-11 12:35:42.421543-07 leaderboard f T4 3.1035724833333336 t \N \N \N {} -6337 1964 2025-03-13 12:31:57.776048-07 2025-03-13 12:44:18.662819-07 test t T4 \N f \N \N \N {} -6675 2065 2025-03-14 15:10:20.43581-07 2025-03-14 15:25:35.47986-07 test f T4 \N f \N \N \N {} -6170 1898 2025-03-11 14:05:54.41326-07 2025-03-11 13:31:34.094196-07 test t T4 \N t \N \N \N {} -6171 1898 2025-03-11 13:56:20.919926-07 2025-03-11 12:56:21.007587-07 benchmark t T4 \N t \N \N \N {} -6172 1898 2025-03-11 13:32:59.303232-07 2025-03-11 14:04:58.378204-07 leaderboard t T4 3.1852416556666667 t \N \N \N {} -6338 1964 2025-03-13 11:48:09.106344-07 2025-03-13 11:48:18.838973-07 test f T4 \N f \N \N \N {} -6676 2066 2025-03-14 15:18:53.997082-07 2025-03-14 14:33:10.917221-07 test f T4 \N f \N \N \N {} -3185 1121 2025-02-28 04:01:47.89985-08 2025-02-28 04:25:50.220487-08 benchmark f A100 \N t \N \N \N {} -3186 1121 2025-02-28 04:48:14.978807-08 2025-02-28 04:05:51.546019-08 leaderboard f A100 0.0031212863333333336 t \N \N \N {} -6173 1899 2025-03-11 13:25:54.795056-07 2025-03-11 13:19:39.098623-07 test t T4 \N f \N \N \N {} -6339 1965 2025-03-13 11:39:17.316162-07 2025-03-13 11:28:19.690857-07 test t T4 \N f \N \N \N {} -3468 1193 2025-02-28 12:15:00.613441-08 2025-02-28 10:38:48.030816-08 test f A100 \N t \N \N \N {} -1421 472 2025-02-25 04:29:57.622938-08 2025-02-25 04:49:21.634274-08 benchmark f A100 \N t \N \N \N {} -1422 472 2025-02-25 03:37:41.571503-08 2025-02-25 03:49:33.02462-08 leaderboard f A100 0.0030871423333333333 t \N \N \N {} -2936 1028 2025-02-27 03:16:50.844905-08 2025-02-27 03:44:10.314673-08 benchmark f A100 \N t \N \N \N {} -3136 1113 2025-02-28 02:58:56.415099-08 2025-02-28 02:44:36.404391-08 test f A100 \N t \N \N \N {} -6492 2012 2025-03-14 08:00:25.963285-07 2025-03-14 06:46:07.163228-07 benchmark f A100 \N t \N \N \N {} -6493 2012 2025-03-14 07:09:08.72814-07 2025-03-14 07:18:27.447251-07 leaderboard f A100 0.002505465 t \N \N \N {} -6599 2045 2025-03-14 12:56:47.350056-07 2025-03-14 13:11:04.653905-07 benchmark f A100 \N t \N \N \N {} -6600 2045 2025-03-14 13:04:51.216706-07 2025-03-14 13:43:13.503645-07 leaderboard f A100 0.0025198196666666667 t \N \N \N {} -6645 2053 2025-03-14 13:47:46.801513-07 2025-03-14 12:15:48.666184-07 benchmark f A100 \N t \N \N \N {} -3192 1122 2025-02-28 03:46:09.723947-08 2025-02-28 04:28:57.570091-08 leaderboard f A100 0.0031000573333333334 t \N \N \N {} -6341 1966 2025-03-13 12:45:43.296749-07 2025-03-13 12:17:33.727157-07 test f T4 \N t \N \N \N {} -6342 1966 2025-03-13 12:13:23.993863-07 2025-03-13 12:35:05.634615-07 benchmark f T4 \N t \N \N \N {} -6343 1966 2025-03-13 13:00:24.511463-07 2025-03-13 12:19:23.080746-07 leaderboard f T4 3.5582660766666665 t \N \N \N {} -6495 2013 2025-03-14 12:06:59.03304-07 2025-03-14 11:21:26.358885-07 test f H100 \N f \N \N \N {} -3198 1123 2025-02-28 04:42:25.083061-08 2025-02-28 04:52:17.118702-08 leaderboard f A100 0.0031039456666666666 t \N \N \N {} -3521 1203 2025-02-28 12:13:18.73249-08 2025-02-28 11:42:59.789557-08 test t A100 \N f \N \N \N {} -3200 1123 2025-02-28 04:09:22.67407-08 2025-02-28 05:42:51.81569-08 benchmark t A100 \N t \N \N \N {} -3201 1123 2025-02-28 04:49:03.536471-08 2025-02-28 05:30:26.750128-08 leaderboard t A100 0.003107339 t \N \N \N {} -6176 1900 2025-03-11 14:27:12.915155-07 2025-03-11 13:27:30.861214-07 test f T4 \N f \N \N \N {} -6344 1966 2025-03-13 12:39:39.896955-07 2025-03-13 11:42:10.53502-07 test t T4 \N t \N \N \N {} -6345 1966 2025-03-13 11:46:33.159161-07 2025-03-13 11:42:35.832436-07 benchmark t T4 \N t \N \N \N {} -6346 1966 2025-03-13 12:49:02.202475-07 2025-03-13 11:43:42.365767-07 leaderboard t T4 3.6324577356666663 t \N \N \N {} -3202 1124 2025-02-28 05:47:04.982464-08 2025-02-28 04:47:56.713443-08 test f A100 \N t \N \N \N {} -3203 1124 2025-02-28 04:08:24.397878-08 2025-02-28 04:24:14.653298-08 benchmark f A100 \N t \N \N \N {} -3204 1124 2025-02-28 05:40:56.722412-08 2025-02-28 05:44:46.320859-08 leaderboard f A100 0.0030972656666666664 t \N \N \N {} -3205 1124 2025-02-28 04:40:01.037489-08 2025-02-28 03:58:41.704134-08 test t A100 \N t \N \N \N {} -3206 1124 2025-02-28 05:42:55.343085-08 2025-02-28 04:23:54.198083-08 benchmark t A100 \N t \N \N \N {} -3207 1124 2025-02-28 05:15:33.617644-08 2025-02-28 05:36:54.360407-08 leaderboard t A100 0.0031034153333333336 t \N \N \N {} -3212 1125 2025-02-28 05:41:25.119279-08 2025-02-28 06:47:46.688795-08 benchmark f A100 \N t \N \N \N {} -3218 1126 2025-02-28 06:15:42.624802-08 2025-02-28 05:18:36.139802-08 benchmark f A100 \N t \N \N \N {} -3224 1127 2025-02-28 05:23:45.306771-08 2025-02-28 06:39:04.576528-08 benchmark f A100 \N t \N \N \N {} -3230 1130 2025-02-28 06:11:34.161879-08 2025-02-28 07:19:05.529092-08 benchmark t A100 \N t \N \N \N {} -3522 1203 2025-02-28 11:17:41.164832-08 2025-02-28 10:45:35.314206-08 test f A100 \N f \N \N \N {} -3238 1133 2025-02-28 05:58:19.469647-08 2025-02-28 05:27:59.168429-08 leaderboard t A100 0.0030885113333333335 t \N \N \N {} -3471 1193 2025-02-28 12:08:38.072989-08 2025-02-28 11:36:29.661671-08 test t A100 \N t \N \N \N {} -1424 472 2025-02-25 05:07:18.96443-08 2025-02-25 04:27:31.619653-08 benchmark t A100 \N t \N \N \N {} -1425 472 2025-02-25 04:26:35.663772-08 2025-02-25 03:52:24.217898-08 leaderboard t A100 0.0031005363333333337 t \N \N \N {} -2937 1029 2025-02-27 04:45:23.397168-08 2025-02-27 04:26:08.472553-08 benchmark f A100 \N t \N \N \N {} -3050 1093 2025-02-27 15:36:27.175994-08 2025-02-27 15:34:55.376904-08 benchmark f A100 \N t \N \N \N {} -3239 1134 2025-02-28 06:50:37.72243-08 2025-02-28 06:25:11.379874-08 benchmark f A100 \N t \N \N \N {} -3240 1133 2025-02-28 05:32:42.89057-08 2025-02-28 06:35:04.012708-08 test f A100 \N t \N \N \N {} -3241 1133 2025-02-28 05:55:48.275123-08 2025-02-28 06:28:43.235753-08 benchmark f A100 \N t \N \N \N {} -3242 1133 2025-02-28 06:28:41.176944-08 2025-02-28 06:11:12.832836-08 leaderboard f A100 0.003086809 t \N \N \N {} -3243 1136 2025-02-28 05:47:39.958836-08 2025-02-28 05:44:16.957515-08 benchmark f A100 \N t \N \N \N {} -3244 1135 2025-02-28 06:38:46.857624-08 2025-02-28 07:16:05.450915-08 test f A100 \N t \N \N \N {} -3245 1135 2025-02-28 06:04:38.048901-08 2025-02-28 07:07:00.823655-08 benchmark f A100 \N t \N \N \N {} -3246 1135 2025-02-28 05:37:48.064623-08 2025-02-28 05:38:45.603311-08 leaderboard f A100 0.0030916216666666664 t \N \N \N {} -3247 1135 2025-02-28 05:52:38.194905-08 2025-02-28 05:43:19.652095-08 test t A100 \N t \N \N \N {} -3523 1204 2025-02-28 11:24:15.172488-08 2025-02-28 11:35:08.725248-08 test f A100 \N t \N \N \N {} -3252 1137 2025-02-28 07:21:26.0063-08 2025-02-28 07:16:56.022377-08 leaderboard f A100 0.0030857416666666667 t \N \N \N {} -3253 1137 2025-02-28 06:00:28.572485-08 2025-02-28 05:34:19.879496-08 test t A100 \N t \N \N \N {} -3254 1137 2025-02-28 06:10:53.662809-08 2025-02-28 06:32:30.162306-08 benchmark t A100 \N t \N \N \N {} -3255 1137 2025-02-28 07:12:35.431057-08 2025-02-28 06:05:27.261706-08 leaderboard t A100 0.0030894023333333334 t \N \N \N {} -3256 1138 2025-02-28 05:44:23.296315-08 2025-02-28 05:54:30.649398-08 benchmark f A100 \N t \N \N \N {} -3258 1139 2025-02-28 06:04:00.85979-08 2025-02-28 06:27:21.740985-08 benchmark f A100 \N f \N \N \N {} -3139 1113 2025-02-28 02:22:31.319401-08 2025-02-28 02:55:45.560083-08 test t A100 \N t \N \N \N {} -1427 473 2025-02-25 04:47:05.317584-08 2025-02-25 03:32:09.61539-08 benchmark t A100 \N t \N \N \N {} -1428 473 2025-02-25 04:44:32.069859-08 2025-02-25 03:55:20.86169-08 leaderboard t A100 0.003175767 t \N \N \N {} -2938 1030 2025-02-27 04:38:20.668328-08 2025-02-27 04:24:06.448431-08 test f A100 \N t \N \N \N {} -2939 1030 2025-02-27 03:05:03.419869-08 2025-02-27 04:22:37.87157-08 benchmark f A100 \N t \N \N \N {} -2940 1030 2025-02-27 04:07:07.030129-08 2025-02-27 04:03:57.310379-08 leaderboard f A100 0.00007918067999999999 t \N \N \N {} -3142 1114 2025-02-28 01:49:44.822785-08 2025-02-28 02:25:24.680714-08 test t A100 \N t \N \N \N {} -3259 1139 2025-02-28 06:24:17.521761-08 2025-02-28 07:13:37.812929-08 test t A100 \N t \N \N \N {} -3562 1210 2025-02-28 11:57:02.979824-08 2025-02-28 10:51:28.47295-08 test t A100 \N t \N \N \N {} -1430 473 2025-02-25 04:40:01.662393-08 2025-02-25 04:30:55.139642-08 benchmark f A100 \N t \N \N \N {} -1431 473 2025-02-25 03:31:35.707171-08 2025-02-25 03:31:53.832049-08 leaderboard f A100 0.00317240225 t \N \N \N {} -1432 474 2025-02-25 04:06:52.039054-08 2025-02-25 04:58:28.878029-08 test f A100 \N f \N \N \N {} -1438 479 2025-02-25 05:28:40.021595-08 2025-02-25 04:21:12.668713-08 test f A100 \N f \N \N \N {} -1445 484 2025-02-25 04:56:56.648978-08 2025-02-25 04:48:16.147785-08 benchmark t A100 \N t \N \N \N {} -1446 484 2025-02-25 05:51:53.636258-08 2025-02-25 04:20:30.28663-08 leaderboard t A100 0.0030884286666666666 t \N \N \N {} -2942 1030 2025-02-27 04:11:40.863984-08 2025-02-27 03:12:21.326746-08 benchmark t A100 \N t \N \N \N {} -2943 1030 2025-02-27 04:47:50.715647-08 2025-02-27 04:01:18.005466-08 leaderboard t A100 0.0000793426 t \N \N \N {} -3260 1139 2025-02-28 07:19:43.310162-08 2025-02-28 06:04:41.287437-08 benchmark t A100 \N f \N \N \N {} -3526 1204 2025-02-28 12:03:21.457321-08 2025-02-28 12:30:39.578159-08 test t A100 \N t \N \N \N {} -6647 2054 2025-03-14 13:43:53.190099-07 2025-03-14 12:39:40.431314-07 test f A100 \N t \N \N \N {} -6500 2015 2025-03-14 12:17:09.225085-07 2025-03-14 10:53:14.727282-07 leaderboard f H100 0.006131182333333333 t \N \N \N {} -6607 2046 2025-03-14 13:24:07.723525-07 2025-03-14 12:31:15.468896-07 test f A100 \N t \N \N \N {} -6504 2016 2025-03-14 12:12:39.778966-07 2025-03-14 12:30:56.454439-07 benchmark f A100 \N t \N \N \N {} -3274 1143 2025-02-28 07:21:32.682632-08 2025-02-28 07:30:37.256411-08 leaderboard f A100 0.003102605 t \N \N \N {} -6184 1903 2025-03-11 17:13:03.62363-07 2025-03-11 16:22:00.434413-07 test f T4 \N t \N \N \N {} -6185 1903 2025-03-11 16:06:37.941692-07 2025-03-11 16:41:23.331167-07 benchmark f T4 \N t \N \N \N {} -6186 1903 2025-03-11 16:13:32.336442-07 2025-03-11 16:38:58.569653-07 leaderboard f T4 3.2504637173333335 t \N \N \N {} -6352 1969 2025-03-13 12:36:05.935244-07 2025-03-13 13:06:47.416038-07 test f T4 \N f \N \N \N {} -6505 2017 2025-03-14 12:15:29.300377-07 2025-03-14 12:34:01.640423-07 test t A100 \N t \N \N \N {} -6506 2017 2025-03-14 11:18:20.450795-07 2025-03-14 12:30:54.661761-07 benchmark t A100 \N t \N \N \N {} -6608 2046 2025-03-14 13:48:29.521438-07 2025-03-14 13:48:51.889234-07 benchmark f A100 \N t \N \N \N {} -6609 2046 2025-03-14 12:28:36.914817-07 2025-03-14 12:50:31.439118-07 leaderboard f A100 0.002515373 t \N \N \N {} -3279 1144 2025-02-28 06:01:10.470211-08 2025-02-28 07:19:57.064988-08 benchmark t A100 \N t \N \N \N {} -3280 1144 2025-02-28 05:49:45.171368-08 2025-02-28 05:55:10.670943-08 leaderboard t A100 0.0031060543333333336 t \N \N \N {} -3624 1239 2025-02-28 16:58:22.066529-08 2025-02-28 16:33:30.877062-08 benchmark t A100 \N t \N \N \N {} -3281 1145 2025-02-28 05:37:52.569256-08 2025-02-28 07:15:46.585683-08 benchmark f A100 \N t \N \N \N {} -3282 1146 2025-02-28 05:57:19.501056-08 2025-02-28 05:49:46.099644-08 benchmark f A100 \N t \N \N \N {} -3287 1148 2025-02-28 07:03:20.664723-08 2025-02-28 07:36:21.224833-08 test f A100 \N t \N \N \N {} -3288 1148 2025-02-28 06:40:03.960027-08 2025-02-28 07:14:31.066145-08 benchmark f A100 \N t \N \N \N {} -3289 1148 2025-02-28 06:38:52.008227-08 2025-02-28 06:21:28.630464-08 leaderboard f A100 0.0037143906666666665 t \N \N \N {} -3290 1148 2025-02-28 07:14:12.948729-08 2025-02-28 06:08:46.103883-08 test t A100 \N t \N \N \N {} -3291 1148 2025-02-28 07:54:28.192996-08 2025-02-28 06:24:00.652714-08 benchmark t A100 \N t \N \N \N {} -3292 1148 2025-02-28 06:29:06.723096-08 2025-02-28 06:39:10.572344-08 leaderboard t A100 0.0036469966666666663 t \N \N \N {} -3293 1148 2025-02-28 06:40:23.470841-08 2025-02-28 07:24:25.091246-08 test f H100 \N t \N \N \N {} -3294 1148 2025-02-28 07:40:06.006249-08 2025-02-28 07:18:53.588443-08 benchmark f H100 \N t \N \N \N {} -3148 1115 2025-02-28 02:02:27.848298-08 2025-02-28 02:01:06.813683-08 test f A100 \N t \N \N \N {} -1448 484 2025-02-25 05:29:00.477152-08 2025-02-25 05:41:06.371795-08 benchmark f A100 \N t \N \N \N {} -1449 484 2025-02-25 04:52:55.95894-08 2025-02-25 04:18:57.804532-08 leaderboard f A100 0.003093351 t \N \N \N {} -2944 1031 2025-02-27 07:43:30.027296-08 2025-02-27 09:06:35.166274-08 benchmark f A100 \N t \N \N \N {} -3295 1148 2025-02-28 07:32:20.445776-08 2025-02-28 07:54:04.00939-08 leaderboard f H100 0.0018497506666666668 t \N \N \N {} -3296 1148 2025-02-28 07:09:18.873569-08 2025-02-28 07:00:06.853282-08 test t T4 \N t \N \N \N {} -3298 1148 2025-02-28 06:42:57.127187-08 2025-02-28 07:48:36.011787-08 leaderboard t T4 0.010518033333333334 t \N \N \N {} -3299 1148 2025-02-28 07:34:33.292144-08 2025-02-28 07:45:13.263944-08 test t H100 \N t \N \N \N {} -3305 1148 2025-02-28 06:56:08.877192-08 2025-02-28 07:30:09.470963-08 test f L4 \N t \N \N \N {} -3306 1148 2025-02-28 06:49:29.091858-08 2025-02-28 06:59:56.481035-08 benchmark f L4 \N t \N \N \N {} -3316 1151 2025-02-28 07:32:43.276426-08 2025-02-28 06:52:43.01807-08 test t H100 \N f \N \N \N {} -3317 1151 2025-02-28 06:04:40.63296-08 2025-02-28 06:57:06.096217-08 test t T4 \N f \N \N \N {} -3318 1151 2025-02-28 06:13:37.060218-08 2025-02-28 06:40:07.898882-08 test f L4 \N f \N \N \N {} -3529 1205 2025-02-28 11:00:20.479694-08 2025-02-28 11:27:21.065676-08 test f A100 \N t \N \N \N {} -3319 1151 2025-02-28 07:44:25.505195-08 2025-02-28 06:17:10.426155-08 test t L4 \N f \N \N \N {} -3569 1212 2025-02-28 12:31:15.223201-08 2025-02-28 12:02:50.307398-08 benchmark f H100 \N t \N \N \N {} -3327 1153 2025-02-28 07:48:24.592846-08 2025-02-28 06:08:43.919391-08 test t A100 \N f \N \N \N {} -3328 1153 2025-02-28 06:13:02.122552-08 2025-02-28 07:59:20.324753-08 test f A100 \N f \N \N \N {} -3535 1206 2025-02-28 11:07:39.749164-08 2025-02-28 11:05:51.393804-08 test f A100 \N t \N \N \N {} -3330 1153 2025-02-28 06:48:38.54272-08 2025-02-28 06:12:06.160611-08 test t H100 \N f \N \N \N {} -3331 1153 2025-02-28 06:44:57.334042-08 2025-02-28 06:27:45.550938-08 test f H100 \N f \N \N \N {} -3332 1153 2025-02-28 06:51:06.071549-08 2025-02-28 07:43:30.61761-08 test f T4 \N f \N \N \N {} -3333 1153 2025-02-28 07:22:29.016321-08 2025-02-28 07:25:37.384757-08 test t T4 \N f \N \N \N {} -3538 1206 2025-02-28 11:19:04.318559-08 2025-02-28 11:59:22.984501-08 test t A100 \N t \N \N \N {} -3337 1154 2025-02-28 06:12:20.751759-08 2025-02-28 07:24:13.732841-08 leaderboard t A100 0.003106905 t \N \N \N {} -3338 1154 2025-02-28 06:28:19.088439-08 2025-02-28 07:42:01.461767-08 test f A100 \N t \N \N \N {} -3339 1154 2025-02-28 07:24:35.655311-08 2025-02-28 07:21:32.784212-08 benchmark f A100 \N t \N \N \N {} -3340 1154 2025-02-28 06:30:11.131902-08 2025-02-28 07:23:31.696175-08 leaderboard f A100 0.0030856966666666665 t \N \N \N {} -3341 1155 2025-02-28 06:29:19.511228-08 2025-02-28 07:29:02.42522-08 test t A100 \N t \N \N \N {} -3342 1155 2025-02-28 07:51:44.284677-08 2025-02-28 07:48:29.018028-08 benchmark t A100 \N t \N \N \N {} -3343 1155 2025-02-28 06:47:28.573079-08 2025-02-28 06:44:05.949627-08 leaderboard t A100 0.0030834483333333335 t \N \N \N {} -3363 1161 2025-02-28 09:17:07.230642-08 2025-02-28 08:40:19.665408-08 test t T4 \N f \N \N \N {} -3348 1156 2025-02-28 07:16:58.973512-08 2025-02-28 07:33:23.078046-08 benchmark f A100 \N t \N \N \N {} -6188 1904 2025-03-11 18:10:38.388791-07 2025-03-11 18:14:58.210124-07 test t T4 \N f \N \N \N {} -6354 1970 2025-03-13 12:17:14.735768-07 2025-03-13 13:49:45.117322-07 test f T4 \N f \N \N \N {} -6508 2017 2025-03-14 11:42:27.682678-07 2025-03-14 12:36:52.887607-07 test f A100 \N t \N \N \N {} -6610 2047 2025-03-14 13:00:31.603918-07 2025-03-14 13:30:16.003681-07 test f A100 \N t \N \N \N {} -3570 1213 2025-02-28 11:12:09.580297-08 2025-02-28 11:15:38.840673-08 benchmark f H100 \N t \N \N \N {} -3541 1207 2025-02-28 11:14:12.554775-08 2025-02-28 11:14:24.949948-08 test t A100 \N t \N \N \N {} -3364 1161 2025-02-28 08:18:06.851362-08 2025-02-28 09:42:01.27446-08 test f T4 \N f \N \N \N {} -3365 1162 2025-02-28 09:22:47.122104-08 2025-02-28 08:23:26.975718-08 test f L4 \N f \N \N \N {} -3051 1093 2025-02-27 15:52:12.940276-08 2025-02-27 17:09:05.153707-08 leaderboard f A100 0.0031512003333333334 t \N \N \N {} -1451 485 2025-02-25 04:52:22.63147-08 2025-02-25 05:18:27.658437-08 benchmark t A100 \N t \N \N \N {} -1452 485 2025-02-25 05:30:56.8532-08 2025-02-25 05:31:55.77683-08 leaderboard t A100 0.0030837706666666663 t \N \N \N {} -2945 1032 2025-02-27 08:03:40.62355-08 2025-02-27 07:23:04.933128-08 test f A100 \N f \N \N \N {} -3151 1115 2025-02-28 03:28:35.730379-08 2025-02-28 02:37:28.944792-08 test t A100 \N t \N \N \N {} -3178 1120 2025-02-28 04:33:49.102329-08 2025-02-28 04:08:21.310974-08 test f A100 \N t \N \N \N {} -1454 485 2025-02-25 04:56:31.907725-08 2025-02-25 05:13:12.842041-08 benchmark f A100 \N t \N \N \N {} -1455 485 2025-02-25 04:05:06.520603-08 2025-02-25 04:56:19.51449-08 leaderboard f A100 0.003085375 t \N \N \N {} -2946 1033 2025-02-27 08:49:30.211741-08 2025-02-27 09:05:35.900691-08 test f T4 \N f \N \N \N {} -3003 1070 2025-02-27 13:00:06.212495-08 2025-02-27 11:57:37.768801-08 test f A100 \N f \N \N \N {} -3053 1094 2025-02-27 16:12:39.715401-08 2025-02-27 16:16:10.311482-08 benchmark f A100 \N t \N \N \N {} -3366 1162 2025-02-28 09:33:20.151769-08 2025-02-28 08:19:11.959347-08 test t L4 \N f \N \N \N {} -3571 1214 2025-02-28 12:01:55.895534-08 2025-02-28 12:13:51.232283-08 benchmark f H100 \N f \N \N \N {} -3373 1164 2025-02-28 09:54:50.944709-08 2025-02-28 09:48:36.826131-08 leaderboard f L4 0.01711390133333333 t \N \N \N {} -3374 1164 2025-02-28 10:03:17.913215-08 2025-02-28 08:48:38.575349-08 test t L4 \N t \N \N \N {} -3375 1164 2025-02-28 10:12:12.736365-08 2025-02-28 09:31:26.847036-08 benchmark t L4 \N t \N \N \N {} -3376 1164 2025-02-28 10:13:36.426125-08 2025-02-28 10:11:53.721613-08 leaderboard t L4 0.01711174266666667 t \N \N \N {} -3054 1094 2025-02-27 16:06:27.122167-08 2025-02-27 16:37:13.76371-08 leaderboard f A100 0.003111287 t \N \N \N {} -3154 1116 2025-02-28 02:29:06.631341-08 2025-02-28 01:58:59.085604-08 test f A100 \N t \N \N \N {} -1457 486 2025-02-25 04:36:15.216458-08 2025-02-25 05:15:34.163173-08 benchmark f A100 \N t \N \N \N {} -1458 486 2025-02-25 04:14:50.938805-08 2025-02-25 05:01:24.288432-08 leaderboard f A100 0.0030878703333333335 t \N \N \N {} -2947 1034 2025-02-27 09:13:44.098036-08 2025-02-27 09:11:18.488648-08 test f A100 \N f \N \N \N {} -3056 1094 2025-02-27 16:41:39.934588-08 2025-02-27 16:25:19.394991-08 benchmark t A100 \N t \N \N \N {} -3213 1125 2025-02-28 06:48:12.252064-08 2025-02-28 06:03:15.742155-08 leaderboard f A100 0.0030832093333333335 t \N \N \N {} -1460 486 2025-02-25 04:41:59.887933-08 2025-02-25 04:23:57.454866-08 benchmark t A100 \N t \N \N \N {} -1461 486 2025-02-25 05:49:45.663044-08 2025-02-25 04:12:57.109398-08 leaderboard t A100 0.003092806 t \N \N \N {} -2948 1035 2025-02-27 08:17:18.304303-08 2025-02-27 07:43:31.506965-08 test f T4 \N f \N \N \N {} -3005 1072 2025-02-27 13:09:50.152666-08 2025-02-27 12:57:15.8831-08 test f A100 \N t \N \N \N {} -3057 1094 2025-02-27 17:34:54.982672-08 2025-02-27 16:26:07.35543-08 leaderboard t A100 0.0031275906666666667 t \N \N \N {} -3080 1103 2025-02-28 02:02:56.286012-08 2025-02-28 02:38:01.79013-08 benchmark t A100 \N t \N \N \N {} -3081 1103 2025-02-28 01:56:35.783923-08 2025-02-28 01:56:41.958069-08 leaderboard t A100 0.0031360743333333336 t \N \N \N {} -3219 1126 2025-02-28 05:38:06.586102-08 2025-02-28 05:05:43.074581-08 leaderboard f A100 0.003108399 t \N \N \N {} -1463 487 2025-02-25 04:52:33.166187-08 2025-02-25 04:41:41.65243-08 benchmark t H100 \N t \N \N \N {} -1464 487 2025-02-25 05:44:02.421962-08 2025-02-25 04:30:44.563192-08 leaderboard t H100 0.0013963186666666667 t \N \N \N {} -2949 1036 2025-02-27 09:05:30.430291-08 2025-02-27 08:16:44.717534-08 test f A100 \N f \N \N \N {} -3006 1073 2025-02-27 14:03:17.549065-08 2025-02-27 12:33:09.482347-08 benchmark f A100 \N t \N \N \N {} -3583 1228 2025-02-28 13:36:44.96965-08 2025-02-28 13:58:50.097299-08 leaderboard f A100 0.0021488616666666665 t \N \N \N {} -3082 1104 2025-02-28 01:20:46.117646-08 2025-02-28 01:48:56.099424-08 test t A100 \N t \N \N \N {} -3083 1104 2025-02-28 02:40:27.969759-08 2025-02-28 03:00:37.086929-08 benchmark t A100 \N t \N \N \N {} -1466 487 2025-02-25 06:00:31.318157-08 2025-02-25 05:21:10.22327-08 benchmark f H100 \N t \N \N \N {} -1467 487 2025-02-25 05:08:04.83803-08 2025-02-25 05:35:28.456713-08 leaderboard f H100 0.0013990763333333333 t \N \N \N {} -1468 488 2025-02-25 04:11:00.167833-08 2025-02-25 05:53:43.66447-08 test f A100 \N f \N \N \N {} -3386 1166 2025-02-28 08:58:24.708767-08 2025-02-28 08:59:35.546096-08 test f L4 \N t \N \N \N {} -3572 1216 2025-02-28 13:25:32.928609-08 2025-02-28 13:06:59.918087-08 test f H100 \N f \N \N \N {} -3402 1173 2025-02-28 08:50:27.553378-08 2025-02-28 09:25:30.916359-08 leaderboard f L4 0.017073246333333333 t \N \N \N {} -3403 1173 2025-02-28 09:24:43.249263-08 2025-02-28 10:19:27.328955-08 test t L4 \N f \N \N \N {} -3404 1174 2025-02-28 09:36:18.604104-08 2025-02-28 10:01:25.201171-08 test t L4 \N t \N \N \N {} -3407 1174 2025-02-28 10:04:35.458188-08 2025-02-28 09:06:27.208576-08 test f L4 \N f \N \N \N {} -3409 1175 2025-02-28 10:42:07.749021-08 2025-02-28 10:23:25.763853-08 test t L4 \N f \N \N \N {} -3410 1176 2025-02-28 09:35:38.594297-08 2025-02-28 10:42:52.909956-08 test f L4 \N t \N \N \N {} -3411 1176 2025-02-28 10:40:17.639715-08 2025-02-28 09:51:35.220213-08 benchmark f L4 \N t \N \N \N {} -3412 1176 2025-02-28 10:11:43.370341-08 2025-02-28 10:27:44.06876-08 leaderboard f L4 0.017120271333333333 t \N \N \N {} -3547 1208 2025-02-28 12:27:27.702985-08 2025-02-28 11:17:47.788966-08 test f A100 \N t \N \N \N {} -3413 1176 2025-02-28 11:01:01.943548-08 2025-02-28 09:36:24.833158-08 test t L4 \N t \N \N \N {} -3414 1176 2025-02-28 09:58:29.179458-08 2025-02-28 09:26:37.706314-08 benchmark t L4 \N t \N \N \N {} -3415 1176 2025-02-28 10:55:29.876048-08 2025-02-28 09:14:25.427957-08 leaderboard t L4 0.017113951333333332 t \N \N \N {} -3416 1177 2025-02-28 10:07:18.386046-08 2025-02-28 10:14:01.64194-08 test f L4 \N f \N \N \N {} -3417 1177 2025-02-28 09:37:22.22405-08 2025-02-28 10:32:18.953094-08 test t L4 \N f \N \N \N {} -3550 1208 2025-02-28 11:50:19.101588-08 2025-02-28 12:45:50.950117-08 test t A100 \N t \N \N \N {} -3422 1180 2025-02-28 10:56:47.404757-08 2025-02-28 10:56:22.71621-08 test f L4 \N f \N \N \N {} -1470 490 2025-02-25 04:41:11.741201-08 2025-02-25 06:11:53.932837-08 benchmark f A100 \N t \N \N \N {} -2950 1037 2025-02-27 07:35:16.658999-08 2025-02-27 09:21:21.845387-08 test f A100 \N t \N \N \N {} -3007 1074 2025-02-27 13:18:44.138568-08 2025-02-27 13:54:19.684997-08 test t A100 \N t \N \N \N {} -3008 1074 2025-02-27 12:10:25.097092-08 2025-02-27 12:22:43.495573-08 benchmark t A100 \N t \N \N \N {} -1472 491 2025-02-25 04:16:51.40767-08 2025-02-25 04:44:20.032912-08 benchmark t A100 \N t \N \N \N {} -1473 491 2025-02-25 04:39:56.631178-08 2025-02-25 04:50:34.949043-08 leaderboard t A100 0.02900779833333333 t \N \N \N {} -3084 1104 2025-02-28 01:25:02.828823-08 2025-02-28 01:40:54.029102-08 leaderboard t A100 0.003145336 t \N \N \N {} -3092 1105 2025-02-28 03:07:23.548326-08 2025-02-28 02:21:49.2888-08 benchmark f A100 \N t \N \N \N {} -3093 1105 2025-02-28 03:14:38.771483-08 2025-02-28 02:41:25.038891-08 leaderboard f A100 0.003120996 t \N \N \N {} -3792 1279 2025-03-01 02:43:50.248573-08 2025-03-01 02:16:59.092747-08 test t A100 \N t \N \N \N {} -1475 491 2025-02-25 05:21:21.516015-08 2025-02-25 05:04:36.503808-08 benchmark f A100 \N t \N \N \N {} -1476 491 2025-02-25 04:41:54.691799-08 2025-02-25 05:44:48.514036-08 leaderboard f A100 0.02915152266666667 t \N \N \N {} -3423 1180 2025-02-28 09:29:33.8094-08 2025-02-28 10:27:43.704792-08 test t L4 \N f \N \N \N {} -3424 1181 2025-02-28 09:43:15.513902-08 2025-02-28 09:32:49.900783-08 test f L4 \N f \N \N \N {} -3425 1181 2025-02-28 09:43:45.066653-08 2025-02-28 10:25:58.378242-08 test t L4 \N f \N \N \N {} -3426 1182 2025-02-28 10:47:18.289906-08 2025-02-28 10:12:47.391263-08 test f L4 \N f \N \N \N {} -3427 1182 2025-02-28 09:47:39.824926-08 2025-02-28 11:31:24.156151-08 test t L4 \N f \N \N \N {} -3430 1184 2025-02-28 10:56:20.592407-08 2025-02-28 09:53:03.031846-08 test t L4 \N t \N \N \N {} -3431 1184 2025-02-28 11:16:58.29005-08 2025-02-28 10:48:40.540813-08 benchmark t L4 \N t \N \N \N {} -3432 1184 2025-02-28 09:40:03.831648-08 2025-02-28 11:23:08.875773-08 leaderboard t L4 0.017144596666666668 t \N \N \N {} -3433 1184 2025-02-28 10:51:16.362382-08 2025-02-28 11:01:47.958995-08 test f L4 \N f \N \N \N {} -3553 1209 2025-02-28 12:15:04.393062-08 2025-02-28 12:20:51.635003-08 test t A100 \N t \N \N \N {} -3436 1185 2025-02-28 10:58:38.735282-08 2025-02-28 09:55:56.205615-08 leaderboard t L4 0.017078157666666666 t \N \N \N {} -3437 1185 2025-02-28 09:43:25.915772-08 2025-02-28 11:05:27.656365-08 test f L4 \N f \N \N \N {} -3438 1186 2025-02-28 10:35:22.023921-08 2025-02-28 10:01:02.327904-08 test f L4 \N t \N \N \N {} -3443 1186 2025-02-28 10:41:33.074483-08 2025-02-28 10:48:09.890989-08 leaderboard t L4 0.017077551 t \N \N \N {} -3455 1190 2025-02-28 11:31:02.465158-08 2025-02-28 11:33:20.889525-08 benchmark f A100 \N t \N \N \N {} -3444 1187 2025-02-28 11:17:11.801344-08 2025-02-28 10:45:20.9261-08 test f L4 \N f \N \N \N {} -3445 1187 2025-02-28 11:46:40.120384-08 2025-02-28 11:16:52.048543-08 test t L4 \N f \N \N \N {} -3446 1188 2025-02-28 12:11:52.882164-08 2025-02-28 11:38:47.364032-08 test t A100 \N t \N \N \N {} -3447 1188 2025-02-28 11:53:16.182886-08 2025-02-28 11:57:38.888939-08 benchmark t A100 \N t \N \N \N {} -3448 1188 2025-02-28 11:31:39.142552-08 2025-02-28 10:34:32.539419-08 leaderboard t A100 0.003089445 t \N \N \N {} -3460 1191 2025-02-28 10:50:29.600852-08 2025-02-28 10:55:28.066601-08 test f A100 \N t \N \N \N {} -3451 1188 2025-02-28 11:25:05.48421-08 2025-02-28 11:26:09.847138-08 leaderboard f A100 0.003099992 t \N \N \N {} -3452 1189 2025-02-28 11:27:07.929922-08 2025-02-28 10:27:25.882031-08 test f A100 \N f \N \N \N {} -2956 1040 2025-02-27 07:53:09.243149-08 2025-02-27 08:42:54.201109-08 test t A100 \N t \N \N \N {} -3014 1076 2025-02-27 13:56:41.106043-08 2025-02-27 13:46:16.794281-08 benchmark f A100 \N t \N \N \N {} -1487 496 2025-02-25 06:02:20.071956-08 2025-02-25 06:20:28.361733-08 test t A100 \N f \N \N \N {} -1488 496 2025-02-25 06:37:53.255009-08 2025-02-25 07:06:16.467792-08 test f A100 \N f \N \N \N {} -3453 1189 2025-02-28 11:50:14.581562-08 2025-02-28 10:31:03.166396-08 test t A100 \N f \N \N \N {} -3463 1191 2025-02-28 10:39:12.659548-08 2025-02-28 11:48:27.810322-08 test t A100 \N t \N \N \N {} -3556 1209 2025-02-28 12:30:43.675467-08 2025-02-28 12:41:17.817806-08 test f A100 \N t \N \N \N {} -6514 2018 2025-03-14 12:18:15.299016-07 2025-03-14 11:04:40.892319-07 test f A100 \N t \N \N \N {} -6515 2018 2025-03-14 11:49:32.366043-07 2025-03-14 11:56:59.199313-07 benchmark f A100 \N t \N \N \N {} -6360 1972 2025-03-13 12:35:05.799956-07 2025-03-13 13:55:16.538902-07 benchmark f T4 \N f \N \N \N {} -6517 2019 2025-03-14 11:49:17.362182-07 2025-03-14 10:59:40.482475-07 benchmark f A100 \N t \N \N \N {} -6363 1973 2025-03-13 15:12:13.132853-07 2025-03-13 13:39:11.582645-07 test t T4 \N t \N \N \N {} -6364 1973 2025-03-13 13:54:16.988636-07 2025-03-13 14:15:30.672524-07 benchmark t T4 \N f \N \N \N {} -6521 2020 2025-03-14 11:11:59.364683-07 2025-03-14 12:39:27.185178-07 test t A100 \N t \N \N \N {} -3479 1196 2025-02-28 10:53:45.214965-08 2025-02-28 11:40:18.287407-08 benchmark f A100 \N t \N \N \N {} -3480 1196 2025-02-28 12:21:44.871556-08 2025-02-28 12:18:32.615765-08 leaderboard f A100 0.0030916253333333333 t \N \N \N {} -3481 1195 2025-02-28 10:38:20.149926-08 2025-02-28 11:37:11.075833-08 test f A100 \N t \N \N \N {} -3482 1195 2025-02-28 11:40:32.30581-08 2025-02-28 11:07:23.741189-08 benchmark f A100 \N f \N \N \N {} -3483 1195 2025-02-28 11:06:17.163408-08 2025-02-28 11:09:41.125501-08 test t A100 \N t \N \N \N {} -6196 1908 2025-03-11 17:42:13.201789-07 2025-03-11 16:36:53.748973-07 test f T4 \N f \N \N \N {} -6394 1987 2025-03-13 16:17:38.428534-07 2025-03-13 17:49:29.688605-07 test t T4 \N f \N \N \N {} -6367 1974 2025-03-13 15:18:15.442709-07 2025-03-13 14:52:02.574384-07 test t T4 \N t \N \N \N {} -6611 2047 2025-03-14 12:25:13.352133-07 2025-03-14 12:54:20.14153-07 benchmark f A100 \N t \N \N \N {} -3492 1198 2025-02-28 10:31:42.778359-08 2025-02-28 12:29:56.888072-08 benchmark f A100 \N t \N \N \N {} -3493 1198 2025-02-28 10:35:47.467941-08 2025-02-28 11:50:52.415689-08 leaderboard f A100 0.0030801673333333336 t \N \N \N {} -6198 1909 2025-03-11 17:57:18.149458-07 2025-03-11 17:27:06.275858-07 test f T4 \N f \N \N \N {} -6369 1975 2025-03-13 16:40:00.881683-07 2025-03-13 16:06:45.585849-07 test f T4 \N f \N \N \N {} -6916 2156 2025-03-16 15:11:39.340389-07 2025-03-16 16:54:21.426913-07 test f T4 \N t \N \N \N {} -3495 1198 2025-02-28 12:22:48.855653-08 2025-02-28 11:22:36.934387-08 benchmark t A100 \N t \N \N \N {} -3496 1198 2025-02-28 11:02:01.041551-08 2025-02-28 10:55:18.662378-08 leaderboard t A100 0.0030712036666666665 t \N \N \N {} -6199 1910 2025-03-11 17:51:06.602448-07 2025-03-11 17:45:19.999575-07 test t T4 \N f \N \N \N {} -6370 1975 2025-03-13 17:09:02.372261-07 2025-03-13 15:49:54.86776-07 test t T4 \N f \N \N \N {} -6528 2021 2025-03-14 11:20:09.013884-07 2025-03-14 11:51:31.5343-07 benchmark f A100 \N t \N \N \N {} -6529 2021 2025-03-14 12:17:59.33897-07 2025-03-14 11:57:07.878145-07 leaderboard f A100 0.0024379966666666663 t \N \N \N {} -6613 2047 2025-03-14 13:28:02.649127-07 2025-03-14 13:08:42.984819-07 test t A100 \N t \N \N \N {} -6677 2066 2025-03-14 14:28:21.53377-07 2025-03-14 15:18:12.092115-07 test t T4 \N f \N \N \N {} -3498 1199 2025-02-28 10:55:47.187252-08 2025-02-28 11:02:06.984812-08 benchmark f A100 \N t \N \N \N {} -3499 1199 2025-02-28 11:57:15.471579-08 2025-02-28 11:00:10.750543-08 leaderboard f A100 0.0030867046666666663 t \N \N \N {} -6200 1910 2025-03-11 18:13:57.459745-07 2025-03-11 17:39:14.073278-07 test f T4 \N f \N \N \N {} -6371 1976 2025-03-13 16:10:15.976652-07 2025-03-13 16:59:38.423812-07 test t T4 \N f \N \N \N {} -6530 2022 2025-03-14 12:42:51.830687-07 2025-03-14 12:44:20.985583-07 test f A100 \N t \N \N \N {} -6531 2022 2025-03-14 12:43:29.503929-07 2025-03-14 11:48:37.299314-07 benchmark f A100 \N t \N \N \N {} -6532 2022 2025-03-14 12:33:03.009856-07 2025-03-14 11:10:10.079119-07 leaderboard f A100 0.0031564046666666666 t \N \N \N {} -6614 2047 2025-03-14 12:41:54.04883-07 2025-03-14 12:42:12.07854-07 benchmark t A100 \N t \N \N \N {} -6615 2047 2025-03-14 12:28:03.957753-07 2025-03-14 12:16:08.077601-07 leaderboard t A100 0.0025148893333333333 t \N \N \N {} -3501 1199 2025-02-28 10:44:00.815458-08 2025-02-28 12:25:29.005606-08 benchmark t A100 \N t \N \N \N {} -1489 497 2025-02-25 06:09:45.421735-08 2025-02-25 06:06:56.895044-08 test t A100 \N f \N \N \N {} -1490 497 2025-02-25 07:30:16.061013-08 2025-02-25 05:58:05.353035-08 test f A100 \N f \N \N \N {} -1493 499 2025-02-25 07:29:41.937766-08 2025-02-25 07:35:35.73218-08 test t A100 \N f \N \N \N {} -1494 499 2025-02-25 06:55:16.544585-08 2025-02-25 06:42:04.831851-08 test f A100 \N f \N \N \N {} -1495 500 2025-02-25 06:11:38.525649-08 2025-02-25 06:06:09.247788-08 test f A100 \N f \N \N \N {} -1496 500 2025-02-25 06:28:17.374206-08 2025-02-25 07:36:57.6453-08 test t A100 \N f \N \N \N {} -1497 501 2025-02-25 06:22:45.354033-08 2025-02-25 06:04:40.972205-08 test t A100 \N f \N \N \N {} -1498 501 2025-02-25 07:26:32.437486-08 2025-02-25 06:26:05.660958-08 test f A100 \N f \N \N \N {} -2957 1040 2025-02-27 08:15:29.867269-08 2025-02-27 08:02:02.503639-08 benchmark t A100 \N t \N \N \N {} -2958 1040 2025-02-27 08:46:34.959091-08 2025-02-27 08:49:51.207345-08 leaderboard t A100 0.0031735606666666665 t \N \N \N {} -3106 1108 2025-02-28 02:29:24.19387-08 2025-02-28 02:25:44.234748-08 test t A100 \N t \N \N \N {} -2772 966 2025-02-26 19:49:15.757238-08 2025-02-26 20:10:22.740071-08 leaderboard t A100 0.014294890199999999 t \N \N \N {} -3502 1199 2025-02-28 11:15:50.983592-08 2025-02-28 11:56:22.474618-08 leaderboard t A100 0.00308841 t \N \N \N {} -1500 502 2025-02-25 05:50:41.376697-08 2025-02-25 06:42:15.925754-08 benchmark f T4 \N t \N \N \N {} -1501 502 2025-02-25 06:35:11.719711-08 2025-02-25 06:16:59.633058-08 leaderboard f T4 0.016468595 t \N \N \N {} -3015 1077 2025-02-27 14:04:51.482078-08 2025-02-27 14:44:42.488782-08 benchmark f H100 \N t \N \N \N {} -3109 1108 2025-02-28 02:01:50.848448-08 2025-02-28 01:49:08.698992-08 test f A100 \N t \N \N \N {} -3157 1116 2025-02-28 02:17:56.372163-08 2025-02-28 02:21:38.342465-08 test t A100 \N t \N \N \N {} -6201 1911 2025-03-11 18:01:48.518324-07 2025-03-11 18:32:00.643951-07 test t T4 \N f \N \N \N {} -6372 1976 2025-03-13 15:35:23.586028-07 2025-03-13 16:38:44.74512-07 test f T4 \N f \N \N \N {} -6533 2022 2025-03-14 12:55:57.39393-07 2025-03-14 12:29:44.638225-07 test t A100 \N t \N \N \N {} -6534 2022 2025-03-14 11:24:57.714064-07 2025-03-14 12:27:54.539497-07 benchmark t A100 \N t \N \N \N {} -6535 2022 2025-03-14 11:34:47.019969-07 2025-03-14 11:00:15.310359-07 leaderboard t A100 0.0039016193333333334 t \N \N \N {} -6616 2048 2025-03-14 12:53:00.225497-07 2025-03-14 13:45:31.447067-07 test t A100 \N t \N \N \N {} -6617 2048 2025-03-14 12:44:51.583069-07 2025-03-14 13:05:24.372941-07 benchmark t A100 \N t \N \N \N {} -3504 1200 2025-02-28 10:37:28.152845-08 2025-02-28 12:03:13.176444-08 benchmark f A100 \N t \N \N \N {} -3505 1200 2025-02-28 11:14:32.252149-08 2025-02-28 11:47:51.650604-08 leaderboard f A100 0.003092401 t \N \N \N {} -6202 1911 2025-03-11 17:19:23.061602-07 2025-03-11 18:30:37.372737-07 test f T4 \N f \N \N \N {} -6373 1977 2025-03-13 16:45:46.79345-07 2025-03-13 17:04:17.571101-07 test t T4 \N t \N \N \N {} -6375 1977 2025-03-13 15:59:28.148453-07 2025-03-13 15:45:43.825301-07 test f T4 \N t \N \N \N {} -6376 1977 2025-03-13 15:47:35.023166-07 2025-03-13 15:51:13.628825-07 benchmark f T4 \N f \N \N \N {} -6539 2023 2025-03-14 12:34:47.676474-07 2025-03-14 11:26:38.083854-07 test f A100 \N t \N \N \N {} -6540 2023 2025-03-14 12:11:02.732324-07 2025-03-14 12:35:39.482955-07 benchmark f A100 \N t \N \N \N {} -3510 1201 2025-02-28 12:09:30.867012-08 2025-02-28 11:14:15.654068-08 benchmark f A100 \N t \N \N \N {} -3511 1201 2025-02-28 12:22:28.128389-08 2025-02-28 10:55:21.592984-08 leaderboard f A100 0.0030901086666666666 t \N \N \N {} -6204 1912 2025-03-11 18:30:54.347773-07 2025-03-11 18:09:47.823091-07 test t T4 \N f \N \N \N {} -6205 1913 2025-03-11 17:40:25.001398-07 2025-03-11 16:51:30.586544-07 test f T4 \N f \N \N \N {} -6542 2024 2025-03-14 11:14:42.747731-07 2025-03-14 12:11:19.545486-07 benchmark f A100 \N t \N \N \N {} -6619 2048 2025-03-14 12:04:08.070953-07 2025-03-14 12:48:51.357583-07 test f A100 \N t \N \N \N {} -6650 2054 2025-03-14 13:53:44.337385-07 2025-03-14 13:48:46.599335-07 test t A100 \N t \N \N \N {} -6656 2058 2025-03-14 13:54:10.740579-07 2025-03-14 12:34:46.676855-07 test t A100 \N t \N \N \N {} -3516 1202 2025-02-28 12:33:09.097002-08 2025-02-28 10:58:45.829133-08 benchmark f A100 \N t \N \N \N {} -3517 1202 2025-02-28 11:58:25.410318-08 2025-02-28 11:30:47.992124-08 leaderboard f A100 0.003084266 t \N \N \N {} -6206 1913 2025-03-11 18:05:22.9157-07 2025-03-11 17:36:14.408717-07 test t T4 \N f \N \N \N {} -6379 1980 2025-03-13 16:36:27.760868-07 2025-03-13 16:20:53.675757-07 test t T4 \N f \N \N \N {} -6548 2025 2025-03-14 11:20:57.236222-07 2025-03-14 12:24:25.970334-07 leaderboard f A100 0.002452236 t \N \N \N {} -3524 1204 2025-02-28 12:20:39.907459-08 2025-02-28 11:12:35.546414-08 benchmark f A100 \N t \N \N \N {} -1503 502 2025-02-25 06:45:45.886953-08 2025-02-25 06:50:12.165567-08 benchmark t T4 \N t \N \N \N {} -1504 502 2025-02-25 07:36:18.732222-08 2025-02-25 06:56:09.60856-08 leaderboard t T4 0.016514245333333333 t \N \N \N {} -1505 503 2025-02-25 07:24:08.994752-08 2025-02-25 07:42:21.014151-08 test t A100 \N f \N \N \N {} -1506 503 2025-02-25 07:27:58.277977-08 2025-02-25 06:58:20.391073-08 test f A100 \N f \N \N \N {} -3112 1109 2025-02-28 02:49:13.950386-08 2025-02-28 03:20:33.833093-08 test t A100 \N t \N \N \N {} -3525 1204 2025-02-28 10:57:17.218778-08 2025-02-28 12:11:28.522376-08 leaderboard f A100 0.0030873746666666667 t \N \N \N {} -6549 2026 2025-03-14 11:43:25.469312-07 2025-03-14 11:20:32.899243-07 test t A100 \N t \N \N \N {} -6550 2026 2025-03-14 12:20:36.010926-07 2025-03-14 12:14:30.57356-07 benchmark t A100 \N t \N \N \N {} -3527 1204 2025-02-28 11:04:54.217825-08 2025-02-28 11:23:20.827974-08 benchmark t A100 \N t \N \N \N {} -3528 1204 2025-02-28 12:01:09.198033-08 2025-02-28 12:15:21.496381-08 leaderboard t A100 0.003084299 t \N \N \N {} -6382 1981 2025-03-13 16:02:38.157698-07 2025-03-13 16:43:08.39172-07 test f T4 \N f \N \N \N {} -6551 2026 2025-03-14 11:33:10.201136-07 2025-03-14 12:55:26.704593-07 leaderboard t A100 0.0025385736666666664 t \N \N \N {} -6623 2049 2025-03-14 12:51:48.541281-07 2025-03-14 13:37:41.702339-07 benchmark f A100 \N t \N \N \N {} -3530 1205 2025-02-28 11:05:21.637922-08 2025-02-28 10:59:24.063245-08 benchmark f A100 \N t \N \N \N {} -3531 1205 2025-02-28 11:51:46.708741-08 2025-02-28 10:54:12.608529-08 leaderboard f A100 0.003087388 t \N \N \N {} -6383 1982 2025-03-13 16:30:23.243012-07 2025-03-13 17:19:59.354647-07 test f T4 \N f \N \N \N {} -6552 2026 2025-03-14 12:22:01.49041-07 2025-03-14 13:00:33.669546-07 test f A100 \N t \N \N \N {} -6553 2026 2025-03-14 11:20:49.862991-07 2025-03-14 11:11:51.945168-07 benchmark f A100 \N t \N \N \N {} -3533 1205 2025-02-28 10:59:21.335705-08 2025-02-28 11:32:43.758531-08 benchmark t A100 \N t \N \N \N {} -3534 1205 2025-02-28 11:42:18.774429-08 2025-02-28 11:30:52.036125-08 leaderboard t A100 0.0030747123333333334 t \N \N \N {} -6211 1916 2025-03-11 18:51:26.328567-07 2025-03-11 17:34:02.97454-07 test f T4 \N f \N \N \N {} -6384 1982 2025-03-13 16:29:22.478385-07 2025-03-13 16:29:50.49526-07 test t T4 \N f \N \N \N {} -6554 2026 2025-03-14 11:32:39.712087-07 2025-03-14 13:01:51.543072-07 leaderboard f A100 0.0024447563333333333 t \N \N \N {} -6624 2049 2025-03-14 13:31:46.298149-07 2025-03-14 13:02:46.422741-07 leaderboard f A100 0.003148123 t \N \N \N {} -6659 2058 2025-03-14 12:36:09.483207-07 2025-03-14 12:28:00.634473-07 test f A100 \N t \N \N \N {} -6971 2169 2025-03-16 19:08:50.226241-07 2025-03-16 19:52:33.539167-07 leaderboard t T4 0.00026675565000000004 t \N \N \N {} -3536 1206 2025-02-28 12:01:21.310531-08 2025-02-28 10:59:24.032043-08 benchmark f A100 \N t \N \N \N {} -1508 504 2025-02-25 07:36:12.320858-08 2025-02-25 06:45:09.929248-08 benchmark f A100 \N t \N \N \N {} -1509 504 2025-02-25 06:24:05.118745-08 2025-02-25 07:05:31.47031-08 leaderboard f A100 0.003201205285714286 t \N \N \N {} -3016 1078 2025-02-27 15:09:58.383782-08 2025-02-27 15:09:34.428187-08 benchmark f A100 \N t \N \N \N {} -3086 1104 2025-02-28 02:37:38.797917-08 2025-02-28 02:36:42.886532-08 benchmark f A100 \N t \N \N \N {} -3537 1206 2025-02-28 11:44:02.859148-08 2025-02-28 12:17:13.765459-08 leaderboard f A100 0.0030771996666666664 t \N \N \N {} -6555 2027 2025-03-14 11:55:38.194672-07 2025-03-14 13:12:55.354266-07 benchmark f A100 \N t \N \N \N {} -6625 2049 2025-03-14 12:03:46.633551-07 2025-03-14 13:14:01.521077-07 test t A100 \N t \N \N \N {} -3539 1206 2025-02-28 12:05:54.440089-08 2025-02-28 11:48:11.736334-08 benchmark t A100 \N t \N \N \N {} -3540 1206 2025-02-28 11:50:30.166906-08 2025-02-28 11:34:39.086558-08 leaderboard t A100 0.003082872 t \N \N \N {} -6626 2049 2025-03-14 12:41:30.656497-07 2025-03-14 12:04:51.089204-07 benchmark t A100 \N t \N \N \N {} -3542 1207 2025-02-28 10:49:11.612247-08 2025-02-28 12:30:07.673052-08 benchmark t A100 \N t \N \N \N {} -3543 1207 2025-02-28 11:35:24.228637-08 2025-02-28 11:37:10.231465-08 leaderboard t A100 0.0031054823333333333 t \N \N \N {} -3546 1207 2025-02-28 11:42:05.635418-08 2025-02-28 12:05:11.256023-08 leaderboard f A100 0.0030748303333333333 t \N \N \N {} -3593 1230 2025-02-28 13:36:11.392976-08 2025-02-28 15:07:41.559929-08 benchmark f A100 \N t \N \N \N {} -3594 1231 2025-02-28 13:56:55.748686-08 2025-02-28 14:50:55.966775-08 test f H100 \N t \N \N \N {} -3595 1231 2025-02-28 14:21:28.207811-08 2025-02-28 13:22:24.302597-08 benchmark f H100 \N t \N \N \N {} -3596 1231 2025-02-28 13:34:32.178088-08 2025-02-28 14:18:02.106124-08 leaderboard f H100 0.0012008532 t \N \N \N {} -3597 1231 2025-02-28 14:33:14.730553-08 2025-02-28 13:51:57.351344-08 test t H100 \N t \N \N \N {} -3598 1231 2025-02-28 13:27:10.531166-08 2025-02-28 14:13:56.712585-08 benchmark t H100 \N t \N \N \N {} -3650 1248 2025-03-01 01:03:42.861509-08 2025-03-01 01:06:00.433924-08 leaderboard t A100 0.0031414876666666664 t \N \N \N {} -3652 1248 2025-03-01 02:29:49.148831-08 2025-03-01 01:52:31.634785-08 benchmark f A100 \N t \N \N \N {} -3658 1249 2025-03-01 01:37:20.868883-08 2025-03-01 01:41:30.386305-08 benchmark t A100 \N t \N \N \N {} -3659 1249 2025-03-01 02:08:57.983273-08 2025-03-01 02:33:34.854721-08 leaderboard t A100 0.0031066326666666665 t \N \N \N {} -3660 1250 2025-03-01 00:47:36.371435-08 2025-03-01 02:29:41.827776-08 test t A100 \N t \N \N \N {} -1511 504 2025-02-25 07:09:54.962145-08 2025-02-25 06:23:56.896069-08 benchmark t A100 \N t \N \N \N {} -1512 504 2025-02-25 06:32:05.54038-08 2025-02-25 07:46:23.871842-08 leaderboard t A100 0.0032573723333333333 t \N \N \N {} -3017 1079 2025-02-27 15:33:40.79789-08 2025-02-27 15:03:19.237768-08 benchmark f H100 \N t \N \N \N {} -3087 1104 2025-02-28 01:35:18.867193-08 2025-02-28 03:11:29.495526-08 leaderboard f A100 0.0031234573333333336 t \N \N \N {} -3095 1106 2025-02-28 02:29:26.843816-08 2025-02-28 03:12:54.70341-08 benchmark t A100 \N t \N \N \N {} -3661 1250 2025-03-01 02:05:16.755613-08 2025-03-01 01:23:08.151871-08 benchmark t A100 \N t \N \N \N {} -3662 1250 2025-03-01 01:38:13.707281-08 2025-03-01 01:47:51.283498-08 leaderboard t A100 0.0031076263333333336 t \N \N \N {} -3663 1250 2025-03-01 01:59:32.222492-08 2025-03-01 01:21:35.81262-08 test f A100 \N t \N \N \N {} -3664 1250 2025-03-01 00:54:01.041112-08 2025-03-01 01:19:58.345142-08 benchmark f A100 \N t \N \N \N {} -3892 1297 2025-03-01 03:24:01.588752-08 2025-03-01 03:18:14.954764-08 test f A100 \N t \N \N \N {} -3665 1250 2025-03-01 01:14:04.759133-08 2025-03-01 02:40:17.88009-08 leaderboard f A100 0.0031095133333333335 t \N \N \N {} -3666 1251 2025-03-01 00:49:00.146116-08 2025-03-01 01:10:29.020161-08 test t A100 \N t \N \N \N {} -3667 1251 2025-03-01 01:22:57.30705-08 2025-03-01 01:48:27.82583-08 benchmark t A100 \N t \N \N \N {} -3668 1251 2025-03-01 00:53:18.345319-08 2025-03-01 01:17:18.281427-08 leaderboard t A100 0.0031184623333333334 t \N \N \N {} -3675 1252 2025-03-01 00:51:13.234543-08 2025-03-01 01:19:47.389964-08 test t A100 \N t \N \N \N {} -3684 1254 2025-03-01 01:05:05.887393-08 2025-03-01 02:05:56.520759-08 benchmark f A100 \N t \N \N \N {} -3685 1255 2025-03-01 01:33:40.444936-08 2025-03-01 02:40:57.193542-08 benchmark f A100 \N t \N \N \N {} -3686 1256 2025-03-01 02:04:57.939143-08 2025-03-01 01:28:58.395269-08 benchmark f A100 \N t \N \N \N {} -3687 1257 2025-03-01 02:11:44.828858-08 2025-03-01 02:14:45.504175-08 benchmark f A100 \N t \N \N \N {} -3703 1263 2025-03-01 01:53:07.320683-08 2025-03-01 01:53:17.511001-08 test f A100 \N t \N \N \N {} -3688 1259 2025-03-01 02:02:24.550405-08 2025-03-01 01:42:30.841662-08 benchmark f H100 \N t \N \N \N {} -3689 1259 2025-03-01 02:51:13.657136-08 2025-03-01 01:36:15.328544-08 benchmark f A100 \N t \N \N \N {} -3695 1261 2025-03-01 02:37:45.341146-08 2025-03-01 02:45:50.374217-08 benchmark f A100 \N t \N \N \N {} -3696 1261 2025-03-01 02:43:06.297215-08 2025-03-01 02:38:52.020845-08 leaderboard f A100 0.0030843926666666664 t \N \N \N {} -1514 505 2025-02-25 06:11:31.116371-08 2025-02-25 07:53:55.571435-08 benchmark f A100 \N t \N \N \N {} -1515 505 2025-02-25 06:32:19.052576-08 2025-02-25 07:10:25.937469-08 leaderboard f A100 0.0032101916 t \N \N \N {} -3018 1080 2025-02-27 14:22:23.426466-08 2025-02-27 14:44:08.206039-08 benchmark f H100 \N f \N \N \N {} -3088 1105 2025-02-28 02:12:41.458282-08 2025-02-28 02:42:49.673656-08 test t A100 \N t \N \N \N {} -3096 1106 2025-02-28 03:02:13.970567-08 2025-02-28 02:27:13.461777-08 leaderboard t A100 0.0030811183333333335 t \N \N \N {} -3115 1109 2025-02-28 02:52:03.948052-08 2025-02-28 02:24:21.911819-08 test f A100 \N t \N \N \N {} -3705 1263 2025-03-01 02:09:53.923033-08 2025-03-01 02:59:38.816636-08 leaderboard f A100 0.0031043356666666665 t \N \N \N {} -3706 1263 2025-03-01 02:45:35.826522-08 2025-03-01 01:31:39.66555-08 test t A100 \N t \N \N \N {} -3707 1263 2025-03-01 02:23:56.866303-08 2025-03-01 02:48:25.991039-08 benchmark t A100 \N t \N \N \N {} -3708 1263 2025-03-01 02:48:53.22977-08 2025-03-01 01:38:57.844846-08 leaderboard t A100 0.0030862586666666665 t \N \N \N {} -3709 1264 2025-03-01 02:07:56.190346-08 2025-03-01 01:40:21.086921-08 test f A100 \N t \N \N \N {} -3710 1264 2025-03-01 03:21:38.843116-08 2025-03-01 01:42:57.620283-08 benchmark f A100 \N t \N \N \N {} -3711 1264 2025-03-01 01:50:57.95035-08 2025-03-01 02:14:10.580881-08 leaderboard f A100 0.003111637 t \N \N \N {} -3922 1304 2025-03-01 04:37:37.399916-08 2025-03-01 05:09:08.597247-08 test t A100 \N t \N \N \N {} -3713 1264 2025-03-01 02:34:54.769991-08 2025-03-01 02:40:30.444044-08 benchmark t A100 \N t \N \N \N {} -3714 1264 2025-03-01 03:26:13.41665-08 2025-03-01 01:31:26.999441-08 leaderboard t A100 0.003092787 t \N \N \N {} -3715 1265 2025-03-01 02:52:42.719228-08 2025-03-01 03:08:21.753062-08 test f A100 \N t \N \N \N {} -3716 1265 2025-03-01 01:34:00.130632-08 2025-03-01 01:35:33.719501-08 benchmark f A100 \N t \N \N \N {} -3717 1265 2025-03-01 03:18:58.998043-08 2025-03-01 01:56:17.857259-08 leaderboard f A100 0.00310427 t \N \N \N {} -3718 1265 2025-03-01 02:49:09.783091-08 2025-03-01 01:54:04.181859-08 test t A100 \N t \N \N \N {} -3719 1265 2025-03-01 01:35:45.872919-08 2025-03-01 01:33:30.016652-08 benchmark t A100 \N t \N \N \N {} -3720 1265 2025-03-01 03:24:17.497602-08 2025-03-01 01:42:24.245018-08 leaderboard t A100 0.0030956306666666666 t \N \N \N {} -3925 1305 2025-03-01 04:56:09.680619-08 2025-03-01 04:07:20.707758-08 test t A100 \N t \N \N \N {} -5248 1613 2025-03-06 08:34:35.577688-08 2025-03-06 08:01:22.528601-08 benchmark f H100 \N t \N \N \N {} -3726 1267 2025-03-01 02:36:57.461321-08 2025-03-01 02:35:02.941022-08 benchmark f A100 \N t \N \N \N {} -3727 1267 2025-03-01 01:38:43.401017-08 2025-03-01 02:32:47.651112-08 leaderboard f A100 0.0030772653333333336 t \N \N \N {} -3728 1268 2025-03-01 02:49:19.195623-08 2025-03-01 02:53:55.254272-08 test t A100 \N t \N \N \N {} -1517 505 2025-02-25 07:05:29.686612-08 2025-02-25 06:37:54.120803-08 benchmark t A100 \N t \N \N \N {} -1518 505 2025-02-25 07:41:30.63433-08 2025-02-25 06:35:14.486014-08 leaderboard t A100 0.0031655055 t \N \N \N {} -3019 1081 2025-02-27 14:48:37.142158-08 2025-02-27 15:06:31.119054-08 benchmark f H100 \N t \N \N \N {} -3097 1106 2025-02-28 03:19:00.154781-08 2025-02-28 02:16:02.356601-08 test f A100 \N t \N \N \N {} -3640 1244 2025-03-01 00:18:40.054434-08 2025-02-28 23:54:18.664151-08 test t A100 \N t \N \N \N {} -3740 1270 2025-03-01 02:37:04.861998-08 2025-03-01 03:28:09.585563-08 benchmark f A100 \N t \N \N \N {} -3741 1271 2025-03-01 02:44:07.033471-08 2025-03-01 03:41:23.057192-08 test f A100 \N t \N \N \N {} -3742 1271 2025-03-01 02:44:35.686645-08 2025-03-01 02:00:04.66024-08 benchmark f A100 \N t \N \N \N {} -3743 1271 2025-03-01 02:38:32.681534-08 2025-03-01 02:37:50.930025-08 leaderboard f A100 0.0030790513333333333 t \N \N \N {} -3744 1271 2025-03-01 03:21:20.979999-08 2025-03-01 02:59:00.012827-08 test t A100 \N t \N \N \N {} -3961 1321 2025-03-01 05:32:54.083384-08 2025-03-01 05:23:18.742132-08 test f A100 \N t \N \N \N {} -3745 1271 2025-03-01 03:15:49.381338-08 2025-03-01 02:53:14.040077-08 benchmark t A100 \N t \N \N \N {} -3746 1271 2025-03-01 02:06:31.769676-08 2025-03-01 01:46:51.112379-08 leaderboard t A100 0.0031014993333333333 t \N \N \N {} -3747 1272 2025-03-01 02:53:41.383476-08 2025-03-01 01:51:32.778962-08 test f A100 \N t \N \N \N {} -3748 1272 2025-03-01 02:45:32.603439-08 2025-03-01 03:10:17.870749-08 benchmark f A100 \N t \N \N \N {} -3749 1272 2025-03-01 02:08:50.626266-08 2025-03-01 02:33:18.807788-08 leaderboard f A100 0.0030842223333333333 t \N \N \N {} -3750 1272 2025-03-01 02:05:35.450725-08 2025-03-01 01:53:09.147022-08 test t A100 \N t \N \N \N {} -3751 1272 2025-03-01 02:35:41.645448-08 2025-03-01 02:31:48.954685-08 benchmark t A100 \N t \N \N \N {} -3756 1273 2025-03-01 03:40:36.388523-08 2025-03-01 02:50:54.185135-08 test t A100 \N t \N \N \N {} -3757 1273 2025-03-01 03:17:02.112839-08 2025-03-01 03:24:26.296631-08 benchmark t A100 \N t \N \N \N {} -3758 1273 2025-03-01 02:23:04.796296-08 2025-03-01 03:14:47.333595-08 leaderboard t A100 0.0031108526666666666 t \N \N \N {} -3759 1274 2025-03-01 03:30:42.935942-08 2025-03-01 02:39:37.720415-08 test f A100 \N t \N \N \N {} -3760 1274 2025-03-01 01:53:05.004026-08 2025-03-01 03:03:30.821872-08 benchmark f A100 \N t \N \N \N {} -3967 1322 2025-03-01 06:17:47.572392-08 2025-03-01 05:09:34.491694-08 test t A100 \N t \N \N \N {} -3761 1274 2025-03-01 02:33:50.346797-08 2025-03-01 02:26:36.964124-08 leaderboard f A100 0.0030782356666666667 t \N \N \N {} -3762 1274 2025-03-01 01:58:07.815646-08 2025-03-01 01:59:41.282385-08 test t A100 \N t \N \N \N {} -3763 1274 2025-03-01 03:17:32.966317-08 2025-03-01 01:57:52.417038-08 benchmark t A100 \N t \N \N \N {} -3764 1274 2025-03-01 02:01:00.831839-08 2025-03-01 02:45:44.696937-08 leaderboard t A100 0.0030974313333333335 t \N \N \N {} -3765 1275 2025-03-01 01:54:45.759236-08 2025-03-01 02:30:33.498327-08 test t A100 \N t \N \N \N {} -3766 1275 2025-03-01 01:51:25.781893-08 2025-03-01 01:50:19.627497-08 benchmark t A100 \N t \N \N \N {} -3767 1275 2025-03-01 02:55:04.68876-08 2025-03-01 03:34:18.634501-08 leaderboard t A100 0.003100908 t \N \N \N {} -3769 1275 2025-03-01 02:04:44.900517-08 2025-03-01 01:53:44.454925-08 benchmark f A100 \N t \N \N \N {} -3770 1275 2025-03-01 03:44:33.933845-08 2025-03-01 03:07:56.729501-08 leaderboard f A100 0.003100629 t \N \N \N {} -3771 1276 2025-03-01 03:45:41.230682-08 2025-03-01 03:07:19.802554-08 test f A100 \N t \N \N \N {} -3772 1276 2025-03-01 02:49:49.866518-08 2025-03-01 02:09:15.199733-08 benchmark f A100 \N t \N \N \N {} -3773 1276 2025-03-01 01:49:21.076445-08 2025-03-01 03:19:58.2239-08 leaderboard f A100 0.0030967063333333334 t \N \N \N {} -1520 506 2025-02-25 07:09:15.225758-08 2025-02-25 07:45:41.962123-08 benchmark t H100 \N t \N \N \N {} -1521 506 2025-02-25 07:34:19.048083-08 2025-02-25 07:59:41.638877-08 leaderboard t H100 0.00147709 t \N \N \N {} -3020 1082 2025-02-27 16:12:37.20378-08 2025-02-27 16:11:58.805622-08 benchmark f H100 \N t \N \N \N {} -3118 1110 2025-02-28 01:59:42.263954-08 2025-02-28 02:02:39.491672-08 test t A100 \N t \N \N \N {} -3774 1276 2025-03-01 02:29:59.337188-08 2025-03-01 03:02:44.666583-08 test t A100 \N t \N \N \N {} -5249 1613 2025-03-06 08:14:23.750809-08 2025-03-06 08:08:33.670096-08 leaderboard f H100 0.0014276973333333333 t \N \N \N {} -3781 1277 2025-03-01 03:22:11.445902-08 2025-03-01 02:55:41.41361-08 benchmark f A100 \N t \N \N \N {} -3782 1277 2025-03-01 03:39:09.245513-08 2025-03-01 03:08:32.889614-08 leaderboard f A100 0.0030874316666666666 t \N \N \N {} -3783 1278 2025-03-01 03:31:43.822781-08 2025-03-01 02:37:43.947702-08 test t A100 \N t \N \N \N {} -6787 2100 2025-03-15 05:14:40.803878-07 2025-03-15 04:12:35.487565-07 test t A100 \N t \N \N \N {} -6239 1924 2025-03-12 01:52:17.600422-07 2025-03-12 01:58:47.314377-07 test f T4 \N f \N \N \N {} -6241 1925 2025-03-12 01:51:25.617795-07 2025-03-12 00:25:45.175368-07 benchmark f T4 \N t \N \N \N {} -6242 1925 2025-03-12 00:18:20.920266-07 2025-03-12 00:37:09.552561-07 leaderboard f T4 3.623034557 t \N \N \N {} -6400 1990 2025-03-13 16:37:41.150108-07 2025-03-13 17:03:46.340373-07 test f T4 \N f \N \N \N {} -6684 2070 2025-03-14 16:12:52.115243-07 2025-03-14 15:40:16.793308-07 test t T4 \N f \N \N \N {} -3796 1280 2025-03-01 03:59:00.997836-08 2025-03-01 02:29:44.953873-08 benchmark f A100 \N t \N \N \N {} -3797 1280 2025-03-01 03:51:51.435647-08 2025-03-01 02:43:07.654041-08 leaderboard f A100 0.0030720253333333335 t \N \N \N {} -6243 1925 2025-03-12 01:00:01.616489-07 2025-03-12 00:47:35.44717-07 test t T4 \N t \N \N \N {} -6402 1991 2025-03-13 17:32:53.433867-07 2025-03-13 18:39:52.759282-07 benchmark f T4 \N t \N \N \N {} -3799 1280 2025-03-01 02:05:15.848809-08 2025-03-01 03:19:03.034027-08 benchmark t A100 \N t \N \N \N {} -3800 1280 2025-03-01 03:51:27.480737-08 2025-03-01 03:58:54.299511-08 leaderboard t A100 0.0031044756666666665 t \N \N \N {} -6246 1926 2025-03-12 01:05:44.944448-07 2025-03-12 01:36:04.272554-07 test t T4 \N t \N \N \N {} -6247 1926 2025-03-12 02:16:02.972538-07 2025-03-12 00:53:47.157218-07 benchmark t T4 \N t \N \N \N {} -6248 1926 2025-03-12 02:04:35.195202-07 2025-03-12 00:39:34.3763-07 leaderboard t T4 3.6946682543333336 t \N \N \N {} -6403 1991 2025-03-13 17:33:19.394407-07 2025-03-13 18:00:50.499601-07 leaderboard f T4 0.012992361650000001 t \N \N \N {} -6564 2035 2025-03-14 13:11:15.918389-07 2025-03-14 13:28:42.918799-07 benchmark f A100 \N t \N \N \N {} -3802 1281 2025-03-01 02:49:11.571703-08 2025-03-01 03:03:34.155129-08 benchmark f A100 \N t \N \N \N {} -3803 1281 2025-03-01 03:11:47.348485-08 2025-03-01 02:42:24.967997-08 leaderboard f A100 0.0030754246666666665 t \N \N \N {} -6249 1926 2025-03-12 02:18:23.699919-07 2025-03-12 01:38:21.807805-07 test f T4 \N t \N \N \N {} -6641 2053 2025-03-14 13:13:01.717161-07 2025-03-14 12:56:42.018712-07 test t A100 \N t \N \N \N {} -3827 1286 2025-03-01 03:21:43.151398-08 2025-03-01 04:03:44.372245-08 benchmark f A100 \N t \N \N \N {} -3828 1286 2025-03-01 02:16:32.267354-08 2025-03-01 02:27:13.261291-08 leaderboard f A100 0.0032163636666666666 t \N \N \N {} -6258 1934 2025-03-12 07:02:26.8957-07 2025-03-12 06:48:21.923788-07 benchmark f A100 \N t \N \N \N {} -6412 1994 2025-03-13 17:40:48.083573-07 2025-03-13 17:33:28.539709-07 test f T4 \N f \N \N \N {} -6573 2039 2025-03-14 11:47:52.741123-07 2025-03-14 13:13:09.01997-07 test t A100 \N t \N \N \N {} -6574 2039 2025-03-14 13:30:31.134825-07 2025-03-14 12:56:58.461643-07 benchmark t A100 \N t \N \N \N {} -6575 2039 2025-03-14 13:14:01.573829-07 2025-03-14 13:06:52.578339-07 leaderboard t A100 0.0025201923333333337 t \N \N \N {} -1523 506 2025-02-25 07:26:02.548226-08 2025-02-25 07:25:46.716931-08 benchmark f H100 \N t \N \N \N {} -1529 510 2025-02-25 06:46:53.396184-08 2025-02-25 07:55:23.310917-08 benchmark f A100 \N t \N \N \N {} -1530 511 2025-02-25 08:01:36.384337-08 2025-02-25 07:34:22.650715-08 benchmark f A100 \N t \N \N \N {} -3021 1083 2025-02-27 15:20:59.403393-08 2025-02-27 15:45:28.074308-08 test t H100 \N t \N \N \N {} -3022 1083 2025-02-27 15:38:29.43689-08 2025-02-27 15:22:38.825121-08 benchmark t H100 \N t \N \N \N {} -3023 1083 2025-02-27 16:15:56.367001-08 2025-02-27 16:18:18.215051-08 leaderboard t H100 0.0025371098599999997 t \N \N \N {} -3070 1102 2025-02-28 02:33:54.637798-08 2025-02-28 01:22:56.978086-08 test f A100 \N t \N \N \N {} -1547 512 2025-02-25 06:52:50.899054-08 2025-02-25 06:49:27.130424-08 benchmark t T4 \N t \N \N \N {} -1548 512 2025-02-25 06:32:04.498659-08 2025-02-25 07:33:16.893589-08 leaderboard t T4 0.021058454833333334 t \N \N \N {} -1549 512 2025-02-25 07:27:14.309213-08 2025-02-25 07:47:52.099855-08 test f L4 \N t \N \N \N {} -1550 512 2025-02-25 06:57:32.191599-08 2025-02-25 07:03:02.042374-08 benchmark f L4 \N t \N \N \N {} -1552 512 2025-02-25 06:58:24.333124-08 2025-02-25 06:33:18.568674-08 test t L4 \N t \N \N \N {} -1556 513 2025-02-25 06:14:45.491004-08 2025-02-25 07:16:46.376307-08 benchmark t A100 \N t \N \N \N {} -1557 513 2025-02-25 07:34:02.46493-08 2025-02-25 08:06:10.020194-08 leaderboard t A100 0.007567747333333333 t \N \N \N {} -3024 1083 2025-02-27 14:39:16.285794-08 2025-02-27 16:33:31.980742-08 test f H100 \N t \N \N \N {} -3025 1083 2025-02-27 16:31:07.646609-08 2025-02-27 14:51:20.50896-08 benchmark f H100 \N t \N \N \N {} -3026 1083 2025-02-27 14:40:27.566259-08 2025-02-27 16:02:44.891034-08 leaderboard f H100 0.00193133143 t \N \N \N {} -3071 1102 2025-02-28 01:56:21.879786-08 2025-02-28 02:17:53.253173-08 benchmark f A100 \N t \N \N \N {} -1559 513 2025-02-25 07:00:51.226524-08 2025-02-25 06:53:52.941813-08 benchmark f A100 \N t \N \N \N {} -1560 513 2025-02-25 07:40:54.286852-08 2025-02-25 07:20:37.31234-08 leaderboard f A100 0.007643499333333333 t \N \N \N {} -3027 1084 2025-02-27 14:58:06.567407-08 2025-02-27 14:38:42.214639-08 benchmark f A100 \N t \N \N \N {} -3072 1102 2025-02-28 02:51:58.714497-08 2025-02-28 02:17:05.194701-08 leaderboard f A100 0.0031002953333333335 t \N \N \N {} -3089 1105 2025-02-28 03:09:52.010667-08 2025-02-28 01:20:13.790445-08 benchmark t A100 \N t \N \N \N {} -3090 1105 2025-02-28 02:51:45.323434-08 2025-02-28 02:33:52.770692-08 leaderboard t A100 0.0030956803333333336 t \N \N \N {} -1562 514 2025-02-25 07:35:28.616691-08 2025-02-25 06:29:07.554479-08 benchmark f A100 \N t \N \N \N {} -1563 514 2025-02-25 07:01:33.808022-08 2025-02-25 06:30:57.182348-08 leaderboard f A100 0.00319349475 t \N \N \N {} -3028 1085 2025-02-27 15:14:39.199509-08 2025-02-27 15:48:14.221616-08 test f H100 \N t \N \N \N {} -3029 1085 2025-02-27 15:48:18.376186-08 2025-02-27 15:01:22.031548-08 benchmark f H100 \N t \N \N \N {} -3030 1085 2025-02-27 15:34:36.352873-08 2025-02-27 15:03:55.584707-08 leaderboard f H100 0.0018411454 t \N \N \N {} -3073 1102 2025-02-28 02:05:44.512427-08 2025-02-28 02:46:15.64099-08 test t A100 \N t \N \N \N {} -1565 514 2025-02-25 06:29:07.165341-08 2025-02-25 07:51:19.989787-08 benchmark t A100 \N t \N \N \N {} -1566 514 2025-02-25 06:29:37.966737-08 2025-02-25 06:28:42.906056-08 leaderboard t A100 0.0032033456 t \N \N \N {} -1573 516 2025-02-25 06:33:48.857537-08 2025-02-25 07:43:18.501261-08 test t A100 \N t \N \N \N {} -1574 516 2025-02-25 08:15:17.851507-08 2025-02-25 08:32:05.853279-08 benchmark t A100 \N t \N \N \N {} -1579 517 2025-02-25 06:55:30.527319-08 2025-02-25 07:43:47.516317-08 test f T4 \N t \N \N \N {} -1581 519 2025-02-25 07:22:27.620876-08 2025-02-25 07:28:11.702548-08 test t H100 \N t \N \N \N {} -1589 522 2025-02-25 08:42:40.422194-08 2025-02-25 08:02:12.607584-08 benchmark f A100 \N t \N \N \N {} -1593 526 2025-02-25 07:56:16.683261-08 2025-02-25 08:23:55.488514-08 test f A100 \N t \N \N \N {} -1594 527 2025-02-25 07:07:18.475511-08 2025-02-25 08:55:21.597339-08 benchmark f A100 \N t \N \N \N {} -1609 533 2025-02-25 08:14:37.090411-08 2025-02-25 07:43:04.159111-08 test f H100 \N t \N \N \N {} -1625 539 2025-02-25 08:06:46.284758-08 2025-02-25 09:01:35.105248-08 test t H100 \N t \N \N \N {} -6919 2156 2025-03-16 16:25:14.914949-07 2025-03-16 16:52:24.968404-07 test t T4 \N t \N \N \N {} -3830 1287 2025-03-01 02:52:44.123034-08 2025-03-01 03:04:16.432487-08 benchmark f A100 \N t \N \N \N {} -3831 1287 2025-03-01 03:18:09.205414-08 2025-03-01 02:51:41.151421-08 leaderboard f A100 0.0030734603333333337 t \N \N \N {} -6414 1995 2025-03-13 18:08:53.756809-07 2025-03-13 17:29:29.189385-07 test f T4 \N f \N \N \N {} -6579 2040 2025-03-14 12:48:20.53274-07 2025-03-14 13:09:22.939558-07 test t A100 \N t \N \N \N {} -6580 2040 2025-03-14 12:55:54.163861-07 2025-03-14 12:30:14.281856-07 benchmark t A100 \N t \N \N \N {} -6581 2040 2025-03-14 12:55:49.972531-07 2025-03-14 13:31:53.117136-07 leaderboard t A100 0.0025155676666666666 t \N \N \N {} -6664 2060 2025-03-14 13:51:59.840678-07 2025-03-14 12:26:11.365457-07 test f T4 \N f \N \N \N {} -3836 1288 2025-03-01 04:15:45.238418-08 2025-03-01 03:47:16.55003-08 benchmark f A100 \N t \N \N \N {} -3837 1288 2025-03-01 03:57:06.451424-08 2025-03-01 02:21:13.360645-08 leaderboard f A100 0.0030780183333333337 t \N \N \N {} -3840 1288 2025-03-01 02:23:11.113845-08 2025-03-01 03:57:50.503081-08 leaderboard t A100 0.0030832926666666664 t \N \N \N {} -6263 1939 2025-03-12 07:51:31.81151-07 2025-03-12 07:26:02.800143-07 benchmark f A100 \N t \N \N \N {} -6264 1939 2025-03-12 08:16:12.289193-07 2025-03-12 08:05:41.403712-07 leaderboard f A100 0.00006743858 t \N \N \N {} -6416 1996 2025-03-13 18:45:10.175348-07 2025-03-13 18:56:40.818899-07 test t T4 \N f \N \N \N {} -3842 1289 2025-03-01 04:13:05.572388-08 2025-03-01 02:54:57.38981-08 benchmark f A100 \N t \N \N \N {} -3843 1289 2025-03-01 02:45:06.634242-08 2025-03-01 03:39:15.781501-08 leaderboard f A100 0.0030902456666666665 t \N \N \N {} -6265 1939 2025-03-12 08:51:35.314623-07 2025-03-12 08:24:58.103706-07 test t A100 \N t \N \N \N {} -6266 1939 2025-03-12 07:29:33.603048-07 2025-03-12 08:44:42.162501-07 benchmark t A100 \N t \N \N \N {} -6267 1939 2025-03-12 07:09:09.345344-07 2025-03-12 07:39:08.752326-07 leaderboard t A100 0.00006758480769230768 t \N \N \N {} -3845 1289 2025-03-01 04:09:16.526703-08 2025-03-01 02:35:11.693428-08 benchmark t A100 \N t \N \N \N {} -3846 1289 2025-03-01 03:33:27.94603-08 2025-03-01 02:24:48.469479-08 leaderboard t A100 0.0030774643333333334 t \N \N \N {} -6269 1940 2025-03-12 08:08:47.902846-07 2025-03-12 07:11:33.996302-07 benchmark f H100 \N t \N \N \N {} -6270 1940 2025-03-12 07:11:39.417039-07 2025-03-12 08:15:41.878762-07 leaderboard f H100 0.00004438749 t \N \N \N {} -6418 1997 2025-03-13 18:59:25.941543-07 2025-03-13 19:24:02.914954-07 test f T4 \N f \N \N \N {} -3848 1290 2025-03-01 03:36:59.507399-08 2025-03-01 03:50:52.826622-08 benchmark f A100 \N t \N \N \N {} -3849 1290 2025-03-01 03:20:17.52254-08 2025-03-01 04:10:31.201707-08 leaderboard f A100 0.0030958916666666667 t \N \N \N {} -6587 2041 2025-03-14 12:56:09.267865-07 2025-03-14 12:56:01.714341-07 leaderboard t A100 0.0025133303333333334 t \N \N \N {} -3857 1291 2025-03-01 03:54:20.136882-08 2025-03-01 02:57:32.144781-08 benchmark f A100 \N t \N \N \N {} -1595 528 2025-02-25 07:57:34.081957-08 2025-02-25 08:46:00.770848-08 benchmark f A100 \N t \N \N \N {} -1596 529 2025-02-25 08:42:32.027812-08 2025-02-25 08:28:47.379598-08 benchmark f H100 \N t \N \N \N {} -1597 530 2025-02-25 08:13:24.437551-08 2025-02-25 08:25:42.046848-08 benchmark f H100 \N t \N \N \N {} -1598 531 2025-02-25 07:18:37.587178-08 2025-02-25 07:26:28.050318-08 test t A100 \N f \N \N \N {} -1599 531 2025-02-25 07:10:05.242952-08 2025-02-25 07:51:39.460156-08 test f A100 \N f \N \N \N {} -1600 532 2025-02-25 07:30:03.467759-08 2025-02-25 07:48:58.024839-08 test f A100 \N t \N \N \N {} -1676 550 2025-02-25 08:20:23.376176-08 2025-02-25 09:14:51.808165-08 leaderboard f H100 0.0015442018125 t \N \N \N {} -1601 532 2025-02-25 09:03:02.38871-08 2025-02-25 09:05:51.634571-08 benchmark f A100 \N t \N \N \N {} -1602 532 2025-02-25 08:03:27.143172-08 2025-02-25 08:12:43.058027-08 leaderboard f A100 0.003081489 t \N \N \N {} -1610 533 2025-02-25 08:22:53.82146-08 2025-02-25 08:19:04.111413-08 benchmark f H100 \N t \N \N \N {} -1611 533 2025-02-25 08:59:03.973218-08 2025-02-25 08:01:47.560678-08 leaderboard f H100 0.0010464732307692308 t \N \N \N {} -1612 534 2025-02-25 08:13:49.798132-08 2025-02-25 07:38:47.976643-08 benchmark f A100 \N t \N \N \N {} -1613 535 2025-02-25 07:35:30.962867-08 2025-02-25 08:49:27.767886-08 benchmark f H100 \N f \N \N \N {} -1623 539 2025-02-25 09:08:19.427448-08 2025-02-25 07:54:22.18938-08 benchmark f H100 \N t \N \N \N {} -1624 539 2025-02-25 09:12:30.335139-08 2025-02-25 09:19:28.18725-08 leaderboard f H100 0.001406501 t \N \N \N {} -1632 540 2025-02-25 08:41:53.357227-08 2025-02-25 07:46:39.377359-08 benchmark t H100 \N t \N \N \N {} -1633 540 2025-02-25 07:58:37.599753-08 2025-02-25 08:48:17.13888-08 leaderboard t H100 0.0014054696666666667 t \N \N \N {} -1635 542 2025-02-25 07:55:32.669761-08 2025-02-25 08:25:28.836254-08 test f A100 \N t \N \N \N {} -1636 542 2025-02-25 07:43:32.658836-08 2025-02-25 08:54:50.899175-08 benchmark f A100 \N t \N \N \N {} -1637 542 2025-02-25 09:16:00.130562-08 2025-02-25 08:20:39.142553-08 leaderboard f A100 0.003090287 t \N \N \N {} -1638 542 2025-02-25 07:35:49.713063-08 2025-02-25 08:24:45.44148-08 test t A100 \N t \N \N \N {} -2773 966 2025-02-26 20:06:46.265649-08 2025-02-26 20:37:56.859709-08 test f A100 \N t \N \N \N {} -6281 1943 2025-03-12 08:27:24.754837-07 2025-03-12 09:00:16.045266-07 test t L4 \N t \N \N \N {} -6282 1943 2025-03-12 09:15:13.475839-07 2025-03-12 08:12:28.990502-07 benchmark t L4 \N t \N \N \N {} -6283 1943 2025-03-12 07:28:57.790932-07 2025-03-12 07:49:47.805831-07 leaderboard t L4 0.00009934762 t \N \N \N {} -6665 2060 2025-03-14 14:00:15.697848-07 2025-03-14 13:40:02.788256-07 test t T4 \N f \N \N \N {} -3863 1292 2025-03-01 04:08:38.155179-08 2025-03-01 03:06:47.8337-08 benchmark t A100 \N t \N \N \N {} -6287 1944 2025-03-12 10:01:22.337882-07 2025-03-12 10:02:16.243525-07 test f T4 \N t \N \N \N {} -6288 1944 2025-03-12 10:35:51.44506-07 2025-03-12 10:53:31.762253-07 benchmark f T4 \N t \N \N \N {} -6289 1944 2025-03-12 11:15:49.75933-07 2025-03-12 10:30:20.890743-07 leaderboard f T4 3.5460847016666666 t \N \N \N {} -6421 1999 2025-03-14 05:54:56.594885-07 2025-03-14 05:07:15.151352-07 benchmark f A100 \N t \N \N \N {} -6422 1999 2025-03-14 04:48:17.257143-07 2025-03-14 04:38:30.49237-07 leaderboard f A100 0.002516429 t \N \N \N {} -6666 2061 2025-03-14 15:01:59.053891-07 2025-03-14 14:18:38.868311-07 test t T4 \N f \N \N \N {} -6291 1944 2025-03-12 09:57:38.536736-07 2025-03-12 10:33:16.849388-07 benchmark t T4 \N t \N \N \N {} -1639 542 2025-02-25 09:00:22.32772-08 2025-02-25 07:59:32.226862-08 benchmark t A100 \N t \N \N \N {} -1640 542 2025-02-25 09:11:58.980108-08 2025-02-25 08:19:49.179574-08 leaderboard t A100 0.0030916486666666665 t \N \N \N {} -1641 543 2025-02-25 07:54:29.239831-08 2025-02-25 09:35:38.991324-08 test t A100 \N t \N \N \N {} -1646 543 2025-02-25 08:14:16.552698-08 2025-02-25 08:35:18.8746-08 leaderboard f A100 0.0030903186666666666 t \N \N \N {} -1656 545 2025-02-25 08:18:12.922307-08 2025-02-25 09:29:21.123215-08 leaderboard t A100 0.0031894605 t \N \N \N {} -1657 546 2025-02-25 08:26:28.000804-08 2025-02-25 09:50:00.709982-08 test t A100 \N t \N \N \N {} -1658 546 2025-02-25 09:48:18.886362-08 2025-02-25 09:10:41.999421-08 benchmark t A100 \N t \N \N \N {} -1659 546 2025-02-25 07:55:49.954375-08 2025-02-25 09:05:11.341596-08 leaderboard t A100 0.00063344525 t \N \N \N {} -1660 546 2025-02-25 08:01:12.970128-08 2025-02-25 08:24:15.501382-08 test f A100 \N t \N \N \N {} -1661 546 2025-02-25 09:18:23.952092-08 2025-02-25 08:15:13.029454-08 benchmark f A100 \N t \N \N \N {} -6292 1944 2025-03-12 10:58:18.817212-07 2025-03-12 10:46:59.456595-07 leaderboard t T4 3.53122528 t \N \N \N {} -1662 546 2025-02-25 08:35:51.067428-08 2025-02-25 08:31:39.935762-08 leaderboard f A100 0.0005320072 t \N \N \N {} -1663 547 2025-02-25 09:32:55.917437-08 2025-02-25 08:44:26.311971-08 benchmark f H100 \N f \N \N \N {} -1684 557 2025-02-25 09:44:40.003266-08 2025-02-25 08:13:24.412498-08 benchmark f A100 \N f \N \N \N {} -1664 548 2025-02-25 08:06:02.985208-08 2025-02-25 08:56:36.303961-08 benchmark f H100 \N t \N \N \N {} -1666 549 2025-02-25 09:38:40.901485-08 2025-02-25 09:32:01.788835-08 benchmark t H100 \N t \N \N \N {} -1667 549 2025-02-25 08:26:34.238735-08 2025-02-25 08:25:38.843118-08 leaderboard t H100 0.000413247 t \N \N \N {} -1668 549 2025-02-25 09:52:24.604979-08 2025-02-25 08:02:29.9005-08 test f H100 \N t \N \N \N {} -1685 557 2025-02-25 09:19:53.854754-08 2025-02-25 08:46:52.219004-08 test t A100 \N t \N \N \N {} -1669 549 2025-02-25 08:53:05.362645-08 2025-02-25 08:47:47.898667-08 benchmark f H100 \N t \N \N \N {} -1670 549 2025-02-25 08:59:06.217181-08 2025-02-25 09:09:07.897493-08 leaderboard f H100 0.0004161602 t \N \N \N {} -6423 1999 2025-03-14 04:42:51.926225-07 2025-03-14 04:23:11.348669-07 test t A100 \N t \N \N \N {} -1678 553 2025-02-25 10:02:18.136454-08 2025-02-25 08:09:06.800521-08 test f H100 \N f \N \N \N {} -1682 556 2025-02-25 08:53:34.265116-08 2025-02-25 08:39:01.371239-08 benchmark f A100 \N t \N \N \N {} -1686 557 2025-02-25 08:35:22.195064-08 2025-02-25 09:08:11.056354-08 benchmark t A100 \N f \N \N \N {} -6424 1999 2025-03-14 05:09:18.158024-07 2025-03-14 04:42:27.024761-07 benchmark t A100 \N t \N \N \N {} -6667 2061 2025-03-14 15:25:37.443228-07 2025-03-14 15:06:32.582085-07 test f T4 \N f \N \N \N {} -6643 2053 2025-03-14 13:12:40.43597-07 2025-03-14 13:44:06.267317-07 leaderboard t A100 0.0024720393333333337 t \N \N \N {} -6668 2062 2025-03-14 14:17:49.224385-07 2025-03-14 14:26:32.460019-07 test t T4 \N f \N \N \N {} -3876 1294 2025-03-01 04:14:10.442655-08 2025-03-01 02:49:35.269118-08 leaderboard t A100 0.0030766496666666666 t \N \N \N {} -6294 1945 2025-03-12 10:57:16.296692-07 2025-03-12 11:33:24.443736-07 test f T4 \N f \N \N \N {} -6426 2000 2025-03-14 06:30:53.7156-07 2025-03-14 06:12:43.209048-07 test f H100 \N t \N \N \N {} -6427 2000 2025-03-14 06:57:59.87542-07 2025-03-14 06:15:38.776251-07 benchmark f H100 \N t \N \N \N {} -6428 2000 2025-03-14 07:35:27.590998-07 2025-03-14 06:32:04.615938-07 leaderboard f H100 0.0014073136666666668 t \N \N \N {} -6591 2042 2025-03-14 11:50:38.099796-07 2025-03-14 12:40:25.372521-07 test f A100 \N t \N \N \N {} -6644 2053 2025-03-14 13:49:44.146284-07 2025-03-14 12:26:28.495965-07 test f A100 \N t \N \N \N {} -6669 2062 2025-03-14 16:02:16.728023-07 2025-03-14 14:51:42.161638-07 test f T4 \N f \N \N \N {} -6436 2001 2025-03-14 06:50:57.915641-07 2025-03-14 07:00:20.008226-07 benchmark t A100 \N t \N \N \N {} -6437 2001 2025-03-14 07:44:58.201024-07 2025-03-14 07:24:40.594633-07 leaderboard t A100 0.002543368 t \N \N \N {} -3887 1296 2025-03-01 04:21:55.785115-08 2025-03-01 04:09:44.553825-08 benchmark f A100 \N t \N \N \N {} -3888 1296 2025-03-01 03:39:58.6244-08 2025-03-01 03:09:40.552669-08 leaderboard f A100 0.00309473 t \N \N \N {} -6299 1948 2025-03-12 10:32:56.767467-07 2025-03-12 11:22:11.851694-07 test t T4 \N t \N \N \N {} -6300 1948 2025-03-12 10:31:11.093009-07 2025-03-12 11:06:33.740742-07 benchmark t T4 \N f \N \N \N {} -6438 2002 2025-03-14 07:39:39.918952-07 2025-03-14 07:42:19.454653-07 test t A100 \N t \N \N \N {} -3891 1297 2025-03-01 04:29:07.565859-08 2025-03-01 03:39:00.355245-08 leaderboard t A100 0.0030872086666666665 t \N \N \N {} -6301 1949 2025-03-12 11:11:45.381428-07 2025-03-12 11:41:44.803347-07 test f T4 \N t \N \N \N {} -6302 1949 2025-03-12 11:19:11.369574-07 2025-03-12 11:47:00.702215-07 benchmark f T4 \N t \N \N \N {} -6303 1949 2025-03-12 11:08:31.94301-07 2025-03-12 11:22:31.160474-07 leaderboard f T4 5.264521755 t \N \N \N {} -3894 1297 2025-03-01 03:08:30.539157-08 2025-03-01 04:20:39.317282-08 leaderboard f A100 0.003077314 t \N \N \N {} -3895 1298 2025-03-01 03:26:56.396066-08 2025-03-01 04:02:10.736561-08 benchmark f A100 \N t \N \N \N {} -3896 1298 2025-03-01 04:30:06.792705-08 2025-03-01 05:08:52.916106-08 benchmark f H100 \N t \N \N \N {} -3897 1299 2025-03-01 03:32:41.876039-08 2025-03-01 03:28:04.603739-08 benchmark f H100 \N t \N \N \N {} -3902 1301 2025-03-01 03:45:08.15341-08 2025-03-01 03:30:46.472923-08 benchmark t A100 \N t \N \N \N {} -3903 1301 2025-03-01 05:18:11.482755-08 2025-03-01 04:15:04.738792-08 leaderboard t A100 0.0017082903333333333 t \N \N \N {} -3904 1301 2025-03-01 05:20:05.351706-08 2025-03-01 04:57:15.691666-08 test f H100 \N t \N \N \N {} -3905 1301 2025-03-01 04:11:22.209627-08 2025-03-01 03:29:30.979359-08 benchmark f H100 \N t \N \N \N {} -3909 1301 2025-03-01 05:05:05.368527-08 2025-03-01 05:21:07.368377-08 leaderboard t H100 0.0009410016 t \N \N \N {} -3911 1302 2025-03-01 04:15:21.468517-08 2025-03-01 04:16:28.01241-08 benchmark f A100 \N t \N \N \N {} -3912 1302 2025-03-01 05:15:06.388507-08 2025-03-01 05:04:17.52923-08 benchmark f H100 \N f \N \N \N {} -3914 1303 2025-03-01 03:58:49.506515-08 2025-03-01 05:03:02.215649-08 benchmark t A100 \N t \N \N \N {} -3915 1303 2025-03-01 05:02:46.480424-08 2025-03-01 05:10:24.739066-08 leaderboard t A100 0.0017033548333333334 t \N \N \N {} -3916 1303 2025-03-01 03:49:31.466637-08 2025-03-01 04:14:50.882192-08 test f A100 \N t \N \N \N {} -3917 1303 2025-03-01 04:37:55.798922-08 2025-03-01 05:09:32.972724-08 benchmark f A100 \N t \N \N \N {} -3918 1303 2025-03-01 04:24:18.36739-08 2025-03-01 04:58:17.307429-08 leaderboard f A100 0.0017008582 t \N \N \N {} -6304 1949 2025-03-12 10:47:37.368806-07 2025-03-12 12:09:25.453957-07 test t T4 \N t \N \N \N {} -1687 558 2025-02-25 09:27:10.084906-08 2025-02-25 09:48:43.848525-08 test f A100 \N f \N \N \N {} -1688 558 2025-02-25 08:29:44.125579-08 2025-02-25 08:24:09.285219-08 test t A100 \N f \N \N \N {} -1689 559 2025-02-25 09:17:25.101881-08 2025-02-25 09:24:03.785066-08 test f A100 \N t \N \N \N {} -1690 559 2025-02-25 08:36:17.748573-08 2025-02-25 09:33:30.838585-08 benchmark f A100 \N f \N \N \N {} -1691 559 2025-02-25 08:55:41.256091-08 2025-02-25 08:48:26.806972-08 test t A100 \N t \N \N \N {} -1692 559 2025-02-25 10:04:14.443146-08 2025-02-25 08:43:41.839368-08 benchmark t A100 \N f \N \N \N {} -1694 561 2025-02-25 10:05:36.595463-08 2025-02-25 10:09:40.331096-08 test f A100 \N t \N \N \N {} -3567 1211 2025-02-28 11:38:42.437274-08 2025-02-28 11:48:25.289361-08 leaderboard f H100 0.0011942561428571429 t \N \N \N {} -1696 562 2025-02-25 09:44:07.680498-08 2025-02-25 09:28:05.067515-08 test f A100 \N t \N \N \N {} -1697 562 2025-02-25 09:28:26.771379-08 2025-02-25 09:08:24.955188-08 benchmark f A100 \N t \N \N \N {} -1698 562 2025-02-25 08:47:34.033962-08 2025-02-25 08:35:38.541602-08 leaderboard f A100 0.023603107 t \N \N \N {} -1699 562 2025-02-25 09:50:30.387686-08 2025-02-25 10:01:59.310649-08 test t A100 \N t \N \N \N {} -1700 562 2025-02-25 08:34:58.738598-08 2025-02-25 08:38:55.902288-08 benchmark t A100 \N t \N \N \N {} -1701 562 2025-02-25 09:37:56.413765-08 2025-02-25 10:02:25.234169-08 leaderboard t A100 0.02387513042857143 t \N \N \N {} -6305 1949 2025-03-12 11:11:14.460487-07 2025-03-12 11:31:45.219851-07 benchmark t T4 \N t \N \N \N {} -6306 1949 2025-03-12 10:28:32.79482-07 2025-03-12 10:15:38.766761-07 leaderboard t T4 5.288479138666667 t \N \N \N {} -6443 2002 2025-03-14 07:34:37.078718-07 2025-03-14 07:14:50.86738-07 leaderboard f A100 0.0025309973333333337 t \N \N \N {} -6595 2043 2025-03-14 11:56:33.434461-07 2025-03-14 12:42:20.017678-07 test t A100 \N f \N \N \N {} -6685 2070 2025-03-14 15:59:15.307918-07 2025-03-14 15:02:13.119994-07 test f T4 \N f \N \N \N {} -3962 1321 2025-03-01 05:24:43.049186-08 2025-03-01 05:35:56.69-08 benchmark f A100 \N t \N \N \N {} -3963 1321 2025-03-01 06:08:58.229359-08 2025-03-01 05:22:31.386839-08 leaderboard f A100 0.0030768063333333337 t \N \N \N {} -6310 1951 2025-03-12 12:07:14.331628-07 2025-03-12 12:08:49.086672-07 test f T4 \N f \N \N \N {} -6453 2004 2025-03-14 06:42:37.075587-07 2025-03-14 07:33:35.882343-07 test t A100 \N t \N \N \N {} -6454 2004 2025-03-14 06:29:13.454127-07 2025-03-14 07:27:34.849727-07 benchmark t A100 \N t \N \N \N {} -6455 2004 2025-03-14 07:29:30.832561-07 2025-03-14 06:24:40.619546-07 leaderboard t A100 0.0025143146666666664 t \N \N \N {} -3965 1321 2025-03-01 05:11:28.825751-08 2025-03-01 04:35:18.89017-08 benchmark t A100 \N t \N \N \N {} -3966 1321 2025-03-01 06:16:12.443097-08 2025-03-01 04:37:00.745753-08 leaderboard t A100 0.0030776773333333336 t \N \N \N {} -6456 2005 2025-03-14 07:23:16.64199-07 2025-03-14 07:01:50.750143-07 test f A100 \N t \N \N \N {} -6457 2005 2025-03-14 06:22:29.76973-07 2025-03-14 06:09:06.773695-07 benchmark f A100 \N t \N \N \N {} -1703 565 2025-02-25 08:37:05.35978-08 2025-02-25 08:21:52.880091-08 test t A100 \N f \N \N \N {} -1713 572 2025-02-25 10:08:35.53301-08 2025-02-25 09:47:59.416619-08 benchmark f A100 \N t \N \N \N {} -1714 574 2025-02-25 08:57:21.205944-08 2025-02-25 08:58:46.708203-08 test f T4 \N f \N \N \N {} -1715 574 2025-02-25 08:22:30.782939-08 2025-02-25 09:22:33.716306-08 test t T4 \N f \N \N \N {} -1716 577 2025-02-25 09:25:17.463083-08 2025-02-25 09:32:17.15906-08 test t T4 \N f \N \N \N {} -1717 577 2025-02-25 08:30:00.171838-08 2025-02-25 09:40:29.944237-08 test f T4 \N f \N \N \N {} -1719 576 2025-02-25 10:16:42.240136-08 2025-02-25 09:11:50.029271-08 test f A100 \N t \N \N \N {} -3983 1326 2025-03-01 05:25:23.898128-08 2025-03-01 04:57:04.509129-08 test t A100 \N t \N \N \N {} -3992 1327 2025-03-01 05:54:28.752918-08 2025-03-01 05:57:31.677252-08 test t A100 \N t \N \N \N {} -3984 1326 2025-03-01 06:08:48.324057-08 2025-03-01 05:49:30.098232-08 benchmark t A100 \N t \N \N \N {} -3985 1326 2025-03-01 05:42:14.249969-08 2025-03-01 04:34:37.024307-08 leaderboard t A100 0.0030766156666666663 t \N \N \N {} -3986 1326 2025-03-01 04:55:11.733421-08 2025-03-01 05:15:01.56334-08 test f A100 \N t \N \N \N {} -3987 1326 2025-03-01 04:56:41.549319-08 2025-03-01 06:23:31.903277-08 benchmark f A100 \N t \N \N \N {} -3988 1326 2025-03-01 06:05:23.530527-08 2025-03-01 05:11:56.630856-08 leaderboard f A100 0.003088671 t \N \N \N {} -3991 1327 2025-03-01 05:48:54.261383-08 2025-03-01 06:04:01.141671-08 leaderboard f A100 0.003079475 t \N \N \N {} -3993 1327 2025-03-01 06:15:07.619494-08 2025-03-01 05:26:34.114027-08 benchmark t A100 \N t \N \N \N {} -3994 1327 2025-03-01 05:16:38.809307-08 2025-03-01 05:49:08.846786-08 leaderboard t A100 0.0031005003333333335 t \N \N \N {} -3995 1328 2025-03-01 04:33:03.029344-08 2025-03-01 04:45:00.030881-08 test f A100 \N t \N \N \N {} -3996 1328 2025-03-01 05:09:28.825437-08 2025-03-01 06:19:26.3327-08 benchmark f A100 \N t \N \N \N {} -1720 576 2025-02-25 08:55:00.326542-08 2025-02-25 10:00:29.575335-08 benchmark f A100 \N t \N \N \N {} -1721 576 2025-02-25 08:21:46.705866-08 2025-02-25 10:00:43.93478-08 leaderboard f A100 0.00010041933333333332 t \N \N \N {} -1722 576 2025-02-25 10:17:49.927789-08 2025-02-25 08:37:55.11806-08 test t A100 \N t \N \N \N {} -1723 576 2025-02-25 09:33:22.088092-08 2025-02-25 09:24:57.641368-08 benchmark t A100 \N t \N \N \N {} -1724 576 2025-02-25 08:39:15.730368-08 2025-02-25 08:39:06.879053-08 leaderboard t A100 0.00009493443478260869 t \N \N \N {} -3997 1328 2025-03-01 05:05:03.87852-08 2025-03-01 05:17:53.273059-08 leaderboard f A100 0.0030810756666666667 t \N \N \N {} -4001 1329 2025-03-01 05:01:28.939311-08 2025-03-01 05:15:07.131981-08 test t A100 \N t \N \N \N {} -4002 1329 2025-03-01 06:12:19.11185-08 2025-03-01 06:29:57.519732-08 benchmark t A100 \N t \N \N \N {} -4003 1329 2025-03-01 05:41:46.640503-08 2025-03-01 05:30:00.149015-08 leaderboard t A100 0.003084419 t \N \N \N {} -4004 1329 2025-03-01 04:37:14.969684-08 2025-03-01 05:59:19.610966-08 test f A100 \N t \N \N \N {} -4005 1329 2025-03-01 05:09:39.146484-08 2025-03-01 05:15:15.340274-08 benchmark f A100 \N t \N \N \N {} -4022 1332 2025-03-01 06:29:41.552466-08 2025-03-01 06:00:54.805207-08 test t A100 \N t \N \N \N {} -2084 702 2025-02-25 14:51:18.373776-08 2025-02-25 14:07:09.597234-08 test t T4 \N t \N \N \N {} -1728 582 2025-02-25 09:02:10.715863-08 2025-02-25 08:32:41.855511-08 test f T4 \N t \N \N \N {} -1729 582 2025-02-25 10:08:04.120077-08 2025-02-25 09:31:28.613967-08 benchmark f T4 \N f \N \N \N {} -1730 582 2025-02-25 09:12:24.120001-08 2025-02-25 08:35:23.070755-08 test t T4 \N t \N \N \N {} -1731 582 2025-02-25 08:45:58.419751-08 2025-02-25 09:50:28.503841-08 benchmark t T4 \N f \N \N \N {} -1732 581 2025-02-25 08:57:10.342617-08 2025-02-25 09:35:43.172418-08 test f T4 \N f \N \N \N {} -4023 1332 2025-03-01 05:59:07.048515-08 2025-03-01 05:19:06.578904-08 benchmark t A100 \N t \N \N \N {} -4024 1332 2025-03-01 06:30:59.985589-08 2025-03-01 05:19:38.789542-08 leaderboard t A100 0.0032329573333333334 t \N \N \N {} -4026 1333 2025-03-01 04:39:47.738838-08 2025-03-01 06:18:45.96386-08 benchmark f A100 \N t \N \N \N {} -4027 1333 2025-03-01 06:14:49.837768-08 2025-03-01 04:40:08.352975-08 leaderboard f A100 0.003070329 t \N \N \N {} -4028 1333 2025-03-01 05:10:32.553371-08 2025-03-01 04:42:50.943725-08 test t A100 \N t \N \N \N {} -4038 1335 2025-03-01 04:42:30.486974-08 2025-03-01 06:10:26.032842-08 test f A100 \N f \N \N \N {} -4039 1336 2025-03-01 06:21:06.572554-08 2025-03-01 06:39:44.644728-08 test t A100 \N t \N \N \N {} -4040 1336 2025-03-01 05:35:17.533391-08 2025-03-01 05:00:53.904121-08 benchmark t A100 \N t \N \N \N {} -4041 1336 2025-03-01 04:42:43.164597-08 2025-03-01 05:32:09.887961-08 leaderboard t A100 0.003087946 t \N \N \N {} -4043 1336 2025-03-01 05:22:32.085041-08 2025-03-01 05:29:36.766261-08 benchmark f A100 \N t \N \N \N {} -4044 1336 2025-03-01 04:46:43.10424-08 2025-03-01 05:19:36.970936-08 leaderboard f A100 0.0030770363333333336 t \N \N \N {} -4047 1337 2025-03-01 06:26:58.612027-08 2025-03-01 05:02:55.827739-08 leaderboard t A100 0.0030799923333333333 t \N \N \N {} -4048 1337 2025-03-01 04:51:49.552059-08 2025-03-01 05:32:32.010537-08 test f A100 \N t \N \N \N {} -4049 1337 2025-03-01 06:28:22.821883-08 2025-03-01 05:36:52.041982-08 benchmark f A100 \N t \N \N \N {} -4050 1337 2025-03-01 06:44:28.006933-08 2025-03-01 06:34:44.60449-08 leaderboard f A100 0.0030728746666666665 t \N \N \N {} -1733 581 2025-02-25 09:41:55.102032-08 2025-02-25 09:45:47.156909-08 test t T4 \N f \N \N \N {} -1739 583 2025-02-25 08:29:33.716534-08 2025-02-25 09:20:58.544378-08 benchmark f H100 \N t \N \N \N {} -1740 583 2025-02-25 09:22:37.035257-08 2025-02-25 08:36:17.972523-08 leaderboard f H100 0.00007956952 t \N \N \N {} -1747 587 2025-02-25 10:20:06.796411-08 2025-02-25 09:35:43.20184-08 test f T4 \N f \N \N \N {} -1750 589 2025-02-25 09:17:20.905239-08 2025-02-25 08:39:09.677788-08 test f A100 \N f \N \N \N {} -1754 593 2025-02-25 10:35:01.237155-08 2025-02-25 09:42:17.876251-08 benchmark f H100 \N f \N \N \N {} -2232 731 2025-02-25 20:55:56.356497-08 2025-02-25 21:54:01.459326-08 test f H100 \N f \N \N \N {} -1752 590 2025-02-25 09:26:44.50262-08 2025-02-25 09:02:57.304272-08 test f T4 \N t \N \N \N {} -1753 592 2025-02-25 10:16:57.848423-08 2025-02-25 09:02:46.758153-08 benchmark f H100 \N f \N \N \N {} -1755 594 2025-02-25 09:52:50.948573-08 2025-02-25 09:17:35.552434-08 test t T4 \N t \N \N \N {} -4051 1338 2025-03-01 04:53:46.779577-08 2025-03-01 05:51:05.617144-08 test t A100 \N t \N \N \N {} -4052 1338 2025-03-01 05:14:44.787303-08 2025-03-01 05:10:58.544283-08 benchmark t A100 \N t \N \N \N {} -4056 1338 2025-03-01 06:06:59.390985-08 2025-03-01 05:45:01.431744-08 leaderboard f A100 0.003124134 t \N \N \N {} -4057 1339 2025-03-01 05:45:53.840411-08 2025-03-01 04:57:01.712759-08 test f A100 \N t \N \N \N {} -4058 1339 2025-03-01 05:12:08.845443-08 2025-03-01 06:00:32.90691-08 benchmark f A100 \N t \N \N \N {} -4059 1339 2025-03-01 06:17:04.287969-08 2025-03-01 06:40:13.98261-08 leaderboard f A100 0.0030740783333333336 t \N \N \N {} -4060 1339 2025-03-01 05:30:41.282216-08 2025-03-01 06:19:05.26954-08 test t A100 \N t \N \N \N {} -4826 1490 2025-03-02 13:31:40.085228-08 2025-03-02 14:56:41.325187-08 test t A100 \N t \N \N \N {} -4069 1341 2025-03-01 06:46:40.043845-08 2025-03-01 05:08:54.482442-08 test t A100 \N t \N \N \N {} -4070 1341 2025-03-01 06:53:39.351776-08 2025-03-01 05:56:37.814647-08 benchmark t A100 \N t \N \N \N {} -4071 1341 2025-03-01 05:41:03.872073-08 2025-03-01 04:57:16.263647-08 leaderboard t A100 0.003135153 t \N \N \N {} -4072 1341 2025-03-01 06:36:12.592251-08 2025-03-01 05:33:09.049244-08 test f A100 \N t \N \N \N {} -4073 1341 2025-03-01 04:57:36.121576-08 2025-03-01 06:04:24.508424-08 benchmark f A100 \N t \N \N \N {} -4074 1341 2025-03-01 06:20:16.915193-08 2025-03-01 05:10:41.943093-08 leaderboard f A100 0.0031289636666666665 t \N \N \N {} -4079 1342 2025-03-01 06:28:17.155594-08 2025-03-01 05:18:07.512479-08 benchmark t A100 \N t \N \N \N {} -4080 1342 2025-03-01 05:17:17.167143-08 2025-03-01 06:19:18.451357-08 leaderboard t A100 0.003087757 t \N \N \N {} -1762 596 2025-02-25 09:48:18.872842-08 2025-02-25 09:08:03.874583-08 benchmark f H100 \N f \N \N \N {} -1763 596 2025-02-25 09:26:41.784533-08 2025-02-25 08:49:26.718913-08 benchmark f A100 \N f \N \N \N {} -1765 598 2025-02-25 10:31:00.584025-08 2025-02-25 09:31:07.814689-08 test f T4 \N f \N \N \N {} -4081 1343 2025-03-01 05:02:29.799238-08 2025-03-01 05:20:01.319572-08 test t A100 \N t \N \N \N {} -4082 1343 2025-03-01 06:56:56.021121-08 2025-03-01 05:37:48.321713-08 benchmark t A100 \N t \N \N \N {} -4083 1343 2025-03-01 05:51:20.985976-08 2025-03-01 06:10:38.11579-08 leaderboard t A100 0.003137047 t \N \N \N {} -4087 1344 2025-03-01 06:50:56.204942-08 2025-03-01 06:57:42.428539-08 test f A100 \N t \N \N \N {} -4088 1344 2025-03-01 05:59:31.101205-08 2025-03-01 05:10:00.245441-08 benchmark f A100 \N t \N \N \N {} -4089 1344 2025-03-01 06:31:19.255093-08 2025-03-01 05:37:19.624461-08 leaderboard f A100 0.0031286543333333334 t \N \N \N {} -4090 1344 2025-03-01 05:39:26.723837-08 2025-03-01 05:18:13.835391-08 test t A100 \N t \N \N \N {} -4091 1344 2025-03-01 05:16:22.102322-08 2025-03-01 05:57:13.821456-08 benchmark t A100 \N t \N \N \N {} -4092 1344 2025-03-01 06:48:00.846042-08 2025-03-01 06:36:07.876646-08 leaderboard t A100 0.0031320843333333334 t \N \N \N {} -4095 1346 2025-03-01 06:54:50.741501-08 2025-03-01 06:37:05.660285-08 test f A100 \N t \N \N \N {} -4096 1346 2025-03-01 06:43:56.667076-08 2025-03-01 05:03:01.487169-08 benchmark f A100 \N t \N \N \N {} -4097 1346 2025-03-01 06:20:12.926762-08 2025-03-01 05:15:59.317062-08 leaderboard f A100 0.0030682336666666664 t \N \N \N {} -4098 1346 2025-03-01 06:31:50.959915-08 2025-03-01 06:24:17.817323-08 test t A100 \N t \N \N \N {} -4099 1346 2025-03-01 05:34:30.160891-08 2025-03-01 05:36:09.807594-08 benchmark t A100 \N t \N \N \N {} -4100 1346 2025-03-01 06:40:10.997004-08 2025-03-01 06:45:08.049407-08 leaderboard t A100 0.003080214 t \N \N \N {} -4103 1348 2025-03-01 06:49:47.231825-08 2025-03-01 05:57:18.775954-08 test f A100 \N t \N \N \N {} -4104 1348 2025-03-01 06:30:42.302161-08 2025-03-01 06:25:51.821249-08 benchmark f A100 \N t \N \N \N {} -4105 1348 2025-03-01 05:19:35.876581-08 2025-03-01 05:20:21.682221-08 leaderboard f A100 0.0030814403333333336 t \N \N \N {} -4106 1348 2025-03-01 06:43:59.230765-08 2025-03-01 06:48:14.067513-08 test t A100 \N t \N \N \N {} -4107 1348 2025-03-01 06:33:05.642007-08 2025-03-01 05:34:35.401784-08 benchmark t A100 \N t \N \N \N {} -4108 1348 2025-03-01 06:33:53.834077-08 2025-03-01 06:05:52.452967-08 leaderboard t A100 0.0030692503333333335 t \N \N \N {} -4112 1349 2025-03-01 05:20:23.470116-08 2025-03-01 06:42:43.607444-08 test f A100 \N t \N \N \N {} -4113 1349 2025-03-01 06:18:41.622091-08 2025-03-01 06:59:47.322159-08 benchmark f A100 \N t \N \N \N {} -4114 1349 2025-03-01 06:55:36.386367-08 2025-03-01 06:13:36.564538-08 leaderboard f A100 0.0030793546666666666 t \N \N \N {} -4115 1350 2025-03-01 06:47:22.861306-08 2025-03-01 06:36:04.525113-08 test f A100 \N t \N \N \N {} -4116 1350 2025-03-01 05:51:42.091206-08 2025-03-01 06:44:09.234411-08 benchmark f A100 \N t \N \N \N {} -4117 1350 2025-03-01 07:00:25.183656-08 2025-03-01 06:56:10.192153-08 leaderboard f A100 0.003075028 t \N \N \N {} -4121 1351 2025-03-01 07:03:16.315538-08 2025-03-01 05:35:39.265352-08 test f A100 \N t \N \N \N {} -4122 1351 2025-03-01 06:26:56.34005-08 2025-03-01 06:15:23.856925-08 benchmark f A100 \N t \N \N \N {} -4123 1351 2025-03-01 06:27:06.070522-08 2025-03-01 05:20:19.58787-08 leaderboard f A100 0.003077125 t \N \N \N {} -4124 1351 2025-03-01 06:56:18.609617-08 2025-03-01 06:45:35.932387-08 test t A100 \N t \N \N \N {} -4125 1351 2025-03-01 05:59:31.787912-08 2025-03-01 05:12:15.522556-08 benchmark t A100 \N t \N \N \N {} -4127 1352 2025-03-01 06:15:58.415656-08 2025-03-01 05:15:59.770051-08 test t A100 \N t \N \N \N {} -4128 1352 2025-03-01 05:38:07.3835-08 2025-03-01 05:42:40.878001-08 benchmark t A100 \N t \N \N \N {} -4129 1352 2025-03-01 06:36:57.941443-08 2025-03-01 06:23:59.790542-08 leaderboard t A100 0.0030807613333333336 t \N \N \N {} -4130 1352 2025-03-01 06:43:58.284545-08 2025-03-01 05:17:08.992478-08 test f A100 \N t \N \N \N {} -4131 1352 2025-03-01 05:18:15.942457-08 2025-03-01 06:36:47.992588-08 benchmark f A100 \N t \N \N \N {} -4132 1352 2025-03-01 05:45:57.090043-08 2025-03-01 05:13:09.294944-08 leaderboard f A100 0.003084765 t \N \N \N {} -4133 1353 2025-03-01 05:45:21.57909-08 2025-03-01 05:50:08.24199-08 test t A100 \N t \N \N \N {} -4142 1354 2025-03-01 05:19:50.397498-08 2025-03-01 06:24:38.013703-08 test t A100 \N t \N \N \N {} -1767 600 2025-02-25 09:57:39.280293-08 2025-02-25 09:01:18.92218-08 benchmark f H100 \N t \N \N \N {} -1769 602 2025-02-25 09:26:39.998077-08 2025-02-25 10:45:34.040607-08 benchmark f H100 \N t \N \N \N {} -1770 603 2025-02-25 09:14:10.5262-08 2025-02-25 10:34:36.545027-08 benchmark f H100 \N t \N \N \N {} -1771 604 2025-02-25 09:18:20.318954-08 2025-02-25 09:47:55.570928-08 test t H100 \N t \N \N \N {} -4141 1354 2025-03-01 05:54:11.96859-08 2025-03-01 06:24:29.905592-08 leaderboard f A100 0.0030812046666666665 t \N \N \N {} -4145 1355 2025-03-01 05:52:29.99551-08 2025-03-01 05:50:58.136374-08 test f A100 \N t \N \N \N {} -4146 1355 2025-03-01 06:50:16.294841-08 2025-03-01 06:10:42.413759-08 benchmark f A100 \N t \N \N \N {} -4147 1355 2025-03-01 06:54:23.821235-08 2025-03-01 07:02:38.84073-08 leaderboard f A100 0.00309179 t \N \N \N {} -4148 1355 2025-03-01 05:36:48.581155-08 2025-03-01 06:55:13.189694-08 test t A100 \N t \N \N \N {} -4149 1355 2025-03-01 06:20:18.889242-08 2025-03-01 06:29:20.71451-08 benchmark t A100 \N t \N \N \N {} -4150 1355 2025-03-01 05:46:07.870361-08 2025-03-01 06:38:31.9395-08 leaderboard t A100 0.0030817543333333335 t \N \N \N {} -4151 1356 2025-03-01 07:33:56.778034-08 2025-03-01 06:51:39.921874-08 test f A100 \N f \N \N \N {} -4154 1359 2025-03-01 09:55:27.574174-08 2025-03-01 08:42:50.314131-08 benchmark f A100 \N t \N \N \N {} -4155 1359 2025-03-01 09:47:12.768835-08 2025-03-01 09:52:39.842141-08 benchmark f H100 \N t \N \N \N {} -4156 1360 2025-03-01 09:34:23.502047-08 2025-03-01 09:57:57.454449-08 benchmark f A100 \N t \N \N \N {} -4157 1360 2025-03-01 08:24:34.434752-08 2025-03-01 08:43:59.423015-08 benchmark f H100 \N t \N \N \N {} -4158 1361 2025-03-01 09:17:52.106156-08 2025-03-01 10:06:57.89908-08 test t H100 \N t \N \N \N {} -4159 1361 2025-03-01 09:16:48.1668-08 2025-03-01 08:15:25.825071-08 benchmark t H100 \N t \N \N \N {} -4160 1361 2025-03-01 09:01:03.843546-08 2025-03-01 09:36:32.754952-08 leaderboard t H100 0.0014479743333333333 t \N \N \N {} -4161 1361 2025-03-01 08:25:39.569213-08 2025-03-01 08:40:52.057697-08 test f H100 \N t \N \N \N {} -4162 1361 2025-03-01 08:47:33.253096-08 2025-03-01 09:19:00.121841-08 benchmark f H100 \N t \N \N \N {} -4163 1361 2025-03-01 09:21:19.831956-08 2025-03-01 09:37:55.855839-08 leaderboard f H100 0.001452041 t \N \N \N {} -4164 1363 2025-03-01 08:46:09.799691-08 2025-03-01 10:24:44.599745-08 benchmark f H100 \N t \N \N \N {} -4211 1390 2025-03-01 15:10:09.501721-08 2025-03-01 15:41:51.166433-08 benchmark f A100 \N t \N \N \N {} -4168 1364 2025-03-01 09:03:53.682811-08 2025-03-01 10:14:06.09417-08 leaderboard t A100 0.0031122263333333336 t \N \N \N {} -4169 1364 2025-03-01 09:49:16.863307-08 2025-03-01 10:08:01.584412-08 test f A100 \N t \N \N \N {} -1772 604 2025-02-25 10:42:51.901096-08 2025-02-25 10:19:33.430206-08 benchmark t H100 \N t \N \N \N {} -1773 604 2025-02-25 09:32:09.301008-08 2025-02-25 10:21:06.499152-08 leaderboard t H100 0.006142186 t \N \N \N {} -1774 604 2025-02-25 10:43:27.145802-08 2025-02-25 09:56:58.387594-08 test f H100 \N t \N \N \N {} -1775 604 2025-02-25 10:39:50.506911-08 2025-02-25 09:47:48.72776-08 benchmark f H100 \N t \N \N \N {} -1776 604 2025-02-25 09:10:42.599596-08 2025-02-25 09:30:41.654648-08 leaderboard f H100 0.006143060666666667 t \N \N \N {} -2414 816 2025-02-26 12:08:46.674357-08 2025-02-26 11:49:40.827855-08 benchmark f H100 \N f \N \N \N {} -1777 605 2025-02-25 10:52:46.64778-08 2025-02-25 09:29:41.278017-08 benchmark f H100 \N t \N \N \N {} -4213 1391 2025-03-01 15:19:09.143259-08 2025-03-01 14:01:35.420716-08 benchmark f H100 \N f \N \N \N {} -1778 605 2025-02-25 10:28:01.040475-08 2025-02-25 09:43:43.248513-08 benchmark f A100 \N t \N \N \N {} -1779 608 2025-02-25 10:43:12.211893-08 2025-02-25 10:07:05.017448-08 benchmark f H100 \N t \N \N \N {} -1780 609 2025-02-25 10:20:07.015553-08 2025-02-25 09:42:49.0735-08 benchmark f H100 \N t \N \N \N {} -2233 732 2025-02-25 22:08:54.161836-08 2025-02-25 22:20:50.391923-08 test f H100 \N t \N \N \N {} -1781 610 2025-02-25 10:34:24.601084-08 2025-02-25 10:19:19.681462-08 benchmark f H100 \N f \N \N \N {} -4677 1467 2025-03-02 05:46:48.283452-08 2025-03-02 06:33:48.786791-08 test f A100 \N t \N \N \N {} -1782 611 2025-02-25 10:20:28.964054-08 2025-02-25 09:52:02.934599-08 benchmark f H100 \N t \N \N \N {} -1791 616 2025-02-25 10:57:56.194437-08 2025-02-25 11:14:23.934399-08 benchmark t A100 \N t \N \N \N {} -1797 616 2025-02-25 10:18:29.893893-08 2025-02-25 10:05:32.012644-08 benchmark t T4 \N t \N \N \N {} -1802 616 2025-02-25 10:35:10.93135-08 2025-02-25 10:50:54.582852-08 test t H100 \N t \N \N \N {} -1807 616 2025-02-25 10:38:00.594029-08 2025-02-25 09:44:40.215164-08 leaderboard f L4 0.0002189721 t \N \N \N {} -1808 616 2025-02-25 10:30:31.534914-08 2025-02-25 10:05:10.928409-08 test t L4 \N t \N \N \N {} -1809 616 2025-02-25 10:42:40.135466-08 2025-02-25 10:28:37.438567-08 benchmark t L4 \N t \N \N \N {} -1813 619 2025-02-25 10:40:45.957535-08 2025-02-25 10:54:37.204319-08 benchmark f H100 \N t \N \N \N {} -1814 620 2025-02-25 10:47:50.841428-08 2025-02-25 09:37:03.436577-08 test t A100 \N t \N \N \N {} -1815 620 2025-02-25 11:04:50.249753-08 2025-02-25 09:57:25.703303-08 benchmark t A100 \N t \N \N \N {} -1820 621 2025-02-25 10:25:46.173072-08 2025-02-25 11:00:06.872432-08 test f A100 \N t \N \N \N {} -1825 621 2025-02-25 10:28:18.242991-08 2025-02-25 10:32:06.378285-08 leaderboard f H100 0.00006850659090909092 t \N \N \N {} -1826 621 2025-02-25 09:41:56.614729-08 2025-02-25 10:07:25.694167-08 test t A100 \N t \N \N \N {} -1827 621 2025-02-25 09:37:04.460998-08 2025-02-25 09:48:35.57183-08 benchmark t A100 \N t \N \N \N {} -1828 621 2025-02-25 10:08:41.300361-08 2025-02-25 11:30:23.969988-08 leaderboard t A100 0.00012062903571428571 t \N \N \N {} -1830 621 2025-02-25 09:45:11.698381-08 2025-02-25 10:09:44.153963-08 benchmark t H100 \N t \N \N \N {} -1838 621 2025-02-25 10:17:56.497108-08 2025-02-25 10:28:25.597743-08 test f L4 \N t \N \N \N {} -1841 621 2025-02-25 10:04:54.287311-08 2025-02-25 10:49:27.389838-08 test t L4 \N t \N \N \N {} -1842 621 2025-02-25 10:15:18.832896-08 2025-02-25 09:43:06.885167-08 benchmark t L4 \N t \N \N \N {} -1843 621 2025-02-25 11:14:37.289396-08 2025-02-25 09:37:11.924231-08 leaderboard t L4 0.00015086871428571428 t \N \N \N {} -1846 624 2025-02-25 10:33:21.190201-08 2025-02-25 11:11:47.766913-08 benchmark f A100 \N t \N \N \N {} -1847 625 2025-02-25 11:32:53.839235-08 2025-02-25 11:35:34.878827-08 benchmark f H100 \N t \N \N \N {} -1848 626 2025-02-25 10:29:31.639414-08 2025-02-25 09:50:52.046112-08 test f H100 \N t \N \N \N {} -1849 626 2025-02-25 10:56:50.659567-08 2025-02-25 11:09:07.456975-08 benchmark f H100 \N t \N \N \N {} -1850 626 2025-02-25 11:01:56.450365-08 2025-02-25 11:13:04.781975-08 leaderboard f H100 0.0014884195 t \N \N \N {} -3801 1281 2025-03-01 02:21:25.649213-08 2025-03-01 03:35:34.124629-08 test f A100 \N t \N \N \N {} -1852 626 2025-02-25 09:52:11.744323-08 2025-02-25 10:17:21.865184-08 benchmark t H100 \N t \N \N \N {} -1853 626 2025-02-25 11:11:09.345105-08 2025-02-25 09:56:58.318051-08 leaderboard t H100 0.0015167958125 t \N \N \N {} -1864 629 2025-02-25 10:03:17.408394-08 2025-02-25 10:31:15.377248-08 test t T4 \N f \N \N \N {} -1865 630 2025-02-25 10:33:02.54222-08 2025-02-25 09:57:27.268701-08 benchmark f A100 \N t \N \N \N {} -1866 631 2025-02-25 11:40:59.404486-08 2025-02-25 11:06:13.593002-08 benchmark f A100 \N t \N \N \N {} -1867 632 2025-02-25 10:30:21.975844-08 2025-02-25 10:10:22.159184-08 benchmark f A100 \N t \N \N \N {} -4171 1364 2025-03-01 09:02:25.076208-08 2025-03-01 09:47:26.827613-08 leaderboard f A100 0.003101713 t \N \N \N {} -4172 1365 2025-03-01 10:16:31.355759-08 2025-03-01 09:11:20.987034-08 benchmark f A100 \N t \N \N \N {} -4173 1366 2025-03-01 10:41:44.391735-08 2025-03-01 10:08:59.397316-08 test t A100 \N t \N \N \N {} -4174 1366 2025-03-01 10:41:36.980935-08 2025-03-01 10:58:56.581662-08 benchmark t A100 \N t \N \N \N {} -4686 1469 2025-03-02 07:07:48.25556-08 2025-03-02 07:20:29.156987-08 test f A100 \N t \N \N \N {} -4177 1366 2025-03-01 10:12:47.989298-08 2025-03-01 09:39:27.09578-08 benchmark f A100 \N t \N \N \N {} -1868 633 2025-02-25 11:43:07.576973-08 2025-02-25 10:22:50.962065-08 benchmark f A100 \N t \N \N \N {} -2454 860 2025-02-26 14:41:46.094612-08 2025-02-26 14:44:15.460594-08 test f A100 \N t \N \N \N {} -1869 634 2025-02-25 10:28:18.751593-08 2025-02-25 11:15:24.986518-08 benchmark f A100 \N t \N \N \N {} -1870 635 2025-02-25 11:53:39.07753-08 2025-02-25 11:27:22.45986-08 test f A100 \N t \N \N \N {} -1871 635 2025-02-25 11:53:25.443777-08 2025-02-25 10:35:30.145788-08 benchmark f A100 \N t \N \N \N {} -1874 635 2025-02-25 11:16:09.682168-08 2025-02-25 11:49:38.387512-08 benchmark t A100 \N t \N \N \N {} -1876 636 2025-02-25 11:49:31.647654-08 2025-02-25 11:20:21.285756-08 test f H100 \N t \N \N \N {} -1882 637 2025-02-25 11:02:50.678205-08 2025-02-25 11:53:07.5339-08 test f A100 \N t \N \N \N {} -1883 637 2025-02-25 12:15:08.69237-08 2025-02-25 12:05:12.143404-08 benchmark f A100 \N t \N \N \N {} -1884 637 2025-02-25 12:15:07.966893-08 2025-02-25 12:16:54.89578-08 leaderboard f A100 0.003200573 t \N \N \N {} -2419 822 2025-02-26 13:19:13.535475-08 2025-02-26 13:18:00.548275-08 test f T4 \N f \N \N \N {} -1889 639 2025-02-25 13:15:11.381768-08 2025-02-25 11:52:40.943412-08 benchmark f A100 \N t \N \N \N {} -1890 640 2025-02-25 13:03:11.780508-08 2025-02-25 11:39:48.194084-08 benchmark f A100 \N t \N \N \N {} -1892 641 2025-02-25 12:41:04.706804-08 2025-02-25 12:31:14.054076-08 benchmark t A100 \N t \N \N \N {} -1893 641 2025-02-25 12:48:58.704728-08 2025-02-25 12:33:09.050832-08 leaderboard t A100 0.003076926 t \N \N \N {} -1894 641 2025-02-25 13:10:49.145744-08 2025-02-25 11:53:53.607428-08 test f A100 \N t \N \N \N {} -1899 644 2025-02-25 12:19:53.732245-08 2025-02-25 13:40:01.838553-08 benchmark f A100 \N t \N \N \N {} -1900 646 2025-02-25 12:52:16.315942-08 2025-02-25 12:55:52.154965-08 test f T4 \N f \N \N \N {} -1901 647 2025-02-25 12:46:38.219456-08 2025-02-25 13:22:45.626265-08 benchmark f A100 \N t \N \N \N {} -1913 658 2025-02-25 13:14:34.483591-08 2025-02-25 12:48:18.641973-08 leaderboard f L4 0.017395948 t \N \N \N {} -1902 648 2025-02-25 13:22:19.470394-08 2025-02-25 13:20:10.149185-08 benchmark f A100 \N t \N \N \N {} -1903 649 2025-02-25 13:39:40.813885-08 2025-02-25 13:36:01.166239-08 benchmark f A100 \N t \N \N \N {} -1917 659 2025-02-25 12:18:43.146652-08 2025-02-25 13:44:37.824554-08 test t H100 \N t \N \N \N {} -1918 659 2025-02-25 12:38:33.511656-08 2025-02-25 12:09:10.447116-08 benchmark t H100 \N t \N \N \N {} -1919 659 2025-02-25 12:30:56.836878-08 2025-02-25 12:57:56.451178-08 leaderboard t H100 0.0014114212 t \N \N \N {} -1920 659 2025-02-25 13:27:03.426084-08 2025-02-25 14:05:30.708212-08 test f H100 \N t \N \N \N {} -1921 659 2025-02-25 13:32:15.353997-08 2025-02-25 13:54:42.055867-08 benchmark f H100 \N t \N \N \N {} -1922 659 2025-02-25 13:10:35.530509-08 2025-02-25 13:20:46.182913-08 leaderboard f H100 0.00142643575 t \N \N \N {} -3807 1283 2025-03-01 03:58:57.932103-08 2025-03-01 02:04:21.585952-08 test t A100 \N t \N \N \N {} -4178 1366 2025-03-01 09:15:06.396332-08 2025-03-01 09:50:59.628509-08 leaderboard f A100 0.0031052183333333335 t \N \N \N {} -4179 1367 2025-03-01 10:00:51.737363-08 2025-03-01 10:00:20.686025-08 benchmark f A100 \N t \N \N \N {} -4245 1401 2025-03-01 17:40:55.269982-08 2025-03-01 17:02:11.828026-08 benchmark f H100 \N t \N \N \N {} -4180 1368 2025-03-01 09:53:14.622803-08 2025-03-01 10:45:13.461477-08 benchmark f A100 \N t \N \N \N {} -4181 1369 2025-03-01 10:19:49.557836-08 2025-03-01 10:31:18.303541-08 benchmark f A100 \N t \N \N \N {} -4182 1370 2025-03-01 10:05:46.891703-08 2025-03-01 10:47:36.638039-08 benchmark f A100 \N t \N \N \N {} -4183 1371 2025-03-01 10:09:50.741844-08 2025-03-01 09:32:36.999656-08 benchmark f A100 \N t \N \N \N {} -4246 1401 2025-03-01 16:27:23.159511-08 2025-03-01 16:38:26.273265-08 benchmark f A100 \N t \N \N \N {} -4186 1374 2025-03-01 10:29:07.179112-08 2025-03-01 10:42:10.711955-08 benchmark f A100 \N f \N \N \N {} -4187 1375 2025-03-01 10:45:16.587627-08 2025-03-01 10:28:33.902324-08 benchmark f A100 \N f \N \N \N {} -4188 1376 2025-03-01 12:01:30.707689-08 2025-03-01 11:24:17.478908-08 test f A100 \N t \N \N \N {} -4189 1377 2025-03-01 12:12:57.585435-08 2025-03-01 11:53:33.240089-08 benchmark f A100 \N t \N \N \N {} -4190 1379 2025-03-01 12:16:36.769477-08 2025-03-01 13:18:08.829651-08 test f A100 \N t \N \N \N {} -4199 1388 2025-03-01 12:52:38.473441-08 2025-03-01 13:32:16.90025-08 test f A100 \N t \N \N \N {} -4200 1388 2025-03-01 13:20:31.349585-08 2025-03-01 12:39:30.194675-08 benchmark f A100 \N t \N \N \N {} -4201 1388 2025-03-01 13:30:38.204623-08 2025-03-01 12:55:51.306591-08 leaderboard f A100 0.4955258506666667 t \N \N \N {} -4805 1487 2025-03-02 12:59:51.989286-08 2025-03-02 12:35:17.947042-08 benchmark t T4 \N f \N \N \N {} -4202 1388 2025-03-01 12:42:31.113413-08 2025-03-01 13:17:21.163086-08 test t A100 \N t \N \N \N {} -4207 1389 2025-03-01 13:01:25.628609-08 2025-03-01 14:02:58.147439-08 leaderboard f H100 0.3205130943333333 t \N \N \N {} -4208 1389 2025-03-01 12:35:43.992201-08 2025-03-01 13:54:37.702884-08 test t H100 \N t \N \N \N {} -4209 1389 2025-03-01 13:46:31.426364-08 2025-03-01 14:02:32.562267-08 benchmark t H100 \N t \N \N \N {} -4210 1389 2025-03-01 12:35:26.710521-08 2025-03-01 13:30:46.516965-08 leaderboard t H100 0.31866500166666667 t \N \N \N {} -4212 1390 2025-03-01 14:35:10.227405-08 2025-03-01 15:03:07.466266-08 benchmark f H100 \N f \N \N \N {} -4214 1392 2025-03-01 15:54:46.023585-08 2025-03-01 15:50:44.87514-08 benchmark f A100 \N t \N \N \N {} -4215 1393 2025-03-01 15:05:15.318883-08 2025-03-01 15:06:05.433626-08 benchmark f A100 \N t \N \N \N {} -4216 1394 2025-03-01 16:24:31.141388-08 2025-03-01 16:30:42.940699-08 test f A100 \N t \N \N \N {} -4217 1394 2025-03-01 15:20:57.053534-08 2025-03-01 15:01:30.702462-08 benchmark f A100 \N t \N \N \N {} -2651 906 2025-02-26 14:32:29.620169-08 2025-02-26 14:03:45.689019-08 benchmark f A100 \N f \N \N \N {} -4773 1485 2025-03-02 12:07:24.955555-08 2025-03-02 12:12:01.099174-08 test t T4 \N t \N \N \N {} -4774 1485 2025-03-02 12:20:07.259834-08 2025-03-02 12:27:22.223302-08 benchmark t T4 \N t \N \N \N {} -4775 1485 2025-03-02 13:25:29.463588-08 2025-03-02 12:38:52.204635-08 leaderboard t T4 0.013066945 t \N \N \N {} -4776 1486 2025-03-02 13:33:31.030105-08 2025-03-02 12:15:35.189097-08 test t A100 \N t \N \N \N {} -4777 1486 2025-03-02 12:13:19.346928-08 2025-03-02 12:46:44.113151-08 benchmark t A100 \N f \N \N \N {} -4778 1486 2025-03-02 13:36:11.751592-08 2025-03-02 12:27:50.077759-08 test f A100 \N t \N \N \N {} -4779 1486 2025-03-02 13:14:12.733535-08 2025-03-02 13:18:37.639912-08 benchmark f A100 \N f \N \N \N {} -6738 2086 2025-03-15 03:23:36.712806-07 2025-03-15 04:23:06.297428-07 test f A100 \N t \N \N \N {} -4781 1486 2025-03-02 12:38:20.279542-08 2025-03-02 13:10:45.650515-08 benchmark t L4 \N t \N \N \N {} -1947 661 2025-02-25 13:28:58.74373-08 2025-02-25 13:47:27.943218-08 benchmark f H100 \N t \N \N \N {} -1949 663 2025-02-25 14:01:25.359523-08 2025-02-25 14:10:06.975147-08 benchmark f H100 \N t \N \N \N {} -1951 665 2025-02-25 12:52:25.628404-08 2025-02-25 13:09:46.048653-08 test f T4 \N t \N \N \N {} -1952 666 2025-02-25 13:17:01.172423-08 2025-02-25 13:04:42.018743-08 test f T4 \N t \N \N \N {} -5081 1553 2025-03-04 16:37:31.290979-08 2025-03-04 17:11:58.415963-08 leaderboard f H100 0.0014990966666666666 t \N \N \N {} -5082 1554 2025-03-04 17:14:21.826358-08 2025-03-04 18:23:09.521817-08 test f A100 \N f \N \N \N {} -5106 1565 2025-03-04 17:28:02.642565-08 2025-03-04 17:19:33.683442-08 leaderboard t A100 0.006331331333333333 t \N \N \N {} -5083 1555 2025-03-04 16:57:56.662828-08 2025-03-04 17:02:59.352083-08 test f A100 \N f \N \N \N {} -5258 1613 2025-03-06 07:52:29.561261-08 2025-03-06 07:25:55.418568-08 leaderboard t T4 0.016500428666666667 t \N \N \N {} -5120 1569 2025-03-04 23:12:31.354705-08 2025-03-04 21:49:58.854631-08 test t A100 \N t \N \N \N {} -5121 1569 2025-03-04 23:09:51.663868-08 2025-03-04 21:37:24.887769-08 benchmark t A100 \N t \N \N \N {} -5122 1569 2025-03-04 22:02:16.559653-08 2025-03-04 22:32:25.782751-08 leaderboard t A100 0.001608786 t \N \N \N {} -5123 1569 2025-03-04 22:05:47.583362-08 2025-03-04 21:33:49.87689-08 test t L4 \N t \N \N \N {} -5124 1569 2025-03-04 21:35:17.104808-08 2025-03-04 22:09:20.389921-08 benchmark t L4 \N t \N \N \N {} -5125 1569 2025-03-04 22:38:25.160967-08 2025-03-04 21:26:50.027334-08 leaderboard t L4 0.009029581666666666 t \N \N \N {} -5126 1569 2025-03-04 22:29:13.641854-08 2025-03-04 23:15:17.197089-08 test f H100 \N t \N \N \N {} -5127 1569 2025-03-04 22:01:12.379517-08 2025-03-04 23:10:54.429836-08 benchmark f H100 \N t \N \N \N {} -5128 1569 2025-03-04 23:11:31.285632-08 2025-03-04 21:42:40.185619-08 leaderboard f H100 0.0010705946666666668 t \N \N \N {} -5129 1569 2025-03-04 21:57:38.963122-08 2025-03-04 21:31:36.054672-08 test t H100 \N t \N \N \N {} -5130 1569 2025-03-04 21:27:09.873699-08 2025-03-04 22:20:40.268062-08 benchmark t H100 \N t \N \N \N {} -6778 2099 2025-03-15 03:30:57.66264-07 2025-03-15 04:43:08.964646-07 test f A100 \N t \N \N \N {} -5134 1569 2025-03-04 22:04:14.888682-08 2025-03-04 22:46:38.905995-08 leaderboard f T4 0.014848202 t \N \N \N {} -5135 1569 2025-03-04 22:03:01.49793-08 2025-03-04 22:36:42.090475-08 test t T4 \N t \N \N \N {} -5136 1569 2025-03-04 22:08:23.328887-08 2025-03-04 22:16:18.514342-08 benchmark t T4 \N t \N \N \N {} -5137 1569 2025-03-04 23:14:20.184391-08 2025-03-04 22:39:50.12814-08 leaderboard t T4 0.014794430666666665 t \N \N \N {} -5141 1570 2025-03-04 23:13:42.914895-08 2025-03-04 21:59:06.843057-08 test f A100 \N t \N \N \N {} -5142 1570 2025-03-04 21:36:21.746898-08 2025-03-04 22:16:20.82538-08 benchmark f A100 \N t \N \N \N {} -5143 1570 2025-03-04 22:33:56.277934-08 2025-03-04 22:10:31.797238-08 leaderboard f A100 0.0017140163333333332 t \N \N \N {} -5144 1570 2025-03-04 23:21:19.677221-08 2025-03-04 22:48:43.348393-08 test t A100 \N t \N \N \N {} -5145 1570 2025-03-04 23:16:49.330286-08 2025-03-04 22:14:55.550056-08 benchmark t A100 \N t \N \N \N {} -5146 1570 2025-03-04 22:23:09.667416-08 2025-03-04 21:43:24.622016-08 leaderboard t A100 0.0017186463333333333 t \N \N \N {} -5147 1570 2025-03-04 23:10:37.127588-08 2025-03-04 22:50:15.83577-08 test t H100 \N t \N \N \N {} -5148 1570 2025-03-04 22:15:05.089919-08 2025-03-04 21:59:44.284611-08 benchmark t H100 \N t \N \N \N {} -5149 1570 2025-03-04 22:40:39.695868-08 2025-03-04 22:51:12.192064-08 leaderboard t H100 0.0010933593333333333 t \N \N \N {} -5150 1570 2025-03-04 22:06:34.697175-08 2025-03-04 22:22:03.050409-08 test f H100 \N t \N \N \N {} -5151 1570 2025-03-04 23:14:32.375663-08 2025-03-04 23:16:06.022274-08 benchmark f H100 \N t \N \N \N {} -6781 2099 2025-03-15 04:17:18.910592-07 2025-03-15 04:27:25.440486-07 test t A100 \N t \N \N \N {} -5154 1571 2025-03-04 21:29:49.913428-08 2025-03-04 22:56:49.726281-08 benchmark t A100 \N t \N \N \N {} -5155 1571 2025-03-04 22:09:17.380828-08 2025-03-04 23:17:01.528387-08 leaderboard t A100 0.00149756 t \N \N \N {} -5156 1571 2025-03-04 22:42:35.691391-08 2025-03-04 22:30:28.371632-08 test f A100 \N t \N \N \N {} -1953 666 2025-02-25 13:11:27.663789-08 2025-02-25 12:56:49.679792-08 benchmark f T4 \N t \N \N \N {} -1954 666 2025-02-25 14:01:28.591074-08 2025-02-25 12:36:59.206687-08 leaderboard f T4 0.03533333425 t \N \N \N {} -1955 666 2025-02-25 13:24:34.96066-08 2025-02-25 12:23:48.496925-08 test t T4 \N t \N \N \N {} -1956 666 2025-02-25 13:11:21.334723-08 2025-02-25 13:18:01.451228-08 benchmark t T4 \N t \N \N \N {} -1957 666 2025-02-25 14:11:36.256788-08 2025-02-25 12:59:40.696196-08 leaderboard t T4 0.035418843285714284 t \N \N \N {} -1958 667 2025-02-25 13:33:38.816072-08 2025-02-25 13:27:18.187004-08 benchmark f A100 \N t \N \N \N {} -1975 679 2025-02-25 13:39:40.730805-08 2025-02-25 12:47:36.780104-08 leaderboard f T4 0.053832773 t \N \N \N {} -5170 1574 2025-03-04 22:47:12.679139-08 2025-03-04 22:51:50.789451-08 test t H100 \N f \N \N \N {} -5177 1575 2025-03-04 23:31:30.176049-08 2025-03-04 23:39:24.402655-08 benchmark t H100 \N t \N \N \N {} -5175 1575 2025-03-04 23:09:05.999145-08 2025-03-04 23:03:45.299121-08 leaderboard f H100 0.0010994093333333334 t \N \N \N {} -5176 1575 2025-03-04 21:58:21.866372-08 2025-03-04 23:01:50.540448-08 test t H100 \N t \N \N \N {} -5197 1589 2025-03-05 04:48:37.908985-08 2025-03-05 05:42:21.123456-08 test f H100 \N f \N \N \N {} -5178 1575 2025-03-04 23:49:34.473234-08 2025-03-04 22:47:13.576304-08 leaderboard t H100 0.001105298 t \N \N \N {} -5179 1576 2025-03-05 05:05:54.983075-08 2025-03-05 04:15:53.893439-08 benchmark f H100 \N t \N \N \N {} -1969 674 2025-02-25 14:18:09.782068-08 2025-02-25 12:29:36.512538-08 benchmark f A100 \N t \N \N \N {} -1971 675 2025-02-25 13:57:33.707833-08 2025-02-25 14:11:11.249992-08 benchmark f A100 \N t \N \N \N {} -1973 679 2025-02-25 13:04:53.521829-08 2025-02-25 14:18:59.793772-08 test f T4 \N t \N \N \N {} -1974 679 2025-02-25 14:13:13.965846-08 2025-02-25 13:57:26.833503-08 benchmark f T4 \N t \N \N \N {} -5180 1577 2025-03-05 03:52:35.002755-08 2025-03-05 04:21:45.713683-08 benchmark f H100 \N t \N \N \N {} -5181 1578 2025-03-05 05:17:31.803633-08 2025-03-05 05:50:42.297338-08 test t H100 \N t \N \N \N {} -5182 1578 2025-03-05 04:56:30.788431-08 2025-03-05 05:45:31.103522-08 benchmark t H100 \N t \N \N \N {} -5183 1578 2025-03-05 05:01:35.150294-08 2025-03-05 05:14:55.594706-08 leaderboard t H100 0.0014070166666666668 t \N \N \N {} -5184 1578 2025-03-05 05:28:07.773821-08 2025-03-05 05:16:37.68584-08 test f H100 \N t \N \N \N {} -5233 1611 2025-03-06 07:25:53.030783-08 2025-03-06 07:55:02.790842-08 test f T4 \N f \N \N \N {} -5190 1582 2025-03-05 04:44:16.876495-08 2025-03-05 04:31:50.278622-08 test f H100 \N f \N \N \N {} -5199 1591 2025-03-05 05:58:19.154295-08 2025-03-05 05:51:19.562708-08 test f H100 \N f \N \N \N {} -5200 1592 2025-03-05 06:51:32.869767-08 2025-03-05 07:03:41.586486-08 test f A100 \N f \N \N \N {} -5201 1593 2025-03-05 11:53:54.672796-08 2025-03-05 10:22:24.681764-08 test f T4 \N f \N \N \N {} -5202 1593 2025-03-05 11:34:29.199094-08 2025-03-05 11:32:56.415861-08 test f H100 \N f \N \N \N {} -5203 1594 2025-03-05 10:15:31.539292-08 2025-03-05 11:41:36.518701-08 test f T4 \N f \N \N \N {} -5204 1595 2025-03-05 11:50:17.097474-08 2025-03-05 10:50:25.088498-08 test f T4 \N f \N \N \N {} -5205 1596 2025-03-05 10:17:15.840178-08 2025-03-05 11:21:12.907749-08 test f T4 \N t \N \N \N {} -5206 1597 2025-03-05 10:38:59.781202-08 2025-03-05 11:53:31.710828-08 benchmark f T4 \N t \N \N \N {} -5207 1598 2025-03-05 15:43:10.18498-08 2025-03-05 15:44:26.256063-08 test f T4 \N f \N \N \N {} -5208 1599 2025-03-05 16:08:47.877222-08 2025-03-05 16:17:45.165244-08 test f T4 \N f \N \N \N {} -5215 1605 2025-03-05 22:28:57.064698-08 2025-03-05 22:38:29.672952-08 test t A100 \N t \N \N \N {} -5216 1605 2025-03-06 00:10:40.145439-08 2025-03-05 23:04:41.009576-08 benchmark t A100 \N t \N \N \N {} -5217 1605 2025-03-06 00:13:35.847747-08 2025-03-05 23:40:14.544266-08 leaderboard t A100 0.0022575486666666666 t \N \N \N {} -5244 1613 2025-03-06 07:19:13.628319-08 2025-03-06 06:35:37.599294-08 test f A100 \N t \N \N \N {} -5245 1613 2025-03-06 06:54:50.780479-08 2025-03-06 06:45:07.226442-08 benchmark f A100 \N t \N \N \N {} -5246 1613 2025-03-06 07:28:04.913981-08 2025-03-06 08:10:28.487649-08 leaderboard f A100 0.0025635133333333335 t \N \N \N {} -5276 1614 2025-03-06 07:19:59.675744-08 2025-03-06 07:43:31.134657-08 leaderboard f H100 0.00140798875 t \N \N \N {} -5277 1614 2025-03-06 08:41:38.090423-08 2025-03-06 08:37:03.818704-08 test t H100 \N t \N \N \N {} -5278 1614 2025-03-06 08:18:49.218286-08 2025-03-06 08:16:42.816157-08 benchmark t H100 \N t \N \N \N {} -5279 1614 2025-03-06 07:03:16.465007-08 2025-03-06 06:46:29.690376-08 leaderboard t H100 0.0014296095 t \N \N \N {} -5280 1615 2025-03-06 08:22:03.432718-08 2025-03-06 08:24:28.297651-08 test f A100 \N f \N \N \N {} -5281 1617 2025-03-06 12:18:02.879373-08 2025-03-06 11:00:55.592702-08 test f L4 \N f \N \N \N {} -5286 1616 2025-03-06 12:36:10.700755-08 2025-03-06 11:50:29.347443-08 test t T4 \N t \N \N \N {} -5287 1616 2025-03-06 12:25:41.61897-08 2025-03-06 11:16:54.998394-08 benchmark t T4 \N t \N \N \N {} -5312 1620 2025-03-06 12:33:59.587669-08 2025-03-06 11:40:59.729965-08 test f L4 \N t \N \N \N {} -5313 1620 2025-03-06 10:43:35.438642-08 2025-03-06 12:38:29.763377-08 benchmark f L4 \N t \N \N \N {} -5314 1620 2025-03-06 12:06:29.829934-08 2025-03-06 12:34:56.969836-08 leaderboard f L4 0.017135117666666668 t \N \N \N {} -1977 679 2025-02-25 12:51:05.905455-08 2025-02-25 12:53:16.176109-08 benchmark t T4 \N t \N \N \N {} -1978 679 2025-02-25 13:37:32.257654-08 2025-02-25 13:03:09.630302-08 leaderboard t T4 0.05386631333333334 t \N \N \N {} -3812 1283 2025-03-01 02:43:07.132486-08 2025-03-01 03:36:19.063234-08 test f A100 \N t \N \N \N {} -5584 1707 2025-03-09 09:22:46.945115-07 2025-03-09 08:37:58.159049-07 test t T4 \N t \N \N \N {} -5550 1693 2025-03-08 16:00:57.23753-08 2025-03-08 17:10:45.887607-08 test f H100 \N f \N \N \N {} -5551 1694 2025-03-08 17:58:29.601266-08 2025-03-08 17:28:23.434527-08 test t H100 \N f \N \N \N {} -5552 1694 2025-03-08 16:17:00.585648-08 2025-03-08 17:26:10.548997-08 test f H100 \N f \N \N \N {} -5553 1695 2025-03-08 17:31:35.366001-08 2025-03-08 16:43:40.087454-08 test t H100 \N f \N \N \N {} -5568 1702 2025-03-08 17:12:40.242062-08 2025-03-08 18:24:17.952735-08 test f H100 \N f \N \N \N {} -5582 1707 2025-03-09 08:41:44.387525-07 2025-03-09 09:18:33.589319-07 benchmark f T4 \N t \N \N \N {} -5557 1697 2025-03-08 18:09:07.446602-08 2025-03-08 16:49:50.71993-08 test f H100 \N f \N \N \N {} -5558 1697 2025-03-08 16:45:59.890505-08 2025-03-08 17:49:20.795913-08 test t H100 \N f \N \N \N {} -5559 1698 2025-03-08 16:44:33.058529-08 2025-03-08 16:55:51.404262-08 test t H100 \N f \N \N \N {} -5560 1698 2025-03-08 17:45:21.683334-08 2025-03-08 17:40:25.662216-08 test f H100 \N f \N \N \N {} -5561 1699 2025-03-08 17:23:07.204592-08 2025-03-08 16:29:40.8521-08 test f H100 \N f \N \N \N {} -5562 1699 2025-03-08 17:14:04.744552-08 2025-03-08 16:45:33.633138-08 test t H100 \N f \N \N \N {} -5565 1701 2025-03-08 17:19:00.706175-08 2025-03-08 16:50:25.370782-08 test t H100 \N f \N \N \N {} -5566 1701 2025-03-08 18:00:39.905743-08 2025-03-08 18:04:31.681329-08 test f H100 \N f \N \N \N {} -5583 1707 2025-03-09 10:07:02.952329-07 2025-03-09 08:33:06.895971-07 leaderboard f T4 0.016582775333333334 t \N \N \N {} -5569 1703 2025-03-08 17:21:28.286711-08 2025-03-08 17:59:25.372338-08 test f H100 \N f \N \N \N {} -5570 1703 2025-03-08 18:05:15.430458-08 2025-03-08 17:19:42.190919-08 test t H100 \N f \N \N \N {} -5571 1704 2025-03-09 09:07:05.945234-07 2025-03-09 09:40:55.738939-07 test f T4 \N f \N \N \N {} -5572 1704 2025-03-09 08:38:56.049572-07 2025-03-09 09:19:08.106745-07 test t T4 \N f \N \N \N {} -5573 1705 2025-03-09 09:08:47.834128-07 2025-03-09 08:44:55.26167-07 test t T4 \N f \N \N \N {} -6852 2125 2025-03-15 10:54:23.765567-07 2025-03-15 10:56:50.760245-07 test t A100 \N t \N \N \N {} -5574 1705 2025-03-09 08:19:09.822926-07 2025-03-09 08:17:05.627722-07 test f T4 \N f \N \N \N {} -5575 1706 2025-03-09 08:15:37.716285-07 2025-03-09 09:07:48.253727-07 test t T4 \N t \N \N \N {} -5576 1706 2025-03-09 09:03:21.820355-07 2025-03-09 09:23:13.212892-07 benchmark t T4 \N t \N \N \N {} -5577 1706 2025-03-09 09:24:37.224862-07 2025-03-09 09:06:00.619832-07 leaderboard t T4 0.017249751 t \N \N \N {} -5578 1706 2025-03-09 09:20:25.180918-07 2025-03-09 08:18:24.424228-07 test f T4 \N t \N \N \N {} -5579 1706 2025-03-09 08:21:53.936645-07 2025-03-09 08:29:28.573712-07 benchmark f T4 \N t \N \N \N {} -5580 1706 2025-03-09 08:30:06.61725-07 2025-03-09 09:22:13.972549-07 leaderboard f T4 0.017213831 t \N \N \N {} -6855 2126 2025-03-15 10:01:43.438198-07 2025-03-15 09:26:54.425537-07 test f A100 \N t \N \N \N {} -5586 1707 2025-03-09 08:33:05.304746-07 2025-03-09 08:45:18.547459-07 leaderboard t T4 0.016620549 t \N \N \N {} -2800 976 2025-02-26 21:36:15.417887-08 2025-02-26 21:39:28.329229-08 leaderboard f A100 0.0031626593333333335 t \N \N \N {} -5634 1718 2025-03-09 09:13:24.672764-07 2025-03-09 10:18:48.792588-07 test f T4 \N t \N \N \N {} -5635 1718 2025-03-09 10:16:44.041935-07 2025-03-09 09:08:48.252549-07 benchmark f T4 \N t \N \N \N {} -5641 1719 2025-03-09 10:29:41.062663-07 2025-03-09 10:42:23.185635-07 benchmark t T4 \N t \N \N \N {} -5642 1719 2025-03-09 10:03:38.725028-07 2025-03-09 10:55:41.903081-07 leaderboard t T4 0.01683973533333333 t \N \N \N {} -5643 1720 2025-03-09 09:57:20.289686-07 2025-03-09 10:34:13.152652-07 test f T4 \N t \N \N \N {} -5644 1720 2025-03-09 09:16:46.353693-07 2025-03-09 10:33:41.020996-07 benchmark f T4 \N t \N \N \N {} -5650 1721 2025-03-09 10:43:52.72403-07 2025-03-09 09:09:04.6489-07 benchmark t T4 \N t \N \N \N {} -5651 1721 2025-03-09 10:26:40.021059-07 2025-03-09 10:38:45.997727-07 leaderboard t T4 0.016620553333333333 t \N \N \N {} -5653 1722 2025-03-09 09:41:53.232041-07 2025-03-09 10:03:19.288174-07 benchmark t T4 \N t \N \N \N {} -5659 1723 2025-03-09 10:23:53.251832-07 2025-03-09 10:13:49.143494-07 benchmark f T4 \N t \N \N \N {} -2801 976 2025-02-26 21:49:50.078075-08 2025-02-26 21:02:25.183977-08 test t A100 \N t \N \N \N {} -6096 1844 2025-03-10 22:34:55.225767-07 2025-03-10 23:16:53.927941-07 leaderboard t A100 0.02345483125 t \N \N \N {} -6097 1845 2025-03-10 22:27:27.260452-07 2025-03-10 23:10:29.127667-07 test f H100 \N t \N \N \N {} -6098 1845 2025-03-10 22:33:31.470385-07 2025-03-10 23:33:08.346801-07 benchmark f H100 \N t \N \N \N {} -6099 1845 2025-03-10 23:19:50.390365-07 2025-03-10 23:55:52.423957-07 leaderboard f H100 0.007574126333333333 t \N \N \N {} -6100 1845 2025-03-10 23:23:59.756697-07 2025-03-10 22:36:01.810723-07 test t H100 \N t \N \N \N {} -6103 1846 2025-03-10 23:34:33.401447-07 2025-03-10 22:38:46.785595-07 test t L4 \N t \N \N \N {} -6104 1846 2025-03-10 22:40:13.180982-07 2025-03-10 22:03:25.587196-07 benchmark t L4 \N t \N \N \N {} -6105 1846 2025-03-10 22:07:31.471617-07 2025-03-10 22:36:23.340517-07 leaderboard t L4 0.289734965 t \N \N \N {} -6106 1846 2025-03-10 22:19:05.931425-07 2025-03-10 22:31:11.627074-07 test f L4 \N t \N \N \N {} -225 37 2025-02-23 10:59:25.499888-08 2025-02-23 11:56:22.371923-08 test f T4 \N t \N \N \N {} -332 80 2025-02-23 15:08:05.190515-08 2025-02-23 14:44:23.958644-08 test f H100 \N t \N \N \N {} -203 36 2025-02-23 11:46:05.335042-08 2025-02-23 12:11:22.163693-08 benchmark f H100 \N t \N \N \N {} -358 94 2025-02-23 18:49:28.760988-08 2025-02-23 19:12:31.385313-08 leaderboard f H100 0.012098538666666665 t \N \N \N {} -359 95 2025-02-23 17:48:36.820226-08 2025-02-23 19:17:34.067307-08 test f H100 \N t \N \N \N {} -1206 374 2025-02-24 20:28:11.986011-08 2025-02-24 19:36:30.92237-08 benchmark f A100 \N f \N \N \N {} -1207 375 2025-02-24 20:12:22.417421-08 2025-02-24 20:46:01.292845-08 benchmark f A100 \N f \N \N \N {} -1414 471 2025-02-25 03:42:52.544828-08 2025-02-25 04:20:26.74565-08 test f A100 \N t \N \N \N {} -1208 376 2025-02-24 19:48:12.808077-08 2025-02-24 20:45:57.879274-08 benchmark f A100 \N t \N \N \N {} -1209 377 2025-02-24 21:24:44.702336-08 2025-02-24 20:39:40.644905-08 benchmark f A100 \N f \N \N \N {} -1210 378 2025-02-24 21:34:59.47251-08 2025-02-24 21:22:02.439541-08 benchmark f A100 \N t \N \N \N {} -1211 379 2025-02-24 21:22:12.927474-08 2025-02-24 19:58:49.367481-08 benchmark f A100 \N t \N \N \N {} -1243 404 2025-02-24 22:06:51.766358-08 2025-02-24 22:05:43.599472-08 benchmark f A100 \N t \N \N \N {} -1216 384 2025-02-24 21:02:40.893555-08 2025-02-24 20:57:25.702865-08 benchmark f A100 \N f \N \N \N {} -1229 393 2025-02-24 20:52:22.785486-08 2025-02-24 21:45:46.13494-08 leaderboard t A100 0.0033359293333333337 t \N \N \N {} -1230 393 2025-02-24 21:16:21.658637-08 2025-02-24 20:25:01.054235-08 test f A100 \N t \N \N \N {} -1231 393 2025-02-24 22:03:36.537687-08 2025-02-24 21:08:30.465614-08 benchmark f A100 \N t \N \N \N {} -1240 401 2025-02-24 21:15:55.733324-08 2025-02-24 21:57:58.972958-08 test f A100 \N f \N \N \N {} -1241 402 2025-02-24 20:44:20.914092-08 2025-02-24 21:56:00.068563-08 test f A100 \N f \N \N \N {} -1248 409 2025-02-24 22:06:04.453015-08 2025-02-24 22:17:03.583941-08 test f A100 \N f \N \N \N {} -1417 471 2025-02-25 03:51:19.78766-08 2025-02-25 03:47:58.900665-08 test t A100 \N t \N \N \N {} -1249 410 2025-02-24 21:39:05.838922-08 2025-02-24 20:58:53.013953-08 test f A100 \N f \N \N \N {} -1250 411 2025-02-24 22:40:19.562086-08 2025-02-24 21:27:25.950857-08 test f A100 \N t \N \N \N {} -1404 469 2025-02-25 03:53:45.5055-08 2025-02-25 04:07:42.563768-08 leaderboard f A100 0.003393497 t \N \N \N {} -2922 1024 2025-02-27 04:31:40.23618-08 2025-02-27 02:56:46.161479-08 test f T4 \N t \N \N \N {} -2923 1024 2025-02-27 02:49:24.009049-08 2025-02-27 03:11:16.56453-08 benchmark f T4 \N t \N \N \N {} -2924 1024 2025-02-27 04:11:37.18876-08 2025-02-27 04:34:12.87404-08 leaderboard f T4 0.00039354559999999996 t \N \N \N {} -3121 1110 2025-02-28 03:11:43.352425-08 2025-02-28 01:42:01.408142-08 test f A100 \N t \N \N \N {} -3466 1192 2025-02-28 10:34:17.327676-08 2025-02-28 12:02:48.511506-08 test t A100 \N f \N \N \N {} -2926 1024 2025-02-27 03:07:44.576399-08 2025-02-27 04:23:59.379675-08 benchmark t T4 \N t \N \N \N {} -2927 1024 2025-02-27 03:35:18.806103-08 2025-02-27 04:01:32.334755-08 leaderboard t T4 0.000564142375 t \N \N \N {} -3124 1111 2025-02-28 03:22:39.920088-08 2025-02-28 03:21:57.983095-08 test f A100 \N t \N \N \N {} -3174 1119 2025-02-28 03:45:25.024721-08 2025-02-28 03:11:06.974641-08 leaderboard f A100 0.0031492663333333335 t \N \N \N {} -1409 470 2025-02-25 03:16:55.220474-08 2025-02-25 04:31:56.202607-08 benchmark t A100 \N t \N \N \N {} -1410 470 2025-02-25 04:11:58.013793-08 2025-02-25 03:21:32.360753-08 leaderboard t A100 0.0030943103333333334 t \N \N \N {} -2928 1025 2025-02-27 04:12:51.460546-08 2025-02-27 02:49:12.29781-08 test f L4 \N t \N \N \N {} -2929 1025 2025-02-27 03:26:34.775483-08 2025-02-27 04:12:58.357761-08 benchmark f L4 \N t \N \N \N {} -2930 1025 2025-02-27 03:19:52.147083-08 2025-02-27 03:27:21.649998-08 leaderboard f L4 0.00011601083783783784 t \N \N \N {} -3047 1093 2025-02-27 15:53:15.961217-08 2025-02-27 15:41:20.80553-08 benchmark t A100 \N t \N \N \N {} -3048 1093 2025-02-27 15:45:02.22711-08 2025-02-27 15:31:35.36741-08 leaderboard t A100 0.0031404533333333337 t \N \N \N {} -1433 475 2025-02-25 04:29:39.664763-08 2025-02-25 05:08:55.732423-08 test f A100 \N f \N \N \N {} -1436 477 2025-02-25 05:11:23.154044-08 2025-02-25 03:41:03.100879-08 test f A100 \N f \N \N \N {} -1437 478 2025-02-25 05:37:15.695274-08 2025-02-25 03:55:07.870743-08 test f A100 \N f \N \N \N {} -1441 481 2025-02-25 04:15:28.477993-08 2025-02-25 05:06:41.311478-08 test f A100 \N f \N \N \N {} -1442 481 2025-02-25 05:43:19.547838-08 2025-02-25 05:02:59.725963-08 test t A100 \N f \N \N \N {} -2941 1030 2025-02-27 04:33:34.388412-08 2025-02-27 03:16:04.017304-08 test t A100 \N t \N \N \N {} -3145 1114 2025-02-28 02:20:48.546337-08 2025-02-28 03:25:08.187009-08 test f A100 \N t \N \N \N {} -3002 1069 2025-02-27 11:57:26.315579-08 2025-02-27 12:26:49.008997-08 test f A100 \N f \N \N \N {} -3052 1094 2025-02-27 17:23:36.313978-08 2025-02-27 17:02:03.79401-08 test f A100 \N t \N \N \N {} -3004 1071 2025-02-27 12:13:32.872228-08 2025-02-27 13:48:22.621332-08 test f A100 \N f \N \N \N {} -3055 1094 2025-02-27 15:46:34.78066-08 2025-02-27 17:32:46.992293-08 test t A100 \N t \N \N \N {} -2951 1038 2025-02-27 08:08:02.949594-08 2025-02-27 07:45:52.672741-08 test f A100 \N t \N \N \N {} -1539 512 2025-02-25 07:42:24.70614-08 2025-02-25 07:55:31.991399-08 leaderboard t H100 0.0014827313333333333 t \N \N \N {} -1540 512 2025-02-25 06:33:27.810792-08 2025-02-25 07:40:47.978218-08 test f H100 \N t \N \N \N {} -1541 512 2025-02-25 07:57:36.75449-08 2025-02-25 07:43:37.029383-08 benchmark f H100 \N t \N \N \N {} -1542 512 2025-02-25 06:29:41.231954-08 2025-02-25 06:14:39.960451-08 leaderboard f H100 0.0014742606666666668 t \N \N \N {} -1543 512 2025-02-25 07:38:34.722488-08 2025-02-25 07:23:26.785114-08 test f T4 \N t \N \N \N {} -1544 512 2025-02-25 06:25:49.430545-08 2025-02-25 07:43:59.473865-08 benchmark f T4 \N t \N \N \N {} -1545 512 2025-02-25 07:44:49.156876-08 2025-02-25 07:09:53.612336-08 leaderboard f T4 0.021663530142857143 t \N \N \N {} -1546 512 2025-02-25 06:35:54.036675-08 2025-02-25 07:10:22.696453-08 test t T4 \N t \N \N \N {} -1561 514 2025-02-25 08:21:20.423985-08 2025-02-25 07:53:25.914837-08 test f A100 \N t \N \N \N {} -1564 514 2025-02-25 06:26:23.245478-08 2025-02-25 07:52:14.438492-08 test t A100 \N t \N \N \N {} -1551 512 2025-02-25 07:08:46.613751-08 2025-02-25 06:42:56.111896-08 leaderboard f L4 0.017496731666666668 t \N \N \N {} -1553 512 2025-02-25 06:58:42.387657-08 2025-02-25 08:00:31.070279-08 benchmark t L4 \N t \N \N \N {} -1554 512 2025-02-25 07:24:12.688624-08 2025-02-25 06:31:02.770404-08 leaderboard t L4 0.017451008666666667 t \N \N \N {} -3231 1130 2025-02-28 05:20:47.157439-08 2025-02-28 06:11:32.587664-08 leaderboard t A100 0.0031105466666666664 t \N \N \N {} -3091 1105 2025-02-28 02:11:54.38507-08 2025-02-28 02:14:17.819311-08 test f A100 \N t \N \N \N {} -3250 1137 2025-02-28 07:15:32.403653-08 2025-02-28 05:44:54.894859-08 test f A100 \N t \N \N \N {} -1567 515 2025-02-25 06:40:37.056239-08 2025-02-25 08:18:23.071234-08 test f A100 \N t \N \N \N {} -1568 515 2025-02-25 07:51:44.723769-08 2025-02-25 06:52:35.487372-08 benchmark f A100 \N t \N \N \N {} -1569 515 2025-02-25 08:12:57.053326-08 2025-02-25 07:23:24.825804-08 leaderboard f A100 0.00322647325 t \N \N \N {} -1570 515 2025-02-25 07:59:41.537278-08 2025-02-25 08:18:25.884866-08 test t A100 \N t \N \N \N {} -1571 515 2025-02-25 07:33:12.913858-08 2025-02-25 07:10:10.306586-08 benchmark t A100 \N t \N \N \N {} -1572 515 2025-02-25 07:41:19.245397-08 2025-02-25 07:29:16.962881-08 leaderboard t A100 0.003220757 t \N \N \N {} -1575 516 2025-02-25 07:41:18.473174-08 2025-02-25 07:29:29.868748-08 leaderboard t A100 0.0032304103333333336 t \N \N \N {} -1738 583 2025-02-25 09:31:47.936241-08 2025-02-25 09:08:32.636598-08 test f H100 \N t \N \N \N {} -1748 587 2025-02-25 10:24:59.666061-08 2025-02-25 09:27:46.691352-08 test t T4 \N f \N \N \N {} -1749 588 2025-02-25 08:59:07.160327-08 2025-02-25 08:38:22.92218-08 benchmark f H100 \N f \N \N \N {} -4803 1487 2025-03-02 12:55:09.203143-08 2025-03-02 13:27:27.333426-08 benchmark t A100 \N f \N \N \N {} -1756 594 2025-02-25 09:11:38.300189-08 2025-02-25 10:28:52.150779-08 benchmark t T4 \N t \N \N \N {} -1757 594 2025-02-25 08:46:53.338218-08 2025-02-25 09:57:23.906953-08 leaderboard t T4 0.017254103 t \N \N \N {} -1758 594 2025-02-25 08:57:48.338091-08 2025-02-25 09:10:25.874419-08 test f T4 \N t \N \N \N {} -1759 594 2025-02-25 09:38:33.456671-08 2025-02-25 09:43:17.759144-08 benchmark f T4 \N t \N \N \N {} -1760 594 2025-02-25 09:40:53.716064-08 2025-02-25 10:08:25.184778-08 leaderboard f T4 0.017332067125 t \N \N \N {} -4804 1487 2025-03-02 12:53:07.905973-08 2025-03-02 13:20:31.048316-08 test t T4 \N t \N \N \N {} -1783 612 2025-02-25 10:30:16.608473-08 2025-02-25 10:08:59.631399-08 benchmark f A100 \N t \N \N \N {} -2280 767 2025-02-26 03:31:52.024635-08 2025-02-26 03:22:12.199287-08 leaderboard f A100 0.0008225438000000001 t \N \N \N {} -2281 767 2025-02-26 03:40:30.007645-08 2025-02-26 04:29:52.334337-08 test t A100 \N t \N \N \N {} -2300 774 2025-02-26 04:14:40.857031-08 2025-02-26 04:33:29.483727-08 test f A100 \N t \N \N \N {} -3588 1229 2025-02-28 14:32:08.489693-08 2025-02-28 14:05:23.563906-08 benchmark f H100 \N t \N \N \N {} -2282 767 2025-02-26 03:24:59.166944-08 2025-02-26 04:45:17.998433-08 benchmark t A100 \N t \N \N \N {} -2284 768 2025-02-26 04:18:53.859554-08 2025-02-26 03:24:39.46805-08 benchmark f A100 \N t \N \N \N {} -3283 1147 2025-02-28 06:11:40.284397-08 2025-02-28 07:19:05.74238-08 benchmark f H100 \N t \N \N \N {} -2483 870 2025-02-26 13:16:44.041088-08 2025-02-26 13:47:23.050449-08 benchmark f A100 \N t \N \N \N {} -2484 870 2025-02-26 13:10:26.731301-08 2025-02-26 14:42:14.746386-08 leaderboard f A100 0.0035295413333333333 t \N \N \N {} -2485 870 2025-02-26 13:33:50.524318-08 2025-02-26 14:10:40.249698-08 test f T4 \N t \N \N \N {} -2486 870 2025-02-26 13:05:54.760424-08 2025-02-26 13:38:28.475426-08 benchmark f T4 \N t \N \N \N {} -2499 869 2025-02-26 13:54:53.311947-08 2025-02-26 13:58:02.187272-08 leaderboard t H100 0.002322158 t \N \N \N {} -3392 1168 2025-02-28 09:12:51.277351-08 2025-02-28 09:23:36.312793-08 test f L4 \N f \N \N \N {} -2510 872 2025-02-26 13:10:41.998428-08 2025-02-26 13:52:20.643803-08 test f H100 \N t \N \N \N {} -2505 872 2025-02-26 14:18:21.923745-08 2025-02-26 14:06:00.875303-08 benchmark t H100 \N t \N \N \N {} -2506 872 2025-02-26 13:16:25.959445-08 2025-02-26 14:06:50.761927-08 leaderboard t H100 0.0014855651428571429 t \N \N \N {} -2507 872 2025-02-26 14:54:25.186275-08 2025-02-26 13:13:47.344332-08 test f A100 \N t \N \N \N {} -2593 884 2025-02-26 14:14:00.181524-08 2025-02-26 13:46:27.570181-08 benchmark f L4 \N t \N \N \N {} -2594 884 2025-02-26 13:32:06.001069-08 2025-02-26 15:05:08.151616-08 leaderboard f L4 0.017391052333333334 t \N \N \N {} -2595 884 2025-02-26 15:04:28.988097-08 2025-02-26 15:18:03.931934-08 test t L4 \N t \N \N \N {} -3420 1179 2025-02-28 10:40:09.83029-08 2025-02-28 10:08:07.480948-08 test f L4 \N f \N \N \N {} -2596 884 2025-02-26 14:56:24.039776-08 2025-02-26 14:30:30.751724-08 benchmark t L4 \N t \N \N \N {} -2597 884 2025-02-26 14:35:42.960818-08 2025-02-26 15:14:14.400532-08 leaderboard t L4 0.017414738333333332 t \N \N \N {} -4203 1388 2025-03-01 13:04:56.163242-08 2025-03-01 14:12:53.003168-08 benchmark t A100 \N t \N \N \N {} -4136 1353 2025-03-01 05:57:54.388305-08 2025-03-01 05:50:17.87171-08 test f A100 \N t \N \N \N {} -4137 1353 2025-03-01 05:16:52.584167-08 2025-03-01 06:12:22.385227-08 benchmark f A100 \N t \N \N \N {} -4138 1353 2025-03-01 05:33:09.219491-08 2025-03-01 06:16:54.189813-08 leaderboard f A100 0.003070895 t \N \N \N {} -4139 1354 2025-03-01 06:20:09.60044-08 2025-03-01 05:18:22.96192-08 test f A100 \N t \N \N \N {} -4140 1354 2025-03-01 05:58:29.731003-08 2025-03-01 05:41:56.193045-08 benchmark f A100 \N t \N \N \N {} -4218 1394 2025-03-01 15:09:41.391316-08 2025-03-01 15:57:05.307002-08 leaderboard f A100 0.003101874 t \N \N \N {} -4695 1470 2025-03-02 07:38:44.249494-08 2025-03-02 07:11:45.382141-08 test f A100 \N t \N \N \N {} -4219 1394 2025-03-01 14:45:22.969713-08 2025-03-01 14:48:00.593278-08 test f H100 \N t \N \N \N {} -4220 1394 2025-03-01 15:38:36.009293-08 2025-03-01 16:26:03.890826-08 benchmark f H100 \N t \N \N \N {} -4221 1394 2025-03-01 15:14:09.768318-08 2025-03-01 14:35:45.346475-08 leaderboard f H100 0.00139569175 t \N \N \N {} -4222 1394 2025-03-01 14:47:10.848925-08 2025-03-01 16:25:02.786375-08 test t A100 \N t \N \N \N {} -4430 1443 2025-03-01 23:16:02.466485-08 2025-03-01 23:58:09.823321-08 benchmark f T4 \N t \N \N \N {} -4431 1443 2025-03-01 23:17:08.006949-08 2025-03-02 00:41:05.844634-08 leaderboard f T4 0.009837714666666665 t \N \N \N {} -4432 1443 2025-03-02 00:29:59.441844-08 2025-03-02 00:59:33.051229-08 test t T4 \N t \N \N \N {} -4433 1443 2025-03-02 00:28:05.820758-08 2025-03-01 23:33:08.733358-08 benchmark t T4 \N t \N \N \N {} -4434 1443 2025-03-01 23:22:06.634593-08 2025-03-01 23:36:43.202641-08 leaderboard t T4 0.009814015333333334 t \N \N \N {} -4435 1444 2025-03-02 00:23:07.374461-08 2025-03-02 00:27:41.123548-08 test f A100 \N t \N \N \N {} -4436 1444 2025-03-02 00:26:01.958483-08 2025-03-01 23:49:21.361713-08 benchmark f A100 \N t \N \N \N {} -6459 2005 2025-03-14 06:48:41.779094-07 2025-03-14 06:41:15.666539-07 test t A100 \N t \N \N \N {} -4439 1444 2025-03-01 23:56:43.720976-08 2025-03-02 00:29:41.591684-08 benchmark f T4 \N t \N \N \N {} -4440 1444 2025-03-02 01:00:37.296922-08 2025-03-01 23:59:24.879229-08 leaderboard f T4 0.009901969333333333 t \N \N \N {} -4441 1444 2025-03-02 00:32:35.468671-08 2025-03-02 00:07:20.667198-08 test f H100 \N t \N \N \N {} -4442 1444 2025-03-02 00:23:25.716537-08 2025-03-01 23:07:19.512954-08 benchmark f H100 \N t \N \N \N {} -4443 1444 2025-03-01 23:26:32.233267-08 2025-03-02 00:51:40.562071-08 leaderboard f H100 0.0011824463333333333 t \N \N \N {} -4447 1444 2025-03-01 23:33:37.582909-08 2025-03-02 00:50:04.426217-08 test t A100 \N t \N \N \N {} -4448 1444 2025-03-01 23:21:44.542236-08 2025-03-02 00:08:03.502567-08 benchmark t A100 \N t \N \N \N {} -4449 1444 2025-03-01 23:37:38.569687-08 2025-03-02 00:55:50.035923-08 leaderboard t A100 0.002102921 t \N \N \N {} -4450 1444 2025-03-02 00:52:28.079251-08 2025-03-01 23:41:04.354527-08 test t H100 \N t \N \N \N {} -4451 1444 2025-03-01 23:53:41.734266-08 2025-03-02 00:14:25.295609-08 benchmark t H100 \N t \N \N \N {} -4452 1444 2025-03-01 23:41:52.983649-08 2025-03-02 00:12:57.932353-08 leaderboard t H100 0.001193107 t \N \N \N {} -4453 1444 2025-03-02 00:27:47.992493-08 2025-03-01 23:08:52.737482-08 test f L4 \N t \N \N \N {} -4454 1444 2025-03-01 23:32:46.550951-08 2025-03-01 23:59:44.887779-08 benchmark f L4 \N t \N \N \N {} -4455 1444 2025-03-02 01:06:16.006685-08 2025-03-02 01:03:15.272313-08 leaderboard f L4 0.009334652333333334 t \N \N \N {} -4456 1444 2025-03-02 00:34:05.739298-08 2025-03-01 23:57:09.516919-08 test t L4 \N t \N \N \N {} -4457 1444 2025-03-01 23:54:57.096397-08 2025-03-02 00:38:20.034987-08 benchmark t L4 \N t \N \N \N {} -6704 2080 2025-03-15 03:44:28.237409-07 2025-03-15 02:59:07.329339-07 test t A100 \N t \N \N \N {} -4460 1445 2025-03-02 00:36:15.053645-08 2025-03-02 00:04:32.394667-08 benchmark f A100 \N t \N \N \N {} -4461 1445 2025-03-02 00:39:57.735714-08 2025-03-01 23:20:00.105801-08 leaderboard f A100 0.0018449429166666666 t \N \N \N {} -4462 1445 2025-03-02 00:55:37.500529-08 2025-03-02 00:06:09.985896-08 test t H100 \N t \N \N \N {} -4463 1445 2025-03-02 01:06:57.541572-08 2025-03-02 00:21:30.815554-08 benchmark t H100 \N t \N \N \N {} -4464 1445 2025-03-02 00:13:41.720834-08 2025-03-01 23:43:07.361692-08 leaderboard t H100 0.0010767913333333333 t \N \N \N {} -4468 1445 2025-03-02 01:09:32.900149-08 2025-03-01 23:58:50.176924-08 test f T4 \N t \N \N \N {} -4469 1445 2025-03-02 00:07:37.426171-08 2025-03-02 01:02:05.35442-08 benchmark f T4 \N t \N \N \N {} -4470 1445 2025-03-02 00:31:13.200358-08 2025-03-02 00:29:49.702127-08 leaderboard f T4 0.009909986333333334 t \N \N \N {} -4471 1445 2025-03-02 00:16:25.62727-08 2025-03-02 00:16:09.130264-08 test t T4 \N t \N \N \N {} -4472 1445 2025-03-02 00:04:53.640853-08 2025-03-01 23:24:24.363564-08 benchmark t T4 \N t \N \N \N {} -4473 1445 2025-03-02 01:02:57.604673-08 2025-03-01 23:55:02.547237-08 leaderboard t T4 0.009850056666666666 t \N \N \N {} -4474 1445 2025-03-01 23:14:41.583535-08 2025-03-02 01:03:01.853819-08 test t A100 \N t \N \N \N {} -4475 1445 2025-03-01 23:54:31.749676-08 2025-03-02 00:19:06.090943-08 benchmark t A100 \N t \N \N \N {} -4476 1445 2025-03-01 23:28:11.845822-08 2025-03-02 00:35:45.681919-08 leaderboard t A100 0.001977769 t \N \N \N {} -4477 1445 2025-03-01 23:10:57.421619-08 2025-03-01 23:46:37.516111-08 test f L4 \N t \N \N \N {} -4478 1445 2025-03-02 00:00:13.580017-08 2025-03-01 23:29:52.639891-08 benchmark f L4 \N t \N \N \N {} -6707 2080 2025-03-15 03:00:01.742093-07 2025-03-15 02:58:02.613361-07 test f A100 \N t \N \N \N {} -4488 1447 2025-03-02 00:17:43.55991-08 2025-03-02 01:03:56.914497-08 test t H100 \N t \N \N \N {} -4489 1447 2025-03-02 01:08:54.903269-08 2025-03-01 23:34:35.298743-08 benchmark t H100 \N f \N \N \N {} -4490 1447 2025-03-01 23:20:16.331584-08 2025-03-02 01:02:40.427181-08 test f A100 \N t \N \N \N {} -4491 1447 2025-03-01 23:58:12.688525-08 2025-03-02 00:00:00.160252-08 benchmark f A100 \N f \N \N \N {} -4492 1447 2025-03-02 00:06:10.369337-08 2025-03-02 00:53:30.708303-08 test f T4 \N t \N \N \N {} -6710 2081 2025-03-15 03:34:31.806339-07 2025-03-15 04:12:10.063053-07 test f A100 \N t \N \N \N {} -4493 1447 2025-03-02 01:03:35.98745-08 2025-03-02 00:54:11.188019-08 benchmark f T4 \N f \N \N \N {} -4494 1447 2025-03-02 00:30:24.438417-08 2025-03-01 23:49:43.972048-08 test t T4 \N t \N \N \N {} -4495 1447 2025-03-02 00:06:52.894921-08 2025-03-02 00:53:07.74224-08 benchmark t T4 \N f \N \N \N {} -4496 1447 2025-03-01 23:56:15.387541-08 2025-03-02 01:11:05.860239-08 test f L4 \N t \N \N \N {} -4497 1447 2025-03-01 23:59:26.042618-08 2025-03-02 00:05:19.797422-08 benchmark f L4 \N f \N \N \N {} -4676 1467 2025-03-02 06:38:57.431816-08 2025-03-02 05:44:56.24281-08 leaderboard t A100 0.0030988743333333333 t \N \N \N {} -4679 1467 2025-03-02 06:07:05.077247-08 2025-03-02 06:27:34.618874-08 leaderboard f A100 0.0030814753333333333 t \N \N \N {} -4681 1468 2025-03-02 05:51:53.90541-08 2025-03-02 05:57:04.320667-08 benchmark f A100 \N t \N \N \N {} -4682 1468 2025-03-02 06:59:21.935782-08 2025-03-02 07:08:58.21826-08 leaderboard f A100 0.0030820743333333333 t \N \N \N {} -4683 1468 2025-03-02 07:33:48.301938-08 2025-03-02 05:51:18.662826-08 test t A100 \N t \N \N \N {} -4684 1468 2025-03-02 06:04:29.029074-08 2025-03-02 06:40:01.015883-08 benchmark t A100 \N t \N \N \N {} -4685 1468 2025-03-02 05:46:36.618897-08 2025-03-02 07:31:14.275548-08 leaderboard t A100 0.0030780586666666666 t \N \N \N {} -4689 1469 2025-03-02 05:56:50.321497-08 2025-03-02 05:54:49.230629-08 test t A100 \N t \N \N \N {} -4690 1469 2025-03-02 06:10:42.998637-08 2025-03-02 06:36:41.015153-08 benchmark t A100 \N t \N \N \N {} -4691 1469 2025-03-02 06:43:44.220952-08 2025-03-02 07:42:47.37538-08 leaderboard t A100 0.00307954 t \N \N \N {} -4692 1470 2025-03-02 07:27:34.955858-08 2025-03-02 07:19:49.777082-08 test t A100 \N t \N \N \N {} -4797 1487 2025-03-02 12:47:39.303587-08 2025-03-02 14:24:58.396542-08 benchmark t H100 \N f \N \N \N {} -4798 1487 2025-03-02 14:03:11.635121-08 2025-03-02 12:41:11.568145-08 test f A100 \N t \N \N \N {} -4799 1487 2025-03-02 14:16:43.466988-08 2025-03-02 13:43:02.452058-08 benchmark f A100 \N f \N \N \N {} -4800 1487 2025-03-02 12:52:53.590829-08 2025-03-02 13:24:26.405768-08 test f H100 \N t \N \N \N {} -4801 1487 2025-03-02 12:48:49.45642-08 2025-03-02 14:27:42.149075-08 benchmark f H100 \N f \N \N \N {} -6752 2093 2025-03-15 03:33:18.596707-07 2025-03-15 05:00:28.91036-07 test f A100 \N t \N \N \N {} -4846 1493 2025-03-02 14:07:28.816942-08 2025-03-02 13:42:53.598883-08 leaderboard f A100 0.001957988 t \N \N \N {} -4847 1493 2025-03-02 13:42:25.008987-08 2025-03-02 15:06:27.494787-08 test t A100 \N t \N \N \N {} -4848 1493 2025-03-02 15:03:52.371645-08 2025-03-02 15:22:56.10921-08 benchmark t A100 \N t \N \N \N {} -4849 1493 2025-03-02 13:36:12.4802-08 2025-03-02 13:52:29.977751-08 leaderboard t A100 0.0019754496666666665 t \N \N \N {} -4850 1494 2025-03-02 14:37:31.400329-08 2025-03-02 14:01:28.604796-08 test f L4 \N t \N \N \N {} -4851 1494 2025-03-02 14:43:42.095203-08 2025-03-02 13:36:47.15097-08 benchmark f L4 \N t \N \N \N {} -4974 1533 2025-03-03 22:57:29.38809-08 2025-03-03 21:55:26.67234-08 test f H100 \N t \N \N \N {} -4865 1495 2025-03-02 14:13:05.528764-08 2025-03-02 14:22:55.062967-08 benchmark t A100 \N t \N \N \N {} -4866 1495 2025-03-02 15:19:34.20592-08 2025-03-02 15:11:10.458129-08 leaderboard t A100 0.0017338363333333333 t \N \N \N {} -4867 1495 2025-03-02 13:51:41.741994-08 2025-03-02 14:01:52.182359-08 test f A100 \N t \N \N \N {} -4880 1501 2025-03-03 08:43:05.642759-08 2025-03-03 09:27:05.185793-08 benchmark f A100 \N t \N \N \N {} -4881 1502 2025-03-03 09:35:30.629754-08 2025-03-03 09:05:15.805475-08 benchmark f A100 \N t \N \N \N {} -4894 1507 2025-03-03 13:08:55.752365-08 2025-03-03 13:41:41.116144-08 test f A100 \N t \N \N \N {} -4895 1507 2025-03-03 14:18:00.216385-08 2025-03-03 14:01:18.093034-08 benchmark f A100 \N t \N \N \N {} -4896 1507 2025-03-03 12:36:28.706864-08 2025-03-03 13:53:27.430536-08 leaderboard f A100 0.023420895 t \N \N \N {} -4902 1508 2025-03-03 13:21:12.73487-08 2025-03-03 13:02:32.458846-08 leaderboard f H100 0.007582660333333333 t \N \N \N {} -4904 1510 2025-03-03 14:37:41.941114-08 2025-03-03 13:46:22.666922-08 test f T4 \N f \N \N \N {} -5251 1613 2025-03-06 07:16:47.168832-08 2025-03-06 08:00:37.45677-08 benchmark t L4 \N t \N \N \N {} -4942 1522 2025-03-03 14:30:23.866503-08 2025-03-03 15:41:40.516523-08 benchmark f H100 \N t \N \N \N {} -4943 1522 2025-03-03 14:48:33.754294-08 2025-03-03 15:31:43.342939-08 leaderboard f H100 0.0009028792280701754 t \N \N \N {} -5163 1571 2025-03-04 22:04:28.946672-08 2025-03-04 22:40:14.074477-08 benchmark t H100 \N t \N \N \N {} -5164 1571 2025-03-04 22:14:23.494998-08 2025-03-04 21:56:46.443828-08 leaderboard t H100 0.0010517243333333333 t \N \N \N {} -5165 1572 2025-03-04 22:44:09.245212-08 2025-03-04 22:55:47.144621-08 test f H100 \N f \N \N \N {} -5166 1572 2025-03-04 22:51:29.817905-08 2025-03-04 22:29:55.447229-08 test t H100 \N f \N \N \N {} -5167 1573 2025-03-04 23:41:43.36022-08 2025-03-04 22:29:50.136285-08 test f H100 \N f \N \N \N {} -5168 1573 2025-03-04 23:23:11.4983-08 2025-03-04 22:11:11.957865-08 test t H100 \N f \N \N \N {} -5169 1574 2025-03-04 22:13:23.349969-08 2025-03-04 22:18:11.101194-08 test f H100 \N f \N \N \N {} -5588 1708 2025-03-09 10:19:34.720879-07 2025-03-09 08:50:34.606837-07 test t T4 \N f \N \N \N {} -5589 1709 2025-03-09 08:35:57.715637-07 2025-03-09 08:57:16.363193-07 test f T4 \N t \N \N \N {} -5590 1709 2025-03-09 08:48:05.388801-07 2025-03-09 08:51:52.822296-07 benchmark f T4 \N t \N \N \N {} -5598 1710 2025-03-09 09:29:22.059881-07 2025-03-09 09:55:48.650399-07 test f T4 \N t \N \N \N {} -5599 1710 2025-03-09 08:55:39.716901-07 2025-03-09 09:35:45.035771-07 benchmark f T4 \N t \N \N \N {} -5600 1710 2025-03-09 08:57:39.621985-07 2025-03-09 09:06:05.154876-07 leaderboard f T4 0.016820244 t \N \N \N {} -5603 1712 2025-03-09 08:59:13.701641-07 2025-03-09 10:05:42.666778-07 test f T4 \N t \N \N \N {} -5604 1712 2025-03-09 08:36:53.940611-07 2025-03-09 09:15:28.314509-07 benchmark f T4 \N t \N \N \N {} -5605 1712 2025-03-09 09:42:44.562696-07 2025-03-09 08:51:10.667131-07 leaderboard f T4 0.017130335 t \N \N \N {} -5606 1712 2025-03-09 09:25:40.097472-07 2025-03-09 09:24:28.034725-07 test t T4 \N t \N \N \N {} -5609 1713 2025-03-09 09:05:10.861954-07 2025-03-09 09:44:03.851528-07 test f T4 \N f \N \N \N {} -5610 1713 2025-03-09 09:44:19.160845-07 2025-03-09 09:34:04.669254-07 test t T4 \N f \N \N \N {} -5611 1714 2025-03-09 08:42:31.949236-07 2025-03-09 08:41:53.417604-07 test f T4 \N t \N \N \N {} -5612 1714 2025-03-09 09:57:39.262941-07 2025-03-09 09:05:14.002676-07 benchmark f T4 \N f \N \N \N {} -5615 1715 2025-03-09 09:17:39.516615-07 2025-03-09 09:12:41.592177-07 test t T4 \N t \N \N \N {} -5616 1715 2025-03-09 10:38:09.131269-07 2025-03-09 10:32:40.256801-07 benchmark t T4 \N t \N \N \N {} -5617 1715 2025-03-09 10:30:21.604304-07 2025-03-09 09:39:45.046006-07 leaderboard t T4 0.020089393 t \N \N \N {} -5618 1715 2025-03-09 09:22:37.846053-07 2025-03-09 09:51:45.425382-07 test f T4 \N t \N \N \N {} -5619 1715 2025-03-09 10:06:35.483321-07 2025-03-09 08:49:05.978541-07 benchmark f T4 \N t \N \N \N {} -5620 1715 2025-03-09 09:57:04.884393-07 2025-03-09 09:09:40.630581-07 leaderboard f T4 0.020089969 t \N \N \N {} -5621 1716 2025-03-09 09:09:36.619819-07 2025-03-09 09:58:56.525307-07 test t T4 \N t \N \N \N {} -5922 1799 2025-03-10 12:08:29.726347-07 2025-03-10 12:16:05.005078-07 test t T4 \N f \N \N \N {} -5927 1802 2025-03-10 12:32:14.926187-07 2025-03-10 12:54:34.768223-07 test t T4 \N f \N \N \N {} -5928 1802 2025-03-10 12:50:16.888351-07 2025-03-10 13:21:42.853839-07 test f T4 \N f \N \N \N {} -5929 1803 2025-03-10 13:37:22.444553-07 2025-03-10 13:05:55.49148-07 test t T4 \N f \N \N \N {} -5930 1803 2025-03-10 12:23:55.172369-07 2025-03-10 12:26:21.880798-07 test f T4 \N f \N \N \N {} -5935 1806 2025-03-10 13:48:38.054827-07 2025-03-10 13:23:13.229669-07 test t T4 \N f \N \N \N {} -5978 1820 2025-03-10 13:29:06.016869-07 2025-03-10 14:22:13.274644-07 leaderboard f H100 0.0002589177142857143 t \N \N \N {} -5959 1814 2025-03-10 12:44:05.167824-07 2025-03-10 14:16:11.26654-07 test f T4 \N f \N \N \N {} -5979 1820 2025-03-10 14:17:15.853549-07 2025-03-10 13:56:33.556629-07 test t H100 \N t \N \N \N {} -5964 1816 2025-03-10 13:24:28.295909-07 2025-03-10 12:59:46.71286-07 test f T4 \N f \N \N \N {} -5987 1819 2025-03-10 12:59:46.080421-07 2025-03-10 13:46:59.872067-07 leaderboard t A100 0.0007810198461538461 t \N \N \N {} -5968 1818 2025-03-10 12:49:46.935079-07 2025-03-10 14:29:30.320975-07 benchmark f T4 \N t \N \N \N {} -5970 1818 2025-03-10 12:50:38.37402-07 2025-03-10 12:59:21.234188-07 test t T4 \N t \N \N \N {} -2078 702 2025-02-25 14:41:18.894451-08 2025-02-25 14:59:05.107708-08 test t H100 \N t \N \N \N {} -2079 702 2025-02-25 13:46:28.574004-08 2025-02-25 14:33:01.405183-08 benchmark t H100 \N t \N \N \N {} -2080 702 2025-02-25 14:30:42.768365-08 2025-02-25 13:11:14.979116-08 leaderboard t H100 0.0019116912857142856 t \N \N \N {} -2081 702 2025-02-25 14:40:48.443884-08 2025-02-25 13:54:57.498846-08 test f T4 \N t \N \N \N {} -2082 702 2025-02-25 14:30:54.980313-08 2025-02-25 14:44:49.165667-08 benchmark f T4 \N t \N \N \N {} -2085 702 2025-02-25 14:18:01.193831-08 2025-02-25 14:59:07.45264-08 benchmark t T4 \N t \N \N \N {} -2086 702 2025-02-25 14:20:00.930365-08 2025-02-25 13:58:38.993991-08 leaderboard t T4 0.02001272633333333 t \N \N \N {} -6950 2162 2025-03-16 16:48:51.39716-07 2025-03-16 15:12:01.465889-07 leaderboard f T4 0.00032559321276595747 t \N \N \N {} -6974 2169 2025-03-16 19:35:21.550002-07 2025-03-16 20:31:39.018156-07 leaderboard f T4 0.00024278 t \N \N \N {} -6976 2170 2025-03-16 20:19:56.143081-07 2025-03-16 19:14:22.00651-07 benchmark f T4 \N t \N \N \N {} -6977 2170 2025-03-16 20:16:18.708649-07 2025-03-16 20:36:29.899891-07 leaderboard f T4 0.00021283630303030305 t \N \N \N {} -6978 2170 2025-03-16 20:24:22.625394-07 2025-03-16 20:12:10.318919-07 test t T4 \N t \N \N \N {} -6979 2170 2025-03-16 20:24:19.649393-07 2025-03-16 19:07:58.941721-07 benchmark t T4 \N t \N \N \N {} -6980 2170 2025-03-16 19:29:57.914998-07 2025-03-16 20:10:27.993196-07 leaderboard t T4 0.00021353688461538462 t \N \N \N {} -6981 2171 2025-03-16 20:30:12.137849-07 2025-03-16 20:46:29.948675-07 test t T4 \N t \N \N \N {} -6983 2171 2025-03-16 20:50:17.763964-07 2025-03-16 19:14:31.186365-07 leaderboard t T4 0.0002254876842105263 t \N \N \N {} -6984 2171 2025-03-16 21:04:10.03094-07 2025-03-16 19:42:37.771415-07 test f T4 \N t \N \N \N {} -7060 2185 2025-03-16 21:00:49.129817-07 2025-03-16 20:45:01.000696-07 test t L4 \N t \N \N \N {} -6985 2171 2025-03-16 20:30:05.903023-07 2025-03-16 20:53:27.393446-07 benchmark f T4 \N t \N \N \N {} -6986 2171 2025-03-16 20:39:51.238598-07 2025-03-16 21:10:32.960644-07 leaderboard f T4 0.00024411110344827586 t \N \N \N {} -6987 2172 2025-03-16 20:16:03.916165-07 2025-03-16 20:44:26.852206-07 test t T4 \N t \N \N \N {} -6995 2173 2025-03-16 19:35:01.975689-07 2025-03-16 20:14:16.885975-07 leaderboard t T4 0.00023485917647058822 t \N \N \N {} -6996 2173 2025-03-16 20:21:16.631389-07 2025-03-16 20:29:29.668201-07 test f T4 \N t \N \N \N {} -6997 2173 2025-03-16 20:27:58.230216-07 2025-03-16 20:09:23.910606-07 benchmark f T4 \N t \N \N \N {} -6998 2173 2025-03-16 20:58:20.871815-07 2025-03-16 20:25:33.751661-07 leaderboard f T4 0.000249013375 t \N \N \N {} -6999 2174 2025-03-16 19:59:32.772964-07 2025-03-16 20:00:12.299702-07 test t T4 \N f \N \N \N {} -7000 2174 2025-03-16 20:57:34.509848-07 2025-03-16 20:51:30.601681-07 test f T4 \N f \N \N \N {} -7001 2175 2025-03-16 21:17:45.195814-07 2025-03-16 20:18:47.813213-07 test t T4 \N t \N \N \N {} -7002 2175 2025-03-16 21:20:26.692848-07 2025-03-16 19:32:57.604151-07 benchmark t T4 \N t \N \N \N {} -7003 2175 2025-03-16 20:44:57.359938-07 2025-03-16 19:58:30.404808-07 leaderboard t T4 0.0002332306 t \N \N \N {} -7004 2175 2025-03-16 21:10:00.970602-07 2025-03-16 21:02:06.246562-07 test f T4 \N t \N \N \N {} -7005 2175 2025-03-16 20:07:09.492932-07 2025-03-16 20:42:42.346737-07 benchmark f T4 \N t \N \N \N {} -7008 2176 2025-03-16 20:09:25.465826-07 2025-03-16 20:12:12.087915-07 benchmark t T4 \N t \N \N \N {} -7009 2176 2025-03-16 20:39:02.786771-07 2025-03-16 20:32:18.347951-07 leaderboard t T4 0.0003024322631578947 t \N \N \N {} -7010 2176 2025-03-16 20:09:27.479373-07 2025-03-16 19:53:27.585268-07 test f T4 \N t \N \N \N {} -7011 2176 2025-03-16 20:40:20.373215-07 2025-03-16 20:35:48.949342-07 benchmark f T4 \N t \N \N \N {} -7012 2176 2025-03-16 20:27:53.725911-07 2025-03-16 21:32:11.632172-07 leaderboard f T4 0.0003064759545454545 t \N \N \N {} -7014 2177 2025-03-16 20:47:48.960713-07 2025-03-16 21:32:50.346065-07 benchmark t T4 \N t \N \N \N {} -7015 2177 2025-03-16 20:22:27.227663-07 2025-03-16 21:27:05.019116-07 leaderboard t T4 0.00023873836363636365 t \N \N \N {} -7016 2177 2025-03-16 20:52:07.570894-07 2025-03-16 21:28:14.032602-07 test f T4 \N t \N \N \N {} -7017 2177 2025-03-16 21:04:02.088955-07 2025-03-16 20:13:59.587543-07 benchmark f T4 \N t \N \N \N {} -7018 2177 2025-03-16 20:52:17.778254-07 2025-03-16 21:07:11.801446-07 leaderboard f T4 0.00021685985 t \N \N \N {} -7019 2178 2025-03-16 20:51:58.999601-07 2025-03-16 20:03:37.893564-07 test f T4 \N t \N \N \N {} -7043 2182 2025-03-16 21:03:58.995548-07 2025-03-16 20:05:41.20667-07 test f H100 \N t \N \N \N {} -7049 2183 2025-03-16 21:44:08.747705-07 2025-03-16 20:09:06.554136-07 test f L4 \N t \N \N \N {} -7050 2183 2025-03-16 21:20:04.412484-07 2025-03-16 21:04:19.112094-07 benchmark f L4 \N t \N \N \N {} -7051 2183 2025-03-16 20:20:24.080976-07 2025-03-16 20:12:16.039498-07 leaderboard f L4 0.00013962746913580247 t \N \N \N {} -7052 2183 2025-03-16 21:06:27.300343-07 2025-03-16 21:35:48.036723-07 test t L4 \N t \N \N \N {} -7053 2183 2025-03-16 21:08:28.281165-07 2025-03-16 21:05:19.303361-07 benchmark t L4 \N t \N \N \N {} -7054 2183 2025-03-16 21:03:17.39211-07 2025-03-16 21:12:38.71214-07 leaderboard t L4 0.00013929977922077922 t \N \N \N {} -7057 2185 2025-03-16 20:57:18.459356-07 2025-03-16 20:58:40.616284-07 test f L4 \N t \N \N \N {} -7058 2185 2025-03-16 21:09:41.620917-07 2025-03-16 20:26:34.270924-07 benchmark f L4 \N t \N \N \N {} -7072 2187 2025-03-16 21:18:46.105748-07 2025-03-16 21:30:58.658604-07 test t A100 \N t \N \N \N {} -3823 1286 2025-03-01 02:51:18.738565-08 2025-03-01 02:35:56.135696-08 test t A100 \N t \N \N \N {} -2423 828 2025-02-26 14:04:34.908137-08 2025-02-26 12:48:44.157236-08 benchmark f H100 \N t \N \N \N {} -2844 989 2025-02-26 23:05:37.389856-08 2025-02-26 23:57:49.949237-08 test t H100 \N t \N \N \N {} -3826 1286 2025-03-01 03:10:29.776175-08 2025-03-01 02:15:26.657539-08 test f A100 \N t \N \N \N {} -2853 990 2025-02-26 23:48:13.530611-08 2025-02-27 00:27:37.952577-08 test f H100 \N t \N \N \N {} -3829 1287 2025-03-01 03:52:39.094858-08 2025-03-01 03:40:13.588698-08 test f A100 \N t \N \N \N {} -2860 992 2025-02-26 23:57:24.598857-08 2025-02-26 22:46:51.241065-08 leaderboard t H100 0.001483009888888889 t \N \N \N {} -3832 1287 2025-03-01 02:40:18.855128-08 2025-03-01 02:20:51.580001-08 test t A100 \N t \N \N \N {} -3575 1222 2025-02-28 14:25:08.626031-08 2025-02-28 13:57:58.851842-08 test f H100 \N t \N \N \N {} -2861 992 2025-02-27 00:10:48.414508-08 2025-02-27 00:15:26.826737-08 test f H100 \N t \N \N \N {} -3835 1288 2025-03-01 03:56:27.936941-08 2025-03-01 03:22:45.323479-08 test f A100 \N t \N \N \N {} -3838 1288 2025-03-01 02:49:58.666605-08 2025-03-01 03:28:40.936194-08 test t A100 \N t \N \N \N {} -3586 1228 2025-02-28 13:04:01.825429-08 2025-02-28 13:19:18.114759-08 leaderboard t A100 0.0018734915714285713 t \N \N \N {} -2208 723 2025-02-25 15:12:24.768432-08 2025-02-25 14:15:55.618693-08 benchmark f A100 \N t \N \N \N {} -3841 1289 2025-03-01 02:47:05.119415-08 2025-03-01 04:04:07.592057-08 test f A100 \N t \N \N \N {} -4808 1487 2025-03-02 13:15:32.005735-08 2025-03-02 14:08:45.412831-08 test t L4 \N t \N \N \N {} -2209 723 2025-02-25 15:18:48.649607-08 2025-02-25 15:21:36.817173-08 benchmark f H100 \N t \N \N \N {} -6755 2093 2025-03-15 04:31:09.91887-07 2025-03-15 04:32:31.426367-07 test t A100 \N t \N \N \N {} -2217 724 2025-02-25 14:40:36.314858-08 2025-02-25 15:59:23.436752-08 benchmark t H100 \N t \N \N \N {} -2227 726 2025-02-25 21:11:10.262768-08 2025-02-25 21:16:28.616268-08 test f H100 \N f \N \N \N {} -2971 1053 2025-02-27 10:22:46.566956-08 2025-02-27 11:14:00.450534-08 test f H100 \N f \N \N \N {} -2229 728 2025-02-25 21:49:59.544732-08 2025-02-25 21:13:13.071273-08 test f H100 \N f \N \N \N {} -2249 748 2025-02-26 01:20:54.630857-08 2025-02-26 01:48:02.906843-08 test f H100 \N f \N \N \N {} -2251 750 2025-02-26 01:02:46.596404-08 2025-02-26 01:40:29.396697-08 benchmark f H100 \N f \N \N \N {} -2203 721 2025-02-25 14:21:58.314111-08 2025-02-25 15:40:17.725093-08 test f A100 \N f \N \N \N {} -2204 721 2025-02-25 15:30:04.482461-08 2025-02-25 15:34:55.709429-08 test f H100 \N f \N \N \N {} -2205 721 2025-02-25 15:37:24.227133-08 2025-02-25 14:52:16.301155-08 test f T4 \N f \N \N \N {} -2206 721 2025-02-25 14:14:52.248428-08 2025-02-25 14:35:57.115198-08 test f L4 \N f \N \N \N {} -2207 722 2025-02-25 14:23:22.656547-08 2025-02-25 14:28:52.286564-08 test f A100 \N t \N \N \N {} -2231 730 2025-02-25 22:25:36.727385-08 2025-02-25 22:07:18.246829-08 test f H100 \N f \N \N \N {} -2252 751 2025-02-26 01:22:15.758845-08 2025-02-26 00:47:07.586743-08 benchmark f H100 \N f \N \N \N {} -2210 724 2025-02-25 15:01:55.831184-08 2025-02-25 15:27:25.721588-08 test f H100 \N t \N \N \N {} -2211 724 2025-02-25 16:00:18.379653-08 2025-02-25 15:14:19.774224-08 benchmark f H100 \N t \N \N \N {} -2212 724 2025-02-25 15:21:00.820815-08 2025-02-25 14:21:32.48071-08 leaderboard f H100 0.00347018762 t \N \N \N {} -2213 724 2025-02-25 15:55:13.720814-08 2025-02-25 14:39:28.319218-08 test t A100 \N t \N \N \N {} -2214 724 2025-02-25 15:15:10.350587-08 2025-02-25 15:20:05.920535-08 benchmark t A100 \N t \N \N \N {} -2222 724 2025-02-25 15:51:08.090008-08 2025-02-25 14:52:07.078563-08 test t L4 \N f \N \N \N {} -2223 724 2025-02-25 15:59:35.150135-08 2025-02-25 15:41:40.617138-08 test t T4 \N f \N \N \N {} -2224 724 2025-02-25 15:59:58.549919-08 2025-02-25 16:16:59.086077-08 test f T4 \N f \N \N \N {} -2225 724 2025-02-25 15:31:29.288566-08 2025-02-25 14:20:08.177759-08 test f L4 \N f \N \N \N {} -2236 735 2025-02-25 22:08:20.946921-08 2025-02-25 22:28:19.364454-08 test f H100 \N f \N \N \N {} -2241 740 2025-02-25 23:14:33.485835-08 2025-02-25 23:51:46.740346-08 benchmark f A100 \N t \N \N \N {} -2242 741 2025-02-25 23:35:50.055644-08 2025-02-25 22:49:24.455708-08 benchmark f A100 \N t \N \N \N {} -2243 742 2025-02-26 00:25:26.264646-08 2025-02-25 23:17:09.093175-08 benchmark f A100 \N f \N \N \N {} -2245 744 2025-02-26 00:42:02.958881-08 2025-02-26 00:45:16.278457-08 test f H100 \N f \N \N \N {} -2254 753 2025-02-26 02:08:05.477951-08 2025-02-26 01:13:20.051352-08 benchmark f H100 \N f \N \N \N {} -2255 754 2025-02-26 00:59:44.093208-08 2025-02-26 02:58:08.265275-08 benchmark f H100 \N f \N \N \N {} -2259 758 2025-02-26 01:17:58.081473-08 2025-02-26 01:34:49.549302-08 benchmark f H100 \N t \N \N \N {} -2260 759 2025-02-26 01:37:29.394797-08 2025-02-26 02:47:14.747208-08 benchmark f H100 \N t \N \N \N {} -2261 760 2025-02-26 01:39:24.386253-08 2025-02-26 02:18:01.358833-08 benchmark f H100 \N t \N \N \N {} -2262 761 2025-02-26 03:15:59.591174-08 2025-02-26 01:36:18.508508-08 benchmark f H100 \N t \N \N \N {} -2265 762 2025-02-26 02:01:34.231469-08 2025-02-26 03:54:41.609703-08 leaderboard t H100 0.0014416166666666667 t \N \N \N {} -2274 766 2025-02-26 04:05:07.529508-08 2025-02-26 04:58:07.255773-08 leaderboard f A100 0.0008179115 t \N \N \N {} -2275 766 2025-02-26 03:48:00.408098-08 2025-02-26 04:08:59.825239-08 test t A100 \N t \N \N \N {} -2301 774 2025-02-26 04:32:05.77669-08 2025-02-26 04:38:11.895003-08 benchmark f A100 \N t \N \N \N {} -2302 774 2025-02-26 04:13:31.801702-08 2025-02-26 04:01:09.656019-08 leaderboard f A100 0.0031047866666666663 t \N \N \N {} -2303 774 2025-02-26 04:24:37.740211-08 2025-02-26 03:39:00.774209-08 test t A100 \N t \N \N \N {} -2308 777 2025-02-26 04:25:35.388029-08 2025-02-26 03:30:27.492815-08 benchmark f A100 \N t \N \N \N {} -2309 778 2025-02-26 03:49:41.847565-08 2025-02-26 05:07:38.629246-08 test t A100 \N t \N \N \N {} -2310 778 2025-02-26 04:40:13.137449-08 2025-02-26 03:29:03.613607-08 benchmark t A100 \N t \N \N \N {} -2316 781 2025-02-26 08:04:59.695461-08 2025-02-26 08:20:17.559059-08 benchmark t A100 \N t \N \N \N {} -2317 781 2025-02-26 07:31:58.361931-08 2025-02-26 07:59:50.437989-08 leaderboard t A100 0.0031859682 t \N \N \N {} -2324 783 2025-02-26 08:10:22.678801-08 2025-02-26 07:07:39.819759-08 benchmark t A100 \N t \N \N \N {} -2325 783 2025-02-26 08:24:49.179778-08 2025-02-26 08:36:36.413259-08 leaderboard t A100 0.00325122275 t \N \N \N {} -2335 785 2025-02-26 08:25:39.641455-08 2025-02-26 06:58:31.005281-08 benchmark t A100 \N t \N \N \N {} -2336 785 2025-02-26 08:09:56.776695-08 2025-02-26 08:13:07.269228-08 leaderboard t A100 0.004335441666666667 t \N \N \N {} -2340 787 2025-02-26 07:39:15.682026-08 2025-02-26 08:18:25.113435-08 test f A100 \N f \N \N \N {} -2341 788 2025-02-26 07:31:19.708086-08 2025-02-26 08:57:53.961399-08 test t A100 \N f \N \N \N {} -2342 788 2025-02-26 08:25:02.656473-08 2025-02-26 08:25:08.005045-08 test f A100 \N f \N \N \N {} -2343 789 2025-02-26 08:43:37.93693-08 2025-02-26 08:07:41.07638-08 test t A100 \N t \N \N \N {} -2347 789 2025-02-26 09:06:31.119554-08 2025-02-26 07:11:28.595374-08 benchmark f A100 \N t \N \N \N {} -2348 789 2025-02-26 07:29:52.547362-08 2025-02-26 08:50:51.272086-08 leaderboard f A100 0.003266832777777778 t \N \N \N {} -2359 793 2025-02-26 09:11:48.758042-08 2025-02-26 08:04:27.206933-08 test t H100 \N f \N \N \N {} -2360 793 2025-02-26 09:01:14.507106-08 2025-02-26 08:36:00.01203-08 test f H100 \N f \N \N \N {} -2361 794 2025-02-26 10:31:01.311335-08 2025-02-26 10:24:32.132628-08 test f T4 \N t \N \N \N {} -2365 795 2025-02-26 10:42:54.506552-08 2025-02-26 10:33:48.974343-08 benchmark f T4 \N f \N \N \N {} -2366 796 2025-02-26 10:39:57.933478-08 2025-02-26 09:13:35.87411-08 test f T4 \N t \N \N \N {} -2367 796 2025-02-26 08:55:18.203941-08 2025-02-26 10:26:58.100193-08 test f A100 \N f \N \N \N {} -2368 796 2025-02-26 09:46:15.118953-08 2025-02-26 10:28:06.814641-08 test f L4 \N f \N \N \N {} -2373 800 2025-02-26 11:48:00.696003-08 2025-02-26 11:05:20.167368-08 test f L4 \N t \N \N \N {} -2374 800 2025-02-26 11:07:59.074012-08 2025-02-26 12:43:47.36544-08 benchmark f L4 \N t \N \N \N {} -2375 800 2025-02-26 12:41:54.306115-08 2025-02-26 11:06:41.948227-08 leaderboard f L4 0.019241309 t \N \N \N {} -2376 800 2025-02-26 11:45:20.290145-08 2025-02-26 12:31:25.711521-08 test t L4 \N t \N \N \N {} -2377 800 2025-02-26 12:39:01.06552-08 2025-02-26 12:57:56.856584-08 benchmark t L4 \N t \N \N \N {} -2378 800 2025-02-26 11:19:43.410096-08 2025-02-26 12:49:08.158954-08 leaderboard t L4 0.01928871 t \N \N \N {} -2379 801 2025-02-26 11:38:56.285241-08 2025-02-26 12:19:54.264843-08 test f A100 \N t \N \N \N {} -2382 804 2025-02-26 12:57:43.215115-08 2025-02-26 13:30:55.355298-08 benchmark f H100 \N t \N \N \N {} -2383 805 2025-02-26 13:12:00.965515-08 2025-02-26 12:47:43.718436-08 test t H100 \N t \N \N \N {} -2384 805 2025-02-26 12:02:09.112152-08 2025-02-26 13:33:00.538551-08 benchmark t H100 \N t \N \N \N {} -2385 805 2025-02-26 12:07:29.57397-08 2025-02-26 11:40:32.117689-08 leaderboard t H100 0.006281822 t \N \N \N {} -2386 805 2025-02-26 13:23:11.459374-08 2025-02-26 12:01:38.625408-08 test f H100 \N t \N \N \N {} -2387 805 2025-02-26 12:40:46.998681-08 2025-02-26 13:19:43.340082-08 benchmark f H100 \N t \N \N \N {} -2388 805 2025-02-26 12:28:02.085984-08 2025-02-26 12:00:56.151234-08 leaderboard f H100 0.006283131666666667 t \N \N \N {} -2400 812 2025-02-26 12:35:43.081119-08 2025-02-26 11:46:25.709461-08 leaderboard f A100 0.0073987305599999996 t \N \N \N {} -2399 812 2025-02-26 13:00:44.684656-08 2025-02-26 12:40:03.546738-08 benchmark f A100 \N t \N \N \N {} -2412 814 2025-02-26 11:55:21.072277-08 2025-02-26 13:21:18.388476-08 benchmark f H100 \N f \N \N \N {} -2406 812 2025-02-26 11:58:36.640565-08 2025-02-26 12:25:12.325022-08 benchmark t H100 \N t \N \N \N {} -2413 815 2025-02-26 12:34:40.3804-08 2025-02-26 12:01:23.839657-08 benchmark f H100 \N f \N \N \N {} -2407 812 2025-02-26 13:09:11.965272-08 2025-02-26 13:07:20.626098-08 leaderboard t H100 0.0034966516099999997 t \N \N \N {} -2408 813 2025-02-26 12:32:37.101507-08 2025-02-26 13:16:23.500546-08 benchmark f H100 \N t \N \N \N {} -2409 812 2025-02-26 13:29:54.187186-08 2025-02-26 13:23:28.632317-08 test t L4 \N f \N \N \N {} -2424 829 2025-02-26 13:07:19.553-08 2025-02-26 14:01:46.739578-08 benchmark f H100 \N f \N \N \N {} -2425 830 2025-02-26 14:02:43.362147-08 2025-02-26 12:33:07.345304-08 benchmark f H100 \N t \N \N \N {} -2426 831 2025-02-26 12:31:38.224085-08 2025-02-26 13:19:43.236285-08 benchmark f H100 \N t \N \N \N {} -3850 1290 2025-03-01 04:03:10.861329-08 2025-03-01 03:39:38.665791-08 test t A100 \N t \N \N \N {} -2428 834 2025-02-26 13:15:44.571141-08 2025-02-26 13:44:55.710615-08 benchmark f H100 \N t \N \N \N {} -2429 835 2025-02-26 13:42:00.820827-08 2025-02-26 13:22:37.497485-08 benchmark f H100 \N t \N \N \N {} -2430 836 2025-02-26 13:48:13.271344-08 2025-02-26 13:35:52.895605-08 benchmark f H100 \N t \N \N \N {} -2431 839 2025-02-26 12:27:35.852032-08 2025-02-26 13:54:55.127064-08 test f H100 \N t \N \N \N {} -2432 839 2025-02-26 13:13:16.24415-08 2025-02-26 12:31:35.696649-08 benchmark f H100 \N t \N \N \N {} -2433 839 2025-02-26 13:25:16.844207-08 2025-02-26 12:41:49.998945-08 leaderboard f H100 0.0014103766666666668 t \N \N \N {} -2434 839 2025-02-26 13:02:21.500262-08 2025-02-26 13:19:32.907466-08 test t H100 \N t \N \N \N {} -3275 1144 2025-02-28 05:52:25.859901-08 2025-02-28 06:09:38.762195-08 test f A100 \N t \N \N \N {} -2437 843 2025-02-26 14:26:52.109887-08 2025-02-26 14:21:54.916482-08 benchmark f A100 \N t \N \N \N {} -2438 845 2025-02-26 12:52:37.260995-08 2025-02-26 12:43:22.265476-08 test f L4 \N t \N \N \N {} -2439 845 2025-02-26 13:15:51.000933-08 2025-02-26 13:07:58.39425-08 benchmark f L4 \N t \N \N \N {} -2440 845 2025-02-26 13:28:47.225016-08 2025-02-26 13:45:32.97832-08 leaderboard f L4 0.043612971333333334 t \N \N \N {} -2443 845 2025-02-26 14:12:08.997237-08 2025-02-26 12:35:40.416269-08 leaderboard t L4 0.04366572966666667 t \N \N \N {} -2444 850 2025-02-26 14:13:58.990169-08 2025-02-26 12:46:54.480342-08 benchmark f A100 \N t \N \N \N {} -2445 851 2025-02-26 14:31:38.156136-08 2025-02-26 14:03:21.128088-08 test f A100 \N f \N \N \N {} -2446 851 2025-02-26 14:23:33.344316-08 2025-02-26 12:39:36.843848-08 test t A100 \N f \N \N \N {} -3853 1291 2025-03-01 03:13:24.343849-08 2025-03-01 02:57:41.913482-08 test t A100 \N t \N \N \N {} -2448 854 2025-02-26 14:40:16.012015-08 2025-02-26 13:30:53.984566-08 benchmark f A100 \N t \N \N \N {} -2449 855 2025-02-26 13:12:11.591988-08 2025-02-26 14:39:17.48303-08 benchmark f A100 \N t \N \N \N {} -2450 857 2025-02-26 13:02:50.277788-08 2025-02-26 13:15:55.340603-08 benchmark f A100 \N t \N \N \N {} -2456 860 2025-02-26 12:50:27.406788-08 2025-02-26 14:27:11.783431-08 leaderboard f A100 0.0030875686666666665 t \N \N \N {} -2457 860 2025-02-26 13:17:23.373759-08 2025-02-26 14:15:06.821417-08 test t A100 \N t \N \N \N {} -2458 860 2025-02-26 13:49:59.089223-08 2025-02-26 14:34:48.510454-08 benchmark t A100 \N t \N \N \N {} -2459 860 2025-02-26 12:54:53.87546-08 2025-02-26 13:27:24.625628-08 leaderboard t A100 0.003084496 t \N \N \N {} -2460 862 2025-02-26 14:25:24.115849-08 2025-02-26 14:24:38.388715-08 benchmark f A100 \N t \N \N \N {} -2461 863 2025-02-26 12:52:35.025536-08 2025-02-26 13:59:55.535093-08 benchmark f A100 \N t \N \N \N {} -3278 1144 2025-02-28 06:16:01.039147-08 2025-02-28 06:22:29.16157-08 test t A100 \N t \N \N \N {} -2467 865 2025-02-26 14:29:40.579994-08 2025-02-26 13:01:17.531306-08 benchmark t A100 \N t \N \N \N {} -2468 865 2025-02-26 14:27:27.068975-08 2025-02-26 13:15:03.336598-08 leaderboard t A100 0.0068730191799999995 t \N \N \N {} -2469 867 2025-02-26 14:38:45.220709-08 2025-02-26 13:20:49.410663-08 benchmark f H100 \N t \N \N \N {} -2470 870 2025-02-26 14:35:14.099811-08 2025-02-26 13:16:45.84219-08 test t L4 \N t \N \N \N {} -2471 870 2025-02-26 14:54:17.97184-08 2025-02-26 14:53:55.242909-08 benchmark t L4 \N t \N \N \N {} -3163 1117 2025-02-28 02:58:49.256531-08 2025-02-28 02:51:45.758919-08 test f A100 \N t \N \N \N {} -2472 870 2025-02-26 14:49:05.199571-08 2025-02-26 13:43:51.365921-08 leaderboard t L4 0.01752669466666667 t \N \N \N {} -2473 870 2025-02-26 14:29:38.067462-08 2025-02-26 13:42:20.183691-08 test t H100 \N t \N \N \N {} -2474 870 2025-02-26 14:28:13.427627-08 2025-02-26 14:43:39.03146-08 benchmark t H100 \N t \N \N \N {} -2475 870 2025-02-26 14:44:05.364863-08 2025-02-26 14:18:20.873024-08 leaderboard t H100 0.0014439003333333333 t \N \N \N {} -2476 870 2025-02-26 14:09:52.898678-08 2025-02-26 14:05:33.382355-08 test t T4 \N t \N \N \N {} -2482 870 2025-02-26 14:26:54.587486-08 2025-02-26 13:04:15.11736-08 test f A100 \N t \N \N \N {} -3391 1168 2025-02-28 09:57:12.308014-08 2025-02-28 09:07:40.869103-08 test t L4 \N f \N \N \N {} -2477 870 2025-02-26 14:11:49.901716-08 2025-02-26 13:57:28.933272-08 benchmark t T4 \N t \N \N \N {} -2478 870 2025-02-26 14:52:49.914318-08 2025-02-26 14:30:14.957771-08 leaderboard t T4 0.018135662333333333 t \N \N \N {} -2479 870 2025-02-26 13:04:33.014453-08 2025-02-26 13:28:38.801176-08 test f H100 \N t \N \N \N {} -2480 870 2025-02-26 13:05:56.396107-08 2025-02-26 13:11:26.670759-08 benchmark f H100 \N t \N \N \N {} -2487 870 2025-02-26 13:53:37.8607-08 2025-02-26 13:42:40.91502-08 leaderboard f T4 0.018169958333333333 t \N \N \N {} -3284 1147 2025-02-28 06:34:56.869895-08 2025-02-28 06:59:54.488727-08 benchmark f A100 \N t \N \N \N {} -2488 870 2025-02-26 13:55:29.990434-08 2025-02-26 13:42:48.566834-08 test t A100 \N t \N \N \N {} -2489 870 2025-02-26 13:33:21.862277-08 2025-02-26 13:18:14.133316-08 benchmark t A100 \N t \N \N \N {} -2490 870 2025-02-26 14:25:53.367799-08 2025-02-26 14:32:23.689127-08 leaderboard t A100 0.0035314313333333334 t \N \N \N {} -2491 870 2025-02-26 13:48:35.450822-08 2025-02-26 14:06:57.276021-08 test f L4 \N t \N \N \N {} -2492 870 2025-02-26 13:52:29.744988-08 2025-02-26 13:53:25.403361-08 benchmark f L4 \N t \N \N \N {} -3285 1147 2025-02-28 06:24:43.203152-08 2025-02-28 06:13:29.756699-08 benchmark f T4 \N t \N \N \N {} -2493 870 2025-02-26 13:59:31.991194-08 2025-02-26 13:59:26.579581-08 leaderboard f L4 0.017607324 t \N \N \N {} -2494 869 2025-02-26 13:53:41.469056-08 2025-02-26 14:38:54.569715-08 test f H100 \N t \N \N \N {} -2495 869 2025-02-26 13:23:26.566841-08 2025-02-26 14:00:54.453804-08 benchmark f H100 \N t \N \N \N {} -2496 869 2025-02-26 14:13:25.627702-08 2025-02-26 13:16:17.10637-08 leaderboard f H100 0.0029435355699999996 t \N \N \N {} -2497 869 2025-02-26 13:45:34.876263-08 2025-02-26 13:53:58.749847-08 test t H100 \N t \N \N \N {} -2498 869 2025-02-26 13:46:26.837661-08 2025-02-26 13:24:26.415607-08 benchmark t H100 \N t \N \N \N {} -2503 872 2025-02-26 14:19:49.973867-08 2025-02-26 13:58:04.924559-08 leaderboard t A100 0.0031960086666666665 t \N \N \N {} -2524 872 2025-02-26 14:59:20.978098-08 2025-02-26 14:04:05.964393-08 leaderboard t L4 0.01734968933333333 t \N \N \N {} -2525 873 2025-02-26 14:17:24.445616-08 2025-02-26 13:21:44.500572-08 benchmark f T4 \N t \N \N \N {} -3311 1149 2025-02-28 07:38:30.746822-08 2025-02-28 06:13:50.041675-08 benchmark f H100 \N t \N \N \N {} -2526 874 2025-02-26 14:02:11.631299-08 2025-02-26 14:54:22.504542-08 benchmark f H100 \N f \N \N \N {} -2528 875 2025-02-26 13:53:59.206204-08 2025-02-26 13:59:51.952233-08 benchmark t L4 \N t \N \N \N {} -2529 875 2025-02-26 13:45:06.978432-08 2025-02-26 14:27:23.069181-08 leaderboard t L4 0.017381977333333333 t \N \N \N {} -2530 875 2025-02-26 13:14:04.120622-08 2025-02-26 13:37:23.311234-08 test f H100 \N t \N \N \N {} -2536 875 2025-02-26 14:17:45.203727-08 2025-02-26 13:52:51.20816-08 test t A100 \N t \N \N \N {} -3398 1172 2025-02-28 10:30:25.502848-08 2025-02-28 09:32:32.438948-08 test t L4 \N f \N \N \N {} -2531 875 2025-02-26 13:27:29.331193-08 2025-02-26 13:29:35.204364-08 benchmark f H100 \N t \N \N \N {} -2538 875 2025-02-26 13:53:08.870239-08 2025-02-26 14:00:49.668667-08 leaderboard t A100 0.0032747976666666666 t \N \N \N {} -2539 875 2025-02-26 13:14:52.010055-08 2025-02-26 14:18:34.81441-08 test f T4 \N t \N \N \N {} -2540 875 2025-02-26 13:20:18.964191-08 2025-02-26 13:13:55.628835-08 benchmark f T4 \N t \N \N \N {} -2541 875 2025-02-26 14:54:26.113811-08 2025-02-26 13:43:25.579169-08 leaderboard f T4 0.018149821333333333 t \N \N \N {} -3349 1156 2025-02-28 06:29:55.568265-08 2025-02-28 08:02:02.535704-08 leaderboard f A100 0.0031082066666666664 t \N \N \N {} -2542 875 2025-02-26 14:51:18.161936-08 2025-02-26 14:37:36.137121-08 test t H100 \N t \N \N \N {} -2543 875 2025-02-26 14:35:54.200978-08 2025-02-26 13:13:31.055337-08 benchmark t H100 \N t \N \N \N {} -2544 875 2025-02-26 14:33:45.385769-08 2025-02-26 13:26:20.622935-08 leaderboard t H100 0.0014337893333333334 t \N \N \N {} -2545 875 2025-02-26 13:53:24.267369-08 2025-02-26 13:18:55.978128-08 test t T4 \N t \N \N \N {} -2546 875 2025-02-26 13:38:11.861473-08 2025-02-26 14:59:51.393224-08 benchmark t T4 \N t \N \N \N {} -2567 877 2025-02-26 15:03:59.772268-08 2025-02-26 14:40:45.912645-08 test t A100 \N t \N \N \N {} -2547 875 2025-02-26 14:32:26.84369-08 2025-02-26 13:13:58.565371-08 leaderboard t T4 0.01822390733333333 t \N \N \N {} -2552 877 2025-02-26 13:20:01.898564-08 2025-02-26 13:58:51.317431-08 benchmark f L4 \N t \N \N \N {} -2557 876 2025-02-26 15:02:00.99544-08 2025-02-26 13:29:31.499121-08 benchmark f T4 \N t \N \N \N {} -2558 877 2025-02-26 13:46:19.16509-08 2025-02-26 14:56:55.471863-08 test f A100 \N t \N \N \N {} -2562 877 2025-02-26 14:11:45.890202-08 2025-02-26 14:42:19.158457-08 benchmark f H100 \N t \N \N \N {} -2574 877 2025-02-26 14:53:25.554208-08 2025-02-26 14:16:55.579672-08 benchmark t T4 \N t \N \N \N {} -2580 880 2025-02-26 15:07:50.100053-08 2025-02-26 13:18:56.845732-08 benchmark f H100 \N t \N \N \N {} -2585 883 2025-02-26 15:03:39.651289-08 2025-02-26 14:42:53.129821-08 benchmark f T4 \N t \N \N \N {} -2586 884 2025-02-26 14:50:00.889114-08 2025-02-26 15:10:04.434058-08 test f A100 \N t \N \N \N {} -2590 885 2025-02-26 15:19:01.393865-08 2025-02-26 13:28:15.590615-08 benchmark f H100 \N t \N \N \N {} -2591 885 2025-02-26 13:26:24.818556-08 2025-02-26 13:50:12.168949-08 leaderboard f H100 0.0014732981818181819 t \N \N \N {} -2602 884 2025-02-26 14:57:40.130347-08 2025-02-26 14:36:36.467514-08 benchmark t H100 \N t \N \N \N {} -2603 884 2025-02-26 14:47:56.199118-08 2025-02-26 14:47:39.751177-08 leaderboard t H100 0.001451446 t \N \N \N {} -2608 884 2025-02-26 14:52:54.969875-08 2025-02-26 14:45:17.829971-08 benchmark t T4 \N t \N \N \N {} -2609 884 2025-02-26 14:00:42.000568-08 2025-02-26 14:07:23.322658-08 leaderboard t T4 0.01839805933333333 t \N \N \N {} -2623 885 2025-02-26 13:24:51.303162-08 2025-02-26 14:59:25.978603-08 benchmark f L4 \N t \N \N \N {} -2624 885 2025-02-26 14:46:25.13041-08 2025-02-26 14:01:16.028106-08 leaderboard f L4 0.018322243399999997 t \N \N \N {} -2632 885 2025-02-26 13:42:28.590895-08 2025-02-26 15:11:24.84455-08 benchmark f T4 \N t \N \N \N {} -2633 885 2025-02-26 14:17:07.144881-08 2025-02-26 13:42:07.341899-08 leaderboard f T4 0.017316619 t \N \N \N {} -2638 890 2025-02-26 14:38:01.800384-08 2025-02-26 14:21:29.502344-08 benchmark f H100 \N t \N \N \N {} -2639 891 2025-02-26 14:00:22.235154-08 2025-02-26 13:49:58.414404-08 benchmark f H100 \N t \N \N \N {} -2640 893 2025-02-26 14:44:28.627123-08 2025-02-26 14:56:02.319874-08 benchmark f H100 \N t \N \N \N {} -2641 892 2025-02-26 14:06:16.166538-08 2025-02-26 14:45:31.778383-08 benchmark f H100 \N t \N \N \N {} -2646 898 2025-02-26 15:10:17.627593-08 2025-02-26 14:28:58.359942-08 benchmark f H100 \N t \N \N \N {} -2647 899 2025-02-26 13:42:07.536404-08 2025-02-26 14:35:17.861549-08 benchmark f H100 \N t \N \N \N {} -2652 909 2025-02-26 15:08:14.043409-08 2025-02-26 14:36:11.735601-08 test f T4 \N f \N \N \N {} -2674 917 2025-02-26 14:23:43.794578-08 2025-02-26 14:36:33.755414-08 benchmark t T4 \N t \N \N \N {} -2686 922 2025-02-26 15:57:13.427621-08 2025-02-26 15:26:20.611904-08 test f T4 \N t \N \N \N {} -2696 925 2025-02-26 14:17:38.083972-08 2025-02-26 16:03:31.423476-08 leaderboard f A100 0.0032370253333333333 t \N \N \N {} -2713 927 2025-02-26 14:37:41.802897-08 2025-02-26 14:31:27.897832-08 test f H100 \N t \N \N \N {} -2714 927 2025-02-26 15:15:35.837289-08 2025-02-26 14:47:25.94265-08 benchmark f H100 \N t \N \N \N {} -2718 927 2025-02-26 15:59:35.075538-08 2025-02-26 15:41:14.515832-08 leaderboard t H100 0.0008215225714285714 t \N \N \N {} -2724 937 2025-02-26 16:47:17.193058-08 2025-02-26 16:23:22.453844-08 test f H100 \N t \N \N \N {} -2725 937 2025-02-26 15:50:22.227979-08 2025-02-26 16:45:26.76204-08 benchmark f H100 \N t \N \N \N {} -2726 937 2025-02-26 16:51:40.672188-08 2025-02-26 16:03:32.138312-08 leaderboard f H100 0.00079554 t \N \N \N {} -2727 937 2025-02-26 15:40:40.022004-08 2025-02-26 17:18:13.128244-08 test t H100 \N t \N \N \N {} -2728 937 2025-02-26 17:19:47.937296-08 2025-02-26 17:33:18.727563-08 benchmark t H100 \N t \N \N \N {} -2789 974 2025-02-26 20:55:25.229033-08 2025-02-26 20:10:10.409265-08 test t A100 \N t \N \N \N {} -3494 1198 2025-02-28 11:44:26.606686-08 2025-02-28 12:27:34.310314-08 test t A100 \N t \N \N \N {} -3877 1295 2025-03-01 04:30:13.818352-08 2025-03-01 03:53:15.973054-08 test f A100 \N t \N \N \N {} -2734 939 2025-02-26 16:52:57.1295-08 2025-02-26 16:57:05.775532-08 test t H100 \N t \N \N \N {} -2735 939 2025-02-26 16:51:34.603971-08 2025-02-26 16:14:14.968064-08 benchmark t H100 \N t \N \N \N {} -2736 939 2025-02-26 17:22:29.074864-08 2025-02-26 17:19:20.267253-08 leaderboard t H100 0.0004968643 t \N \N \N {} -2737 940 2025-02-26 17:36:57.572482-08 2025-02-26 17:38:12.608294-08 benchmark f H100 \N t \N \N \N {} -2738 941 2025-02-26 16:31:57.595239-08 2025-02-26 17:53:18.674166-08 benchmark f H100 \N t \N \N \N {} -2740 943 2025-02-26 18:29:20.73428-08 2025-02-26 18:00:14.467783-08 test f H100 \N f \N \N \N {} -2742 945 2025-02-26 19:02:41.462274-08 2025-02-26 17:30:22.202193-08 test f H100 \N f \N \N \N {} -2743 946 2025-02-26 18:48:22.350038-08 2025-02-26 17:37:45.056255-08 test f H100 \N t \N \N \N {} -2744 947 2025-02-26 17:40:41.628733-08 2025-02-26 18:01:07.85212-08 benchmark f H100 \N t \N \N \N {} -3497 1199 2025-02-28 10:52:33.356367-08 2025-02-28 11:45:48.622925-08 test f A100 \N t \N \N \N {} -2746 949 2025-02-26 19:17:31.915891-08 2025-02-26 17:48:54.299006-08 test f H100 \N t \N \N \N {} -3590 1229 2025-02-28 14:30:29.574017-08 2025-02-28 13:08:20.706884-08 test t H100 \N t \N \N \N {} -2748 951 2025-02-26 17:49:30.605776-08 2025-02-26 19:07:43.438503-08 benchmark f H100 \N t \N \N \N {} -2749 952 2025-02-26 18:03:40.668218-08 2025-02-26 17:46:00.26634-08 test t H100 \N t \N \N \N {} -3500 1199 2025-02-28 11:02:55.892242-08 2025-02-28 11:47:33.229851-08 test t A100 \N t \N \N \N {} -2750 952 2025-02-26 17:51:37.477609-08 2025-02-26 18:26:25.871776-08 benchmark t H100 \N t \N \N \N {} -2753 952 2025-02-26 18:36:33.794858-08 2025-02-26 18:27:26.243733-08 benchmark f H100 \N t \N \N \N {} -2754 952 2025-02-26 18:28:16.765992-08 2025-02-26 19:25:37.400542-08 leaderboard f H100 0.00005700789 t \N \N \N {} -3503 1200 2025-02-28 12:12:33.080144-08 2025-02-28 10:58:43.275181-08 test f A100 \N t \N \N \N {} -3880 1295 2025-03-01 02:41:14.083823-08 2025-03-01 02:48:54.508486-08 test t A100 \N t \N \N \N {} -2758 956 2025-02-26 19:12:28.514931-08 2025-02-26 19:22:57.495178-08 test f A100 \N f \N \N \N {} -2764 962 2025-02-26 19:58:47.609776-08 2025-02-26 20:12:54.160596-08 test f A100 \N f \N \N \N {} -2777 965 2025-02-26 19:44:07.999345-08 2025-02-26 19:22:11.183958-08 benchmark t A100 \N t \N \N \N {} -2778 965 2025-02-26 20:27:24.433649-08 2025-02-26 21:00:25.129429-08 leaderboard t A100 0.014223114 t \N \N \N {} -2779 967 2025-02-26 21:12:06.665368-08 2025-02-26 19:59:07.508513-08 test f A100 \N t \N \N \N {} -2783 971 2025-02-26 21:16:26.422875-08 2025-02-26 20:09:38.194507-08 test f A100 \N f \N \N \N {} -2784 972 2025-02-26 19:47:01.998273-08 2025-02-26 20:04:32.122274-08 test f A100 \N f \N \N \N {} -2793 975 2025-02-26 21:52:56.123062-08 2025-02-26 20:49:58.093155-08 benchmark t A100 \N t \N \N \N {} -2795 975 2025-02-26 21:40:13.75942-08 2025-02-26 21:41:56.411339-08 test f A100 \N t \N \N \N {} -2802 976 2025-02-26 20:52:48.46039-08 2025-02-26 21:31:20.714827-08 benchmark t A100 \N t \N \N \N {} -2803 976 2025-02-26 20:35:27.685284-08 2025-02-26 20:31:29.866823-08 leaderboard t A100 0.0031533973333333336 t \N \N \N {} -2808 981 2025-02-26 23:31:14.250849-08 2025-02-26 22:50:58.663532-08 test f H100 \N t \N \N \N {} -2809 981 2025-02-26 22:06:44.871153-08 2025-02-26 23:16:53.896715-08 benchmark f H100 \N t \N \N \N {} -2812 981 2025-02-26 23:27:33.898802-08 2025-02-26 22:27:35.757556-08 benchmark t H100 \N t \N \N \N {} -2813 981 2025-02-26 22:50:45.630362-08 2025-02-26 22:38:26.024532-08 leaderboard t H100 0.0014750091999999999 t \N \N \N {} -2821 983 2025-02-26 23:54:19.617224-08 2025-02-26 22:54:43.658755-08 benchmark f T4 \N t \N \N \N {} -2830 984 2025-02-26 22:48:33.644224-08 2025-02-26 23:28:42.682233-08 benchmark f L4 \N t \N \N \N {} -2831 984 2025-02-26 22:41:43.757041-08 2025-02-26 22:20:51.450588-08 leaderboard f L4 0.01786917866666667 t \N \N \N {} -2832 985 2025-02-26 22:21:51.307477-08 2025-02-26 23:57:27.92374-08 test t H100 \N f \N \N \N {} -2833 985 2025-02-26 23:24:09.942894-08 2025-02-26 23:56:01.814252-08 test f H100 \N f \N \N \N {} -2834 986 2025-02-27 00:15:23.058422-08 2025-02-26 22:20:39.444729-08 test t H100 \N f \N \N \N {} -2836 987 2025-02-26 23:41:37.112761-08 2025-02-26 23:11:03.419055-08 test t H100 \N f \N \N \N {} -2837 987 2025-02-26 23:42:42.038768-08 2025-02-26 22:42:24.323417-08 test f H100 \N f \N \N \N {} -2838 988 2025-02-27 00:16:37.194207-08 2025-02-26 23:55:57.063136-08 test t H100 \N t \N \N \N {} -2845 989 2025-02-26 23:26:00.442015-08 2025-02-27 00:38:35.106507-08 benchmark t H100 \N t \N \N \N {} -2846 989 2025-02-27 00:27:01.196646-08 2025-02-26 23:54:58.682026-08 leaderboard t H100 0.0016006286666666667 t \N \N \N {} -2854 990 2025-02-26 23:50:17.183107-08 2025-02-27 00:18:03.383314-08 benchmark f H100 \N t \N \N \N {} -2862 992 2025-02-27 00:36:55.131454-08 2025-02-27 00:15:38.507114-08 benchmark f H100 \N t \N \N \N {} -2863 992 2025-02-27 00:40:16.481455-08 2025-02-26 22:52:39.64442-08 leaderboard f H100 0.0014829375555555555 t \N \N \N {} -2870 994 2025-02-26 23:16:51.538974-08 2025-02-26 23:17:43.660758-08 test t H100 \N f \N \N \N {} -2871 994 2025-02-26 23:04:28.177098-08 2025-02-26 23:47:57.196289-08 test f H100 \N f \N \N \N {} -2872 995 2025-02-26 23:07:49.94189-08 2025-02-26 23:01:43.419125-08 test t H100 \N t \N \N \N {} -2890 1001 2025-02-27 00:11:31.886016-08 2025-02-27 00:30:39.383216-08 benchmark f T4 \N f \N \N \N {} -3098 1106 2025-02-28 01:45:37.330184-08 2025-02-28 01:23:52.672209-08 benchmark f A100 \N t \N \N \N {} -3099 1106 2025-02-28 02:14:12.999333-08 2025-02-28 01:30:27.761931-08 leaderboard f A100 0.003088477 t \N \N \N {} -6141 1883 2025-03-11 12:00:12.478656-07 2025-03-11 10:31:10.503903-07 test f T4 \N f \N \N \N {} -6313 1953 2025-03-12 19:49:16.980515-07 2025-03-12 19:36:25.891323-07 benchmark f A100 \N t \N \N \N {} -3101 1107 2025-02-28 03:01:32.467064-08 2025-02-28 01:43:34.556856-08 benchmark f A100 \N t \N \N \N {} -3104 1107 2025-02-28 02:08:46.087586-08 2025-02-28 03:09:33.504864-08 benchmark t A100 \N t \N \N \N {} -3105 1107 2025-02-28 02:13:06.321782-08 2025-02-28 02:29:03.356769-08 leaderboard t A100 0.003087981 t \N \N \N {} -6143 1884 2025-03-11 10:32:50.120634-07 2025-03-11 10:37:50.909508-07 benchmark f A100 \N t \N \N \N {} -6316 1954 2025-03-12 19:55:01.537607-07 2025-03-12 19:24:32.57323-07 leaderboard f A100 0.0013369068899999998 t \N \N \N {} -6463 2007 2025-03-14 06:32:12.249801-07 2025-03-14 06:46:07.006143-07 benchmark f A100 \N t \N \N \N {} -3107 1108 2025-02-28 02:23:54.191397-08 2025-02-28 03:14:14.624324-08 benchmark t A100 \N t \N \N \N {} -6464 2008 2025-03-14 06:36:56.11805-07 2025-03-14 07:46:19.266759-07 test f A100 \N t \N \N \N {} -3110 1108 2025-02-28 01:35:00.983298-08 2025-02-28 01:52:32.794497-08 benchmark f A100 \N t \N \N \N {} -3111 1108 2025-02-28 02:00:47.78852-08 2025-02-28 02:24:47.205564-08 leaderboard f A100 0.0030859636666666665 t \N \N \N {} -6145 1885 2025-03-11 11:00:03.267504-07 2025-03-11 12:18:13.420225-07 test t T4 \N f \N \N \N {} -6466 2008 2025-03-14 07:55:41.158642-07 2025-03-14 06:50:35.791318-07 leaderboard f A100 0.003151319 t \N \N \N {} -3113 1109 2025-02-28 03:14:09.084685-08 2025-02-28 03:13:52.452047-08 benchmark t A100 \N t \N \N \N {} -3114 1109 2025-02-28 02:43:35.282513-08 2025-02-28 02:18:55.469658-08 leaderboard t A100 0.0030930446666666664 t \N \N \N {} -6146 1886 2025-03-11 11:52:02.632791-07 2025-03-11 12:03:46.235678-07 test t T4 \N f \N \N \N {} -6320 1955 2025-03-12 20:34:48.835946-07 2025-03-12 20:33:18.170617-07 benchmark f A100 \N t \N \N \N {} -6469 2008 2025-03-14 06:40:05.438504-07 2025-03-14 06:15:33.296357-07 leaderboard t A100 0.0031540403333333335 t \N \N \N {} -3116 1109 2025-02-28 02:52:12.076786-08 2025-02-28 02:54:24.827665-08 benchmark f A100 \N t \N \N \N {} -3117 1109 2025-02-28 01:28:21.790912-08 2025-02-28 02:23:58.811177-08 leaderboard f A100 0.003085927 t \N \N \N {} -6147 1886 2025-03-11 12:54:26.920466-07 2025-03-11 11:44:08.259058-07 test f T4 \N f \N \N \N {} -6472 2009 2025-03-14 07:41:35.13284-07 2025-03-14 07:39:10.080386-07 leaderboard t A100 0.0024421096666666663 t \N \N \N {} -6148 1887 2025-03-11 12:01:43.87785-07 2025-03-11 12:26:23.003639-07 test f T4 \N f \N \N \N {} -6474 2009 2025-03-14 06:55:16.190554-07 2025-03-14 07:53:24.794099-07 benchmark f A100 \N t \N \N \N {} -3125 1111 2025-02-28 03:11:12.541581-08 2025-02-28 01:41:13.937321-08 benchmark f A100 \N t \N \N \N {} -3126 1111 2025-02-28 02:42:24.500879-08 2025-02-28 03:15:29.89987-08 leaderboard f A100 0.00316644475 t \N \N \N {} -6149 1887 2025-03-11 11:38:18.005859-07 2025-03-11 12:03:46.05906-07 test t T4 \N f \N \N \N {} -6323 1957 2025-03-13 09:54:02.414286-07 2025-03-13 09:52:30.795121-07 test t T4 \N f \N \N \N {} -6475 2009 2025-03-14 06:45:29.792426-07 2025-03-14 07:50:32.215984-07 leaderboard f A100 0.0024576166666666665 t \N \N \N {} -6597 2044 2025-03-14 13:34:41.655918-07 2025-03-14 12:08:56.842687-07 test t T4 \N f \N \N \N {} -6896 2145 2025-03-16 13:27:48.549328-07 2025-03-16 13:17:35.997934-07 benchmark f T4 \N f \N \N \N {} -6476 2010 2025-03-14 08:15:20.795494-07 2025-03-14 08:16:59.451399-07 test t A100 \N t \N \N \N {} -6477 2010 2025-03-14 08:08:47.27748-07 2025-03-14 06:59:19.727174-07 benchmark t A100 \N t \N \N \N {} -6478 2010 2025-03-14 06:48:33.525468-07 2025-03-14 08:02:14.948812-07 leaderboard t A100 0.002638912 t \N \N \N {} -3131 1112 2025-02-28 02:57:25.964429-08 2025-02-28 01:48:05.5249-08 benchmark f A100 \N t \N \N \N {} -6479 2010 2025-03-14 06:44:08.522027-07 2025-03-14 07:44:23.847039-07 test f A100 \N t \N \N \N {} -6480 2010 2025-03-14 07:56:38.205116-07 2025-03-14 07:54:00.333047-07 benchmark f A100 \N t \N \N \N {} -6152 1889 2025-03-11 13:30:29.989092-07 2025-03-11 12:48:46.455566-07 test t T4 \N t \N \N \N {} -6326 1958 2025-03-13 10:24:57.188611-07 2025-03-13 10:35:01.884206-07 test f T4 \N f \N \N \N {} -6482 2011 2025-03-14 08:02:31.143266-07 2025-03-14 06:41:16.991591-07 test f A100 \N t \N \N \N {} -6598 2045 2025-03-14 12:19:29.381029-07 2025-03-14 12:25:30.310096-07 test f A100 \N t \N \N \N {} -3137 1113 2025-02-28 03:08:43.17474-08 2025-02-28 02:45:51.821714-08 benchmark f A100 \N t \N \N \N {} -3138 1113 2025-02-28 02:20:25.9795-08 2025-02-28 02:34:44.310115-08 leaderboard f A100 0.0030921646666666664 t \N \N \N {} -6154 1889 2025-03-11 13:37:01.73686-07 2025-03-11 12:13:13.284253-07 test f T4 \N t \N \N \N {} -6327 1959 2025-03-13 10:26:24.667252-07 2025-03-13 11:45:14.442974-07 test t T4 \N f \N \N \N {} -3140 1113 2025-02-28 01:57:21.325728-08 2025-02-28 03:23:21.141542-08 benchmark t A100 \N t \N \N \N {} -6156 1890 2025-03-11 12:21:39.727111-07 2025-03-11 12:34:12.250351-07 test t T4 \N t \N \N \N {} -6157 1890 2025-03-11 13:12:21.4034-07 2025-03-11 12:39:13.895393-07 benchmark t T4 \N f \N \N \N {} -6328 1959 2025-03-13 12:08:12.60756-07 2025-03-13 12:05:32.463165-07 test f T4 \N f \N \N \N {} -6899 2148 2025-03-16 12:45:15.052291-07 2025-03-16 12:31:02.61805-07 test t T4 \N t \N \N \N {} -3143 1114 2025-02-28 01:39:46.938722-08 2025-02-28 02:11:45.255844-08 benchmark t A100 \N t \N \N \N {} -6159 1890 2025-03-11 13:25:06.856726-07 2025-03-11 12:59:34.598467-07 benchmark f T4 \N f \N \N \N {} -6329 1960 2025-03-13 11:46:43.371-07 2025-03-13 10:55:57.695267-07 test f T4 \N f \N \N \N {} -6902 2148 2025-03-16 12:34:32.187225-07 2025-03-16 12:33:04.36163-07 test f T4 \N t \N \N \N {} -3146 1114 2025-02-28 03:06:33.724337-08 2025-02-28 03:00:58.980906-08 benchmark f A100 \N t \N \N \N {} -6330 1960 2025-03-13 12:01:53.271866-07 2025-03-13 10:58:04.509016-07 test t T4 \N f \N \N \N {} -6483 2011 2025-03-14 07:37:56.702294-07 2025-03-14 07:18:30.959485-07 benchmark f A100 \N t \N \N \N {} -6331 1961 2025-03-13 12:07:41.574197-07 2025-03-13 11:12:52.328699-07 test f T4 \N f \N \N \N {} -6485 2011 2025-03-14 08:12:16.79755-07 2025-03-14 07:27:22.899891-07 test t A100 \N t \N \N \N {} -6671 2063 2025-03-14 14:46:30.836769-07 2025-03-14 14:56:13.217033-07 test t T4 \N f \N \N \N {} -6332 1961 2025-03-13 11:55:09.823065-07 2025-03-13 11:32:59.676251-07 test t T4 \N f \N \N \N {} -6486 2011 2025-03-14 07:56:31.390698-07 2025-03-14 07:47:31.714989-07 benchmark t A100 \N t \N \N \N {} -6163 1894 2025-03-11 12:16:32.845989-07 2025-03-11 13:15:29.765532-07 benchmark f T4 \N f \N \N \N {} -6333 1962 2025-03-13 12:24:14.859057-07 2025-03-13 12:20:08.73536-07 test t T4 \N f \N \N \N {} -3159 1116 2025-02-28 02:38:21.177305-08 2025-02-28 02:21:09.980476-08 leaderboard t A100 0.0030984103333333334 t \N \N \N {} -6164 1895 2025-03-11 12:39:00.764376-07 2025-03-11 12:37:33.970367-07 benchmark f T4 \N f \N \N \N {} -3162 1117 2025-02-28 03:41:30.098596-08 2025-02-28 03:20:42.331939-08 leaderboard t A100 0.0031041603333333335 t \N \N \N {} -6165 1896 2025-03-11 14:01:29.688942-07 2025-03-11 14:06:15.280779-07 benchmark f T4 \N t \N \N \N {} -6335 1963 2025-03-13 12:06:34.502766-07 2025-03-13 12:03:06.093899-07 test f T4 \N f \N \N \N {} -3169 1118 2025-02-28 03:31:50.129542-08 2025-02-28 03:36:06.764464-08 test f A100 \N t \N \N \N {} -3170 1118 2025-02-28 03:57:44.683588-08 2025-02-28 02:28:12.168907-08 benchmark f A100 \N t \N \N \N {} -3175 1119 2025-02-28 03:54:43.890351-08 2025-02-28 02:21:17.673941-08 test t A100 \N t \N \N \N {} -3176 1119 2025-02-28 03:25:21.208695-08 2025-02-28 02:53:54.097447-08 benchmark t A100 \N t \N \N \N {} -3179 1120 2025-02-28 03:19:52.64551-08 2025-02-28 04:03:22.512601-08 benchmark f A100 \N t \N \N \N {} -3180 1120 2025-02-28 02:37:44.833383-08 2025-02-28 03:49:05.485113-08 leaderboard f A100 0.0030965363333333336 t \N \N \N {} -3182 1120 2025-02-28 03:37:30.50022-08 2025-02-28 02:42:14.289029-08 benchmark t A100 \N t \N \N \N {} -3183 1120 2025-02-28 04:27:43.205983-08 2025-02-28 04:09:58.805071-08 leaderboard t A100 0.0030974113333333336 t \N \N \N {} -3188 1121 2025-02-28 05:16:45.018621-08 2025-02-28 04:16:05.328123-08 benchmark t A100 \N t \N \N \N {} -3189 1121 2025-02-28 03:32:46.838908-08 2025-02-28 04:24:02.880315-08 leaderboard t A100 0.0031272676666666663 t \N \N \N {} -6174 1899 2025-03-11 13:02:25.812706-07 2025-03-11 14:15:36.592099-07 test f T4 \N f \N \N \N {} -6340 1965 2025-03-13 12:42:09.960264-07 2025-03-13 12:51:36.764465-07 test f T4 \N f \N \N \N {} -6494 2013 2025-03-14 11:17:10.359439-07 2025-03-14 12:08:51.813768-07 test t H100 \N f \N \N \N {} -3191 1122 2025-02-28 04:31:59.29779-08 2025-02-28 04:47:11.70033-08 benchmark f A100 \N t \N \N \N {} -6175 1900 2025-03-11 14:09:24.209402-07 2025-03-11 14:27:29.662604-07 test t T4 \N f \N \N \N {} -3194 1122 2025-02-28 04:47:15.662542-08 2025-02-28 03:39:02.874427-08 benchmark t A100 \N t \N \N \N {} -3195 1122 2025-02-28 05:10:42.206283-08 2025-02-28 04:42:15.656228-08 leaderboard t A100 0.0031017193333333333 t \N \N \N {} -3196 1123 2025-02-28 05:13:41.323903-08 2025-02-28 04:55:50.819985-08 test f A100 \N t \N \N \N {} -3197 1123 2025-02-28 04:32:26.171448-08 2025-02-28 05:16:39.991067-08 benchmark f A100 \N t \N \N \N {} -3199 1123 2025-02-28 03:54:47.179034-08 2025-02-28 04:59:40.706458-08 test t A100 \N t \N \N \N {} -3208 1125 2025-02-28 05:52:39.581361-08 2025-02-28 05:55:19.83514-08 test t A100 \N t \N \N \N {} -3209 1125 2025-02-28 06:17:26.794554-08 2025-02-28 05:23:32.742402-08 benchmark t A100 \N t \N \N \N {} -3210 1125 2025-02-28 06:05:54.312077-08 2025-02-28 06:38:52.236143-08 leaderboard t A100 0.0030843956666666665 t \N \N \N {} -3211 1125 2025-02-28 06:16:05.768963-08 2025-02-28 06:25:41.001152-08 test f A100 \N t \N \N \N {} -3214 1126 2025-02-28 05:05:57.182176-08 2025-02-28 05:32:39.383884-08 test t A100 \N t \N \N \N {} -3215 1126 2025-02-28 05:52:39.479649-08 2025-02-28 06:47:24.562236-08 benchmark t A100 \N t \N \N \N {} -3216 1126 2025-02-28 05:10:50.786526-08 2025-02-28 04:55:01.414872-08 leaderboard t A100 0.0030879633333333336 t \N \N \N {} -3217 1126 2025-02-28 05:53:55.969095-08 2025-02-28 05:27:21.527994-08 test f A100 \N t \N \N \N {} -3220 1127 2025-02-28 06:54:22.094293-08 2025-02-28 06:25:04.727769-08 test t A100 \N t \N \N \N {} -3221 1127 2025-02-28 05:59:30.794599-08 2025-02-28 06:35:17.919421-08 benchmark t A100 \N t \N \N \N {} -3222 1127 2025-02-28 06:46:45.538801-08 2025-02-28 06:24:24.183739-08 leaderboard t A100 0.0031056823333333334 t \N \N \N {} -3223 1127 2025-02-28 05:37:12.475011-08 2025-02-28 05:15:06.135555-08 test f A100 \N t \N \N \N {} -3226 1128 2025-02-28 06:50:10.03441-08 2025-02-28 07:04:42.596407-08 benchmark f A100 \N t \N \N \N {} -3227 1129 2025-02-28 05:42:24.496681-08 2025-02-28 05:46:06.806343-08 benchmark f A100 \N t \N \N \N {} -3228 1131 2025-02-28 06:46:23.079469-08 2025-02-28 07:01:32.420227-08 benchmark f A100 \N t \N \N \N {} -3229 1130 2025-02-28 05:31:01.081051-08 2025-02-28 05:35:16.365184-08 test t A100 \N t \N \N \N {} -3232 1130 2025-02-28 05:56:38.654734-08 2025-02-28 06:03:33.120382-08 test f A100 \N t \N \N \N {} -3233 1130 2025-02-28 06:46:27.388033-08 2025-02-28 07:12:24.723128-08 benchmark f A100 \N t \N \N \N {} -3234 1130 2025-02-28 05:46:53.817871-08 2025-02-28 06:42:04.198827-08 leaderboard f A100 0.00310292 t \N \N \N {} -3235 1132 2025-02-28 06:21:32.053551-08 2025-02-28 07:14:16.857496-08 benchmark f A100 \N t \N \N \N {} -3236 1133 2025-02-28 07:07:04.755708-08 2025-02-28 06:46:05.801511-08 test t A100 \N t \N \N \N {} -3237 1133 2025-02-28 07:17:33.624616-08 2025-02-28 07:02:03.837416-08 benchmark t A100 \N t \N \N \N {} -3248 1135 2025-02-28 05:37:54.093967-08 2025-02-28 05:45:04.283128-08 benchmark t A100 \N t \N \N \N {} -3249 1135 2025-02-28 06:33:33.396017-08 2025-02-28 06:15:49.659969-08 leaderboard t A100 0.0031071463333333335 t \N \N \N {} -6177 1901 2025-03-11 14:17:16.056405-07 2025-03-11 12:39:24.253923-07 test t T4 \N f \N \N \N {} -6347 1967 2025-03-13 13:09:40.841714-07 2025-03-13 12:52:38.371098-07 test f T4 \N f \N \N \N {} -6496 2014 2025-03-14 11:32:31.174534-07 2025-03-14 10:45:59.582663-07 test f H100 \N f \N \N \N {} -6601 2045 2025-03-14 13:39:24.594274-07 2025-03-14 12:37:33.021169-07 test t A100 \N t \N \N \N {} -6602 2045 2025-03-14 13:09:25.371778-07 2025-03-14 12:02:22.248161-07 benchmark t A100 \N t \N \N \N {} -6603 2045 2025-03-14 12:21:14.812855-07 2025-03-14 13:54:36.938359-07 leaderboard t A100 0.0025770586666666664 t \N \N \N {} -6646 2053 2025-03-14 12:10:23.321882-07 2025-03-14 13:10:56.484171-07 leaderboard f A100 0.0024435633333333333 t \N \N \N {} -3251 1137 2025-02-28 05:50:43.617739-08 2025-02-28 06:49:39.360729-08 benchmark f A100 \N t \N \N \N {} -3257 1139 2025-02-28 06:31:44.654978-08 2025-02-28 05:38:26.805158-08 test f A100 \N t \N \N \N {} -3261 1140 2025-02-28 06:07:13.230828-08 2025-02-28 05:47:24.282991-08 benchmark f A100 \N t \N \N \N {} -3262 1141 2025-02-28 05:55:39.227255-08 2025-02-28 05:48:38.619947-08 benchmark f A100 \N t \N \N \N {} -6178 1901 2025-03-11 13:27:16.149666-07 2025-03-11 13:38:34.371907-07 test f T4 \N f \N \N \N {} -6348 1967 2025-03-13 13:02:53.563358-07 2025-03-13 13:01:27.915249-07 test t T4 \N f \N \N \N {} -6497 2014 2025-03-14 12:15:59.319003-07 2025-03-14 11:30:37.118914-07 test t H100 \N f \N \N \N {} -6604 2046 2025-03-14 13:43:57.061271-07 2025-03-14 13:07:50.209665-07 test t A100 \N t \N \N \N {} -6605 2046 2025-03-14 12:17:52.139426-07 2025-03-14 12:29:22.656383-07 benchmark t A100 \N t \N \N \N {} -6606 2046 2025-03-14 12:53:29.636257-07 2025-03-14 12:33:45.393318-07 leaderboard t A100 0.00256954 t \N \N \N {} -3264 1142 2025-02-28 07:20:46.488258-08 2025-02-28 06:11:17.956226-08 benchmark f A100 \N t \N \N \N {} -3265 1142 2025-02-28 07:11:08.133535-08 2025-02-28 06:09:41.048274-08 leaderboard f A100 0.0030866826666666666 t \N \N \N {} -6179 1902 2025-03-11 17:31:19.851556-07 2025-03-11 16:59:27.86489-07 test t T4 \N f \N \N \N {} -6349 1968 2025-03-13 12:16:15.487592-07 2025-03-13 13:01:52.748945-07 test t T4 \N f \N \N \N {} -6498 2015 2025-03-14 10:52:26.469597-07 2025-03-14 10:53:06.720164-07 test f H100 \N t \N \N \N {} -6499 2015 2025-03-14 11:34:32.855847-07 2025-03-14 10:47:26.771183-07 benchmark f H100 \N t \N \N \N {} -3267 1142 2025-02-28 07:07:31.550653-08 2025-02-28 06:39:21.840614-08 benchmark t A100 \N t \N \N \N {} -3268 1142 2025-02-28 06:19:53.446652-08 2025-02-28 05:31:10.708334-08 leaderboard t A100 0.0030884806666666665 t \N \N \N {} -6180 1902 2025-03-11 16:42:29.121174-07 2025-03-11 17:29:34.053097-07 test f T4 \N f \N \N \N {} -6350 1968 2025-03-13 11:36:52.447175-07 2025-03-13 11:56:08.746664-07 test f T4 \N f \N \N \N {} -6501 2015 2025-03-14 11:04:29.106551-07 2025-03-14 11:04:06.594233-07 test t H100 \N t \N \N \N {} -6502 2015 2025-03-14 10:46:29.195352-07 2025-03-14 11:29:56.856874-07 benchmark t H100 \N t \N \N \N {} -6503 2015 2025-03-14 11:02:59.180189-07 2025-03-14 11:37:31.767013-07 leaderboard t H100 0.006124215666666667 t \N \N \N {} -3270 1143 2025-02-28 07:20:25.006918-08 2025-02-28 06:39:34.217517-08 benchmark t A100 \N t \N \N \N {} -3271 1143 2025-02-28 06:14:53.755216-08 2025-02-28 05:57:39.774657-08 leaderboard t A100 0.0031000976666666663 t \N \N \N {} -6181 1903 2025-03-11 16:58:37.492878-07 2025-03-11 15:58:30.703546-07 test t T4 \N t \N \N \N {} -6182 1903 2025-03-11 17:18:44.559625-07 2025-03-11 15:57:14.031513-07 benchmark t T4 \N t \N \N \N {} -6183 1903 2025-03-11 16:37:25.986127-07 2025-03-11 16:28:32.33402-07 leaderboard t T4 3.1959500263333336 t \N \N \N {} -6351 1969 2025-03-13 11:55:17.679659-07 2025-03-13 12:30:30.977919-07 test t T4 \N f \N \N \N {} -3273 1143 2025-02-28 07:13:08.532252-08 2025-02-28 06:24:09.587417-08 benchmark f A100 \N t \N \N \N {} -6353 1970 2025-03-13 13:09:01.840724-07 2025-03-13 13:09:06.040381-07 test t T4 \N f \N \N \N {} -6507 2017 2025-03-14 12:15:09.410382-07 2025-03-14 12:08:16.887223-07 leaderboard t A100 0.0024410596666666665 t \N \N \N {} -3300 1148 2025-02-28 07:07:41.615297-08 2025-02-28 07:39:21.848265-08 benchmark t H100 \N t \N \N \N {} -3301 1148 2025-02-28 06:10:52.271101-08 2025-02-28 06:58:11.397489-08 leaderboard t H100 0.001857518 t \N \N \N {} -3302 1148 2025-02-28 06:00:16.905751-08 2025-02-28 06:14:31.098179-08 test t L4 \N t \N \N \N {} -3303 1148 2025-02-28 07:52:17.086381-08 2025-02-28 06:16:18.985978-08 benchmark t L4 \N t \N \N \N {} -3304 1148 2025-02-28 07:32:12.045626-08 2025-02-28 06:24:56.573657-08 leaderboard t L4 0.008826167333333334 t \N \N \N {} -3307 1148 2025-02-28 07:29:43.892232-08 2025-02-28 06:09:46.083173-08 leaderboard f L4 0.008876066 t \N \N \N {} -3308 1148 2025-02-28 06:13:12.466775-08 2025-02-28 07:21:14.017377-08 test f T4 \N t \N \N \N {} -3309 1148 2025-02-28 06:14:05.077253-08 2025-02-28 07:11:50.628783-08 benchmark f T4 \N t \N \N \N {} -3310 1148 2025-02-28 06:45:02.322758-08 2025-02-28 06:31:27.319188-08 leaderboard f T4 0.010512813666666666 t \N \N \N {} -3313 1151 2025-02-28 06:13:28.870279-08 2025-02-28 06:59:04.57498-08 test f H100 \N f \N \N \N {} -3314 1151 2025-02-28 06:53:49.334214-08 2025-02-28 07:39:29.099662-08 test f A100 \N f \N \N \N {} -3315 1151 2025-02-28 06:23:38.170481-08 2025-02-28 06:10:53.877168-08 test t A100 \N f \N \N \N {} -3320 1151 2025-02-28 07:07:06.317012-08 2025-02-28 07:47:22.348612-08 test f T4 \N f \N \N \N {} -3321 1152 2025-02-28 06:41:39.144469-08 2025-02-28 07:15:44.323297-08 test f A100 \N t \N \N \N {} -3322 1152 2025-02-28 06:33:30.747147-08 2025-02-28 07:19:24.710801-08 benchmark f A100 \N t \N \N \N {} -3323 1152 2025-02-28 06:13:17.846448-08 2025-02-28 07:16:53.101236-08 leaderboard f A100 0.003092475 t \N \N \N {} -3532 1205 2025-02-28 11:36:17.404133-08 2025-02-28 11:04:59.294896-08 test t A100 \N t \N \N \N {} -3324 1152 2025-02-28 07:18:58.437402-08 2025-02-28 06:19:11.553801-08 test t A100 \N t \N \N \N {} -3325 1152 2025-02-28 07:55:29.183414-08 2025-02-28 07:03:55.443509-08 benchmark t A100 \N t \N \N \N {} -3326 1152 2025-02-28 07:19:25.423303-08 2025-02-28 07:50:35.236223-08 leaderboard t A100 0.0031075596666666665 t \N \N \N {} -3329 1153 2025-02-28 06:25:00.374655-08 2025-02-28 07:30:25.54331-08 test t L4 \N f \N \N \N {} -3334 1153 2025-02-28 07:49:11.602135-08 2025-02-28 07:52:08.469201-08 test f L4 \N f \N \N \N {} -3335 1154 2025-02-28 07:00:59.730078-08 2025-02-28 06:49:51.977949-08 test t A100 \N t \N \N \N {} -3336 1154 2025-02-28 07:54:33.813629-08 2025-02-28 06:56:00.534875-08 benchmark t A100 \N t \N \N \N {} -3344 1155 2025-02-28 07:18:52.719651-08 2025-02-28 07:58:30.473969-08 test f A100 \N t \N \N \N {} -3345 1155 2025-02-28 06:18:08.185243-08 2025-02-28 06:59:20.358203-08 benchmark f A100 \N t \N \N \N {} -3346 1155 2025-02-28 07:02:45.365698-08 2025-02-28 06:33:19.989005-08 leaderboard f A100 0.00308959 t \N \N \N {} -3347 1156 2025-02-28 07:59:44.021356-08 2025-02-28 06:48:49.388888-08 test f A100 \N t \N \N \N {} -3350 1156 2025-02-28 06:29:45.868814-08 2025-02-28 06:37:20.655964-08 test t A100 \N t \N \N \N {} -3351 1156 2025-02-28 07:13:45.617041-08 2025-02-28 07:15:15.742271-08 benchmark t A100 \N t \N \N \N {} -3352 1156 2025-02-28 07:46:00.514913-08 2025-02-28 06:43:16.480409-08 leaderboard t A100 0.0031107886666666665 t \N \N \N {} -3354 1157 2025-02-28 06:33:28.648011-08 2025-02-28 06:43:24.152901-08 benchmark t A100 \N t \N \N \N {} -3355 1157 2025-02-28 07:45:51.654693-08 2025-02-28 07:44:31.010515-08 leaderboard t A100 0.0030852116666666663 t \N \N \N {} -6189 1905 2025-03-11 17:31:04.799276-07 2025-03-11 17:23:51.876169-07 test t T4 \N f \N \N \N {} -6355 1971 2025-03-13 13:24:12.401709-07 2025-03-13 13:32:14.413028-07 test f T4 \N f \N \N \N {} -6509 2017 2025-03-14 11:29:46.886636-07 2025-03-14 12:19:10.774689-07 benchmark f A100 \N t \N \N \N {} -3357 1157 2025-02-28 08:03:49.481801-08 2025-02-28 08:00:06.310818-08 benchmark f A100 \N t \N \N \N {} -3358 1157 2025-02-28 07:39:56.271986-08 2025-02-28 07:00:59.378014-08 leaderboard f A100 0.0030816896666666666 t \N \N \N {} -3360 1159 2025-02-28 07:39:08.622355-08 2025-02-28 08:38:10.734027-08 test f T4 \N f \N \N \N {} -3361 1160 2025-02-28 09:26:30.209671-08 2025-02-28 09:51:28.232565-08 test f L4 \N f \N \N \N {} -3362 1160 2025-02-28 08:13:42.830297-08 2025-02-28 08:36:53.136944-08 test t L4 \N f \N \N \N {} -3367 1163 2025-02-28 09:08:01.615266-08 2025-02-28 09:04:38.110604-08 test t T4 \N t \N \N \N {} -3368 1163 2025-02-28 08:47:34.73436-08 2025-02-28 09:40:07.599281-08 benchmark t T4 \N t \N \N \N {} -3370 1163 2025-02-28 10:03:37.527045-08 2025-02-28 10:11:34.393096-08 test f T4 \N f \N \N \N {} -3371 1164 2025-02-28 10:01:44.770216-08 2025-02-28 09:27:21.603234-08 test f L4 \N t \N \N \N {} -3372 1164 2025-02-28 08:47:05.293177-08 2025-02-28 10:23:25.384173-08 benchmark f L4 \N t \N \N \N {} -3377 1165 2025-02-28 09:21:40.284625-08 2025-02-28 10:30:06.106699-08 test f L4 \N t \N \N \N {} -3378 1165 2025-02-28 08:56:55.571472-08 2025-02-28 09:49:21.797242-08 benchmark f L4 \N t \N \N \N {} -3379 1165 2025-02-28 10:05:42.150638-08 2025-02-28 09:36:46.848902-08 leaderboard f L4 0.017076701333333333 t \N \N \N {} -3454 1190 2025-02-28 12:13:49.181929-08 2025-02-28 11:09:14.008399-08 test f A100 \N t \N \N \N {} -3380 1165 2025-02-28 09:57:10.477902-08 2025-02-28 08:59:43.970761-08 test t L4 \N t \N \N \N {} -3381 1165 2025-02-28 09:34:07.882518-08 2025-02-28 10:28:42.342832-08 benchmark t L4 \N t \N \N \N {} -3382 1165 2025-02-28 10:25:49.905778-08 2025-02-28 08:45:12.787062-08 leaderboard t L4 0.017087161666666666 t \N \N \N {} -3383 1166 2025-02-28 10:09:06.401999-08 2025-02-28 09:14:44.292073-08 test t L4 \N t \N \N \N {} -3384 1166 2025-02-28 09:27:54.175727-08 2025-02-28 08:41:58.508616-08 benchmark t L4 \N t \N \N \N {} -3387 1166 2025-02-28 09:50:29.259635-08 2025-02-28 10:07:29.279896-08 benchmark f L4 \N t \N \N \N {} -3388 1166 2025-02-28 09:42:07.63904-08 2025-02-28 10:09:52.762874-08 leaderboard f L4 0.01713320033333333 t \N \N \N {} -3389 1167 2025-02-28 10:08:48.950368-08 2025-02-28 09:00:40.626022-08 test t L4 \N f \N \N \N {} -3390 1167 2025-02-28 09:23:41.075458-08 2025-02-28 08:55:11.552134-08 test f L4 \N f \N \N \N {} -3544 1207 2025-02-28 12:21:19.174948-08 2025-02-28 11:46:49.287104-08 test f A100 \N t \N \N \N {} -3394 1169 2025-02-28 09:19:45.607358-08 2025-02-28 10:30:06.679512-08 test t L4 \N f \N \N \N {} -3395 1169 2025-02-28 10:06:46.387305-08 2025-02-28 09:43:30.292106-08 test f L4 \N f \N \N \N {} -3397 1171 2025-02-28 09:03:44.961516-08 2025-02-28 10:20:27.853337-08 test f L4 \N f \N \N \N {} -3405 1174 2025-02-28 10:39:36.975544-08 2025-02-28 09:52:14.8899-08 benchmark t L4 \N t \N \N \N {} -3400 1173 2025-02-28 10:29:49.463718-08 2025-02-28 10:21:22.102387-08 test f L4 \N t \N \N \N {} -3401 1173 2025-02-28 09:21:57.128462-08 2025-02-28 10:30:59.993678-08 benchmark f L4 \N t \N \N \N {} -3434 1185 2025-02-28 11:34:12.254431-08 2025-02-28 10:21:43.385984-08 test t L4 \N t \N \N \N {} -3435 1185 2025-02-28 11:18:57.057952-08 2025-02-28 11:28:37.311547-08 benchmark t L4 \N t \N \N \N {} -3439 1186 2025-02-28 10:55:26.766127-08 2025-02-28 11:07:39.44103-08 benchmark f L4 \N t \N \N \N {} -3440 1186 2025-02-28 10:10:12.112939-08 2025-02-28 11:10:32.049899-08 leaderboard f L4 0.01707861033333333 t \N \N \N {} -3441 1186 2025-02-28 09:42:05.101791-08 2025-02-28 10:27:34.733276-08 test t L4 \N t \N \N \N {} -3442 1186 2025-02-28 10:06:18.981477-08 2025-02-28 10:02:38.94075-08 benchmark t L4 \N t \N \N \N {} -3449 1188 2025-02-28 11:22:41.568508-08 2025-02-28 10:32:23.811511-08 test f A100 \N t \N \N \N {} -3450 1188 2025-02-28 10:37:43.385883-08 2025-02-28 11:17:39.7834-08 benchmark f A100 \N t \N \N \N {} -3456 1190 2025-02-28 10:41:28.207826-08 2025-02-28 12:14:51.122436-08 leaderboard f A100 0.002069882 t \N \N \N {} -3457 1190 2025-02-28 11:29:53.359486-08 2025-02-28 11:55:18.355744-08 test t A100 \N t \N \N \N {} -3458 1190 2025-02-28 10:37:33.37378-08 2025-02-28 11:30:33.125044-08 benchmark t A100 \N t \N \N \N {} -3459 1190 2025-02-28 11:02:25.492442-08 2025-02-28 11:05:52.473542-08 leaderboard t A100 0.002045183 t \N \N \N {} -6190 1905 2025-03-11 16:26:41.241921-07 2025-03-11 16:38:33.020666-07 test f T4 \N f \N \N \N {} -6510 2017 2025-03-14 12:01:53.782121-07 2025-03-14 10:58:53.397351-07 leaderboard f A100 0.0025043896666666664 t \N \N \N {} -3461 1191 2025-02-28 10:52:09.093285-08 2025-02-28 11:24:50.914766-08 benchmark f A100 \N t \N \N \N {} -3462 1191 2025-02-28 12:13:09.43073-08 2025-02-28 11:11:59.107864-08 leaderboard f A100 0.003088071 t \N \N \N {} -6191 1906 2025-03-11 16:40:31.677341-07 2025-03-11 17:30:01.999781-07 test t T4 \N f \N \N \N {} -6356 1971 2025-03-13 12:18:33.704663-07 2025-03-13 12:52:16.173084-07 test t T4 \N f \N \N \N {} -6511 2018 2025-03-14 11:30:51.790617-07 2025-03-14 12:46:33.976449-07 test t A100 \N t \N \N \N {} -6512 2018 2025-03-14 11:50:37.040812-07 2025-03-14 12:00:32.698091-07 benchmark t A100 \N t \N \N \N {} -6513 2018 2025-03-14 11:58:09.461167-07 2025-03-14 10:57:34.439949-07 leaderboard t A100 0.0024541096666666666 t \N \N \N {} -3464 1191 2025-02-28 11:06:30.414003-08 2025-02-28 10:53:55.756396-08 benchmark t A100 \N t \N \N \N {} -3465 1191 2025-02-28 10:54:02.19782-08 2025-02-28 11:07:04.11224-08 leaderboard t A100 0.0030866083333333334 t \N \N \N {} -6192 1906 2025-03-11 18:04:00.474946-07 2025-03-11 18:14:14.534699-07 test f T4 \N f \N \N \N {} -6357 1972 2025-03-13 13:34:07.292711-07 2025-03-13 14:11:37.509333-07 test t T4 \N t \N \N \N {} -6358 1972 2025-03-13 13:17:51.104355-07 2025-03-13 14:01:28.265004-07 benchmark t T4 \N f \N \N \N {} -6516 2018 2025-03-14 10:52:38.328-07 2025-03-14 12:27:11.650928-07 leaderboard f A100 0.0024775536666666667 t \N \N \N {} -3469 1193 2025-02-28 12:13:49.642175-08 2025-02-28 11:55:09.712002-08 benchmark f A100 \N t \N \N \N {} -3470 1193 2025-02-28 11:12:47.050744-08 2025-02-28 11:14:55.313376-08 leaderboard f A100 0.0031209246666666665 t \N \N \N {} -6193 1907 2025-03-11 16:49:57.729617-07 2025-03-11 17:48:28.815284-07 test f T4 \N f \N \N \N {} -6359 1972 2025-03-13 13:07:47.502676-07 2025-03-13 14:27:21.253575-07 test f T4 \N t \N \N \N {} -3472 1193 2025-02-28 10:47:16.91505-08 2025-02-28 11:18:49.513731-08 benchmark t A100 \N t \N \N \N {} -3473 1193 2025-02-28 11:05:06.759594-08 2025-02-28 12:04:22.05689-08 leaderboard t A100 0.003093471 t \N \N \N {} -6194 1907 2025-03-11 17:02:18.345331-07 2025-03-11 16:38:32.775938-07 test t T4 \N f \N \N \N {} -6361 1973 2025-03-13 13:34:46.334274-07 2025-03-13 14:01:13.147474-07 test f T4 \N t \N \N \N {} -6362 1973 2025-03-13 13:37:07.283208-07 2025-03-13 15:08:05.351976-07 benchmark f T4 \N f \N \N \N {} -6518 2020 2025-03-14 11:30:41.762022-07 2025-03-14 11:12:09.117466-07 test f A100 \N t \N \N \N {} -6519 2020 2025-03-14 11:13:39.719926-07 2025-03-14 11:25:21.536972-07 benchmark f A100 \N t \N \N \N {} -6520 2020 2025-03-14 12:06:50.863502-07 2025-03-14 12:44:38.653958-07 leaderboard f A100 0.0025181186666666665 t \N \N \N {} -3476 1196 2025-02-28 10:54:21.072266-08 2025-02-28 11:37:50.81538-08 benchmark t A100 \N t \N \N \N {} -3477 1196 2025-02-28 12:24:50.73577-08 2025-02-28 11:42:01.495948-08 leaderboard t A100 0.0030894043333333336 t \N \N \N {} -6195 1908 2025-03-11 16:33:13.825423-07 2025-03-11 17:00:19.916832-07 test t T4 \N f \N \N \N {} -3484 1195 2025-02-28 11:31:48.964942-08 2025-02-28 11:27:19.249047-08 benchmark t A100 \N f \N \N \N {} -6365 1974 2025-03-13 15:18:06.293763-07 2025-03-13 14:44:47.284631-07 test f T4 \N t \N \N \N {} -3486 1197 2025-02-28 12:26:33.136784-08 2025-02-28 11:24:39.011923-08 benchmark f A100 \N t \N \N \N {} -3487 1197 2025-02-28 11:54:54.56215-08 2025-02-28 12:26:07.45337-08 leaderboard f A100 0.0030942576666666663 t \N \N \N {} -6366 1974 2025-03-13 14:15:36.374645-07 2025-03-13 14:09:23.053095-07 benchmark f T4 \N f \N \N \N {} -6522 2020 2025-03-14 12:18:54.728315-07 2025-03-14 12:44:10.914453-07 benchmark t A100 \N t \N \N \N {} -3489 1197 2025-02-28 11:23:03.004299-08 2025-02-28 11:03:53.165609-08 benchmark t A100 \N t \N \N \N {} -3490 1197 2025-02-28 11:33:31.529975-08 2025-02-28 11:07:28.938258-08 leaderboard t A100 0.0030712816666666667 t \N \N \N {} -6197 1909 2025-03-11 18:17:26.032444-07 2025-03-11 17:19:28.72111-07 test t T4 \N f \N \N \N {} -6368 1974 2025-03-13 14:55:40.171664-07 2025-03-13 14:12:41.864308-07 benchmark t T4 \N f \N \N \N {} -6523 2020 2025-03-14 12:53:35.58379-07 2025-03-14 11:54:41.519798-07 leaderboard t A100 0.002498382 t \N \N \N {} -6524 2021 2025-03-14 12:36:39.402806-07 2025-03-14 11:08:03.626394-07 test t A100 \N t \N \N \N {} -6525 2021 2025-03-14 11:24:50.232214-07 2025-03-14 12:29:14.201976-07 benchmark t A100 \N t \N \N \N {} -6526 2021 2025-03-14 12:39:45.622642-07 2025-03-14 11:23:16.261268-07 leaderboard t A100 0.0025124666666666664 t \N \N \N {} -6612 2047 2025-03-14 13:18:33.788335-07 2025-03-14 13:24:29.736685-07 leaderboard f A100 0.0025605253333333333 t \N \N \N {} -6527 2021 2025-03-14 12:10:21.085092-07 2025-03-14 11:31:54.576597-07 test f A100 \N t \N \N \N {} -6374 1977 2025-03-13 16:02:01.798258-07 2025-03-13 15:38:46.117793-07 benchmark t T4 \N f \N \N \N {} -6536 2023 2025-03-14 12:00:31.556878-07 2025-03-14 12:46:54.788332-07 test t A100 \N t \N \N \N {} -6537 2023 2025-03-14 12:32:44.743863-07 2025-03-14 12:54:06.787263-07 benchmark t A100 \N t \N \N \N {} -6538 2023 2025-03-14 12:49:53.790312-07 2025-03-14 12:00:47.817767-07 leaderboard t A100 0.0025183806666666665 t \N \N \N {} -3507 1200 2025-02-28 12:16:30.548788-08 2025-02-28 11:43:54.947982-08 benchmark t A100 \N t \N \N \N {} -3508 1200 2025-02-28 11:28:38.825541-08 2025-02-28 11:10:09.664227-08 leaderboard t A100 0.003144041 t \N \N \N {} -6203 1912 2025-03-11 17:08:06.730232-07 2025-03-11 17:58:19.821741-07 test f T4 \N f \N \N \N {} -6377 1978 2025-03-13 15:54:45.454946-07 2025-03-13 16:33:05.355765-07 benchmark f T4 \N t \N \N \N {} -6541 2023 2025-03-14 11:06:04.095739-07 2025-03-14 11:57:33.138195-07 leaderboard f A100 0.0024532393333333334 t \N \N \N {} -6618 2048 2025-03-14 12:42:53.553414-07 2025-03-14 13:49:27.329988-07 leaderboard t A100 0.0025746103333333333 t \N \N \N {} -6648 2054 2025-03-14 12:06:53.802343-07 2025-03-14 13:37:02.957155-07 benchmark f A100 \N t \N \N \N {} -6649 2054 2025-03-14 12:36:50.625847-07 2025-03-14 12:35:25.488371-07 leaderboard f A100 0.0024409133333333334 t \N \N \N {} -3513 1201 2025-02-28 12:22:24.212195-08 2025-02-28 12:18:25.901829-08 benchmark t A100 \N t \N \N \N {} -3514 1201 2025-02-28 12:24:03.792716-08 2025-02-28 12:13:02.802944-08 leaderboard t A100 0.0031015336666666664 t \N \N \N {} -6378 1979 2025-03-13 17:23:58.186753-07 2025-03-13 16:06:55.720179-07 benchmark f T4 \N f \N \N \N {} -6543 2025 2025-03-14 12:41:00.561242-07 2025-03-14 11:46:24.982291-07 test t A100 \N t \N \N \N {} -6544 2025 2025-03-14 12:20:54.337724-07 2025-03-14 11:31:16.216919-07 benchmark t A100 \N t \N \N \N {} -6545 2025 2025-03-14 11:25:51.951167-07 2025-03-14 12:23:37.08102-07 leaderboard t A100 0.0025238436666666663 t \N \N \N {} -6620 2048 2025-03-14 12:30:34.657105-07 2025-03-14 12:13:05.701347-07 benchmark f A100 \N t \N \N \N {} -6621 2048 2025-03-14 12:33:05.301709-07 2025-03-14 13:37:25.906329-07 leaderboard f A100 0.002544002 t \N \N \N {} -3519 1202 2025-02-28 11:11:30.376958-08 2025-02-28 11:01:50.55658-08 benchmark t A100 \N t \N \N \N {} -3520 1202 2025-02-28 11:43:16.373234-08 2025-02-28 12:31:24.408715-08 leaderboard t A100 0.003084011 t \N \N \N {} -6207 1914 2025-03-11 17:36:02.063163-07 2025-03-11 18:44:14.758287-07 test f T4 \N f \N \N \N {} -6380 1980 2025-03-13 15:58:51.960023-07 2025-03-13 16:24:59.128925-07 test f T4 \N f \N \N \N {} -6546 2025 2025-03-14 12:53:00.880967-07 2025-03-14 12:23:03.100245-07 test f A100 \N t \N \N \N {} -6547 2025 2025-03-14 11:06:13.671532-07 2025-03-14 11:10:58.899444-07 benchmark f A100 \N t \N \N \N {} -6622 2049 2025-03-14 12:01:06.825573-07 2025-03-14 12:10:07.768513-07 test f A100 \N t \N \N \N {} -6208 1914 2025-03-11 17:46:09.416055-07 2025-03-11 18:43:51.806087-07 test t T4 \N f \N \N \N {} -6381 1981 2025-03-13 17:27:47.16164-07 2025-03-13 17:00:09.810894-07 test t T4 \N f \N \N \N {} -6209 1915 2025-03-11 17:02:30.670995-07 2025-03-11 18:34:35.253755-07 test f T4 \N f \N \N \N {} -6210 1915 2025-03-11 18:19:24.18935-07 2025-03-11 16:56:01.127871-07 test t T4 \N f \N \N \N {} -6212 1916 2025-03-11 18:37:26.217772-07 2025-03-11 18:26:08.828412-07 test t T4 \N f \N \N \N {} -6385 1983 2025-03-13 16:53:42.93878-07 2025-03-13 18:01:17.86942-07 test t T4 \N f \N \N \N {} -6213 1917 2025-03-11 18:49:08.446554-07 2025-03-11 18:23:51.890625-07 test t T4 \N f \N \N \N {} -6386 1983 2025-03-13 16:59:20.147802-07 2025-03-13 17:23:05.485124-07 test f T4 \N f \N \N \N {} -6556 2028 2025-03-14 13:11:22.628618-07 2025-03-14 12:15:44.55829-07 test f A100 \N t \N \N \N {} -6214 1917 2025-03-11 18:57:56.391885-07 2025-03-11 18:40:03.839455-07 test f T4 \N f \N \N \N {} -6387 1984 2025-03-13 17:51:21.236837-07 2025-03-13 16:43:28.14984-07 test t T4 \N f \N \N \N {} -6557 2029 2025-03-14 12:34:22.237304-07 2025-03-14 11:29:44.596311-07 test t T4 \N f \N \N \N {} -6627 2049 2025-03-14 13:14:24.077723-07 2025-03-14 12:11:10.400483-07 leaderboard t A100 0.0031591056666666665 t \N \N \N {} -3545 1207 2025-02-28 11:34:32.128279-08 2025-02-28 10:48:07.092517-08 benchmark f A100 \N t \N \N \N {} -6388 1984 2025-03-13 17:46:54.072499-07 2025-03-13 16:07:38.434852-07 test f T4 \N f \N \N \N {} -6558 2029 2025-03-14 13:24:40.101943-07 2025-03-14 12:23:21.049241-07 test f T4 \N f \N \N \N {} -6628 2050 2025-03-14 13:02:18.585241-07 2025-03-14 12:00:19.719197-07 test f A100 \N t \N \N \N {} -6680 2068 2025-03-14 15:33:14.472771-07 2025-03-14 16:02:51.078416-07 test f T4 \N f \N \N \N {} -3548 1208 2025-02-28 11:47:51.556932-08 2025-02-28 11:49:48.214291-08 benchmark f A100 \N t \N \N \N {} -3549 1208 2025-02-28 12:21:05.688454-08 2025-02-28 11:23:23.692653-08 leaderboard f A100 0.003099421 t \N \N \N {} -6389 1985 2025-03-13 16:57:14.22982-07 2025-03-13 17:30:21.244746-07 test t T4 \N f \N \N \N {} -7055 2184 2025-03-16 20:46:00.165975-07 2025-03-16 21:45:58.753749-07 test f L4 \N f \N \N \N {} -3551 1208 2025-02-28 12:08:51.038645-08 2025-02-28 11:50:46.059027-08 benchmark t A100 \N t \N \N \N {} -3552 1208 2025-02-28 11:11:38.66471-08 2025-02-28 12:18:11.410389-08 leaderboard t A100 0.0030721586666666665 t \N \N \N {} -6390 1985 2025-03-13 16:22:49.557696-07 2025-03-13 17:03:17.529052-07 test f T4 \N f \N \N \N {} -7056 2184 2025-03-16 21:28:43.042415-07 2025-03-16 21:41:54.202912-07 test t L4 \N f \N \N \N {} -3554 1209 2025-02-28 11:42:34.402738-08 2025-02-28 10:58:34.409829-08 benchmark t A100 \N t \N \N \N {} -3555 1209 2025-02-28 12:08:05.685399-08 2025-02-28 12:29:20.539116-08 leaderboard t A100 0.0030725803333333337 t \N \N \N {} -6391 1986 2025-03-13 17:33:05.58602-07 2025-03-13 17:03:31.170452-07 test f T4 \N f \N \N \N {} -6662 2059 2025-03-14 13:23:27.383679-07 2025-03-14 13:38:22.037388-07 test f T4 \N f \N \N \N {} -3557 1209 2025-02-28 10:54:49.669617-08 2025-02-28 12:37:28.504084-08 benchmark f A100 \N t \N \N \N {} -3558 1209 2025-02-28 12:46:50.125484-08 2025-02-28 10:53:05.190346-08 leaderboard f A100 0.003090619 t \N \N \N {} -6392 1986 2025-03-13 18:05:36.821373-07 2025-03-13 16:18:41.422227-07 test t T4 \N f \N \N \N {} -6559 2030 2025-03-14 11:43:33.554172-07 2025-03-14 11:45:17.444192-07 benchmark f A100 \N t \N \N \N {} -3560 1210 2025-02-28 12:25:24.784425-08 2025-02-28 11:26:41.263715-08 benchmark f A100 \N t \N \N \N {} -3561 1210 2025-02-28 10:49:57.408057-08 2025-02-28 10:50:18.625288-08 leaderboard f A100 0.0030909023333333336 t \N \N \N {} -6393 1987 2025-03-13 17:09:40.617657-07 2025-03-13 18:01:53.260744-07 test f T4 \N f \N \N \N {} -3563 1210 2025-02-28 11:47:01.61943-08 2025-02-28 11:03:40.113806-08 benchmark t A100 \N t \N \N \N {} -3592 1229 2025-02-28 14:16:18.378222-08 2025-02-28 14:41:43.485886-08 leaderboard t H100 0.0011789098333333332 t \N \N \N {} -3599 1231 2025-02-28 14:24:45.98762-08 2025-02-28 14:33:38.933844-08 leaderboard t H100 0.0012175512 t \N \N \N {} -3600 1232 2025-02-28 13:31:10.936218-08 2025-02-28 14:19:19.455477-08 test t A100 \N t \N \N \N {} -3601 1232 2025-02-28 14:40:09.495449-08 2025-02-28 13:28:50.540847-08 benchmark t A100 \N t \N \N \N {} -3602 1232 2025-02-28 14:11:02.148305-08 2025-02-28 13:20:25.431282-08 leaderboard t A100 0.0018670176 t \N \N \N {} -3603 1232 2025-02-28 13:32:32.0241-08 2025-02-28 14:31:24.527828-08 test f A100 \N t \N \N \N {} -3604 1232 2025-02-28 13:20:53.784469-08 2025-02-28 14:29:17.100087-08 benchmark f A100 \N t \N \N \N {} -3629 1240 2025-03-01 01:07:27.030682-08 2025-03-01 00:11:34.566732-08 test t A100 \N f \N \N \N {} -3648 1248 2025-03-01 01:50:58.464696-08 2025-03-01 00:44:11.917362-08 test t A100 \N t \N \N \N {} -3605 1232 2025-02-28 14:53:49.887413-08 2025-02-28 15:02:29.191933-08 leaderboard f A100 0.001868504 t \N \N \N {} -3606 1233 2025-02-28 14:00:44.074635-08 2025-02-28 14:09:25.557225-08 benchmark f A100 \N t \N \N \N {} -3607 1234 2025-02-28 14:42:03.73345-08 2025-02-28 15:08:49.030554-08 benchmark f A100 \N f \N \N \N {} -3608 1235 2025-02-28 15:06:39.526344-08 2025-02-28 15:15:55.847946-08 benchmark f A100 \N t \N \N \N {} -3609 1235 2025-02-28 14:36:31.855831-08 2025-02-28 14:45:08.753778-08 benchmark f H100 \N f \N \N \N {} -3610 1236 2025-02-28 14:22:25.170803-08 2025-02-28 15:09:04.222075-08 benchmark f H100 \N t \N \N \N {} -3611 1237 2025-02-28 14:59:01.171066-08 2025-02-28 15:22:44.422251-08 test t H100 \N t \N \N \N {} -3612 1237 2025-02-28 15:35:54.216346-08 2025-02-28 15:38:16.179312-08 benchmark t H100 \N t \N \N \N {} -3613 1237 2025-02-28 14:52:56.878083-08 2025-02-28 14:51:40.998403-08 leaderboard t H100 0.001161027 t \N \N \N {} -3614 1237 2025-02-28 16:12:12.194689-08 2025-02-28 16:07:13.458388-08 test f H100 \N f \N \N \N {} -3615 1238 2025-02-28 16:14:51.726429-08 2025-02-28 16:25:19.334709-08 benchmark f H100 \N t \N \N \N {} -3616 1238 2025-02-28 15:18:16.528728-08 2025-02-28 16:17:03.954782-08 benchmark f A100 \N t \N \N \N {} -3630 1240 2025-03-01 00:33:39.01249-08 2025-03-01 00:07:40.699626-08 test f A100 \N f \N \N \N {} -3617 1239 2025-02-28 15:32:38.229302-08 2025-02-28 15:57:09.263444-08 test t H100 \N t \N \N \N {} -3618 1239 2025-02-28 15:40:41.884458-08 2025-02-28 15:19:49.237295-08 benchmark t H100 \N t \N \N \N {} -3619 1239 2025-02-28 15:26:34.626489-08 2025-02-28 16:48:20.214012-08 leaderboard t H100 0.0011695108 t \N \N \N {} -3620 1239 2025-02-28 15:34:19.352077-08 2025-02-28 15:18:34.628786-08 test f A100 \N t \N \N \N {} -3621 1239 2025-02-28 15:34:17.50586-08 2025-02-28 15:42:25.985409-08 benchmark f A100 \N t \N \N \N {} -3622 1239 2025-02-28 15:22:37.115875-08 2025-02-28 15:55:56.769348-08 leaderboard f A100 0.002215739 t \N \N \N {} -3623 1239 2025-02-28 17:10:36.81778-08 2025-02-28 15:57:08.360846-08 test t A100 \N t \N \N \N {} -3625 1239 2025-02-28 16:44:19.965613-08 2025-02-28 15:49:32.46356-08 leaderboard t A100 0.0020725956666666667 t \N \N \N {} -3626 1239 2025-02-28 15:41:26.021642-08 2025-02-28 15:23:55.7378-08 test f H100 \N t \N \N \N {} -3627 1239 2025-02-28 16:33:01.867796-08 2025-02-28 15:24:47.499545-08 benchmark f H100 \N t \N \N \N {} -3628 1239 2025-02-28 16:49:49.757233-08 2025-02-28 16:35:02.023132-08 leaderboard f H100 0.00116604725 t \N \N \N {} -6234 1922 2025-03-12 00:22:49.917936-07 2025-03-12 00:13:52.488578-07 test t T4 \N f \N \N \N {} -6395 1988 2025-03-13 17:58:47.761639-07 2025-03-13 17:01:13.84194-07 test t T4 \N f \N \N \N {} -6560 2031 2025-03-14 13:05:32.105346-07 2025-03-14 11:29:34.501788-07 benchmark f A100 \N t \N \N \N {} -6629 2050 2025-03-14 12:58:46.09814-07 2025-03-14 12:52:50.872419-07 benchmark f A100 \N t \N \N \N {} -6630 2050 2025-03-14 12:08:39.259423-07 2025-03-14 13:48:19.785521-07 leaderboard f A100 0.002560023 t \N \N \N {} -3638 1243 2025-03-01 00:16:45.170175-08 2025-03-01 00:27:41.072541-08 test t A100 \N f \N \N \N {} -3639 1243 2025-02-28 23:56:11.182103-08 2025-03-01 00:14:59.572871-08 test f A100 \N f \N \N \N {} -6235 1922 2025-03-11 23:57:28.40312-07 2025-03-12 00:24:44.986697-07 test f T4 \N f \N \N \N {} -6396 1988 2025-03-13 16:20:45.984417-07 2025-03-13 16:27:56.674856-07 test f T4 \N f \N \N \N {} -6631 2050 2025-03-14 12:29:57.064337-07 2025-03-14 13:06:46.738173-07 test t A100 \N t \N \N \N {} -3641 1244 2025-03-01 01:27:10.713031-08 2025-03-01 01:18:07.544805-08 benchmark t A100 \N f \N \N \N {} -6236 1923 2025-03-12 01:04:23.501448-07 2025-03-12 00:05:29.54956-07 test t T4 \N f \N \N \N {} -6397 1989 2025-03-13 17:25:39.429146-07 2025-03-13 16:39:56.193057-07 test t T4 \N f \N \N \N {} -6561 2032 2025-03-14 12:04:24.116313-07 2025-03-14 12:06:21.091672-07 benchmark f A100 \N t \N \N \N {} -6632 2050 2025-03-14 12:39:52.486934-07 2025-03-14 13:43:25.534592-07 benchmark t A100 \N t \N \N \N {} -6633 2050 2025-03-14 13:36:06.263877-07 2025-03-14 13:20:17.386606-07 leaderboard t A100 0.002519189 t \N \N \N {} -3643 1244 2025-03-01 01:35:19.685312-08 2025-03-01 00:36:21.094893-08 benchmark f A100 \N f \N \N \N {} -3644 1245 2025-03-01 00:26:06.119792-08 2025-03-01 01:35:27.012514-08 test t A100 \N f \N \N \N {} -3645 1245 2025-03-01 00:43:41.46218-08 2025-02-28 23:55:12.934017-08 test f A100 \N f \N \N \N {} -3646 1246 2025-03-01 00:53:33.727311-08 2025-03-01 02:15:48.757393-08 benchmark f A100 \N f \N \N \N {} -3647 1247 2025-03-01 02:33:55.98627-08 2025-03-01 02:31:22.439703-08 benchmark f A100 \N t \N \N \N {} -6237 1923 2025-03-12 00:58:02.114277-07 2025-03-12 01:54:20.094067-07 test f T4 \N f \N \N \N {} -3649 1248 2025-03-01 01:25:05.925773-08 2025-03-01 01:09:17.783085-08 benchmark t A100 \N t \N \N \N {} -3651 1248 2025-03-01 02:17:03.556096-08 2025-03-01 02:09:10.130241-08 test f A100 \N t \N \N \N {} -3653 1248 2025-03-01 02:26:58.343458-08 2025-03-01 00:43:43.03041-08 leaderboard f A100 0.0031272273333333334 t \N \N \N {} -3654 1249 2025-03-01 02:12:30.998096-08 2025-03-01 00:50:24.715655-08 test f A100 \N t \N \N \N {} -3655 1249 2025-03-01 01:49:55.694137-08 2025-03-01 01:29:49.718531-08 benchmark f A100 \N t \N \N \N {} -3656 1249 2025-03-01 01:38:29.821048-08 2025-03-01 01:46:03.662966-08 leaderboard f A100 0.0031039466666666665 t \N \N \N {} -3889 1297 2025-03-01 02:52:44.650492-08 2025-03-01 03:23:37.6224-08 test t A100 \N t \N \N \N {} -3657 1249 2025-03-01 01:50:49.813103-08 2025-03-01 02:00:22.394174-08 test t A100 \N t \N \N \N {} -3669 1251 2025-03-01 01:21:22.679753-08 2025-03-01 02:00:57.562983-08 test f A100 \N t \N \N \N {} -3670 1251 2025-03-01 02:27:13.746914-08 2025-03-01 01:53:26.460343-08 benchmark f A100 \N t \N \N \N {} -3671 1251 2025-03-01 00:57:08.998138-08 2025-03-01 00:58:17.33663-08 leaderboard f A100 0.0031081036666666664 t \N \N \N {} -3672 1252 2025-03-01 01:11:34.754783-08 2025-03-01 00:52:36.138674-08 test f A100 \N t \N \N \N {} -3681 1253 2025-03-01 01:03:11.979635-08 2025-03-01 02:40:30.398743-08 test f A100 \N t \N \N \N {} -3910 1301 2025-03-01 03:46:47.874489-08 2025-03-01 03:33:33.484922-08 test f A100 \N f \N \N \N {} -3673 1252 2025-03-01 02:02:20.431435-08 2025-03-01 01:05:47.013515-08 benchmark f A100 \N t \N \N \N {} -3674 1252 2025-03-01 02:12:52.290378-08 2025-03-01 01:46:02.154693-08 leaderboard f A100 0.0031048003333333336 t \N \N \N {} -3676 1252 2025-03-01 01:03:12.897497-08 2025-03-01 01:26:39.062279-08 benchmark t A100 \N t \N \N \N {} -3677 1252 2025-03-01 01:38:06.493139-08 2025-03-01 02:09:28.677674-08 leaderboard t A100 0.0031104436666666664 t \N \N \N {} -3678 1253 2025-03-01 02:06:10.058974-08 2025-03-01 01:30:07.280628-08 test t A100 \N t \N \N \N {} -3679 1253 2025-03-01 02:03:05.966122-08 2025-03-01 00:56:13.504406-08 benchmark t A100 \N t \N \N \N {} -3680 1253 2025-03-01 02:45:57.725737-08 2025-03-01 02:34:27.95218-08 leaderboard t A100 0.0031090656666666666 t \N \N \N {} -3682 1253 2025-03-01 02:05:39.373314-08 2025-03-01 01:20:42.226463-08 benchmark f A100 \N t \N \N \N {} -3683 1253 2025-03-01 01:49:07.509476-08 2025-03-01 01:57:14.444777-08 leaderboard f A100 0.0031048203333333335 t \N \N \N {} -3690 1260 2025-03-01 01:35:12.029569-08 2025-03-01 01:45:52.929787-08 benchmark f A100 \N t \N \N \N {} -3691 1261 2025-03-01 01:38:16.278326-08 2025-03-01 01:36:09.051317-08 test t A100 \N t \N \N \N {} -3692 1261 2025-03-01 02:49:47.45514-08 2025-03-01 03:17:46.327605-08 benchmark t A100 \N t \N \N \N {} -3693 1261 2025-03-01 02:16:44.503505-08 2025-03-01 02:01:57.713906-08 leaderboard t A100 0.0030880283333333336 t \N \N \N {} -3694 1261 2025-03-01 02:18:25.475537-08 2025-03-01 02:21:41.927369-08 test f A100 \N t \N \N \N {} -3712 1264 2025-03-01 02:56:03.049752-08 2025-03-01 01:43:52.163817-08 test t A100 \N t \N \N \N {} -3697 1262 2025-03-01 03:19:09.843073-08 2025-03-01 01:30:49.179646-08 test t A100 \N t \N \N \N {} -3698 1262 2025-03-01 03:23:21.681606-08 2025-03-01 02:15:56.73891-08 benchmark t A100 \N t \N \N \N {} -3699 1262 2025-03-01 02:06:27.293399-08 2025-03-01 02:01:32.569519-08 leaderboard t A100 0.0030787253333333336 t \N \N \N {} -3700 1262 2025-03-01 03:02:04.699757-08 2025-03-01 01:27:30.191004-08 test f A100 \N t \N \N \N {} -3701 1262 2025-03-01 01:41:08.419018-08 2025-03-01 02:33:50.455395-08 benchmark f A100 \N t \N \N \N {} -3702 1262 2025-03-01 03:18:19.489873-08 2025-03-01 02:32:54.172929-08 leaderboard f A100 0.0030775323333333336 t \N \N \N {} -3919 1304 2025-03-01 05:14:59.512003-08 2025-03-01 04:15:31.250903-08 test f A100 \N t \N \N \N {} -3704 1263 2025-03-01 03:03:11.700889-08 2025-03-01 02:34:12.760558-08 benchmark f A100 \N t \N \N \N {} -3721 1266 2025-03-01 03:29:58.775718-08 2025-03-01 02:01:14.58848-08 benchmark f A100 \N t \N \N \N {} -3722 1267 2025-03-01 02:00:41.613096-08 2025-03-01 02:39:28.789934-08 test t A100 \N t \N \N \N {} -3723 1267 2025-03-01 02:08:23.198399-08 2025-03-01 02:24:17.413187-08 benchmark t A100 \N t \N \N \N {} -3724 1267 2025-03-01 02:58:51.225291-08 2025-03-01 01:53:44.033391-08 leaderboard t A100 0.0030839456666666666 t \N \N \N {} -3725 1267 2025-03-01 02:45:10.109595-08 2025-03-01 02:41:38.775413-08 test f A100 \N t \N \N \N {} -3928 1305 2025-03-01 05:09:48.841292-08 2025-03-01 05:01:55.591158-08 test f A100 \N t \N \N \N {} -3729 1268 2025-03-01 03:05:42.17627-08 2025-03-01 02:39:10.249741-08 benchmark t A100 \N t \N \N \N {} -3730 1268 2025-03-01 01:47:00.767793-08 2025-03-01 02:57:06.798112-08 leaderboard t A100 0.0031026586666666666 t \N \N \N {} -3731 1268 2025-03-01 02:41:00.300122-08 2025-03-01 03:30:29.20737-08 test f A100 \N t \N \N \N {} -3732 1268 2025-03-01 03:04:23.203277-08 2025-03-01 01:43:41.695278-08 benchmark f A100 \N t \N \N \N {} -3733 1268 2025-03-01 01:51:37.215302-08 2025-03-01 02:38:14.824507-08 leaderboard f A100 0.0030949656666666666 t \N \N \N {} -3734 1269 2025-03-01 02:58:59.331439-08 2025-03-01 02:02:26.027818-08 test t A100 \N t \N \N \N {} -3735 1269 2025-03-01 03:35:14.847212-08 2025-03-01 02:02:44.717076-08 benchmark t A100 \N t \N \N \N {} -3736 1269 2025-03-01 01:53:25.567276-08 2025-03-01 02:33:01.154365-08 leaderboard t A100 0.0031152113333333337 t \N \N \N {} -3737 1269 2025-03-01 02:30:37.175149-08 2025-03-01 02:34:29.93941-08 test f A100 \N t \N \N \N {} -3738 1269 2025-03-01 03:04:08.336127-08 2025-03-01 02:39:05.51646-08 benchmark f A100 \N t \N \N \N {} -3739 1269 2025-03-01 03:26:54.522181-08 2025-03-01 02:30:29.400292-08 leaderboard f A100 0.003116479 t \N \N \N {} -3752 1272 2025-03-01 03:35:25.32574-08 2025-03-01 03:42:34.897532-08 leaderboard t A100 0.003070418 t \N \N \N {} -3964 1321 2025-03-01 04:25:29.824066-08 2025-03-01 05:01:10.21614-08 test t A100 \N t \N \N \N {} -3753 1273 2025-03-01 01:58:03.710454-08 2025-03-01 03:21:45.806033-08 test f A100 \N t \N \N \N {} -3754 1273 2025-03-01 03:22:29.292453-08 2025-03-01 03:42:54.655064-08 benchmark f A100 \N t \N \N \N {} -3755 1273 2025-03-01 02:32:56.792637-08 2025-03-01 03:04:59.058769-08 leaderboard f A100 0.0031262986666666663 t \N \N \N {} -3768 1275 2025-03-01 02:46:08.326312-08 2025-03-01 02:22:21.610827-08 test f A100 \N t \N \N \N {} -3777 1277 2025-03-01 02:21:07.979234-08 2025-03-01 03:47:24.50701-08 test t A100 \N t \N \N \N {} -3775 1276 2025-03-01 01:55:51.302209-08 2025-03-01 02:01:49.922507-08 benchmark t A100 \N t \N \N \N {} -3776 1276 2025-03-01 02:08:35.267407-08 2025-03-01 01:54:26.716726-08 leaderboard t A100 0.0030983713333333335 t \N \N \N {} -3778 1277 2025-03-01 03:32:26.499294-08 2025-03-01 03:46:45.069319-08 benchmark t A100 \N t \N \N \N {} -3779 1277 2025-03-01 03:38:23.774189-08 2025-03-01 02:35:30.930272-08 leaderboard t A100 0.003084539 t \N \N \N {} -3780 1277 2025-03-01 03:22:55.155356-08 2025-03-01 02:43:56.628934-08 test f A100 \N t \N \N \N {} -3784 1278 2025-03-01 02:48:23.56591-08 2025-03-01 02:33:17.354544-08 benchmark t A100 \N t \N \N \N {} -3785 1278 2025-03-01 02:39:45.41231-08 2025-03-01 02:40:12.61527-08 leaderboard t A100 0.0031055783333333335 t \N \N \N {} -3789 1279 2025-03-01 02:01:53.626081-08 2025-03-01 02:47:46.086211-08 test f A100 \N t \N \N \N {} -3786 1278 2025-03-01 02:03:03.53279-08 2025-03-01 03:47:19.946226-08 test f A100 \N t \N \N \N {} -3787 1278 2025-03-01 03:29:33.298347-08 2025-03-01 03:34:20.277338-08 benchmark f A100 \N t \N \N \N {} -3788 1278 2025-03-01 03:00:05.752988-08 2025-03-01 01:56:16.762213-08 leaderboard f A100 0.003097914 t \N \N \N {} -6238 1924 2025-03-12 01:21:46.601814-07 2025-03-12 01:32:26.514257-07 test t T4 \N f \N \N \N {} -6398 1989 2025-03-13 17:08:21.233687-07 2025-03-13 17:06:06.404998-07 test f T4 \N f \N \N \N {} -6562 2033 2025-03-14 13:12:39.948827-07 2025-03-14 13:23:53.347312-07 benchmark f A100 \N t \N \N \N {} -6634 2051 2025-03-14 12:30:34.721734-07 2025-03-14 12:30:27.296794-07 test t A100 \N t \N \N \N {} -3790 1279 2025-03-01 03:32:19.769184-08 2025-03-01 03:50:26.829557-08 benchmark f A100 \N t \N \N \N {} -3791 1279 2025-03-01 02:54:07.655455-08 2025-03-01 02:19:13.748311-08 leaderboard f A100 0.0030992133333333336 t \N \N \N {} -6399 1990 2025-03-13 17:50:57.628826-07 2025-03-13 16:49:18.239868-07 test t T4 \N f \N \N \N {} -6563 2034 2025-03-14 12:07:55.631644-07 2025-03-14 12:27:16.784091-07 benchmark f A100 \N t \N \N \N {} -7059 2185 2025-03-16 22:03:27.033571-07 2025-03-16 21:36:29.376537-07 leaderboard f L4 0.00011869082456140351 t \N \N \N {} -3793 1279 2025-03-01 03:42:52.339097-08 2025-03-01 03:50:15.909886-08 benchmark t A100 \N t \N \N \N {} -3794 1279 2025-03-01 02:12:29.931818-08 2025-03-01 03:14:22.177181-08 leaderboard t A100 0.0030700836666666667 t \N \N \N {} -6240 1925 2025-03-12 01:13:55.791064-07 2025-03-12 00:29:24.037503-07 test f T4 \N t \N \N \N {} -6244 1925 2025-03-12 01:24:08.247115-07 2025-03-12 00:16:50.797071-07 benchmark t T4 \N t \N \N \N {} -6245 1925 2025-03-12 00:39:44.345705-07 2025-03-12 01:03:34.532126-07 leaderboard t T4 3.565283209 t \N \N \N {} -6401 1991 2025-03-13 18:14:55.765999-07 2025-03-13 16:54:11.131116-07 test f T4 \N t \N \N \N {} -6250 1926 2025-03-12 02:15:10.273238-07 2025-03-12 01:01:04.027885-07 benchmark f T4 \N t \N \N \N {} -6251 1926 2025-03-12 00:50:43.580637-07 2025-03-12 00:57:06.425599-07 leaderboard f T4 3.682825032 t \N \N \N {} -6404 1991 2025-03-13 18:23:53.350354-07 2025-03-13 17:41:06.459187-07 test t T4 \N t \N \N \N {} -6405 1991 2025-03-13 18:45:41.07329-07 2025-03-13 17:03:11.875615-07 benchmark t T4 \N t \N \N \N {} -3805 1281 2025-03-01 02:40:30.487878-08 2025-03-01 02:47:22.690155-08 benchmark t A100 \N t \N \N \N {} -3806 1281 2025-03-01 03:18:03.1251-08 2025-03-01 02:07:31.048245-08 leaderboard t A100 0.0030762023333333337 t \N \N \N {} -6406 1991 2025-03-13 16:56:16.674442-07 2025-03-13 17:32:22.195013-07 leaderboard t T4 0.01198132082 t \N \N \N {} -6565 2036 2025-03-14 13:26:34.659744-07 2025-03-14 12:28:45.901302-07 benchmark f A100 \N t \N \N \N {} -6635 2051 2025-03-14 12:12:47.169659-07 2025-03-14 12:30:19.462198-07 benchmark t A100 \N t \N \N \N {} -6636 2051 2025-03-14 13:22:32.747423-07 2025-03-14 13:46:45.493463-07 leaderboard t A100 0.0025760326666666666 t \N \N \N {} -3808 1283 2025-03-01 03:57:31.233254-08 2025-03-01 03:30:41.035131-08 benchmark t A100 \N t \N \N \N {} -3809 1283 2025-03-01 02:53:01.039251-08 2025-03-01 03:11:15.949496-08 leaderboard t A100 0.0030891856666666667 t \N \N \N {} -3810 1282 2025-03-01 02:47:29.668011-08 2025-03-01 03:06:52.828571-08 benchmark f A100 \N t \N \N \N {} -3811 1282 2025-03-01 02:30:35.994805-08 2025-03-01 02:24:05.263461-08 benchmark f H100 \N t \N \N \N {} -6407 1992 2025-03-13 18:08:55.397985-07 2025-03-13 17:16:33.413217-07 test t T4 \N f \N \N \N {} -6566 2037 2025-03-14 12:48:47.844486-07 2025-03-14 12:08:55.718904-07 test f A100 \N f \N \N \N {} -6637 2051 2025-03-14 12:40:08.734201-07 2025-03-14 12:40:23.426756-07 test f A100 \N t \N \N \N {} -3813 1283 2025-03-01 04:00:42.588176-08 2025-03-01 02:20:16.080762-08 benchmark f A100 \N t \N \N \N {} -3814 1283 2025-03-01 03:40:07.853236-08 2025-03-01 03:29:58.258385-08 leaderboard f A100 0.003101622 t \N \N \N {} -3815 1284 2025-03-01 02:25:33.95253-08 2025-03-01 02:28:22.525197-08 test t A100 \N f \N \N \N {} -3816 1284 2025-03-01 03:57:37.041465-08 2025-03-01 02:25:56.015805-08 test f A100 \N f \N \N \N {} -6408 1992 2025-03-13 19:09:40.136801-07 2025-03-13 17:14:34.611515-07 test f T4 \N f \N \N \N {} -6567 2037 2025-03-14 12:36:50.459533-07 2025-03-14 12:05:58.31517-07 test t A100 \N f \N \N \N {} -3818 1285 2025-03-01 02:34:29.255307-08 2025-03-01 02:41:27.552585-08 benchmark f A100 \N t \N \N \N {} -3819 1285 2025-03-01 03:02:33.900221-08 2025-03-01 02:39:16.471311-08 leaderboard f A100 0.003196064 t \N \N \N {} -6409 1993 2025-03-13 17:37:09.987891-07 2025-03-13 17:44:10.720084-07 test t T4 \N f \N \N \N {} -6568 2038 2025-03-14 13:27:33.060334-07 2025-03-14 11:46:55.719973-07 test t A100 \N f \N \N \N {} -6638 2051 2025-03-14 14:00:30.84485-07 2025-03-14 12:56:57.304408-07 benchmark f A100 \N t \N \N \N {} -6639 2051 2025-03-14 13:35:20.196957-07 2025-03-14 12:03:45.180778-07 leaderboard f A100 0.002520242 t \N \N \N {} -3821 1285 2025-03-01 03:35:11.06709-08 2025-03-01 03:50:33.861385-08 benchmark t A100 \N t \N \N \N {} -3822 1285 2025-03-01 04:05:45.187777-08 2025-03-01 03:19:06.671204-08 leaderboard t A100 0.003205114 t \N \N \N {} -6410 1993 2025-03-13 18:31:14.592561-07 2025-03-13 18:03:31.587714-07 test f T4 \N f \N \N \N {} -6569 2038 2025-03-14 11:54:10.44922-07 2025-03-14 12:14:26.005828-07 test f A100 \N f \N \N \N {} -6640 2052 2025-03-14 13:20:11.68013-07 2025-03-14 13:54:08.817793-07 benchmark f A100 \N t \N \N \N {} -6663 2059 2025-03-14 13:43:14.834484-07 2025-03-14 13:10:23.01354-07 test t T4 \N f \N \N \N {} -3824 1286 2025-03-01 03:48:51.460228-08 2025-03-01 03:05:00.868889-08 benchmark t A100 \N t \N \N \N {} -3825 1286 2025-03-01 03:13:52.902037-08 2025-03-01 03:59:39.680247-08 leaderboard t A100 0.0032052243333333336 t \N \N \N {} -6257 1933 2025-03-12 06:29:52.272103-07 2025-03-12 06:33:58.837779-07 benchmark f A100 \N t \N \N \N {} -6411 1994 2025-03-13 17:31:54.500515-07 2025-03-13 17:50:40.571354-07 test t T4 \N f \N \N \N {} -6570 2039 2025-03-14 12:43:54.474387-07 2025-03-14 11:46:53.304327-07 test f A100 \N t \N \N \N {} -6571 2039 2025-03-14 13:36:58.274258-07 2025-03-14 11:48:23.338015-07 benchmark f A100 \N t \N \N \N {} -6572 2039 2025-03-14 12:38:10.932323-07 2025-03-14 12:39:34.102441-07 leaderboard f A100 0.0025144643333333337 t \N \N \N {} -6259 1936 2025-03-12 07:33:01.179724-07 2025-03-12 08:00:02.311465-07 benchmark f A100 \N t \N \N \N {} -6413 1995 2025-03-13 18:23:54.614105-07 2025-03-13 19:13:53.587183-07 test t T4 \N f \N \N \N {} -6576 2040 2025-03-14 12:03:45.01229-07 2025-03-14 12:44:15.413186-07 test f A100 \N t \N \N \N {} -6577 2040 2025-03-14 11:54:53.53347-07 2025-03-14 12:54:34.179949-07 benchmark f A100 \N t \N \N \N {} -6578 2040 2025-03-14 13:10:32.037477-07 2025-03-14 12:17:48.109262-07 leaderboard f A100 0.002512107 t \N \N \N {} -3833 1287 2025-03-01 02:36:47.925069-08 2025-03-01 03:26:45.302835-08 benchmark t A100 \N t \N \N \N {} -3834 1287 2025-03-01 02:32:30.809129-08 2025-03-01 03:12:32.781049-08 leaderboard t A100 0.0030916056666666666 t \N \N \N {} -6260 1937 2025-03-12 07:23:06.50947-07 2025-03-12 08:23:19.792542-07 benchmark f A100 \N f \N \N \N {} -6261 1938 2025-03-12 08:35:16.313743-07 2025-03-12 08:05:05.626744-07 benchmark f A100 \N t \N \N \N {} -6415 1996 2025-03-13 17:38:08.027955-07 2025-03-13 18:18:31.168297-07 test f T4 \N f \N \N \N {} -6582 2041 2025-03-14 13:35:12.454784-07 2025-03-14 11:47:41.393176-07 test f A100 \N t \N \N \N {} -6583 2041 2025-03-14 13:00:57.639982-07 2025-03-14 12:50:35.650726-07 benchmark f A100 \N t \N \N \N {} -3839 1288 2025-03-01 02:49:11.125792-08 2025-03-01 03:21:23.235824-08 benchmark t A100 \N t \N \N \N {} -6262 1939 2025-03-12 07:09:38.373161-07 2025-03-12 08:48:50.014584-07 test f A100 \N t \N \N \N {} -6417 1997 2025-03-13 20:13:42.139063-07 2025-03-13 19:48:53.847265-07 test t T4 \N f \N \N \N {} -6268 1940 2025-03-12 08:48:23.158902-07 2025-03-12 07:34:48.110256-07 test f H100 \N t \N \N \N {} -6271 1940 2025-03-12 08:39:14.809321-07 2025-03-12 08:06:26.839197-07 test t H100 \N t \N \N \N {} -6272 1940 2025-03-12 08:53:57.603406-07 2025-03-12 08:56:46.092949-07 benchmark t H100 \N t \N \N \N {} -6273 1940 2025-03-12 08:30:27.185852-07 2025-03-12 07:53:33.36497-07 leaderboard t H100 0.00004756101351351351 t \N \N \N {} -6584 2041 2025-03-14 11:48:11.361349-07 2025-03-14 13:07:20.826594-07 leaderboard f A100 0.00251554 t \N \N \N {} -3851 1290 2025-03-01 02:42:29.071194-08 2025-03-01 03:06:56.033212-08 benchmark t A100 \N t \N \N \N {} -3852 1290 2025-03-01 03:17:02.156523-08 2025-03-01 02:24:26.38537-08 leaderboard t A100 0.0030752533333333336 t \N \N \N {} -6274 1941 2025-03-12 08:49:13.036616-07 2025-03-12 09:04:52.719316-07 benchmark f T4 \N t \N \N \N {} -6419 1998 2025-03-14 05:01:43.261969-07 2025-03-14 04:51:13.385755-07 benchmark f A100 \N t \N \N \N {} -6585 2041 2025-03-14 12:16:33.083304-07 2025-03-14 11:57:07.432446-07 test t A100 \N t \N \N \N {} -6586 2041 2025-03-14 12:55:15.401484-07 2025-03-14 13:17:33.140416-07 benchmark t A100 \N t \N \N \N {} -3854 1291 2025-03-01 03:39:45.47163-08 2025-03-01 04:08:17.677385-08 benchmark t A100 \N t \N \N \N {} -3855 1291 2025-03-01 04:06:18.714922-08 2025-03-01 04:05:24.645808-08 leaderboard t A100 0.0030727556666666667 t \N \N \N {} -6275 1942 2025-03-12 08:13:42.287391-07 2025-03-12 08:06:35.694977-07 test f T4 \N t \N \N \N {} -6276 1942 2025-03-12 08:50:04.371499-07 2025-03-12 07:30:05.01053-07 benchmark f T4 \N t \N \N \N {} -6277 1942 2025-03-12 08:59:14.826076-07 2025-03-12 08:14:40.196742-07 leaderboard f T4 0.00013039858 t \N \N \N {} -6420 1999 2025-03-14 05:43:54.110159-07 2025-03-14 04:25:26.908913-07 test f A100 \N t \N \N \N {} -3858 1291 2025-03-01 02:47:40.294969-08 2025-03-01 03:21:35.997134-08 leaderboard f A100 0.0030834156666666667 t \N \N \N {} -6278 1942 2025-03-12 07:53:39.313803-07 2025-03-12 08:52:00.346671-07 test t T4 \N t \N \N \N {} -6279 1942 2025-03-12 07:54:27.029152-07 2025-03-12 08:11:56.444947-07 benchmark t T4 \N t \N \N \N {} -6280 1942 2025-03-12 09:01:51.96126-07 2025-03-12 07:48:32.954993-07 leaderboard t T4 0.00017005301923076922 t \N \N \N {} -6588 2042 2025-03-14 12:41:32.755275-07 2025-03-14 13:27:47.933076-07 test t A100 \N t \N \N \N {} -3860 1292 2025-03-01 03:44:44.046571-08 2025-03-01 02:59:32.783521-08 benchmark f A100 \N t \N \N \N {} -3861 1292 2025-03-01 02:54:03.416387-08 2025-03-01 04:17:02.090714-08 leaderboard f A100 0.0030798846666666665 t \N \N \N {} -3864 1292 2025-03-01 03:01:12.811119-08 2025-03-01 03:57:53.126582-08 leaderboard t A100 0.0030685383333333336 t \N \N \N {} -6284 1943 2025-03-12 07:37:15.328744-07 2025-03-12 08:00:32.114323-07 test f L4 \N t \N \N \N {} -6285 1943 2025-03-12 09:15:51.836775-07 2025-03-12 08:18:34.03295-07 benchmark f L4 \N t \N \N \N {} -6286 1943 2025-03-12 07:18:39.964262-07 2025-03-12 08:42:15.886255-07 leaderboard f L4 0.00009671902499999999 t \N \N \N {} -6589 2042 2025-03-14 13:28:36.680878-07 2025-03-14 12:42:33.70216-07 benchmark t A100 \N t \N \N \N {} -3866 1293 2025-03-01 03:45:51.800351-08 2025-03-01 04:07:15.03461-08 benchmark f A100 \N t \N \N \N {} -3867 1293 2025-03-01 03:54:05.969256-08 2025-03-01 03:52:25.243297-08 leaderboard f A100 0.0030785423333333337 t \N \N \N {} -3869 1293 2025-03-01 03:05:36.772437-08 2025-03-01 03:41:52.212108-08 benchmark t A100 \N t \N \N \N {} -3870 1293 2025-03-01 03:43:37.103283-08 2025-03-01 03:30:28.463215-08 leaderboard t A100 0.0030768713333333333 t \N \N \N {} -6290 1944 2025-03-12 10:57:21.104648-07 2025-03-12 10:29:58.980609-07 test t T4 \N t \N \N \N {} -3872 1294 2025-03-01 02:49:18.306021-08 2025-03-01 03:07:55.017888-08 benchmark f A100 \N t \N \N \N {} -3873 1294 2025-03-01 03:18:49.888527-08 2025-03-01 02:34:53.156434-08 leaderboard f A100 0.0030735693333333335 t \N \N \N {} -6293 1945 2025-03-12 11:27:15.263361-07 2025-03-12 10:44:27.72857-07 test t T4 \N f \N \N \N {} -6425 1999 2025-03-14 04:01:43.503576-07 2025-03-14 04:06:07.830224-07 leaderboard t A100 0.0024771706666666666 t \N \N \N {} -6590 2042 2025-03-14 12:07:33.510979-07 2025-03-14 12:55:00.902598-07 leaderboard t A100 0.0031459183333333333 t \N \N \N {} -6642 2053 2025-03-14 13:53:44.288829-07 2025-03-14 13:50:05.960849-07 benchmark t A100 \N t \N \N \N {} -3875 1294 2025-03-01 03:43:43.782738-08 2025-03-01 04:24:15.592112-08 benchmark t A100 \N t \N \N \N {} -3878 1295 2025-03-01 04:12:08.121664-08 2025-03-01 04:27:53.16111-08 benchmark f A100 \N t \N \N \N {} -3879 1295 2025-03-01 03:26:16.469666-08 2025-03-01 03:58:31.78643-08 leaderboard f A100 0.0030710493333333333 t \N \N \N {} -6429 2000 2025-03-14 06:05:54.518003-07 2025-03-14 07:37:02.202589-07 test t H100 \N t \N \N \N {} -6430 2000 2025-03-14 06:35:29.934346-07 2025-03-14 07:22:55.35725-07 benchmark t H100 \N t \N \N \N {} -6431 2000 2025-03-14 06:03:20.051179-07 2025-03-14 06:00:31.398891-07 leaderboard t H100 0.00140618875 t \N \N \N {} -6592 2042 2025-03-14 12:08:29.246929-07 2025-03-14 13:42:58.5473-07 benchmark f A100 \N t \N \N \N {} -6593 2042 2025-03-14 12:19:09.742168-07 2025-03-14 13:19:22.260046-07 leaderboard f A100 0.0038880753333333335 t \N \N \N {} -3881 1295 2025-03-01 03:58:42.298448-08 2025-03-01 04:33:00.759565-08 benchmark t A100 \N t \N \N \N {} -3882 1295 2025-03-01 04:11:32.308003-08 2025-03-01 03:45:03.260733-08 leaderboard t A100 0.0030748093333333336 t \N \N \N {} -6432 2001 2025-03-14 06:20:47.16994-07 2025-03-14 06:30:53.126756-07 test f A100 \N t \N \N \N {} -6433 2001 2025-03-14 06:43:34.047031-07 2025-03-14 05:58:41.550022-07 benchmark f A100 \N t \N \N \N {} -6434 2001 2025-03-14 07:28:13.286534-07 2025-03-14 05:58:23.458927-07 leaderboard f A100 0.0025180643333333336 t \N \N \N {} -6594 2043 2025-03-14 12:13:34.198838-07 2025-03-14 13:41:30.590742-07 test f A100 \N f \N \N \N {} -5073 1550 2025-03-04 16:13:34.029481-08 2025-03-04 17:53:09.887209-08 test f H100 \N f \N \N \N {} -3884 1296 2025-03-01 03:12:53.853598-08 2025-03-01 03:21:33.361057-08 benchmark t A100 \N t \N \N \N {} -3885 1296 2025-03-01 02:52:45.802668-08 2025-03-01 04:33:27.422507-08 leaderboard t A100 0.003072869 t \N \N \N {} -6297 1948 2025-03-12 11:29:51.801512-07 2025-03-12 10:25:13.155691-07 test f T4 \N t \N \N \N {} -6298 1948 2025-03-12 10:35:30.201717-07 2025-03-12 10:27:15.46981-07 benchmark f T4 \N f \N \N \N {} -6435 2001 2025-03-14 07:04:41.915018-07 2025-03-14 07:08:00.946033-07 test t A100 \N t \N \N \N {} -6439 2002 2025-03-14 06:51:44.898139-07 2025-03-14 07:04:48.007592-07 benchmark t A100 \N t \N \N \N {} -6440 2002 2025-03-14 06:58:37.268782-07 2025-03-14 07:30:47.849941-07 leaderboard t A100 0.0025299696666666667 t \N \N \N {} -3890 1297 2025-03-01 04:08:21.671557-08 2025-03-01 03:21:10.925412-08 benchmark t A100 \N t \N \N \N {} -6441 2002 2025-03-14 06:48:37.342621-07 2025-03-14 07:23:19.449449-07 test f A100 \N t \N \N \N {} -6442 2002 2025-03-14 07:08:21.810282-07 2025-03-14 06:12:36.932788-07 benchmark f A100 \N t \N \N \N {} -3893 1297 2025-03-01 03:27:32.585689-08 2025-03-01 03:40:51.676057-08 benchmark f A100 \N t \N \N \N {} -3900 1299 2025-03-01 03:40:55.395999-08 2025-03-01 03:53:19.378987-08 benchmark f A100 \N f \N \N \N {} -3901 1301 2025-03-01 03:23:52.831997-08 2025-03-01 03:24:06.231506-08 test t A100 \N t \N \N \N {} -3906 1301 2025-03-01 04:15:07.380839-08 2025-03-01 03:58:42.535677-08 leaderboard f H100 0.000903244375 t \N \N \N {} -3907 1301 2025-03-01 03:58:13.621695-08 2025-03-01 03:35:37.1111-08 test t H100 \N t \N \N \N {} -3908 1301 2025-03-01 04:58:15.504982-08 2025-03-01 04:38:42.581718-08 benchmark t H100 \N t \N \N \N {} -3913 1303 2025-03-01 03:45:26.565256-08 2025-03-01 05:21:39.968163-08 test t A100 \N t \N \N \N {} -3920 1304 2025-03-01 04:39:50.463012-08 2025-03-01 05:01:19.916314-08 benchmark f A100 \N t \N \N \N {} -3921 1304 2025-03-01 04:39:17.669272-08 2025-03-01 03:55:58.328183-08 leaderboard f A100 0.0030727456666666663 t \N \N \N {} -6670 2063 2025-03-14 14:58:48.236812-07 2025-03-14 15:04:51.635193-07 test f T4 \N f \N \N \N {} -3923 1304 2025-03-01 04:23:40.082947-08 2025-03-01 04:52:37.416251-08 benchmark t A100 \N t \N \N \N {} -3924 1304 2025-03-01 05:06:09.579535-08 2025-03-01 04:30:25.040011-08 leaderboard t A100 0.0030747073333333335 t \N \N \N {} -6307 1950 2025-03-12 11:44:15.74066-07 2025-03-12 10:54:32.286656-07 test f T4 \N f \N \N \N {} -6444 2003 2025-03-14 06:32:45.509148-07 2025-03-14 07:18:07.617634-07 test t A100 \N t \N \N \N {} -6445 2003 2025-03-14 06:43:39.064772-07 2025-03-14 07:26:22.701906-07 benchmark t A100 \N t \N \N \N {} -6446 2003 2025-03-14 07:33:49.087001-07 2025-03-14 06:39:43.653159-07 leaderboard t A100 0.00251628 t \N \N \N {} -3926 1305 2025-03-01 05:27:38.253899-08 2025-03-01 04:35:13.150247-08 benchmark t A100 \N t \N \N \N {} -3927 1305 2025-03-01 04:30:10.546633-08 2025-03-01 04:32:49.219013-08 leaderboard t A100 0.003074227 t \N \N \N {} -6308 1950 2025-03-12 12:25:05.113988-07 2025-03-12 12:37:59.249778-07 test t T4 \N f \N \N \N {} -6447 2003 2025-03-14 07:25:35.263987-07 2025-03-14 05:57:18.436435-07 test f A100 \N t \N \N \N {} -6448 2003 2025-03-14 06:22:22.575249-07 2025-03-14 07:19:37.030539-07 benchmark f A100 \N t \N \N \N {} -6449 2003 2025-03-14 07:33:07.159548-07 2025-03-14 07:43:35.889465-07 leaderboard f A100 0.0025153743333333335 t \N \N \N {} -3929 1305 2025-03-01 05:24:53.102248-08 2025-03-01 05:27:25.918489-08 benchmark f A100 \N t \N \N \N {} -3930 1305 2025-03-01 04:56:51.217802-08 2025-03-01 04:41:26.360821-08 leaderboard f A100 0.0030731703333333337 t \N \N \N {} -6309 1951 2025-03-12 12:37:05.375541-07 2025-03-12 12:21:31.848581-07 test t T4 \N f \N \N \N {} -6450 2004 2025-03-14 07:29:50.054077-07 2025-03-14 06:54:48.788265-07 test f A100 \N t \N \N \N {} -6451 2004 2025-03-14 06:47:18.994761-07 2025-03-14 07:41:04.641274-07 benchmark f A100 \N t \N \N \N {} -6452 2004 2025-03-14 07:10:52.271141-07 2025-03-14 07:05:23.598203-07 leaderboard f A100 0.0025149163333333334 t \N \N \N {} -6686 2071 2025-03-14 15:07:32.035959-07 2025-03-14 16:41:36.954155-07 test t T4 \N f \N \N \N {} -6935 2159 2025-03-16 16:02:23.35434-07 2025-03-16 16:42:39.300711-07 test f T4 \N t \N \N \N {} -6311 1952 2025-03-12 11:40:24.149803-07 2025-03-12 11:50:01.838943-07 test t T4 \N f \N \N \N {} -6458 2005 2025-03-14 07:21:25.580439-07 2025-03-14 07:49:47.315286-07 leaderboard f A100 0.004558669333333333 t \N \N \N {} -6687 2071 2025-03-14 15:04:02.106962-07 2025-03-14 16:27:06.289153-07 test f T4 \N f \N \N \N {} -6883 2136 2025-03-15 15:12:53.005577-07 2025-03-15 15:45:03.98046-07 test f T4 \N t \N \N \N {} -3968 1322 2025-03-01 05:31:02.366882-08 2025-03-01 05:15:44.290903-08 benchmark t A100 \N t \N \N \N {} -3969 1322 2025-03-01 05:37:29.486548-08 2025-03-01 05:45:21.369833-08 leaderboard t A100 0.0030774476666666664 t \N \N \N {} -3970 1322 2025-03-01 05:40:01.603582-08 2025-03-01 05:45:11.281035-08 test f A100 \N t \N \N \N {} -3971 1322 2025-03-01 06:09:29.893232-08 2025-03-01 05:02:33.403442-08 benchmark f A100 \N t \N \N \N {} -3972 1322 2025-03-01 05:06:02.207814-08 2025-03-01 05:55:39.737325-08 leaderboard f A100 0.0030752193333333333 t \N \N \N {} -3973 1323 2025-03-01 05:58:27.485607-08 2025-03-01 05:32:24.84737-08 test f A100 \N t \N \N \N {} -3974 1323 2025-03-01 06:16:37.08018-08 2025-03-01 04:46:10.75807-08 benchmark f A100 \N t \N \N \N {} -3975 1323 2025-03-01 06:18:48.328453-08 2025-03-01 05:34:36.581312-08 leaderboard f A100 0.003101147 t \N \N \N {} -3976 1323 2025-03-01 06:03:28.859556-08 2025-03-01 05:45:30.216393-08 test t A100 \N t \N \N \N {} -3977 1323 2025-03-01 06:05:43.149319-08 2025-03-01 06:06:01.600121-08 benchmark t A100 \N t \N \N \N {} -3978 1323 2025-03-01 05:20:14.946-08 2025-03-01 06:09:26.3369-08 leaderboard t A100 0.0030720786666666665 t \N \N \N {} -3979 1324 2025-03-01 05:38:48.032681-08 2025-03-01 05:38:54.242522-08 test t A100 \N f \N \N \N {} -3980 1324 2025-03-01 05:03:07.857476-08 2025-03-01 05:56:18.026019-08 test f A100 \N f \N \N \N {} -3981 1325 2025-03-01 04:30:24.581687-08 2025-03-01 05:30:26.203251-08 test f A100 \N f \N \N \N {} -3982 1325 2025-03-01 04:56:35.77322-08 2025-03-01 05:06:42.117449-08 test t A100 \N f \N \N \N {} -3989 1327 2025-03-01 04:28:09.627783-08 2025-03-01 06:14:03.4485-08 test f A100 \N t \N \N \N {} -3990 1327 2025-03-01 04:35:12.535221-08 2025-03-01 06:16:55.791101-08 benchmark f A100 \N t \N \N \N {} -3998 1328 2025-03-01 05:02:22.872872-08 2025-03-01 05:53:01.111038-08 test t A100 \N t \N \N \N {} -3999 1328 2025-03-01 05:20:30.293816-08 2025-03-01 04:33:16.504511-08 benchmark t A100 \N t \N \N \N {} -4000 1328 2025-03-01 05:24:59.129999-08 2025-03-01 05:11:00.788762-08 leaderboard t A100 0.0030814433333333333 t \N \N \N {} -4006 1329 2025-03-01 05:03:38.237873-08 2025-03-01 05:49:05.125811-08 leaderboard f A100 0.0030727136666666667 t \N \N \N {} -4007 1330 2025-03-01 06:29:07.869426-08 2025-03-01 06:22:44.330161-08 test t A100 \N t \N \N \N {} -4008 1330 2025-03-01 06:01:42.053545-08 2025-03-01 04:58:15.207722-08 benchmark t A100 \N t \N \N \N {} -4009 1330 2025-03-01 05:03:24.750209-08 2025-03-01 05:57:23.562377-08 leaderboard t A100 0.0030764006666666667 t \N \N \N {} -4010 1330 2025-03-01 05:02:18.387184-08 2025-03-01 04:49:51.849261-08 test f A100 \N t \N \N \N {} -4011 1330 2025-03-01 04:34:41.853129-08 2025-03-01 05:27:20.630573-08 benchmark f A100 \N t \N \N \N {} -4012 1330 2025-03-01 04:49:25.539951-08 2025-03-01 05:57:06.277345-08 leaderboard f A100 0.0030913556666666664 t \N \N \N {} -4013 1331 2025-03-01 04:48:50.723394-08 2025-03-01 05:02:12.598261-08 test f A100 \N t \N \N \N {} -4014 1331 2025-03-01 05:46:30.40955-08 2025-03-01 04:34:39.633219-08 benchmark f A100 \N t \N \N \N {} -4015 1331 2025-03-01 05:38:54.065577-08 2025-03-01 05:25:23.854596-08 leaderboard f A100 0.0030761543333333334 t \N \N \N {} -4016 1331 2025-03-01 04:41:26.822773-08 2025-03-01 06:31:54.977434-08 test t A100 \N t \N \N \N {} -4025 1333 2025-03-01 05:50:11.13569-08 2025-03-01 06:05:47.809639-08 test f A100 \N t \N \N \N {} -4034 1334 2025-03-01 05:19:46.050467-08 2025-03-01 06:13:31.802821-08 test t A100 \N t \N \N \N {} -4042 1336 2025-03-01 06:19:43.447341-08 2025-03-01 05:51:53.054293-08 test f A100 \N t \N \N \N {} -4017 1331 2025-03-01 04:46:15.747877-08 2025-03-01 05:40:33.74129-08 benchmark t A100 \N t \N \N \N {} -4018 1331 2025-03-01 04:50:33.824408-08 2025-03-01 04:35:16.517834-08 leaderboard t A100 0.0030775386666666666 t \N \N \N {} -4019 1332 2025-03-01 06:15:51.207238-08 2025-03-01 06:21:40.29258-08 test f A100 \N t \N \N \N {} -4020 1332 2025-03-01 05:54:40.351278-08 2025-03-01 06:07:21.488972-08 benchmark f A100 \N t \N \N \N {} -4021 1332 2025-03-01 05:31:15.028107-08 2025-03-01 06:30:55.794956-08 leaderboard f A100 0.0032326943333333335 t \N \N \N {} -4029 1333 2025-03-01 04:43:11.484999-08 2025-03-01 06:18:08.597188-08 benchmark t A100 \N t \N \N \N {} -4030 1333 2025-03-01 05:38:23.948276-08 2025-03-01 05:39:32.998484-08 leaderboard t A100 0.0030748286666666667 t \N \N \N {} -4031 1334 2025-03-01 05:48:36.681273-08 2025-03-01 05:09:41.33705-08 test f A100 \N t \N \N \N {} -4032 1334 2025-03-01 05:41:35.653199-08 2025-03-01 05:12:26.846467-08 benchmark f A100 \N t \N \N \N {} -4033 1334 2025-03-01 05:50:46.305933-08 2025-03-01 05:16:24.499999-08 leaderboard f A100 0.0031010393333333335 t \N \N \N {} -4035 1334 2025-03-01 06:38:20.344372-08 2025-03-01 05:01:27.901596-08 benchmark t A100 \N t \N \N \N {} -4036 1334 2025-03-01 05:13:10.923007-08 2025-03-01 05:05:10.395318-08 leaderboard t A100 0.0030793333333333337 t \N \N \N {} -4037 1335 2025-03-01 05:22:53.382722-08 2025-03-01 05:25:53.811459-08 test t A100 \N f \N \N \N {} -4045 1337 2025-03-01 06:43:13.512286-08 2025-03-01 05:52:38.084808-08 test t A100 \N t \N \N \N {} -4046 1337 2025-03-01 06:44:42.843151-08 2025-03-01 06:01:44.654233-08 benchmark t A100 \N t \N \N \N {} -4053 1338 2025-03-01 05:49:19.310951-08 2025-03-01 05:36:21.556206-08 leaderboard t A100 0.0030786213333333333 t \N \N \N {} -4054 1338 2025-03-01 06:08:31.2478-08 2025-03-01 05:15:47.918409-08 test f A100 \N t \N \N \N {} -4055 1338 2025-03-01 06:00:55.317206-08 2025-03-01 05:42:35.691206-08 benchmark f A100 \N t \N \N \N {} -4061 1339 2025-03-01 04:55:54.708149-08 2025-03-01 06:20:05.506072-08 benchmark t A100 \N t \N \N \N {} -4062 1339 2025-03-01 06:29:35.459123-08 2025-03-01 06:23:22.438908-08 leaderboard t A100 0.0030713596666666664 t \N \N \N {} -4063 1340 2025-03-01 05:41:13.291902-08 2025-03-01 05:08:42.07891-08 test f A100 \N t \N \N \N {} -4064 1340 2025-03-01 05:33:54.804638-08 2025-03-01 06:49:34.790694-08 benchmark f A100 \N t \N \N \N {} -4065 1340 2025-03-01 06:22:42.948861-08 2025-03-01 06:31:27.337118-08 leaderboard f A100 0.003198704 t \N \N \N {} -4066 1340 2025-03-01 05:53:22.22062-08 2025-03-01 06:34:56.280809-08 test t A100 \N t \N \N \N {} -4075 1342 2025-03-01 06:23:06.476775-08 2025-03-01 06:20:15.612586-08 test f A100 \N t \N \N \N {} -4084 1343 2025-03-01 06:31:52.402656-08 2025-03-01 06:10:01.556153-08 test f A100 \N t \N \N \N {} -4109 1349 2025-03-01 05:50:58.123614-08 2025-03-01 07:02:47.190517-08 test t A100 \N t \N \N \N {} -4067 1340 2025-03-01 05:51:32.857224-08 2025-03-01 05:50:31.869282-08 benchmark t A100 \N t \N \N \N {} -4068 1340 2025-03-01 06:52:04.233379-08 2025-03-01 05:20:00.968896-08 leaderboard t A100 0.0032254873333333336 t \N \N \N {} -4076 1342 2025-03-01 05:52:15.30752-08 2025-03-01 05:16:01.171421-08 benchmark f A100 \N t \N \N \N {} -4077 1342 2025-03-01 06:48:06.395287-08 2025-03-01 06:20:52.98914-08 leaderboard f A100 0.003085711 t \N \N \N {} -4078 1342 2025-03-01 06:38:10.193601-08 2025-03-01 06:54:28.028198-08 test t A100 \N t \N \N \N {} -4085 1343 2025-03-01 06:30:45.755953-08 2025-03-01 05:42:14.015703-08 benchmark f A100 \N t \N \N \N {} -4086 1343 2025-03-01 05:14:21.820912-08 2025-03-01 06:41:27.76024-08 leaderboard f A100 0.0031491675 t \N \N \N {} -4093 1345 2025-03-01 05:54:34.018691-08 2025-03-01 06:48:03.464696-08 test f A100 \N f \N \N \N {} -4094 1345 2025-03-01 06:07:45.020264-08 2025-03-01 05:56:24.473964-08 test t A100 \N f \N \N \N {} -4101 1347 2025-03-01 06:44:45.774365-08 2025-03-01 05:10:23.773951-08 test f A100 \N f \N \N \N {} -4102 1347 2025-03-01 06:21:29.448139-08 2025-03-01 05:16:01.94432-08 test t A100 \N f \N \N \N {} -4110 1349 2025-03-01 06:50:52.832524-08 2025-03-01 06:21:34.412682-08 benchmark t A100 \N t \N \N \N {} -4111 1349 2025-03-01 05:37:38.785374-08 2025-03-01 05:39:45.670246-08 leaderboard t A100 0.0030744786666666666 t \N \N \N {} -4118 1350 2025-03-01 05:53:22.664423-08 2025-03-01 07:01:25.508428-08 test t A100 \N t \N \N \N {} -4119 1350 2025-03-01 07:06:15.020001-08 2025-03-01 05:36:11.046309-08 benchmark t A100 \N t \N \N \N {} -4120 1350 2025-03-01 06:20:34.501205-08 2025-03-01 07:07:39.442371-08 leaderboard t A100 0.003088505 t \N \N \N {} -4126 1351 2025-03-01 05:29:03.034314-08 2025-03-01 06:25:02.133816-08 leaderboard t A100 0.0030791646666666664 t \N \N \N {} -4134 1353 2025-03-01 06:04:43.547155-08 2025-03-01 05:13:34.33261-08 benchmark t A100 \N t \N \N \N {} -4135 1353 2025-03-01 07:05:13.871186-08 2025-03-01 06:41:07.602177-08 leaderboard t A100 0.003071621 t \N \N \N {} -4143 1354 2025-03-01 05:17:03.588452-08 2025-03-01 05:16:11.035298-08 benchmark t A100 \N t \N \N \N {} -4144 1354 2025-03-01 06:01:34.820165-08 2025-03-01 06:34:58.72374-08 leaderboard t A100 0.00309386 t \N \N \N {} -4165 1363 2025-03-01 09:39:07.8542-08 2025-03-01 09:06:36.459295-08 benchmark f A100 \N t \N \N \N {} -4166 1364 2025-03-01 10:45:48.93923-08 2025-03-01 10:33:36.557942-08 test t A100 \N t \N \N \N {} -4167 1364 2025-03-01 09:43:39.289368-08 2025-03-01 09:24:23.903656-08 benchmark t A100 \N t \N \N \N {} -4170 1364 2025-03-01 10:36:49.26316-08 2025-03-01 09:28:08.907873-08 benchmark f A100 \N t \N \N \N {} -4175 1366 2025-03-01 10:48:21.857968-08 2025-03-01 10:57:43.342896-08 leaderboard t A100 0.0030997733333333337 t \N \N \N {} -4176 1366 2025-03-01 10:27:28.925342-08 2025-03-01 09:25:05.826997-08 test f A100 \N t \N \N \N {} -4184 1372 2025-03-01 10:01:34.032339-08 2025-03-01 10:16:18.107861-08 benchmark f A100 \N t \N \N \N {} -4185 1373 2025-03-01 09:15:05.438331-08 2025-03-01 10:28:47.955472-08 benchmark f A100 \N t \N \N \N {} -4191 1380 2025-03-01 11:36:24.556989-08 2025-03-01 11:40:57.219225-08 benchmark f A100 \N t \N \N \N {} -4192 1381 2025-03-01 12:37:28.809727-08 2025-03-01 11:54:48.723704-08 benchmark f A100 \N f \N \N \N {} -4193 1382 2025-03-01 12:05:31.937467-08 2025-03-01 12:39:42.035292-08 benchmark f A100 \N f \N \N \N {} -4194 1383 2025-03-01 12:24:03.28573-08 2025-03-01 12:49:27.141084-08 benchmark f A100 \N f \N \N \N {} -4195 1384 2025-03-01 12:30:23.318874-08 2025-03-01 11:45:28.231808-08 benchmark f A100 \N t \N \N \N {} -4196 1385 2025-03-01 12:10:07.559639-08 2025-03-01 13:20:27.623427-08 benchmark f A100 \N t \N \N \N {} -4197 1386 2025-03-01 13:54:20.599915-08 2025-03-01 13:59:32.398597-08 test f A100 \N t \N \N \N {} -4198 1387 2025-03-01 14:14:19.137435-08 2025-03-01 13:16:59.246757-08 benchmark f A100 \N t \N \N \N {} -4204 1388 2025-03-01 12:23:09.987084-08 2025-03-01 13:22:39.985792-08 leaderboard t A100 0.496474646 t \N \N \N {} -4205 1389 2025-03-01 13:19:47.926437-08 2025-03-01 13:50:43.82625-08 test f H100 \N t \N \N \N {} -4206 1389 2025-03-01 12:57:36.960021-08 2025-03-01 13:42:33.039405-08 benchmark f H100 \N t \N \N \N {} -4224 1394 2025-03-01 16:02:06.026414-08 2025-03-01 15:58:14.959593-08 leaderboard t A100 0.0031078296666666666 t \N \N \N {} -4225 1394 2025-03-01 15:56:49.702024-08 2025-03-01 14:57:49.257679-08 test t H100 \N t \N \N \N {} -4226 1394 2025-03-01 16:23:12.233422-08 2025-03-01 16:09:26.381534-08 benchmark t H100 \N t \N \N \N {} -4227 1394 2025-03-01 15:22:20.9878-08 2025-03-01 15:51:31.402515-08 leaderboard t H100 0.00140471775 t \N \N \N {} -4230 1397 2025-03-01 17:11:50.531769-08 2025-03-01 17:06:47.495163-08 benchmark f H100 \N t \N \N \N {} -4235 1398 2025-03-01 16:50:13.330905-08 2025-03-01 16:05:18.289309-08 benchmark f H100 \N t \N \N \N {} -4240 1398 2025-03-01 15:52:44.51554-08 2025-03-01 15:40:06.215226-08 test f A100 \N f \N \N \N {} -4241 1399 2025-03-01 17:23:26.394202-08 2025-03-01 16:46:15.926611-08 benchmark f H100 \N f \N \N \N {} -4248 1402 2025-03-01 16:50:49.496325-08 2025-03-01 16:38:17.053665-08 benchmark t H100 \N t \N \N \N {} -4249 1402 2025-03-01 17:29:19.46759-08 2025-03-01 17:17:13.325643-08 leaderboard t H100 0.0013931105 t \N \N \N {} -4250 1403 2025-03-01 16:41:21.078394-08 2025-03-01 16:31:20.029427-08 test f H100 \N t \N \N \N {} -4251 1403 2025-03-01 17:01:41.343912-08 2025-03-01 17:09:49.148125-08 benchmark f H100 \N t \N \N \N {} -4252 1403 2025-03-01 16:09:08.072113-08 2025-03-01 16:30:28.83147-08 leaderboard f H100 0.00139343525 t \N \N \N {} -4415 1443 2025-03-01 23:27:54.443718-08 2025-03-01 23:44:48.408471-08 benchmark t L4 \N t \N \N \N {} -4254 1403 2025-03-01 16:28:43.322824-08 2025-03-01 16:12:28.94178-08 benchmark t H100 \N t \N \N \N {} -4255 1403 2025-03-01 16:59:30.637265-08 2025-03-01 16:54:54.864297-08 leaderboard t H100 0.0014084093333333332 t \N \N \N {} -4256 1402 2025-03-01 17:48:26.163752-08 2025-03-01 16:43:09.49428-08 test f H100 \N t \N \N \N {} -4257 1402 2025-03-01 17:31:46.113694-08 2025-03-01 16:07:29.034922-08 benchmark f H100 \N t \N \N \N {} -4258 1402 2025-03-01 16:44:05.583517-08 2025-03-01 16:25:07.353815-08 leaderboard f H100 0.0014460703333333332 t \N \N \N {} -4259 1404 2025-03-01 16:04:19.796606-08 2025-03-01 17:33:34.248936-08 test t H100 \N t \N \N \N {} -4260 1404 2025-03-01 16:49:13.841981-08 2025-03-01 17:51:00.007281-08 benchmark t H100 \N t \N \N \N {} -4261 1404 2025-03-01 17:21:32.526268-08 2025-03-01 16:39:22.981736-08 leaderboard t H100 0.00139580475 t \N \N \N {} -4262 1404 2025-03-01 16:31:24.233879-08 2025-03-01 17:38:25.925363-08 test f H100 \N t \N \N \N {} -4263 1404 2025-03-01 17:31:33.499326-08 2025-03-01 16:34:05.021142-08 benchmark f H100 \N t \N \N \N {} -4379 1433 2025-03-01 21:51:29.505052-08 2025-03-01 21:42:07.436975-08 benchmark f H100 \N t \N \N \N {} -4264 1404 2025-03-01 16:46:11.323414-08 2025-03-01 17:19:04.9435-08 leaderboard f H100 0.0014059293333333331 t \N \N \N {} -4265 1405 2025-03-01 17:04:29.433941-08 2025-03-01 17:19:54.898023-08 test f L4 \N t \N \N \N {} -4266 1405 2025-03-01 17:15:36.867761-08 2025-03-01 16:06:23.499666-08 benchmark f L4 \N t \N \N \N {} -4267 1405 2025-03-01 17:28:05.852436-08 2025-03-01 16:40:49.105619-08 leaderboard f L4 0.017067446 t \N \N \N {} -4268 1405 2025-03-01 17:14:37.360289-08 2025-03-01 16:02:42.08553-08 test t L4 \N t \N \N \N {} -4384 1437 2025-03-01 22:29:30.981358-08 2025-03-01 22:15:31.729879-08 benchmark f H100 \N f \N \N \N {} -4269 1405 2025-03-01 16:26:20.496455-08 2025-03-01 16:15:13.996431-08 benchmark t L4 \N t \N \N \N {} -4270 1405 2025-03-01 16:08:25.687965-08 2025-03-01 17:10:55.36684-08 leaderboard t L4 0.01706846266666667 t \N \N \N {} -4271 1406 2025-03-01 17:35:32.433009-08 2025-03-01 16:47:33.575921-08 test f T4 \N t \N \N \N {} -4272 1406 2025-03-01 16:29:17.767422-08 2025-03-01 16:51:54.661181-08 benchmark f T4 \N t \N \N \N {} -4273 1406 2025-03-01 16:08:36.107841-08 2025-03-01 16:30:12.375768-08 leaderboard f T4 0.009307217333333334 t \N \N \N {} -4385 1438 2025-03-01 22:33:51.295044-08 2025-03-01 22:12:55.046741-08 benchmark f H100 \N f \N \N \N {} -4274 1406 2025-03-01 17:26:10.061376-08 2025-03-01 16:28:55.30488-08 test t T4 \N f \N \N \N {} -4275 1406 2025-03-01 17:24:14.461681-08 2025-03-01 17:32:10.727373-08 test f L4 \N f \N \N \N {} -4276 1406 2025-03-01 16:51:43.765001-08 2025-03-01 17:08:41.224245-08 test t L4 \N f \N \N \N {} -4351 1425 2025-03-01 18:27:28.317296-08 2025-03-01 19:48:34.537155-08 test t A100 \N t \N \N \N {} -4360 1425 2025-03-01 18:55:29.934952-08 2025-03-01 20:05:32.79375-08 test f T4 \N t \N \N \N {} -4726 1479 2025-03-02 07:19:35.258294-08 2025-03-02 07:04:07.889836-08 test f A100 \N t \N \N \N {} -4341 1424 2025-03-01 19:12:19.828159-08 2025-03-01 20:11:36.50049-08 benchmark f H100 \N t \N \N \N {} -4342 1425 2025-03-01 19:30:03.137888-08 2025-03-01 20:13:32.937364-08 test f H100 \N t \N \N \N {} -4735 1480 2025-03-02 06:33:46.379677-08 2025-03-02 07:28:20.847048-08 test f A100 \N t \N \N \N {} -4343 1425 2025-03-01 19:07:06.678388-08 2025-03-01 19:01:17.242899-08 benchmark f H100 \N t \N \N \N {} -4344 1425 2025-03-01 19:36:47.591098-08 2025-03-01 20:05:13.137174-08 leaderboard f H100 0.0015068819090909092 t \N \N \N {} -4345 1425 2025-03-01 19:40:07.817557-08 2025-03-01 19:16:20.820213-08 test t L4 \N t \N \N \N {} -4355 1425 2025-03-01 19:40:23.407312-08 2025-03-01 18:36:43.639689-08 benchmark t T4 \N t \N \N \N {} -4356 1425 2025-03-01 19:34:42.848141-08 2025-03-01 19:21:17.019994-08 leaderboard t T4 0.019392106333333332 t \N \N \N {} -4364 1425 2025-03-01 19:44:49.950465-08 2025-03-01 19:52:00.834651-08 benchmark f L4 \N t \N \N \N {} -4365 1425 2025-03-01 18:56:32.12492-08 2025-03-01 19:36:54.329004-08 leaderboard f L4 0.018389386333333334 t \N \N \N {} -4366 1426 2025-03-01 22:15:53.709448-08 2025-03-01 21:41:09.208205-08 test f H100 \N f \N \N \N {} -4367 1426 2025-03-01 20:32:14.409145-08 2025-03-01 20:49:12.590979-08 test f A100 \N f \N \N \N {} -4368 1427 2025-03-01 22:21:41.20304-08 2025-03-01 21:10:43.83991-08 test f T4 \N f \N \N \N {} -4369 1427 2025-03-01 20:41:14.21784-08 2025-03-01 22:20:23.41131-08 test f L4 \N f \N \N \N {} -4370 1428 2025-03-01 21:22:14.660722-08 2025-03-01 21:39:17.638743-08 test f L4 \N f \N \N \N {} -4371 1429 2025-03-01 20:56:28.569982-08 2025-03-01 21:27:23.848244-08 test f L4 \N f \N \N \N {} -4375 1432 2025-03-01 21:12:25.058278-08 2025-03-01 21:46:33.33486-08 benchmark f H100 \N t \N \N \N {} -4376 1432 2025-03-01 23:10:02.333804-08 2025-03-01 21:49:56.568079-08 benchmark f L4 \N t \N \N \N {} -4377 1432 2025-03-01 22:54:44.213473-08 2025-03-01 21:21:30.556007-08 benchmark f A100 \N t \N \N \N {} -4876 1500 2025-03-03 09:35:56.137495-08 2025-03-03 09:52:48.005654-08 leaderboard t A100 0.003105324 t \N \N \N {} -4380 1434 2025-03-01 22:15:57.892727-08 2025-03-01 23:36:46.04608-08 benchmark f H100 \N t \N \N \N {} -4381 1435 2025-03-01 22:24:08.366722-08 2025-03-01 22:29:42.067258-08 benchmark f H100 \N t \N \N \N {} -4382 1436 2025-03-01 23:39:19.061091-08 2025-03-01 22:54:39.475489-08 benchmark f H100 \N f \N \N \N {} -4383 1437 2025-03-01 22:35:16.757658-08 2025-03-01 22:50:02.811266-08 benchmark f A100 \N t \N \N \N {} -4386 1439 2025-03-02 00:07:36.475999-08 2025-03-01 23:44:26.098199-08 benchmark f H100 \N t \N \N \N {} -4387 1440 2025-03-02 00:02:57.861102-08 2025-03-02 00:00:32.358477-08 test f H100 \N f \N \N \N {} -4414 1443 2025-03-01 23:54:21.846815-08 2025-03-01 23:32:53.796678-08 test t L4 \N t \N \N \N {} -4388 1440 2025-03-01 23:11:27.786009-08 2025-03-01 23:02:42.012476-08 test t A100 \N f \N \N \N {} -4393 1440 2025-03-01 23:15:26.635065-08 2025-03-01 23:08:51.999345-08 test t L4 \N f \N \N \N {} -4398 1441 2025-03-01 23:17:23.847393-08 2025-03-02 00:04:54.13285-08 test f A100 \N f \N \N \N {} -4403 1442 2025-03-01 22:53:39.143776-08 2025-03-01 23:56:44.359809-08 test t L4 \N f \N \N \N {} -4404 1442 2025-03-01 22:33:19.623771-08 2025-03-01 23:47:56.133494-08 test t H100 \N f \N \N \N {} -4405 1442 2025-03-02 00:04:31.80069-08 2025-03-01 23:26:12.331721-08 test f L4 \N f \N \N \N {} -4406 1442 2025-03-01 23:27:53.503574-08 2025-03-01 22:47:29.500397-08 test f H100 \N f \N \N \N {} -4407 1442 2025-03-02 00:07:32.568134-08 2025-03-01 23:00:08.696649-08 test t T4 \N f \N \N \N {} -4408 1442 2025-03-01 23:29:07.086459-08 2025-03-01 23:23:37.989186-08 test t A100 \N f \N \N \N {} -4409 1442 2025-03-01 22:51:12.097545-08 2025-03-01 22:56:56.425507-08 test f A100 \N f \N \N \N {} -4410 1442 2025-03-01 23:47:29.737111-08 2025-03-02 00:21:02.822694-08 test f T4 \N f \N \N \N {} -4416 1443 2025-03-01 23:58:25.027917-08 2025-03-01 23:05:04.128044-08 leaderboard t L4 0.009129896 t \N \N \N {} -4417 1443 2025-03-01 23:26:40.751758-08 2025-03-02 00:27:51.073423-08 test t A100 \N t \N \N \N {} -4423 1443 2025-03-02 00:04:09.394316-08 2025-03-02 00:13:48.201959-08 test f H100 \N t \N \N \N {} -4424 1443 2025-03-01 23:36:08.305635-08 2025-03-02 00:47:29.293128-08 benchmark f H100 \N t \N \N \N {} -4425 1443 2025-03-01 23:30:11.887008-08 2025-03-02 00:21:43.679513-08 leaderboard f H100 0.0015019263333333332 t \N \N \N {} -4437 1444 2025-03-01 23:22:12.599414-08 2025-03-02 00:09:39.877934-08 leaderboard f A100 0.0021369876666666667 t \N \N \N {} -4438 1444 2025-03-01 23:59:01.602753-08 2025-03-02 00:52:25.788903-08 test f T4 \N t \N \N \N {} -4444 1444 2025-03-01 23:27:58.287274-08 2025-03-01 23:34:31.637125-08 test t T4 \N t \N \N \N {} -4445 1444 2025-03-01 23:28:25.653915-08 2025-03-02 00:46:20.886868-08 benchmark t T4 \N t \N \N \N {} -4446 1444 2025-03-01 23:44:46.267002-08 2025-03-02 00:46:20.218889-08 leaderboard t T4 0.009888984666666666 t \N \N \N {} -4458 1444 2025-03-02 00:52:06.841349-08 2025-03-02 00:45:13.235451-08 leaderboard t L4 0.009366314666666667 t \N \N \N {} -4459 1445 2025-03-02 00:17:32.146735-08 2025-03-02 00:27:53.179825-08 test f A100 \N t \N \N \N {} -4465 1445 2025-03-01 23:25:17.275973-08 2025-03-01 23:51:30.13378-08 test f H100 \N t \N \N \N {} -4466 1445 2025-03-02 00:51:37.846267-08 2025-03-02 00:39:47.26155-08 benchmark f H100 \N t \N \N \N {} -4467 1445 2025-03-01 23:12:50.976391-08 2025-03-02 00:06:29.223376-08 leaderboard f H100 0.0010895946666666667 t \N \N \N {} -4479 1445 2025-03-02 00:18:32.388575-08 2025-03-01 23:33:06.524137-08 leaderboard f L4 0.009311576333333333 t \N \N \N {} -4480 1445 2025-03-02 00:23:10.771659-08 2025-03-02 00:25:16.79615-08 test t L4 \N t \N \N \N {} -4481 1445 2025-03-02 01:06:15.185297-08 2025-03-02 00:56:41.356199-08 benchmark t L4 \N t \N \N \N {} -4486 1447 2025-03-01 23:44:19.043343-08 2025-03-02 00:34:17.729776-08 test t A100 \N t \N \N \N {} -4500 1448 2025-03-01 23:26:04.292969-08 2025-03-02 00:24:20.318749-08 test f A100 \N t \N \N \N {} -4501 1448 2025-03-01 23:52:25.86203-08 2025-03-02 00:46:12.033575-08 benchmark f A100 \N t \N \N \N {} -4502 1448 2025-03-02 00:50:48.885087-08 2025-03-02 00:58:56.374508-08 leaderboard f A100 0.005635142019999999 t \N \N \N {} -4503 1448 2025-03-02 00:36:34.265162-08 2025-03-01 23:44:52.402347-08 test t A100 \N t \N \N \N {} -4514 1450 2025-03-02 00:08:27.757645-08 2025-03-02 00:02:03.781381-08 leaderboard t H100 0.00110452 t \N \N \N {} -4515 1450 2025-03-02 00:14:30.121063-08 2025-03-02 00:04:30.11163-08 test t L4 \N t \N \N \N {} -4516 1450 2025-03-02 00:00:00.221666-08 2025-03-01 23:29:45.13959-08 benchmark t L4 \N t \N \N \N {} -4521 1450 2025-03-02 00:02:47.686536-08 2025-03-01 23:56:26.155139-08 test f H100 \N t \N \N \N {} -4522 1450 2025-03-01 23:44:32.403338-08 2025-03-02 01:02:56.194528-08 benchmark f H100 \N t \N \N \N {} -4523 1450 2025-03-02 00:07:11.12717-08 2025-03-01 23:55:23.35683-08 leaderboard f H100 0.0010997553333333332 t \N \N \N {} -4535 1450 2025-03-02 00:15:33.808245-08 2025-03-02 00:39:03.802273-08 leaderboard f L4 0.009153131 t \N \N \N {} -4536 1451 2025-03-02 01:18:52.170604-08 2025-03-02 01:11:40.804771-08 test t H100 \N t \N \N \N {} -4900 1508 2025-03-03 12:44:25.017899-08 2025-03-03 13:29:37.01192-08 test f H100 \N t \N \N \N {} -4759 1485 2025-03-02 13:16:14.223315-08 2025-03-02 12:01:21.108531-08 benchmark f H100 \N t \N \N \N {} -4905 1511 2025-03-03 14:17:16.597745-08 2025-03-03 13:19:16.395123-08 test t T4 \N f \N \N \N {} -4623 1458 2025-03-02 06:38:45.060119-08 2025-03-02 05:30:45.356488-08 test t A100 \N t \N \N \N {} -6716 2082 2025-03-15 03:44:36.086784-07 2025-03-15 03:23:26.694084-07 test t A100 \N t \N \N \N {} -4632 1460 2025-03-02 06:51:07.426669-08 2025-03-02 06:04:55.597062-08 test t A100 \N t \N \N \N {} -6719 2082 2025-03-15 03:16:11.184399-07 2025-03-15 03:09:17.233425-07 test f A100 \N t \N \N \N {} -4641 1461 2025-03-02 05:59:49.230938-08 2025-03-02 06:17:49.715674-08 test f A100 \N t \N \N \N {} -6722 2083 2025-03-15 03:29:49.696607-07 2025-03-15 04:02:55.058125-07 test f A100 \N t \N \N \N {} -4650 1463 2025-03-02 06:51:19.039666-08 2025-03-02 05:32:00.49752-08 test t A100 \N t \N \N \N {} -6725 2083 2025-03-15 04:23:08.720635-07 2025-03-15 04:21:03.715823-07 test t A100 \N t \N \N \N {} -4659 1464 2025-03-02 07:24:45.335423-08 2025-03-02 05:47:41.040187-08 test f A100 \N t \N \N \N {} -6728 2084 2025-03-15 03:50:48.746611-07 2025-03-15 03:49:46.374297-07 test t A100 \N t \N \N \N {} -4668 1466 2025-03-02 07:09:02.875102-08 2025-03-02 05:37:12.011229-08 test t A100 \N t \N \N \N {} -6731 2084 2025-03-15 02:46:52.42645-07 2025-03-15 04:11:18.181663-07 test f A100 \N t \N \N \N {} -4620 1458 2025-03-02 07:19:02.28608-08 2025-03-02 07:06:35.957428-08 test f A100 \N t \N \N \N {} -4621 1458 2025-03-02 06:23:47.065884-08 2025-03-02 07:03:30.070237-08 benchmark f A100 \N t \N \N \N {} -4622 1458 2025-03-02 07:11:03.17852-08 2025-03-02 06:48:19.70623-08 leaderboard f A100 0.0030958753333333337 t \N \N \N {} -4624 1458 2025-03-02 06:26:45.847704-08 2025-03-02 05:52:44.331704-08 benchmark t A100 \N t \N \N \N {} -4625 1458 2025-03-02 06:51:27.902184-08 2025-03-02 06:23:56.736147-08 leaderboard t A100 0.003088996 t \N \N \N {} -4633 1460 2025-03-02 06:16:38.8786-08 2025-03-02 07:11:21.936323-08 benchmark t A100 \N t \N \N \N {} -4634 1460 2025-03-02 07:04:41.283428-08 2025-03-02 07:16:21.369857-08 leaderboard t A100 0.003092899 t \N \N \N {} -4642 1461 2025-03-02 06:09:41.284959-08 2025-03-02 06:22:42.202445-08 benchmark f A100 \N t \N \N \N {} -4643 1461 2025-03-02 06:39:57.63533-08 2025-03-02 06:46:22.813146-08 leaderboard f A100 0.0030704853333333336 t \N \N \N {} -4651 1463 2025-03-02 06:26:49.901591-08 2025-03-02 06:01:32.015488-08 benchmark t A100 \N t \N \N \N {} -4653 1463 2025-03-02 07:29:04.708213-08 2025-03-02 07:18:09.192245-08 test f A100 \N t \N \N \N {} -4660 1464 2025-03-02 05:32:51.24243-08 2025-03-02 06:13:17.003468-08 benchmark f A100 \N t \N \N \N {} -4661 1464 2025-03-02 07:16:41.081997-08 2025-03-02 05:48:25.892059-08 leaderboard f A100 0.0030758066666666667 t \N \N \N {} -4669 1466 2025-03-02 07:06:29.137455-08 2025-03-02 06:45:12.645699-08 benchmark t A100 \N t \N \N \N {} -4670 1466 2025-03-02 06:40:31.210834-08 2025-03-02 05:42:40.735546-08 leaderboard t A100 0.0030799263333333333 t \N \N \N {} -4678 1467 2025-03-02 06:32:38.464263-08 2025-03-02 06:16:47.589364-08 benchmark f A100 \N t \N \N \N {} -4680 1468 2025-03-02 06:22:28.004766-08 2025-03-02 07:35:00.897908-08 test f A100 \N t \N \N \N {} -4687 1469 2025-03-02 06:37:34.057103-08 2025-03-02 05:51:59.584284-08 benchmark f A100 \N t \N \N \N {} -4688 1469 2025-03-02 06:22:01.378836-08 2025-03-02 06:13:47.362128-08 leaderboard f A100 0.00307773 t \N \N \N {} -4696 1470 2025-03-02 05:54:10.720598-08 2025-03-02 06:58:21.045687-08 benchmark f A100 \N t \N \N \N {} -4697 1470 2025-03-02 06:56:21.605195-08 2025-03-02 07:04:27.428497-08 leaderboard f A100 0.003323739428571429 t \N \N \N {} -4704 1472 2025-03-02 06:51:21.142542-08 2025-03-02 07:19:12.17164-08 benchmark f A100 \N t \N \N \N {} -4705 1473 2025-03-02 06:59:02.14179-08 2025-03-02 06:18:41.378415-08 benchmark f A100 \N t \N \N \N {} -4706 1474 2025-03-02 06:32:28.531936-08 2025-03-02 07:28:17.021525-08 benchmark f A100 \N t \N \N \N {} -4707 1475 2025-03-02 07:43:56.768427-08 2025-03-02 07:18:05.844238-08 benchmark f A100 \N t \N \N \N {} -4709 1476 2025-03-02 07:29:45.620972-08 2025-03-02 06:53:02.850347-08 benchmark t A100 \N t \N \N \N {} -4710 1476 2025-03-02 06:48:36.930559-08 2025-03-02 07:09:21.284629-08 leaderboard t A100 0.003082332 t \N \N \N {} -4718 1477 2025-03-02 07:14:49.953547-08 2025-03-02 07:01:10.444489-08 benchmark f A100 \N t \N \N \N {} -4719 1477 2025-03-02 07:13:14.737688-08 2025-03-02 07:58:55.056874-08 leaderboard f A100 0.003080525 t \N \N \N {} -4727 1479 2025-03-02 08:08:16.530944-08 2025-03-02 06:25:59.616763-08 benchmark f A100 \N t \N \N \N {} -4736 1480 2025-03-02 07:47:58.351644-08 2025-03-02 06:49:23.461444-08 benchmark f A100 \N t \N \N \N {} -4737 1480 2025-03-02 08:02:53.103575-08 2025-03-02 08:13:30.327361-08 leaderboard f A100 0.0030852583333333336 t \N \N \N {} -4752 1485 2025-03-02 11:53:55.657606-08 2025-03-02 11:29:49.203485-08 test f A100 \N t \N \N \N {} -4753 1485 2025-03-02 11:58:43.743381-08 2025-03-02 12:04:12.552688-08 benchmark f A100 \N t \N \N \N {} -4754 1485 2025-03-02 12:59:42.33895-08 2025-03-02 12:36:29.8219-08 leaderboard f A100 0.0017007568181818182 t \N \N \N {} -4760 1485 2025-03-02 13:02:43.584587-08 2025-03-02 11:40:22.88528-08 leaderboard f H100 0.001025282 t \N \N \N {} -4761 1485 2025-03-02 11:54:09.417727-08 2025-03-02 13:05:23.177947-08 test t H100 \N t \N \N \N {} -4764 1485 2025-03-02 12:19:37.770033-08 2025-03-02 11:40:52.739277-08 test f T4 \N t \N \N \N {} -4767 1485 2025-03-02 11:34:48.399203-08 2025-03-02 11:27:10.480623-08 test f L4 \N t \N \N \N {} -4768 1485 2025-03-02 12:21:36.330234-08 2025-03-02 13:16:33.964583-08 benchmark f L4 \N t \N \N \N {} -4769 1485 2025-03-02 11:54:00.372056-08 2025-03-02 12:11:51.743238-08 leaderboard f L4 0.009055283666666665 t \N \N \N {} -4780 1486 2025-03-02 13:32:04.884765-08 2025-03-02 13:19:13.880894-08 test t L4 \N t \N \N \N {} -4788 1486 2025-03-02 12:38:25.692798-08 2025-03-02 12:40:07.438573-08 test f T4 \N t \N \N \N {} -4795 1486 2025-03-02 11:54:55.018029-08 2025-03-02 12:21:15.163724-08 leaderboard f L4 0.013281347 t \N \N \N {} -4827 1490 2025-03-02 14:04:50.955427-08 2025-03-02 14:34:08.371455-08 benchmark t A100 \N t \N \N \N {} -4828 1490 2025-03-02 14:33:00.642669-08 2025-03-02 14:35:15.849785-08 leaderboard t A100 0.0017312735 t \N \N \N {} -4829 1490 2025-03-02 15:19:06.654379-08 2025-03-02 14:54:43.856752-08 test f A100 \N t \N \N \N {} -4830 1490 2025-03-02 13:56:46.528544-08 2025-03-02 15:10:08.582329-08 benchmark f A100 \N t \N \N \N {} -4831 1490 2025-03-02 13:52:33.720668-08 2025-03-02 13:42:04.183005-08 leaderboard f A100 0.0017395216666666667 t \N \N \N {} -4832 1491 2025-03-02 13:31:50.812204-08 2025-03-02 15:31:56.835867-08 test t A100 \N t \N \N \N {} -4833 1491 2025-03-02 15:09:34.132728-08 2025-03-02 13:54:04.869362-08 benchmark t A100 \N t \N \N \N {} -4834 1491 2025-03-02 14:20:30.701842-08 2025-03-02 13:59:22.648224-08 leaderboard t A100 0.0017047336000000001 t \N \N \N {} -4835 1491 2025-03-02 14:45:24.03934-08 2025-03-02 15:10:20.533866-08 test f A100 \N t \N \N \N {} -4836 1491 2025-03-02 14:20:31.244621-08 2025-03-02 15:06:36.572076-08 benchmark f A100 \N t \N \N \N {} -4837 1491 2025-03-02 15:29:45.897744-08 2025-03-02 13:54:34.350922-08 leaderboard f A100 0.0017131206666666668 t \N \N \N {} -6758 2094 2025-03-15 03:45:32.92736-07 2025-03-15 04:07:37.272427-07 test f A100 \N t \N \N \N {} -4844 1493 2025-03-02 13:46:35.98177-08 2025-03-02 14:48:05.043588-08 test f A100 \N t \N \N \N {} -4845 1493 2025-03-02 14:00:59.163247-08 2025-03-02 14:35:29.575037-08 benchmark f A100 \N t \N \N \N {} -6761 2094 2025-03-15 03:27:08.411109-07 2025-03-15 04:01:20.079415-07 test t A100 \N t \N \N \N {} -4852 1494 2025-03-02 15:08:24.34101-08 2025-03-02 14:19:40.913664-08 leaderboard f L4 0.009482677 t \N \N \N {} -4853 1494 2025-03-02 13:50:54.659903-08 2025-03-02 13:57:25.615542-08 test t L4 \N t \N \N \N {} -4854 1494 2025-03-02 14:27:36.134951-08 2025-03-02 13:47:01.295492-08 benchmark t L4 \N t \N \N \N {} -4855 1494 2025-03-02 15:05:50.23843-08 2025-03-02 15:31:13.138166-08 leaderboard t L4 0.009465309666666666 t \N \N \N {} -4856 1494 2025-03-02 14:59:15.831079-08 2025-03-02 15:20:52.999421-08 test t A100 \N t \N \N \N {} -4857 1494 2025-03-02 14:03:52.80639-08 2025-03-02 14:22:25.374431-08 benchmark t A100 \N t \N \N \N {} -4858 1494 2025-03-02 14:26:35.800808-08 2025-03-02 14:28:06.519575-08 leaderboard t A100 0.0017436125 t \N \N \N {} -4859 1494 2025-03-02 15:29:00.03614-08 2025-03-02 14:28:22.752305-08 test f A100 \N t \N \N \N {} -4860 1494 2025-03-02 14:27:41.758642-08 2025-03-02 14:55:37.506929-08 benchmark f A100 \N t \N \N \N {} -4861 1494 2025-03-02 15:13:30.285322-08 2025-03-02 13:38:55.585292-08 leaderboard f A100 0.0017438063333333332 t \N \N \N {} -4862 1494 2025-03-02 15:33:33.758009-08 2025-03-02 14:29:38.081435-08 test f T4 \N f \N \N \N {} -4869 1495 2025-03-02 15:00:43.386949-08 2025-03-02 15:19:19.029978-08 leaderboard f A100 0.0017183623 t \N \N \N {} -4863 1494 2025-03-02 13:48:00.216726-08 2025-03-02 14:43:10.705646-08 test t T4 \N f \N \N \N {} -4864 1495 2025-03-02 14:14:21.117209-08 2025-03-02 14:50:17.471125-08 test t A100 \N t \N \N \N {} -4868 1495 2025-03-02 13:56:41.498396-08 2025-03-02 15:19:57.661184-08 benchmark f A100 \N t \N \N \N {} -4870 1496 2025-03-03 05:33:22.851475-08 2025-03-03 07:04:29.985456-08 test f T4 \N f \N \N \N {} -4872 1498 2025-03-03 10:02:30.253679-08 2025-03-03 10:00:42.925801-08 benchmark f A100 \N t \N \N \N {} -4873 1499 2025-03-03 08:49:19.901664-08 2025-03-03 10:04:25.898649-08 benchmark f A100 \N t \N \N \N {} -4874 1500 2025-03-03 10:15:34.816738-08 2025-03-03 09:04:15.56701-08 test t A100 \N t \N \N \N {} -4875 1500 2025-03-03 09:58:02.568244-08 2025-03-03 10:16:40.069607-08 benchmark t A100 \N t \N \N \N {} -4878 1500 2025-03-03 10:05:00.25862-08 2025-03-03 09:37:20.75377-08 benchmark f A100 \N t \N \N \N {} -4879 1500 2025-03-03 09:50:57.496245-08 2025-03-03 08:44:48.833862-08 leaderboard f A100 0.003117381 t \N \N \N {} -4882 1503 2025-03-03 10:01:52.486048-08 2025-03-03 10:04:44.602379-08 benchmark f A100 \N t \N \N \N {} -4883 1504 2025-03-03 09:35:17.850935-08 2025-03-03 10:09:25.356439-08 benchmark f A100 \N t \N \N \N {} -4885 1506 2025-03-03 13:09:09.154292-08 2025-03-03 13:05:14.933666-08 test f H100 \N t \N \N \N {} -4886 1506 2025-03-03 12:41:09.063313-08 2025-03-03 12:33:34.096179-08 benchmark f H100 \N t \N \N \N {} -4887 1506 2025-03-03 13:59:05.903613-08 2025-03-03 13:48:05.240853-08 leaderboard f H100 0.007589034 t \N \N \N {} -4888 1506 2025-03-03 13:41:18.845874-08 2025-03-03 13:57:20.398849-08 test t H100 \N t \N \N \N {} -4889 1506 2025-03-03 13:43:47.027609-08 2025-03-03 13:34:31.478831-08 benchmark t H100 \N t \N \N \N {} -4890 1506 2025-03-03 14:32:13.712927-08 2025-03-03 13:02:10.756259-08 leaderboard t H100 0.007640819666666667 t \N \N \N {} -4892 1507 2025-03-03 14:08:09.124545-08 2025-03-03 13:52:34.536612-08 benchmark t A100 \N t \N \N \N {} -4893 1507 2025-03-03 14:13:34.686495-08 2025-03-03 13:52:41.545312-08 leaderboard t A100 0.023358184 t \N \N \N {} -4897 1508 2025-03-03 14:08:08.277234-08 2025-03-03 12:42:40.071166-08 test t H100 \N t \N \N \N {} -4898 1508 2025-03-03 13:11:16.067265-08 2025-03-03 13:52:02.911783-08 benchmark t H100 \N t \N \N \N {} -4901 1508 2025-03-03 12:59:43.830263-08 2025-03-03 13:02:51.227677-08 benchmark f H100 \N t \N \N \N {} -4903 1510 2025-03-03 13:17:23.292196-08 2025-03-03 14:30:50.919066-08 test t T4 \N f \N \N \N {} -6766 2097 2025-03-15 03:49:11.850159-07 2025-03-15 04:26:16.901571-07 test t A100 \N t \N \N \N {} -5252 1613 2025-03-06 07:03:50.875686-08 2025-03-06 06:49:34.383024-08 leaderboard t L4 0.017109318 t \N \N \N {} -5253 1613 2025-03-06 07:08:11.029391-08 2025-03-06 07:01:52.953606-08 test f L4 \N t \N \N \N {} -4937 1521 2025-03-03 14:44:11.876868-08 2025-03-03 14:10:59.325044-08 leaderboard f A100 0.0014066483684210525 t \N \N \N {} -5254 1613 2025-03-06 07:19:32.162924-08 2025-03-06 08:10:28.115423-08 benchmark f L4 \N t \N \N \N {} -4938 1521 2025-03-03 15:24:12.517619-08 2025-03-03 14:20:06.658751-08 test t A100 \N t \N \N \N {} -4935 1521 2025-03-03 14:48:27.263394-08 2025-03-03 15:21:43.699775-08 test f A100 \N t \N \N \N {} -4936 1521 2025-03-03 14:31:08.393947-08 2025-03-03 14:45:16.89233-08 benchmark f A100 \N t \N \N \N {} -4939 1521 2025-03-03 15:07:14.225011-08 2025-03-03 14:04:11.041623-08 benchmark t A100 \N t \N \N \N {} -4940 1521 2025-03-03 15:16:14.240133-08 2025-03-03 14:50:14.153588-08 leaderboard t A100 0.001405596387755102 t \N \N \N {} -4941 1522 2025-03-03 15:05:35.069177-08 2025-03-03 14:28:42.867479-08 test f H100 \N t \N \N \N {} -4945 1522 2025-03-03 14:50:55.354936-08 2025-03-03 15:01:22.261798-08 benchmark t H100 \N t \N \N \N {} -4947 1523 2025-03-03 15:51:18.302976-08 2025-03-03 15:33:42.474228-08 benchmark f L4 \N f \N \N \N {} -4950 1524 2025-03-03 14:20:11.679881-08 2025-03-03 15:49:02.916005-08 leaderboard f A100 0.0025553103333333335 t \N \N \N {} -4951 1524 2025-03-03 14:08:29.207972-08 2025-03-03 14:22:21.528102-08 test t H100 \N t \N \N \N {} -4952 1524 2025-03-03 14:38:24.77646-08 2025-03-03 15:05:24.221642-08 benchmark t H100 \N t \N \N \N {} -4953 1524 2025-03-03 14:46:04.864855-08 2025-03-03 15:51:25.144507-08 leaderboard t H100 0.001393842 t \N \N \N {} -4954 1524 2025-03-03 14:30:39.536975-08 2025-03-03 14:17:44.438638-08 test t A100 \N t \N \N \N {} -4955 1524 2025-03-03 14:40:56.30983-08 2025-03-03 14:38:06.121372-08 benchmark t A100 \N t \N \N \N {} -4956 1524 2025-03-03 15:09:02.97402-08 2025-03-03 15:42:51.195857-08 leaderboard t A100 0.0025125443333333334 t \N \N \N {} -5003 1534 2025-03-03 23:49:46.367273-08 2025-03-03 22:04:46.20686-08 benchmark f T4 \N t \N \N \N {} -5256 1613 2025-03-06 07:45:45.096126-08 2025-03-06 08:18:22.747974-08 test t T4 \N t \N \N \N {} -4968 1533 2025-03-03 22:51:29.622688-08 2025-03-03 23:00:37.622546-08 test t A100 \N t \N \N \N {} -4969 1533 2025-03-03 21:55:51.670102-08 2025-03-03 22:25:04.572539-08 benchmark t A100 \N f \N \N \N {} -4970 1533 2025-03-03 22:42:07.031457-08 2025-03-03 23:50:25.299897-08 test f A100 \N t \N \N \N {} -4971 1533 2025-03-03 22:38:20.171762-08 2025-03-03 23:13:31.324985-08 benchmark f A100 \N f \N \N \N {} -4972 1533 2025-03-03 22:03:32.238956-08 2025-03-03 22:00:26.809337-08 test t H100 \N t \N \N \N {} -4973 1533 2025-03-03 23:37:08.619928-08 2025-03-03 22:52:08.648968-08 benchmark t H100 \N f \N \N \N {} -4975 1533 2025-03-03 22:42:28.912392-08 2025-03-03 22:08:16.911585-08 benchmark f H100 \N f \N \N \N {} -4976 1533 2025-03-03 22:57:31.735787-08 2025-03-03 22:02:40.663491-08 test t T4 \N t \N \N \N {} -4977 1533 2025-03-03 23:18:01.411543-08 2025-03-03 22:17:36.143751-08 benchmark t T4 \N f \N \N \N {} -4978 1533 2025-03-03 23:47:51.751613-08 2025-03-03 22:21:29.994607-08 test f T4 \N t \N \N \N {} -4979 1533 2025-03-03 23:38:54.802767-08 2025-03-03 23:19:22.073079-08 benchmark f T4 \N f \N \N \N {} -4980 1533 2025-03-03 22:10:43.459126-08 2025-03-03 23:21:01.183959-08 test f L4 \N t \N \N \N {} -4987 1534 2025-03-03 22:29:57.882848-08 2025-03-03 22:02:34.344571-08 test f A100 \N t \N \N \N {} -4988 1534 2025-03-03 23:17:09.194997-08 2025-03-03 22:00:28.272124-08 benchmark f A100 \N t \N \N \N {} -6772 2098 2025-03-15 05:05:11.153225-07 2025-03-15 03:30:05.714156-07 test t A100 \N t \N \N \N {} -4989 1534 2025-03-03 23:03:04.470836-08 2025-03-03 22:15:42.701149-08 leaderboard f A100 0.001491699 t \N \N \N {} -4990 1534 2025-03-03 23:01:12.271656-08 2025-03-03 23:25:12.093163-08 test f H100 \N t \N \N \N {} -4991 1534 2025-03-03 23:48:25.304808-08 2025-03-03 23:01:02.444844-08 benchmark f H100 \N t \N \N \N {} -4992 1534 2025-03-03 23:36:40.312813-08 2025-03-03 23:14:16.51261-08 leaderboard f H100 0.001046992 t \N \N \N {} -4993 1534 2025-03-03 22:43:08.623067-08 2025-03-03 23:34:08.683584-08 test f L4 \N t \N \N \N {} -4994 1534 2025-03-03 22:04:15.889744-08 2025-03-03 22:26:35.523219-08 benchmark f L4 \N t \N \N \N {} -4995 1534 2025-03-03 22:41:49.509135-08 2025-03-03 23:39:33.494802-08 leaderboard f L4 0.009082157 t \N \N \N {} -4996 1534 2025-03-03 23:19:47.000677-08 2025-03-03 23:51:43.362821-08 test t H100 \N t \N \N \N {} -5000 1534 2025-03-03 22:10:50.514317-08 2025-03-03 23:01:22.855314-08 benchmark t T4 \N t \N \N \N {} -5001 1534 2025-03-03 23:23:50.275013-08 2025-03-03 22:04:37.625675-08 leaderboard t T4 0.013584824666666667 t \N \N \N {} -5002 1534 2025-03-03 22:16:59.138409-08 2025-03-03 22:04:29.040324-08 test f T4 \N t \N \N \N {} -6775 2098 2025-03-15 04:57:02.889001-07 2025-03-15 04:18:28.535969-07 test f A100 \N t \N \N \N {} -5011 1539 2025-03-04 07:31:37.161177-08 2025-03-04 07:58:56.890059-08 test f T4 \N t \N \N \N {} -5012 1539 2025-03-04 08:16:38.896588-08 2025-03-04 07:18:18.877124-08 benchmark f T4 \N t \N \N \N {} -5013 1539 2025-03-04 08:20:34.258391-08 2025-03-04 07:24:16.514602-08 leaderboard f T4 0.009332833333333334 t \N \N \N {} -5014 1539 2025-03-04 06:44:13.355504-08 2025-03-04 07:28:03.052048-08 test t T4 \N t \N \N \N {} -5015 1539 2025-03-04 08:06:37.290401-08 2025-03-04 06:41:47.643875-08 benchmark t T4 \N t \N \N \N {} -5016 1539 2025-03-04 06:59:22.532084-08 2025-03-04 06:53:54.381226-08 leaderboard t T4 0.009299085333333333 t \N \N \N {} -5017 1540 2025-03-04 08:59:30.670557-08 2025-03-04 07:55:07.055003-08 test f A100 \N f \N \N \N {} -5027 1545 2025-03-04 14:11:42.47821-08 2025-03-04 13:44:28.274936-08 leaderboard f A100 0.00004787177777777778 t \N \N \N {} -5032 1545 2025-03-04 14:00:04.919683-08 2025-03-04 13:52:34.794499-08 benchmark f H100 \N t \N \N \N {} -5033 1545 2025-03-04 15:24:15.587724-08 2025-03-04 15:27:11.189949-08 leaderboard f H100 0.00004866111 t \N \N \N {} -5035 1545 2025-03-04 15:30:36.223103-08 2025-03-04 14:27:02.560929-08 benchmark t T4 \N t \N \N \N {} -5036 1545 2025-03-04 15:07:24.286467-08 2025-03-04 14:51:48.245831-08 leaderboard t T4 0.00023255714285714288 t \N \N \N {} -5257 1613 2025-03-06 06:53:37.987844-08 2025-03-06 08:14:43.956638-08 benchmark t T4 \N t \N \N \N {} -5037 1545 2025-03-04 14:19:47.708086-08 2025-03-04 15:37:55.178413-08 test f L4 \N f \N \N \N {} -5038 1545 2025-03-04 13:49:27.790553-08 2025-03-04 15:32:18.011451-08 test t L4 \N f \N \N \N {} -5039 1545 2025-03-04 15:35:59.738048-08 2025-03-04 14:26:26.882713-08 test f T4 \N f \N \N \N {} -5072 1549 2025-03-04 18:02:14.774593-08 2025-03-04 16:24:38.762227-08 test f H100 \N f \N \N \N {} -5075 1552 2025-03-04 16:26:06.712061-08 2025-03-04 17:05:50.91046-08 benchmark f H100 \N t \N \N \N {} -5076 1553 2025-03-04 17:19:12.024738-08 2025-03-04 17:13:44.312105-08 test t H100 \N t \N \N \N {} -5077 1553 2025-03-04 17:19:42.564719-08 2025-03-04 17:12:42.482126-08 benchmark t H100 \N t \N \N \N {} -5078 1553 2025-03-04 18:03:27.829582-08 2025-03-04 16:30:29.070429-08 leaderboard t H100 0.001479608 t \N \N \N {} -5079 1553 2025-03-04 16:43:40.87693-08 2025-03-04 17:28:08.937479-08 test f H100 \N t \N \N \N {} -5080 1553 2025-03-04 16:51:53.271335-08 2025-03-04 16:21:38.563765-08 benchmark f H100 \N t \N \N \N {} -5084 1556 2025-03-04 16:54:52.472201-08 2025-03-04 17:45:47.245193-08 test f A100 \N f \N \N \N {} -5085 1557 2025-03-04 18:28:01.276786-08 2025-03-04 17:44:42.993298-08 test f A100 \N f \N \N \N {} -5086 1558 2025-03-04 18:11:17.534507-08 2025-03-04 17:29:06.539472-08 test f A100 \N f \N \N \N {} -6790 2101 2025-03-15 03:49:21.74589-07 2025-03-15 04:16:48.818713-07 test f A100 \N t \N \N \N {} -5093 1560 2025-03-04 18:23:35.895658-08 2025-03-04 17:04:19.58105-08 test f H100 \N f \N \N \N {} -5094 1561 2025-03-04 18:05:24.892968-08 2025-03-04 18:46:27.229976-08 test f H100 \N t \N \N \N {} -5095 1562 2025-03-04 18:36:54.224297-08 2025-03-04 18:32:29.172193-08 test f A100 \N t \N \N \N {} -5096 1563 2025-03-04 18:52:59.239172-08 2025-03-04 18:15:38.074894-08 test f H100 \N t \N \N \N {} -5097 1563 2025-03-04 18:49:59.0744-08 2025-03-04 17:26:17.186125-08 benchmark f H100 \N t \N \N \N {} -5098 1563 2025-03-04 17:39:36.278703-08 2025-03-04 17:57:36.746337-08 leaderboard f H100 0.001524286 t \N \N \N {} -5099 1564 2025-03-04 18:14:59.270131-08 2025-03-04 18:51:49.99979-08 benchmark f A100 \N t \N \N \N {} -5100 1563 2025-03-04 18:12:08.43731-08 2025-03-04 17:24:24.25148-08 test t H100 \N t \N \N \N {} -5101 1563 2025-03-04 17:22:01.762045-08 2025-03-04 18:00:18.365591-08 benchmark t H100 \N t \N \N \N {} -5102 1563 2025-03-04 17:14:24.254997-08 2025-03-04 18:03:10.882073-08 leaderboard t H100 0.0015306753333333332 t \N \N \N {} -5104 1565 2025-03-04 18:29:07.767766-08 2025-03-04 17:23:19.898042-08 test t A100 \N t \N \N \N {} -5105 1565 2025-03-04 18:37:16.219751-08 2025-03-04 17:10:25.551997-08 benchmark t A100 \N t \N \N \N {} -5108 1565 2025-03-04 18:35:43.150886-08 2025-03-04 18:46:33.299334-08 benchmark f A100 \N t \N \N \N {} -5109 1565 2025-03-04 17:46:26.10294-08 2025-03-04 17:32:00.135585-08 leaderboard f A100 0.006325797333333333 t \N \N \N {} -5117 1569 2025-03-04 22:14:53.928421-08 2025-03-04 22:40:51.332237-08 test f A100 \N t \N \N \N {} -5118 1569 2025-03-04 22:24:33.021103-08 2025-03-04 21:34:40.758739-08 benchmark f A100 \N t \N \N \N {} -5119 1569 2025-03-04 22:58:48.215389-08 2025-03-04 22:50:16.105009-08 leaderboard f A100 0.0016024253333333334 t \N \N \N {} -5131 1569 2025-03-04 22:47:53.005147-08 2025-03-04 21:52:01.609325-08 leaderboard t H100 0.0010646286666666667 t \N \N \N {} -5132 1569 2025-03-04 23:21:58.974923-08 2025-03-04 21:27:57.937643-08 test f T4 \N t \N \N \N {} -5133 1569 2025-03-04 21:35:43.347052-08 2025-03-04 22:03:01.621549-08 benchmark f T4 \N t \N \N \N {} -5138 1569 2025-03-04 22:37:43.97357-08 2025-03-04 23:16:28.792385-08 test f L4 \N t \N \N \N {} -5139 1569 2025-03-04 21:55:16.016933-08 2025-03-04 23:05:51.261929-08 benchmark f L4 \N t \N \N \N {} -5140 1569 2025-03-04 23:05:20.733299-08 2025-03-04 22:29:48.217612-08 leaderboard f L4 0.009060729666666666 t \N \N \N {} -5152 1570 2025-03-04 22:28:21.72317-08 2025-03-04 23:05:56.534035-08 leaderboard f H100 0.0011025386666666667 t \N \N \N {} -5153 1571 2025-03-04 23:27:03.934327-08 2025-03-04 22:25:50.087612-08 test t A100 \N t \N \N \N {} -5171 1574 2025-03-04 22:56:14.353869-08 2025-03-04 23:19:35.268597-08 test t A100 \N f \N \N \N {} -5172 1574 2025-03-04 21:59:17.605165-08 2025-03-04 22:14:53.169362-08 test f A100 \N f \N \N \N {} -5173 1575 2025-03-04 22:04:24.384305-08 2025-03-04 22:14:55.882584-08 test f H100 \N t \N \N \N {} -5174 1575 2025-03-04 23:09:21.089194-08 2025-03-04 22:12:41.373209-08 benchmark f H100 \N t \N \N \N {} -5185 1578 2025-03-05 04:32:15.216004-08 2025-03-05 04:30:27.093417-08 benchmark f H100 \N t \N \N \N {} -5186 1578 2025-03-05 05:12:33.674089-08 2025-03-05 05:32:04.699339-08 leaderboard f H100 0.0014253056666666667 t \N \N \N {} -5187 1579 2025-03-05 05:33:08.325393-08 2025-03-05 04:46:50.693654-08 test f H100 \N t \N \N \N {} -5188 1580 2025-03-05 04:39:23.334662-08 2025-03-05 04:55:15.840654-08 test f H100 \N f \N \N \N {} -5189 1581 2025-03-05 05:09:09.30193-08 2025-03-05 04:30:58.000235-08 test f H100 \N f \N \N \N {} -5191 1583 2025-03-05 06:03:36.083056-08 2025-03-05 05:52:58.768087-08 test f H100 \N f \N \N \N {} -5192 1584 2025-03-05 04:53:13.978814-08 2025-03-05 06:01:29.534419-08 test f H100 \N f \N \N \N {} -5193 1585 2025-03-05 05:16:23.384842-08 2025-03-05 04:40:47.567792-08 test f H100 \N f \N \N \N {} -5194 1586 2025-03-05 05:55:30.771046-08 2025-03-05 04:56:37.274029-08 test f H100 \N f \N \N \N {} -5195 1587 2025-03-05 05:49:50.339689-08 2025-03-05 05:58:41.349328-08 test f H100 \N f \N \N \N {} -5241 1613 2025-03-06 08:16:58.083166-08 2025-03-06 07:04:03.546846-08 test t A100 \N t \N \N \N {} -5196 1588 2025-03-05 05:52:45.188962-08 2025-03-05 06:10:15.320706-08 test f H100 \N f \N \N \N {} -5198 1590 2025-03-05 06:35:32.564142-08 2025-03-05 05:59:51.088599-08 test f H100 \N f \N \N \N {} -5211 1604 2025-03-05 23:01:07.467624-08 2025-03-06 00:15:28.333028-08 benchmark f A100 \N t \N \N \N {} -5209 1600 2025-03-05 16:08:44.73907-08 2025-03-05 16:32:02.657585-08 test f T4 \N f \N \N \N {} -5210 1601 2025-03-05 17:19:12.616402-08 2025-03-05 16:16:23.001998-08 test f T4 \N f \N \N \N {} -5212 1605 2025-03-05 22:59:21.454362-08 2025-03-05 22:28:41.565705-08 test f A100 \N t \N \N \N {} -5213 1605 2025-03-05 23:20:06.529966-08 2025-03-05 23:11:34.874411-08 benchmark f A100 \N t \N \N \N {} -5214 1605 2025-03-05 22:21:25.7678-08 2025-03-05 22:43:21.731097-08 leaderboard f A100 0.00240375 t \N \N \N {} -5218 1606 2025-03-05 22:50:16.943212-08 2025-03-06 00:17:26.530067-08 test f H100 \N t \N \N \N {} -5219 1606 2025-03-05 23:56:01.568548-08 2025-03-05 23:45:15.745008-08 benchmark f H100 \N t \N \N \N {} -5220 1606 2025-03-05 23:02:21.7458-08 2025-03-06 00:12:54.212376-08 leaderboard f H100 0.001836295 t \N \N \N {} -5221 1606 2025-03-05 23:07:53.314931-08 2025-03-05 23:19:58.450174-08 test t H100 \N t \N \N \N {} -5222 1606 2025-03-05 22:21:37.603066-08 2025-03-05 23:33:48.373334-08 benchmark t H100 \N t \N \N \N {} -5223 1606 2025-03-05 23:56:17.695563-08 2025-03-05 23:31:36.226457-08 leaderboard t H100 0.001844128 t \N \N \N {} -5224 1608 2025-03-06 06:41:32.825228-08 2025-03-06 07:35:28.239062-08 test f T4 \N f \N \N \N {} -5226 1610 2025-03-06 07:18:42.460735-08 2025-03-06 06:35:06.778387-08 test t T4 \N t \N \N \N {} -5250 1613 2025-03-06 08:07:54.141701-08 2025-03-06 07:24:21.635977-08 test t L4 \N t \N \N \N {} -5227 1610 2025-03-06 07:38:46.614128-08 2025-03-06 06:58:40.087618-08 benchmark t T4 \N t \N \N \N {} -5228 1610 2025-03-06 07:51:50.188241-08 2025-03-06 06:58:42.083894-08 leaderboard t T4 0.017143785666666668 t \N \N \N {} -5229 1610 2025-03-06 06:31:06.739357-08 2025-03-06 06:37:17.472387-08 test f T4 \N t \N \N \N {} -5230 1610 2025-03-06 07:22:42.771965-08 2025-03-06 07:33:54.418655-08 benchmark f T4 \N t \N \N \N {} -5231 1610 2025-03-06 06:16:53.919416-08 2025-03-06 06:36:47.083912-08 leaderboard f T4 0.01720559766666667 t \N \N \N {} -5232 1611 2025-03-06 08:16:50.440029-08 2025-03-06 07:49:49.111467-08 test t T4 \N f \N \N \N {} -6784 2100 2025-03-15 04:40:09.589806-07 2025-03-15 04:00:29.456068-07 test f A100 \N t \N \N \N {} -5234 1612 2025-03-06 07:34:10.797478-08 2025-03-06 06:55:41.824879-08 benchmark f L4 \N t \N \N \N {} -5235 1612 2025-03-06 07:42:17.581429-08 2025-03-06 07:04:34.801114-08 benchmark f A100 \N t \N \N \N {} -5236 1612 2025-03-06 07:29:27.298493-08 2025-03-06 08:18:54.173232-08 benchmark f H100 \N t \N \N \N {} -5237 1612 2025-03-06 07:45:18.830048-08 2025-03-06 07:09:08.875473-08 benchmark f T4 \N t \N \N \N {} -5238 1613 2025-03-06 07:02:42.383443-08 2025-03-06 06:58:40.326617-08 test t H100 \N t \N \N \N {} -5239 1613 2025-03-06 06:58:41.561231-08 2025-03-06 07:26:52.163278-08 benchmark t H100 \N t \N \N \N {} -5240 1613 2025-03-06 08:17:32.781179-08 2025-03-06 07:19:03.090424-08 leaderboard t H100 0.0014283363333333333 t \N \N \N {} -5242 1613 2025-03-06 07:50:24.304771-08 2025-03-06 06:37:12.655596-08 benchmark t A100 \N t \N \N \N {} -5243 1613 2025-03-06 07:52:14.675021-08 2025-03-06 07:13:22.297664-08 leaderboard t A100 0.0025773366666666667 t \N \N \N {} -5260 1613 2025-03-06 08:18:47.261863-08 2025-03-06 06:59:45.373237-08 benchmark f T4 \N t \N \N \N {} -5261 1613 2025-03-06 06:55:11.400354-08 2025-03-06 07:22:29.939541-08 leaderboard f T4 0.016297266666666668 t \N \N \N {} -5262 1614 2025-03-06 07:10:48.778261-08 2025-03-06 07:33:58.158012-08 test t A100 \N t \N \N \N {} -5263 1614 2025-03-06 07:57:06.330893-08 2025-03-06 07:32:20.266324-08 benchmark t A100 \N t \N \N \N {} -5264 1614 2025-03-06 07:34:46.398663-08 2025-03-06 07:48:31.150592-08 leaderboard t A100 0.002518239 t \N \N \N {} -5265 1614 2025-03-06 08:22:10.296115-08 2025-03-06 08:10:01.676777-08 test f A100 \N t \N \N \N {} -5266 1614 2025-03-06 07:36:30.354753-08 2025-03-06 08:06:26.122608-08 benchmark f A100 \N t \N \N \N {} -6793 2101 2025-03-15 04:34:05.070783-07 2025-03-15 03:38:46.857954-07 test t A100 \N t \N \N \N {} -5267 1614 2025-03-06 08:17:25.703839-08 2025-03-06 07:44:04.763072-08 leaderboard f A100 0.0025364223333333336 t \N \N \N {} -5268 1614 2025-03-06 07:54:07.764514-08 2025-03-06 06:53:43.516588-08 test f L4 \N t \N \N \N {} -5269 1614 2025-03-06 06:49:11.954531-08 2025-03-06 08:09:48.887046-08 benchmark f L4 \N t \N \N \N {} -5270 1614 2025-03-06 07:58:12.691983-08 2025-03-06 07:16:48.763534-08 leaderboard f L4 0.01703991833333333 t \N \N \N {} -5271 1614 2025-03-06 07:29:09.154149-08 2025-03-06 06:51:28.330755-08 test t L4 \N t \N \N \N {} -5272 1614 2025-03-06 08:11:29.656544-08 2025-03-06 07:59:29.594078-08 benchmark t L4 \N t \N \N \N {} -5273 1614 2025-03-06 08:35:09.612272-08 2025-03-06 07:01:28.409958-08 leaderboard t L4 0.01704863566666667 t \N \N \N {} -5274 1614 2025-03-06 07:29:25.840467-08 2025-03-06 08:40:01.921695-08 test f H100 \N t \N \N \N {} -5275 1614 2025-03-06 07:16:27.972728-08 2025-03-06 07:22:01.065139-08 benchmark f H100 \N t \N \N \N {} -5282 1617 2025-03-06 11:49:00.194719-08 2025-03-06 12:03:16.273518-08 test t L4 \N f \N \N \N {} -5283 1616 2025-03-06 12:12:36.448946-08 2025-03-06 11:07:02.723415-08 test f T4 \N t \N \N \N {} -5284 1616 2025-03-06 12:25:43.203938-08 2025-03-06 10:58:59.548248-08 benchmark f T4 \N t \N \N \N {} -5285 1616 2025-03-06 12:14:49.086319-08 2025-03-06 11:05:00.869471-08 leaderboard f T4 0.009402661909090908 t \N \N \N {} -5288 1616 2025-03-06 11:15:57.5079-08 2025-03-06 12:34:26.281327-08 leaderboard t T4 0.009402624535714285 t \N \N \N {} -5289 1617 2025-03-06 11:04:48.91978-08 2025-03-06 11:41:19.26857-08 test f T4 \N f \N \N \N {} -5290 1617 2025-03-06 11:31:11.14058-08 2025-03-06 11:07:15.510427-08 test t T4 \N f \N \N \N {} -5291 1616 2025-03-06 11:20:35.3437-08 2025-03-06 11:41:25.791263-08 test t L4 \N t \N \N \N {} -5292 1616 2025-03-06 11:17:21.149159-08 2025-03-06 11:36:57.390953-08 benchmark t L4 \N t \N \N \N {} -5293 1616 2025-03-06 11:23:09.088018-08 2025-03-06 10:56:39.864963-08 leaderboard t L4 0.009685256699999999 t \N \N \N {} -5294 1616 2025-03-06 11:46:28.551612-08 2025-03-06 10:50:17.314532-08 test f L4 \N t \N \N \N {} -5295 1616 2025-03-06 11:20:19.815835-08 2025-03-06 11:22:30.677937-08 benchmark f L4 \N t \N \N \N {} -5296 1616 2025-03-06 11:38:50.355641-08 2025-03-06 11:50:15.455809-08 leaderboard f L4 0.009650051153846154 t \N \N \N {} -5309 1620 2025-03-06 12:17:08.411894-08 2025-03-06 11:23:40.680848-08 test t L4 \N t \N \N \N {} -6796 2102 2025-03-15 03:47:18.073866-07 2025-03-15 04:58:31.457625-07 test f A100 \N t \N \N \N {} -6799 2102 2025-03-15 03:49:39.535334-07 2025-03-15 04:47:47.163839-07 test t A100 \N t \N \N \N {} -5310 1620 2025-03-06 11:47:19.342547-08 2025-03-06 11:29:52.276452-08 benchmark t L4 \N t \N \N \N {} -5311 1620 2025-03-06 11:30:56.626007-08 2025-03-06 11:54:39.587711-08 leaderboard t L4 0.017108037 t \N \N \N {} -5315 1621 2025-03-06 12:21:24.243905-08 2025-03-06 11:09:28.475948-08 test t L4 \N f \N \N \N {} -5316 1621 2025-03-06 12:05:13.275699-08 2025-03-06 12:39:19.771477-08 test f L4 \N f \N \N \N {} -5591 1709 2025-03-09 08:38:21.814972-07 2025-03-09 08:31:41.454653-07 leaderboard f T4 0.017528556666666667 t \N \N \N {} -6819 2120 2025-03-15 09:28:09.69146-07 2025-03-15 10:53:19.142071-07 test f A100 \N t \N \N \N {} -5592 1709 2025-03-09 09:45:13.630538-07 2025-03-09 08:29:21.654913-07 test t T4 \N t \N \N \N {} -5637 1719 2025-03-09 10:37:18.14285-07 2025-03-09 09:06:06.681007-07 test f T4 \N t \N \N \N {} -5646 1720 2025-03-09 10:27:41.86582-07 2025-03-09 09:54:42.03058-07 test t T4 \N t \N \N \N {} -5665 1722 2025-03-09 09:32:47.436929-07 2025-03-09 09:26:45.271601-07 benchmark f T4 \N t \N \N \N {} -6822 2120 2025-03-15 10:43:09.477007-07 2025-03-15 09:32:55.270251-07 test t A100 \N t \N \N \N {} -6825 2121 2025-03-15 10:03:06.648384-07 2025-03-15 09:27:27.809952-07 test t A100 \N t \N \N \N {} -5666 1722 2025-03-09 09:52:43.506841-07 2025-03-09 10:58:18.20445-07 leaderboard f T4 0.016603317666666666 t \N \N \N {} -6828 2121 2025-03-15 09:33:59.003711-07 2025-03-15 10:39:36.450576-07 test f A100 \N t \N \N \N {} -5667 1724 2025-03-09 10:44:35.101417-07 2025-03-09 09:13:35.191271-07 test f A100 \N t \N \N \N {} -5655 1721 2025-03-09 10:15:26.439475-07 2025-03-09 09:13:07.504617-07 test f T4 \N t \N \N \N {} -5664 1722 2025-03-09 09:28:08.995402-07 2025-03-09 10:57:10.770407-07 test f T4 \N t \N \N \N {} -6831 2122 2025-03-15 09:55:36.80771-07 2025-03-15 09:14:46.211485-07 test t A100 \N t \N \N \N {} -5668 1724 2025-03-09 10:39:56.890456-07 2025-03-09 11:00:53.761033-07 benchmark f A100 \N t \N \N \N {} -5669 1724 2025-03-09 09:11:31.886775-07 2025-03-09 10:51:26.751854-07 leaderboard f A100 0.0026062246666666666 t \N \N \N {} -5673 1724 2025-03-09 10:17:36.739347-07 2025-03-09 09:34:01.324901-07 test t A100 \N t \N \N \N {} -5670 1725 2025-03-09 09:56:06.327259-07 2025-03-09 10:35:57.538152-07 test f H100 \N t \N \N \N {} -5682 1726 2025-03-09 09:24:13.10994-07 2025-03-09 09:44:04.129165-07 test f L4 \N t \N \N \N {} -5671 1725 2025-03-09 09:58:59.213519-07 2025-03-09 09:39:17.681959-07 benchmark f H100 \N t \N \N \N {} -6834 2122 2025-03-15 09:41:59.559997-07 2025-03-15 10:55:52.113033-07 test f A100 \N t \N \N \N {} -5672 1725 2025-03-09 09:41:17.366352-07 2025-03-09 09:41:18.800653-07 leaderboard f H100 0.0014690293333333333 t \N \N \N {} -6864 2127 2025-03-15 09:44:16.174491-07 2025-03-15 10:46:22.719823-07 test t A100 \N t \N \N \N {} -6837 2123 2025-03-15 11:04:53.025505-07 2025-03-15 10:47:38.423663-07 test f A100 \N t \N \N \N {} -5691 1728 2025-03-09 09:50:18.197376-07 2025-03-09 09:21:08.916986-07 test f A100 \N t \N \N \N {} -6840 2123 2025-03-15 09:53:02.595875-07 2025-03-15 09:54:52.862532-07 test t A100 \N t \N \N \N {} -6843 2124 2025-03-15 10:08:06.005806-07 2025-03-15 10:57:35.474858-07 test f A100 \N t \N \N \N {} -5674 1724 2025-03-09 09:30:02.411383-07 2025-03-09 09:10:48.029672-07 benchmark t A100 \N t \N \N \N {} -6846 2124 2025-03-15 09:34:42.825301-07 2025-03-15 10:21:34.275136-07 test t A100 \N t \N \N \N {} -5563 1700 2025-03-08 17:19:44.52692-08 2025-03-08 16:34:26.7346-08 test t H100 \N f \N \N \N {} -6849 2125 2025-03-15 09:45:34.633967-07 2025-03-15 09:27:25.430422-07 test f A100 \N t \N \N \N {} -5564 1700 2025-03-08 17:51:09.804861-08 2025-03-08 16:57:41.781475-08 test f H100 \N f \N \N \N {} -5547 1692 2025-03-08 16:29:46.214302-08 2025-03-08 16:20:04.501518-08 test f H100 \N f \N \N \N {} -5548 1692 2025-03-08 15:58:09.465006-08 2025-03-08 16:18:52.498393-08 test t H100 \N f \N \N \N {} -5549 1693 2025-03-08 16:52:18.575178-08 2025-03-08 17:37:26.953067-08 test t H100 \N f \N \N \N {} -5567 1702 2025-03-08 16:31:44.29566-08 2025-03-08 17:20:51.940485-08 test t H100 \N f \N \N \N {} -5554 1695 2025-03-08 16:24:17.191755-08 2025-03-08 18:01:05.802736-08 test f H100 \N f \N \N \N {} -5555 1696 2025-03-08 17:11:18.772884-08 2025-03-08 17:09:32.444232-08 test t H100 \N f \N \N \N {} -5556 1696 2025-03-08 17:57:34.2415-08 2025-03-08 17:37:04.496604-08 test f H100 \N f \N \N \N {} -5581 1707 2025-03-09 09:14:09.834023-07 2025-03-09 08:25:50.569029-07 test f T4 \N t \N \N \N {} -5585 1707 2025-03-09 09:38:59.880211-07 2025-03-09 08:38:21.050436-07 benchmark t T4 \N t \N \N \N {} -5587 1708 2025-03-09 09:24:39.948132-07 2025-03-09 08:27:28.337093-07 test f T4 \N f \N \N \N {} -5593 1709 2025-03-09 08:46:11.00316-07 2025-03-09 09:09:14.41678-07 benchmark t T4 \N t \N \N \N {} -5594 1709 2025-03-09 09:13:43.175362-07 2025-03-09 09:11:53.850526-07 leaderboard t T4 0.017543475333333333 t \N \N \N {} -5595 1710 2025-03-09 09:43:56.816043-07 2025-03-09 10:16:02.768519-07 test t T4 \N t \N \N \N {} -5596 1710 2025-03-09 08:28:21.825411-07 2025-03-09 09:26:32.812995-07 benchmark t T4 \N t \N \N \N {} -5597 1710 2025-03-09 09:14:02.814794-07 2025-03-09 09:20:14.126574-07 leaderboard t T4 0.016824632333333332 t \N \N \N {} -6858 2126 2025-03-15 10:43:39.789025-07 2025-03-15 09:34:45.620404-07 test t A100 \N t \N \N \N {} -5601 1711 2025-03-09 09:09:47.896726-07 2025-03-09 09:02:44.866972-07 test t T4 \N f \N \N \N {} -5602 1711 2025-03-09 10:23:41.056545-07 2025-03-09 09:36:00.891836-07 test f T4 \N f \N \N \N {} -5700 1729 2025-03-09 10:19:32.974097-07 2025-03-09 09:54:46.566098-07 test f H100 \N t \N \N \N {} -5607 1712 2025-03-09 10:24:03.045125-07 2025-03-09 09:51:21.11739-07 benchmark t T4 \N t \N \N \N {} -5608 1712 2025-03-09 08:34:08.27444-07 2025-03-09 10:22:37.533877-07 leaderboard t T4 0.01711488933333333 t \N \N \N {} -5613 1714 2025-03-09 09:30:39.959969-07 2025-03-09 10:16:30.712056-07 test t T4 \N t \N \N \N {} -5614 1714 2025-03-09 09:55:01.657622-07 2025-03-09 09:16:09.636467-07 benchmark t T4 \N f \N \N \N {} -5781 1751 2025-03-09 22:17:14.433037-07 2025-03-09 21:49:17.871196-07 test t L4 \N t \N \N \N {} -5629 1717 2025-03-09 08:54:48.324259-07 2025-03-09 10:08:29.038511-07 benchmark t T4 \N t \N \N \N {} -5630 1717 2025-03-09 10:42:26.967084-07 2025-03-09 09:41:58.61599-07 leaderboard t T4 0.016894572333333333 t \N \N \N {} -5631 1718 2025-03-09 09:27:03.051502-07 2025-03-09 10:10:12.243584-07 test t T4 \N t \N \N \N {} -5632 1718 2025-03-09 09:39:46.422345-07 2025-03-09 10:18:05.599135-07 benchmark t T4 \N t \N \N \N {} -5636 1718 2025-03-09 09:51:06.131421-07 2025-03-09 09:42:47.922533-07 leaderboard f T4 0.021235485666666668 t \N \N \N {} -5638 1719 2025-03-09 10:26:54.407345-07 2025-03-09 09:15:13.06042-07 benchmark f T4 \N t \N \N \N {} -5639 1719 2025-03-09 09:29:44.472627-07 2025-03-09 09:37:56.334139-07 leaderboard f T4 0.016808872333333332 t \N \N \N {} -5640 1719 2025-03-09 10:32:20.431198-07 2025-03-09 09:39:18.18504-07 test t T4 \N t \N \N \N {} -5645 1720 2025-03-09 09:55:57.197808-07 2025-03-09 09:40:52.349852-07 leaderboard f T4 0.016596006666666666 t \N \N \N {} -5647 1720 2025-03-09 10:17:27.374148-07 2025-03-09 10:50:14.82081-07 benchmark t T4 \N t \N \N \N {} -5648 1720 2025-03-09 09:16:50.615819-07 2025-03-09 10:21:27.988216-07 leaderboard t T4 0.016557458666666667 t \N \N \N {} -5649 1721 2025-03-09 09:42:37.64406-07 2025-03-09 10:22:42.1259-07 test t T4 \N t \N \N \N {} -5652 1722 2025-03-09 10:58:24.342857-07 2025-03-09 09:19:31.766123-07 test t T4 \N t \N \N \N {} -5654 1722 2025-03-09 10:21:05.107452-07 2025-03-09 09:25:54.295116-07 leaderboard t T4 0.016595996333333335 t \N \N \N {} -5656 1721 2025-03-09 09:56:16.90649-07 2025-03-09 09:16:05.410514-07 benchmark f T4 \N t \N \N \N {} -5657 1721 2025-03-09 09:29:05.785369-07 2025-03-09 10:35:50.223531-07 leaderboard f T4 0.016588513666666665 t \N \N \N {} -5658 1723 2025-03-09 09:19:55.392332-07 2025-03-09 09:53:19.722057-07 test f T4 \N t \N \N \N {} -5677 1725 2025-03-09 09:28:24.172642-07 2025-03-09 10:22:11.236381-07 benchmark t H100 \N t \N \N \N {} -5678 1725 2025-03-09 09:26:29.522614-07 2025-03-09 10:22:55.539844-07 leaderboard t H100 0.001474356 t \N \N \N {} -5679 1726 2025-03-09 10:03:52.222717-07 2025-03-09 09:33:44.331339-07 test t L4 \N t \N \N \N {} -5680 1726 2025-03-09 10:46:04.610414-07 2025-03-09 09:20:34.148072-07 benchmark t L4 \N t \N \N \N {} -5681 1726 2025-03-09 09:37:57.602752-07 2025-03-09 10:22:08.448087-07 leaderboard t L4 0.017242522333333333 t \N \N \N {} -5686 1727 2025-03-09 10:05:30.828856-07 2025-03-09 09:43:47.571121-07 benchmark f L4 \N t \N \N \N {} -5687 1727 2025-03-09 11:07:15.116394-07 2025-03-09 10:13:13.37959-07 leaderboard f L4 0.017209885666666667 t \N \N \N {} -5688 1727 2025-03-09 10:18:00.314232-07 2025-03-09 10:57:21.614871-07 test t L4 \N t \N \N \N {} -5689 1727 2025-03-09 10:01:49.26376-07 2025-03-09 09:25:21.45197-07 benchmark t L4 \N t \N \N \N {} -5690 1727 2025-03-09 10:45:23.99607-07 2025-03-09 09:43:33.892968-07 leaderboard t L4 0.017194495333333334 t \N \N \N {} -5695 1729 2025-03-09 11:12:01.892957-07 2025-03-09 10:53:34.36104-07 benchmark t H100 \N t \N \N \N {} -5696 1729 2025-03-09 10:25:09.517125-07 2025-03-09 10:31:41.836024-07 leaderboard t H100 0.00151662 t \N \N \N {} -5697 1728 2025-03-09 10:48:03.533453-07 2025-03-09 09:49:32.322572-07 test t A100 \N t \N \N \N {} -5698 1728 2025-03-09 10:47:46.442739-07 2025-03-09 10:50:32.307978-07 benchmark t A100 \N t \N \N \N {} -5699 1728 2025-03-09 11:11:38.205953-07 2025-03-09 09:57:38.117077-07 leaderboard t A100 0.0027480226666666665 t \N \N \N {} -5701 1729 2025-03-09 10:46:43.436224-07 2025-03-09 10:08:36.297082-07 benchmark f H100 \N t \N \N \N {} -5702 1729 2025-03-09 09:53:35.832862-07 2025-03-09 10:24:24.298067-07 leaderboard f H100 0.001514298 t \N \N \N {} -5790 1752 2025-03-09 21:29:49.172353-07 2025-03-09 21:20:16.735724-07 test t T4 \N t \N \N \N {} -5775 1749 2025-03-09 22:00:09.902505-07 2025-03-09 22:13:49.54977-07 test f A100 \N t \N \N \N {} -5776 1749 2025-03-09 23:05:49.965781-07 2025-03-09 22:51:52.170132-07 benchmark f A100 \N t \N \N \N {} -5777 1749 2025-03-09 21:24:00.00801-07 2025-03-09 22:47:05.154155-07 leaderboard f A100 0.002640859 t \N \N \N {} -5778 1750 2025-03-09 23:04:17.164077-07 2025-03-09 23:12:50.24992-07 test t H100 \N t \N \N \N {} -5779 1750 2025-03-09 21:43:55.396805-07 2025-03-09 22:41:37.388888-07 benchmark t H100 \N t \N \N \N {} -5782 1751 2025-03-09 21:58:32.972565-07 2025-03-09 22:18:54.173023-07 benchmark t L4 \N t \N \N \N {} -5784 1750 2025-03-09 22:33:29.942446-07 2025-03-09 22:47:34.687352-07 test f H100 \N t \N \N \N {} -5785 1750 2025-03-09 22:57:51.36389-07 2025-03-09 23:11:33.809283-07 benchmark f H100 \N t \N \N \N {} -5786 1750 2025-03-09 22:55:32.663609-07 2025-03-09 21:59:50.521514-07 leaderboard f H100 0.001517079 t \N \N \N {} -5787 1752 2025-03-09 21:22:25.235791-07 2025-03-09 22:13:25.850939-07 test f T4 \N t \N \N \N {} -5788 1752 2025-03-09 23:01:38.584323-07 2025-03-09 21:41:17.038278-07 benchmark f T4 \N t \N \N \N {} -5793 1749 2025-03-09 21:33:50.661901-07 2025-03-09 22:14:05.438845-07 test t A100 \N t \N \N \N {} -5794 1749 2025-03-09 23:11:26.826051-07 2025-03-09 22:00:03.831654-07 benchmark t A100 \N t \N \N \N {} -5795 1749 2025-03-09 21:37:34.837957-07 2025-03-09 22:12:24.604584-07 leaderboard t A100 0.0027676723333333337 t \N \N \N {} -5796 1751 2025-03-09 23:08:13.975436-07 2025-03-09 22:41:10.744916-07 test f L4 \N t \N \N \N {} -5797 1751 2025-03-09 22:51:47.852548-07 2025-03-09 21:35:35.48834-07 benchmark f L4 \N t \N \N \N {} -5798 1751 2025-03-09 22:37:22.743083-07 2025-03-09 21:36:38.099972-07 leaderboard f L4 0.017245750666666667 t \N \N \N {} -5997 1823 2025-03-10 13:44:13.361199-07 2025-03-10 14:17:08.320572-07 test f H100 \N t \N \N \N {} -6111 1849 2025-03-11 00:45:42.466684-07 2025-03-11 02:08:01.224061-07 benchmark f A100 \N f \N \N \N {} -5887 1782 2025-03-10 12:48:56.366283-07 2025-03-10 11:26:28.502927-07 test t T4 \N f \N \N \N {} -5888 1782 2025-03-10 11:20:36.181976-07 2025-03-10 11:07:40.511412-07 test f T4 \N f \N \N \N {} -6006 1824 2025-03-10 14:29:01.568504-07 2025-03-10 13:17:50.880972-07 test t L4 \N t \N \N \N {} -5931 1804 2025-03-10 13:24:49.874112-07 2025-03-10 13:12:59.214864-07 test t T4 \N f \N \N \N {} -5932 1804 2025-03-10 13:11:51.554353-07 2025-03-10 12:36:24.247696-07 test f T4 \N f \N \N \N {} -5933 1805 2025-03-10 12:33:56.206912-07 2025-03-10 12:22:23.193916-07 test t T4 \N f \N \N \N {} -5934 1805 2025-03-10 12:16:39.318057-07 2025-03-10 13:04:51.698492-07 test f T4 \N f \N \N \N {} -5885 1781 2025-03-10 10:56:08.127207-07 2025-03-10 12:40:40.827896-07 test f T4 \N f \N \N \N {} -5886 1781 2025-03-10 11:37:13.25838-07 2025-03-10 11:18:09.354031-07 test t T4 \N f \N \N \N {} -5889 1783 2025-03-10 12:26:17.975854-07 2025-03-10 11:35:06.825449-07 test t T4 \N f \N \N \N {} -5893 1785 2025-03-10 12:49:26.520475-07 2025-03-10 12:05:07.923004-07 test t T4 \N f \N \N \N {} -5894 1785 2025-03-10 11:21:13.907922-07 2025-03-10 12:13:55.484547-07 test f T4 \N f \N \N \N {} -5895 1786 2025-03-10 12:53:04.60948-07 2025-03-10 12:56:23.335782-07 test t T4 \N f \N \N \N {} -5896 1786 2025-03-10 11:52:35.543265-07 2025-03-10 12:47:37.44292-07 test f T4 \N f \N \N \N {} -5897 1787 2025-03-10 12:19:59.35342-07 2025-03-10 12:03:09.756897-07 test t T4 \N f \N \N \N {} -5898 1787 2025-03-10 12:26:38.384347-07 2025-03-10 12:38:55.659544-07 test f T4 \N f \N \N \N {} -5904 1790 2025-03-10 13:07:41.471622-07 2025-03-10 11:29:54.088642-07 test f T4 \N f \N \N \N {} -5905 1791 2025-03-10 12:11:05.954905-07 2025-03-10 11:56:04.212348-07 test t T4 \N f \N \N \N {} -5906 1791 2025-03-10 12:06:20.464482-07 2025-03-10 13:05:37.047802-07 test f T4 \N f \N \N \N {} -5936 1806 2025-03-10 12:25:19.66528-07 2025-03-10 13:25:54.809235-07 test f T4 \N f \N \N \N {} -5907 1792 2025-03-10 11:41:05.031269-07 2025-03-10 12:31:10.781699-07 test f T4 \N f \N \N \N {} -5908 1792 2025-03-10 13:24:19.84949-07 2025-03-10 12:05:30.566627-07 test t T4 \N f \N \N \N {} -5909 1793 2025-03-10 13:10:29.314729-07 2025-03-10 12:27:22.377821-07 test f T4 \N f \N \N \N {} -5910 1793 2025-03-10 11:56:32.644344-07 2025-03-10 12:01:46.701558-07 test t T4 \N f \N \N \N {} -5911 1794 2025-03-10 11:51:38.865118-07 2025-03-10 11:51:19.334553-07 test t T4 \N f \N \N \N {} -5943 1809 2025-03-10 14:18:40.194531-07 2025-03-10 13:13:02.518142-07 leaderboard f A100 0.0026386466666666664 t \N \N \N {} -5912 1794 2025-03-10 12:22:55.695011-07 2025-03-10 13:18:30.719097-07 test f T4 \N f \N \N \N {} -5913 1795 2025-03-10 12:18:06.516563-07 2025-03-10 11:38:14.754524-07 test f T4 \N f \N \N \N {} -5914 1795 2025-03-10 12:36:00.298953-07 2025-03-10 12:55:22.229312-07 test t T4 \N f \N \N \N {} -5923 1800 2025-03-10 11:55:59.030364-07 2025-03-10 12:53:27.690509-07 test f T4 \N f \N \N \N {} -5924 1800 2025-03-10 12:07:20.544617-07 2025-03-10 12:44:06.621555-07 test t T4 \N f \N \N \N {} -5925 1801 2025-03-10 12:06:30.111263-07 2025-03-10 12:53:12.669353-07 test t T4 \N f \N \N \N {} -5926 1801 2025-03-10 13:31:32.380898-07 2025-03-10 13:00:06.56229-07 test f T4 \N f \N \N \N {} -5946 1809 2025-03-10 13:29:37.323879-07 2025-03-10 13:21:30.63564-07 leaderboard t A100 0.002635759 t \N \N \N {} -5937 1807 2025-03-10 12:41:08.079867-07 2025-03-10 12:35:49.297728-07 test f T4 \N f \N \N \N {} -5938 1807 2025-03-10 13:35:12.048803-07 2025-03-10 12:54:21.189668-07 test t T4 \N f \N \N \N {} -5939 1808 2025-03-10 13:59:07.441287-07 2025-03-10 13:33:08.669422-07 test t T4 \N f \N \N \N {} -5940 1808 2025-03-10 14:15:23.166819-07 2025-03-10 13:28:24.134307-07 test f T4 \N f \N \N \N {} -5941 1809 2025-03-10 14:13:38.207591-07 2025-03-10 13:49:12.972948-07 test f A100 \N t \N \N \N {} -5942 1809 2025-03-10 13:54:14.281293-07 2025-03-10 13:15:36.646021-07 benchmark f A100 \N t \N \N \N {} -5953 1811 2025-03-10 13:30:04.564524-07 2025-03-10 12:55:33.446063-07 test t T4 \N f \N \N \N {} -5954 1811 2025-03-10 14:13:48.338956-07 2025-03-10 12:46:33.424518-07 test f T4 \N f \N \N \N {} -5955 1812 2025-03-10 12:31:35.951286-07 2025-03-10 14:17:08.111846-07 test f T4 \N f \N \N \N {} -5956 1812 2025-03-10 13:45:55.348085-07 2025-03-10 13:37:16.626985-07 test t T4 \N f \N \N \N {} -5957 1813 2025-03-10 14:19:03.474874-07 2025-03-10 12:40:48.028667-07 test f T4 \N f \N \N \N {} -5958 1813 2025-03-10 13:09:32.274357-07 2025-03-10 12:58:22.955703-07 test t T4 \N f \N \N \N {} -5960 1814 2025-03-10 13:48:21.644188-07 2025-03-10 13:41:37.383129-07 test t T4 \N f \N \N \N {} -5961 1815 2025-03-10 14:25:53.892843-07 2025-03-10 14:27:52.516911-07 test f T4 \N f \N \N \N {} -5962 1815 2025-03-10 13:39:34.9964-07 2025-03-10 14:26:34.410119-07 test t T4 \N f \N \N \N {} -5963 1816 2025-03-10 12:54:03.964782-07 2025-03-10 13:19:23.51163-07 test t T4 \N f \N \N \N {} -5965 1817 2025-03-10 14:33:23.123369-07 2025-03-10 13:20:30.62665-07 test t T4 \N f \N \N \N {} -5966 1817 2025-03-10 14:24:33.780553-07 2025-03-10 13:09:05.912817-07 test f T4 \N f \N \N \N {} -5967 1818 2025-03-10 12:58:03.382174-07 2025-03-10 13:01:07.893499-07 test f T4 \N t \N \N \N {} -5971 1818 2025-03-10 13:56:03.131162-07 2025-03-10 13:29:13.996141-07 benchmark t T4 \N t \N \N \N {} -5989 1821 2025-03-10 14:34:10.506362-07 2025-03-10 13:16:40.681009-07 benchmark t L4 \N t \N \N \N {} -5990 1821 2025-03-10 14:14:40.84149-07 2025-03-10 13:59:04.607394-07 leaderboard t L4 0.0023410633333333336 t \N \N \N {} -5998 1823 2025-03-10 14:14:38.086577-07 2025-03-10 14:04:27.238303-07 benchmark f H100 \N t \N \N \N {} -5999 1823 2025-03-10 13:02:04.67764-07 2025-03-10 13:38:00.727394-07 leaderboard f H100 0.006278458 t \N \N \N {} -6000 1823 2025-03-10 14:25:36.878836-07 2025-03-10 13:56:47.954868-07 test t H100 \N t \N \N \N {} -6001 1823 2025-03-10 14:33:56.104952-07 2025-03-10 13:27:44.07615-07 benchmark t H100 \N t \N \N \N {} -6002 1823 2025-03-10 13:55:54.754715-07 2025-03-10 14:28:21.040943-07 leaderboard t H100 0.006274267666666667 t \N \N \N {} -6007 1824 2025-03-10 14:28:35.829178-07 2025-03-10 14:56:43.794426-07 benchmark t L4 \N t \N \N \N {} -6008 1824 2025-03-10 14:47:33.073271-07 2025-03-10 13:02:30.850136-07 leaderboard t L4 0.07900226333333332 t \N \N \N {} -6009 1825 2025-03-10 13:08:30.376545-07 2025-03-10 14:58:22.56048-07 test t T4 \N t \N \N \N {} -6010 1825 2025-03-10 13:10:54.018143-07 2025-03-10 14:06:35.263338-07 benchmark t T4 \N t \N \N \N {} -6012 1825 2025-03-10 14:42:31.922476-07 2025-03-10 14:59:03.540136-07 test f T4 \N t \N \N \N {} -6093 1844 2025-03-10 22:33:26.258836-07 2025-03-10 23:34:33.63838-07 leaderboard f A100 0.023116982 t \N \N \N {} -6075 1838 2025-03-10 16:41:59.664667-07 2025-03-10 17:22:08.116083-07 test t T4 \N f \N \N \N {} -6076 1838 2025-03-10 17:26:55.475959-07 2025-03-10 17:59:15.681447-07 test f T4 \N f \N \N \N {} -6077 1839 2025-03-10 17:21:38.821552-07 2025-03-10 17:51:32.223604-07 test f T4 \N f \N \N \N {} -6078 1839 2025-03-10 16:49:21.182853-07 2025-03-10 17:57:15.491956-07 test t T4 \N f \N \N \N {} -6079 1840 2025-03-10 18:26:53.2575-07 2025-03-10 18:21:22.708514-07 test t T4 \N f \N \N \N {} -6080 1840 2025-03-10 16:58:04.162955-07 2025-03-10 16:54:29.133532-07 test f T4 \N f \N \N \N {} -6094 1844 2025-03-10 22:39:58.995586-07 2025-03-10 23:17:13.977931-07 test t A100 \N t \N \N \N {} -6081 1841 2025-03-10 17:37:46.462074-07 2025-03-10 17:23:29.564865-07 test f T4 \N f \N \N \N {} -6082 1841 2025-03-10 17:01:30.436669-07 2025-03-10 17:54:05.772936-07 test t T4 \N f \N \N \N {} -6083 1842 2025-03-10 17:25:05.119241-07 2025-03-10 17:23:54.474002-07 test t T4 \N f \N \N \N {} -6084 1842 2025-03-10 17:50:14.778236-07 2025-03-10 17:38:41.04272-07 test f T4 \N f \N \N \N {} -6085 1843 2025-03-10 23:26:01.96551-07 2025-03-10 22:45:08.618919-07 test f T4 \N t \N \N \N {} -6102 1845 2025-03-10 23:18:03.684504-07 2025-03-10 22:13:42.532972-07 leaderboard t H100 0.007583517333333333 t \N \N \N {} -6086 1843 2025-03-10 23:47:15.122747-07 2025-03-10 23:21:01.982853-07 benchmark f T4 \N t \N \N \N {} -6087 1843 2025-03-10 23:09:56.514137-07 2025-03-10 23:41:36.80451-07 leaderboard f T4 1.0437126283333333 t \N \N \N {} -6088 1843 2025-03-10 22:13:21.923671-07 2025-03-10 23:11:52.790625-07 test t T4 \N t \N \N \N {} -6089 1843 2025-03-10 23:06:17.150635-07 2025-03-10 21:59:26.386221-07 benchmark t T4 \N t \N \N \N {} -6090 1843 2025-03-10 22:53:29.015752-07 2025-03-10 22:55:46.841799-07 leaderboard t T4 0.9874019316666667 t \N \N \N {} -6091 1844 2025-03-10 22:58:35.828002-07 2025-03-10 22:04:49.298779-07 test f A100 \N t \N \N \N {} -6092 1844 2025-03-10 23:35:22.877612-07 2025-03-10 22:06:59.964007-07 benchmark f A100 \N t \N \N \N {} -6101 1845 2025-03-10 22:20:24.248784-07 2025-03-10 23:42:44.548166-07 benchmark t H100 \N t \N \N \N {} -6113 1852 2025-03-11 01:44:33.106318-07 2025-03-11 00:54:18.834861-07 test f A100 \N f \N \N \N {} -6114 1853 2025-03-11 02:21:59.844098-07 2025-03-11 00:42:46.671243-07 test f A100 \N t \N \N \N {} -6115 1854 2025-03-11 02:28:36.156334-07 2025-03-11 00:46:06.202296-07 benchmark f A100 \N t \N \N \N {} -6886 2139 2025-03-16 11:38:46.212648-07 2025-03-16 11:28:03.154972-07 test f H100 \N f \N \N \N {} -6312 1952 2025-03-12 12:28:15.470511-07 2025-03-12 11:10:36.793829-07 test f T4 \N f \N \N \N {} -6460 2005 2025-03-14 06:31:39.099707-07 2025-03-14 07:51:37.583527-07 benchmark t A100 \N t \N \N \N {} -6461 2005 2025-03-14 06:46:54.111465-07 2025-03-14 07:07:11.64108-07 leaderboard t A100 0.004546970333333333 t \N \N \N {} -6596 2044 2025-03-14 13:39:50.152918-07 2025-03-14 12:42:49.661409-07 test f T4 \N f \N \N \N {} -6887 2140 2025-03-16 11:24:30.571404-07 2025-03-16 10:17:15.135954-07 test f T4 \N t \N \N \N {} -6651 2054 2025-03-14 12:23:27.508379-07 2025-03-14 12:07:07.961699-07 benchmark t A100 \N t \N \N \N {} -6652 2054 2025-03-14 13:00:27.647397-07 2025-03-14 13:25:59.68305-07 leaderboard t A100 0.0024835623333333335 t \N \N \N {} -6653 2055 2025-03-14 13:40:44.971801-07 2025-03-14 12:14:34.48949-07 benchmark f A100 \N t \N \N \N {} -6654 2056 2025-03-14 12:23:48.644957-07 2025-03-14 13:52:44.535234-07 benchmark f A100 \N t \N \N \N {} -6657 2058 2025-03-14 13:30:54.732584-07 2025-03-14 12:53:01.468344-07 benchmark t A100 \N t \N \N \N {} -6658 2058 2025-03-14 13:18:02.16859-07 2025-03-14 13:30:04.076635-07 leaderboard t A100 0.002514722 t \N \N \N {} -6660 2058 2025-03-14 12:19:31.91691-07 2025-03-14 12:23:40.05458-07 benchmark f A100 \N t \N \N \N {} -6678 2067 2025-03-14 14:51:27.080477-07 2025-03-14 15:45:50.453303-07 test t T4 \N f \N \N \N {} -6679 2067 2025-03-14 16:00:05.640981-07 2025-03-14 15:45:05.704388-07 test f T4 \N f \N \N \N {} -6681 2068 2025-03-14 15:59:19.410815-07 2025-03-14 15:47:46.277659-07 test t T4 \N f \N \N \N {} -6682 2069 2025-03-14 14:50:20.787072-07 2025-03-14 16:19:00.44313-07 test t T4 \N f \N \N \N {} -6683 2069 2025-03-14 15:33:20.870757-07 2025-03-14 16:21:01.08373-07 test f T4 \N f \N \N \N {} -6688 2072 2025-03-14 16:45:06.05283-07 2025-03-14 15:41:49.353865-07 test f T4 \N f \N \N \N {} -6689 2072 2025-03-14 15:20:50.731476-07 2025-03-14 15:36:51.584067-07 test t T4 \N f \N \N \N {} -6888 2141 2025-03-16 11:25:39.687485-07 2025-03-16 10:00:50.121381-07 test f H100 \N t \N \N \N {} -6690 2073 2025-03-14 17:13:14.556768-07 2025-03-14 16:38:50.501123-07 test t T4 \N f \N \N \N {} -6691 2073 2025-03-14 16:16:09.058399-07 2025-03-14 16:49:34.474312-07 test f T4 \N f \N \N \N {} -6692 2074 2025-03-14 16:06:22.83602-07 2025-03-14 16:32:58.584801-07 test t T4 \N f \N \N \N {} -6695 2075 2025-03-14 16:07:22.505809-07 2025-03-14 16:41:15.832579-07 test t T4 \N f \N \N \N {} -6696 2076 2025-03-14 17:00:09.730989-07 2025-03-14 17:45:40.086325-07 test f T4 \N f \N \N \N {} -6697 2076 2025-03-14 16:27:20.361782-07 2025-03-14 17:11:01.011478-07 test t T4 \N f \N \N \N {} -6698 2077 2025-03-14 17:12:38.530745-07 2025-03-14 17:22:03.682351-07 test t T4 \N f \N \N \N {} -6699 2077 2025-03-14 17:57:47.071236-07 2025-03-14 17:32:18.120911-07 test f T4 \N f \N \N \N {} -6700 2078 2025-03-14 17:18:24.324855-07 2025-03-14 16:55:54.52059-07 test t T4 \N f \N \N \N {} -6701 2078 2025-03-14 16:20:14.880505-07 2025-03-14 16:15:25.529956-07 test f T4 \N f \N \N \N {} -6702 2079 2025-03-14 16:16:56.772414-07 2025-03-14 16:16:13.557071-07 test t T4 \N f \N \N \N {} -6703 2079 2025-03-14 16:04:46.914169-07 2025-03-14 17:47:21.761297-07 test f T4 \N f \N \N \N {} -6705 2080 2025-03-15 03:22:21.602325-07 2025-03-15 03:51:58.457278-07 benchmark t A100 \N t \N \N \N {} -6706 2080 2025-03-15 02:53:01.372004-07 2025-03-15 04:22:27.007219-07 leaderboard t A100 0.002516597 t \N \N \N {} -6712 2081 2025-03-15 02:40:30.575706-07 2025-03-15 03:31:55.583471-07 leaderboard f A100 0.0025119373333333337 t \N \N \N {} -6714 2081 2025-03-15 04:01:30.92787-07 2025-03-15 04:10:50.24876-07 benchmark t A100 \N t \N \N \N {} -6715 2081 2025-03-15 03:13:12.177222-07 2025-03-15 04:31:33.220267-07 leaderboard t A100 0.0025109593333333337 t \N \N \N {} -6717 2082 2025-03-15 04:18:09.428811-07 2025-03-15 04:24:16.122702-07 benchmark t A100 \N t \N \N \N {} -6718 2082 2025-03-15 02:49:24.552314-07 2025-03-15 02:42:15.529163-07 leaderboard t A100 0.0025148316666666667 t \N \N \N {} -6720 2082 2025-03-15 04:08:25.063554-07 2025-03-15 03:27:30.400048-07 benchmark f A100 \N t \N \N \N {} -6723 2083 2025-03-15 03:03:30.27571-07 2025-03-15 03:18:11.663245-07 benchmark f A100 \N t \N \N \N {} -6724 2083 2025-03-15 03:42:23.801801-07 2025-03-15 03:28:29.653584-07 leaderboard f A100 0.002519684 t \N \N \N {} -6726 2083 2025-03-15 04:21:45.565499-07 2025-03-15 04:16:51.069546-07 benchmark t A100 \N t \N \N \N {} -6727 2083 2025-03-15 04:07:46.449197-07 2025-03-15 04:25:25.23021-07 leaderboard t A100 0.002565036 t \N \N \N {} -6729 2084 2025-03-15 03:39:18.979856-07 2025-03-15 03:30:24.318469-07 benchmark t A100 \N t \N \N \N {} -6730 2084 2025-03-15 04:36:55.579596-07 2025-03-15 02:45:20.602588-07 leaderboard t A100 0.0025688876666666665 t \N \N \N {} -6732 2084 2025-03-15 03:14:12.489414-07 2025-03-15 02:51:03.824773-07 benchmark f A100 \N t \N \N \N {} -6733 2084 2025-03-15 03:22:20.346621-07 2025-03-15 03:11:13.814391-07 leaderboard f A100 0.00251581 t \N \N \N {} -6734 2085 2025-03-15 04:20:48.927946-07 2025-03-15 04:46:33.035612-07 benchmark f A100 \N t \N \N \N {} -6736 2086 2025-03-15 04:16:49.706067-07 2025-03-15 03:09:15.471405-07 benchmark t A100 \N t \N \N \N {} -6737 2086 2025-03-15 04:03:35.988704-07 2025-03-15 03:48:00.494296-07 leaderboard t A100 0.0025361823333333337 t \N \N \N {} -6739 2086 2025-03-15 03:15:14.837866-07 2025-03-15 04:10:52.660769-07 benchmark f A100 \N t \N \N \N {} -6740 2086 2025-03-15 03:44:14.062436-07 2025-03-15 04:17:31.002026-07 leaderboard f A100 0.0025177116666666665 t \N \N \N {} -6742 2087 2025-03-15 03:11:35.820391-07 2025-03-15 03:29:33.061134-07 benchmark t A100 \N t \N \N \N {} -6743 2087 2025-03-15 04:11:17.86842-07 2025-03-15 04:57:34.429343-07 leaderboard t A100 0.0025124136666666665 t \N \N \N {} -6745 2087 2025-03-15 04:40:18.217533-07 2025-03-15 04:45:54.791513-07 benchmark f A100 \N t \N \N \N {} -6746 2087 2025-03-15 03:26:08.981404-07 2025-03-15 03:26:01.506723-07 leaderboard f A100 0.0025170136666666666 t \N \N \N {} -6747 2088 2025-03-15 03:45:17.641686-07 2025-03-15 03:59:01.555077-07 benchmark f A100 \N t \N \N \N {} -6748 2089 2025-03-15 04:38:05.216274-07 2025-03-15 03:11:47.405569-07 benchmark f A100 \N t \N \N \N {} -6749 2090 2025-03-15 03:38:24.37619-07 2025-03-15 03:54:27.64572-07 benchmark f A100 \N t \N \N \N {} -6750 2091 2025-03-15 03:40:25.330357-07 2025-03-15 05:05:56.066665-07 benchmark f A100 \N t \N \N \N {} -6751 2092 2025-03-15 03:14:13.338645-07 2025-03-15 03:23:06.85116-07 benchmark f A100 \N t \N \N \N {} -6753 2093 2025-03-15 03:42:38.171903-07 2025-03-15 04:13:43.823972-07 benchmark f A100 \N t \N \N \N {} -6754 2093 2025-03-15 04:16:08.4764-07 2025-03-15 04:59:47.179425-07 leaderboard f A100 0.0025626343333333334 t \N \N \N {} -6756 2093 2025-03-15 03:19:26.506082-07 2025-03-15 03:33:18.400847-07 benchmark t A100 \N t \N \N \N {} -6757 2093 2025-03-15 03:50:47.54997-07 2025-03-15 04:41:08.193634-07 leaderboard t A100 0.0025123326666666667 t \N \N \N {} -6759 2094 2025-03-15 04:41:28.996534-07 2025-03-15 03:59:31.091742-07 benchmark f A100 \N t \N \N \N {} -6760 2094 2025-03-15 03:19:48.97712-07 2025-03-15 04:52:46.927488-07 leaderboard f A100 0.002587383 t \N \N \N {} -6762 2094 2025-03-15 03:21:46.748837-07 2025-03-15 03:42:35.174807-07 benchmark t A100 \N t \N \N \N {} -6763 2094 2025-03-15 04:43:41.011534-07 2025-03-15 04:19:37.015055-07 leaderboard t A100 0.0025829123333333333 t \N \N \N {} -6764 2095 2025-03-15 03:22:19.084993-07 2025-03-15 04:23:32.846181-07 benchmark f A100 \N t \N \N \N {} -6765 2096 2025-03-15 04:20:43.869135-07 2025-03-15 03:47:49.996079-07 benchmark f A100 \N t \N \N \N {} -6767 2097 2025-03-15 05:10:33.784233-07 2025-03-15 05:11:32.173991-07 benchmark t A100 \N t \N \N \N {} -6768 2097 2025-03-15 05:07:59.890466-07 2025-03-15 04:39:03.8974-07 leaderboard t A100 0.0024414366666666667 t \N \N \N {} -6770 2097 2025-03-15 04:36:24.390754-07 2025-03-15 04:02:31.398882-07 benchmark f A100 \N t \N \N \N {} -6771 2097 2025-03-15 04:12:36.697244-07 2025-03-15 03:55:22.254114-07 leaderboard f A100 0.0025765703333333334 t \N \N \N {} -6773 2098 2025-03-15 05:10:57.321488-07 2025-03-15 04:50:43.920081-07 benchmark t A100 \N t \N \N \N {} -6774 2098 2025-03-15 04:14:30.497647-07 2025-03-15 04:53:30.016091-07 leaderboard t A100 0.002577079 t \N \N \N {} -6776 2098 2025-03-15 05:09:55.996121-07 2025-03-15 03:56:57.017989-07 benchmark f A100 \N t \N \N \N {} -6777 2098 2025-03-15 04:11:54.507468-07 2025-03-15 03:43:49.234308-07 leaderboard f A100 0.002441317 t \N \N \N {} -6779 2099 2025-03-15 03:45:03.883023-07 2025-03-15 05:13:30.957419-07 benchmark f A100 \N t \N \N \N {} -6780 2099 2025-03-15 03:28:47.790669-07 2025-03-15 04:06:54.990019-07 leaderboard f A100 0.002574019 t \N \N \N {} -6782 2099 2025-03-15 05:16:01.58112-07 2025-03-15 04:49:37.793655-07 benchmark t A100 \N t \N \N \N {} -6783 2099 2025-03-15 04:17:13.541413-07 2025-03-15 05:01:49.023637-07 leaderboard t A100 0.0024406096666666666 t \N \N \N {} -6785 2100 2025-03-15 05:11:53.175254-07 2025-03-15 04:00:48.036801-07 benchmark f A100 \N t \N \N \N {} -6786 2100 2025-03-15 04:50:11.427307-07 2025-03-15 04:59:24.114334-07 leaderboard f A100 0.0025154266666666666 t \N \N \N {} -6788 2100 2025-03-15 03:58:09.913043-07 2025-03-15 04:10:04.670157-07 benchmark t A100 \N t \N \N \N {} -6789 2100 2025-03-15 04:27:52.506737-07 2025-03-15 05:09:40.763406-07 leaderboard t A100 0.0025164226666666667 t \N \N \N {} -6791 2101 2025-03-15 03:51:55.571727-07 2025-03-15 04:36:50.58781-07 benchmark f A100 \N t \N \N \N {} -6792 2101 2025-03-15 05:02:04.728096-07 2025-03-15 03:47:35.278382-07 leaderboard f A100 0.002513957 t \N \N \N {} -6794 2101 2025-03-15 04:13:17.847293-07 2025-03-15 05:12:57.162997-07 benchmark t A100 \N t \N \N \N {} -6795 2101 2025-03-15 04:32:46.623254-07 2025-03-15 04:14:33.053587-07 leaderboard t A100 0.0025157153333333336 t \N \N \N {} -6797 2102 2025-03-15 04:32:32.324876-07 2025-03-15 05:04:55.352884-07 benchmark f A100 \N t \N \N \N {} -6798 2102 2025-03-15 05:21:36.583203-07 2025-03-15 04:18:22.362049-07 leaderboard f A100 0.002519632 t \N \N \N {} -6800 2102 2025-03-15 04:46:37.876337-07 2025-03-15 05:18:37.22304-07 benchmark t A100 \N t \N \N \N {} -6801 2102 2025-03-15 04:37:43.517293-07 2025-03-15 04:33:17.157515-07 leaderboard t A100 0.0025164303333333336 t \N \N \N {} -6802 2103 2025-03-15 04:28:54.778992-07 2025-03-15 04:05:58.240583-07 benchmark f A100 \N t \N \N \N {} -6803 2104 2025-03-15 04:17:37.393026-07 2025-03-15 04:32:32.169131-07 benchmark f A100 \N t \N \N \N {} -6805 2106 2025-03-15 04:31:28.892695-07 2025-03-15 05:14:03.534628-07 benchmark f A100 \N t \N \N \N {} -6809 2110 2025-03-15 05:36:30.308746-07 2025-03-15 05:16:01.72196-07 benchmark f A100 \N f \N \N \N {} -6810 2111 2025-03-15 05:40:36.907231-07 2025-03-15 04:27:34.338625-07 benchmark f A100 \N t \N \N \N {} -6811 2112 2025-03-15 04:14:08.830754-07 2025-03-15 04:10:52.990865-07 benchmark f A100 \N t \N \N \N {} -6812 2113 2025-03-15 04:18:14.027105-07 2025-03-15 04:27:38.546365-07 benchmark f A100 \N t \N \N \N {} -6892 2142 2025-03-16 11:16:36.721079-07 2025-03-16 10:23:08.981733-07 benchmark f H100 \N f \N \N \N {} -6813 2114 2025-03-15 05:32:29.886643-07 2025-03-15 04:16:37.097189-07 benchmark f A100 \N f \N \N \N {} -6814 2115 2025-03-15 04:20:01.707962-07 2025-03-15 04:47:01.479607-07 benchmark f A100 \N t \N \N \N {} -6815 2116 2025-03-15 09:41:27.374128-07 2025-03-15 10:42:21.527333-07 benchmark f A100 \N f \N \N \N {} -6817 2118 2025-03-15 09:34:00.585802-07 2025-03-15 09:31:57.810416-07 benchmark f A100 \N t \N \N \N {} -6818 2119 2025-03-15 09:01:24.585482-07 2025-03-15 09:06:44.508444-07 benchmark f A100 \N t \N \N \N {} -6820 2120 2025-03-15 09:11:32.950497-07 2025-03-15 10:49:33.106269-07 benchmark f A100 \N t \N \N \N {} -6821 2120 2025-03-15 10:40:24.613571-07 2025-03-15 09:19:20.415657-07 leaderboard f A100 0.002443316 t \N \N \N {} -6824 2120 2025-03-15 09:48:57.993341-07 2025-03-15 10:57:27.863934-07 leaderboard t A100 0.0025090733333333333 t \N \N \N {} -6826 2121 2025-03-15 10:59:21.842552-07 2025-03-15 09:27:40.540091-07 benchmark t A100 \N t \N \N \N {} -6827 2121 2025-03-15 11:01:43.071093-07 2025-03-15 09:36:34.551049-07 leaderboard t A100 0.0025179616666666663 t \N \N \N {} -6829 2121 2025-03-15 10:06:55.697797-07 2025-03-15 09:52:44.863131-07 benchmark f A100 \N t \N \N \N {} -6830 2121 2025-03-15 10:04:37.365154-07 2025-03-15 10:01:47.995774-07 leaderboard f A100 0.00258409 t \N \N \N {} -6832 2122 2025-03-15 09:19:20.004818-07 2025-03-15 10:37:41.259917-07 benchmark t A100 \N t \N \N \N {} -6833 2122 2025-03-15 10:30:16.383706-07 2025-03-15 10:47:04.167114-07 leaderboard t A100 0.002514938 t \N \N \N {} -6835 2122 2025-03-15 09:23:17.985207-07 2025-03-15 10:19:25.750256-07 benchmark f A100 \N t \N \N \N {} -6836 2122 2025-03-15 09:33:25.125545-07 2025-03-15 09:07:15.935099-07 leaderboard f A100 0.002580424 t \N \N \N {} -6838 2123 2025-03-15 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{} -6851 2125 2025-03-15 10:43:35.545505-07 2025-03-15 09:58:24.113231-07 leaderboard f A100 0.0025163956666666666 t \N \N \N {} -6853 2125 2025-03-15 10:00:18.694621-07 2025-03-15 09:47:13.923293-07 benchmark t A100 \N t \N \N \N {} -6854 2125 2025-03-15 09:28:35.589355-07 2025-03-15 09:48:08.961608-07 leaderboard t A100 0.0024417513333333334 t \N \N \N {} -6856 2126 2025-03-15 11:02:27.462138-07 2025-03-15 09:43:02.319651-07 benchmark f A100 \N t \N \N \N {} -6857 2126 2025-03-15 10:34:45.228313-07 2025-03-15 10:59:38.816203-07 leaderboard f A100 0.0025180366666666667 t \N \N \N {} -6859 2126 2025-03-15 10:50:57.421372-07 2025-03-15 10:57:16.908572-07 benchmark t A100 \N t \N \N \N {} -6860 2126 2025-03-15 09:48:05.47393-07 2025-03-15 11:11:47.912597-07 leaderboard t A100 0.0024680626666666663 t \N \N \N {} -6862 2127 2025-03-15 10:49:46.799765-07 2025-03-15 11:16:21.43097-07 benchmark f A100 \N t \N \N \N {} -6863 2127 2025-03-15 09:54:25.438285-07 2025-03-15 09:33:00.814065-07 leaderboard f A100 0.0025179106666666667 t \N \N \N {} -6865 2127 2025-03-15 09:30:50.965569-07 2025-03-15 10:48:04.990717-07 benchmark t A100 \N t \N \N \N {} -6866 2127 2025-03-15 10:18:20.707038-07 2025-03-15 11:07:56.251823-07 leaderboard t A100 0.00251993 t \N \N \N {} -6867 2128 2025-03-15 12:31:03.821683-07 2025-03-15 13:36:09.213881-07 test t T4 \N f \N \N \N {} -6873 2131 2025-03-15 13:22:04.183948-07 2025-03-15 14:24:48.619022-07 test f T4 \N f \N \N \N {} -6879 2134 2025-03-15 13:51:24.499821-07 2025-03-15 13:59:05.032478-07 test f T4 \N f \N \N \N {} -6880 2134 2025-03-15 13:38:48.357103-07 2025-03-15 13:25:18.378546-07 test t T4 \N f \N \N \N {} -6881 2135 2025-03-15 13:08:37.869004-07 2025-03-15 14:28:23.740745-07 test t T4 \N f \N \N \N {} -6882 2135 2025-03-15 14:19:21.411146-07 2025-03-15 13:47:56.79824-07 test f T4 \N f \N \N \N {} -6885 2138 2025-03-16 09:39:05.307836-07 2025-03-16 09:57:12.148158-07 test f H100 \N f \N \N \N {} -6898 2147 2025-03-16 12:12:19.701328-07 2025-03-16 12:45:49.124926-07 benchmark f T4 \N t \N \N \N {} -6900 2148 2025-03-16 12:59:18.78657-07 2025-03-16 12:12:58.201063-07 benchmark t T4 \N t \N \N \N {} -6901 2148 2025-03-16 13:01:51.449428-07 2025-03-16 13:37:59.705354-07 leaderboard t T4 0.04730965533333334 t \N \N \N {} -6903 2148 2025-03-16 12:58:16.904312-07 2025-03-16 12:26:52.007968-07 benchmark f T4 \N t \N \N \N {} -6904 2148 2025-03-16 13:30:00.224366-07 2025-03-16 12:52:44.892037-07 leaderboard f T4 0.04833626233333334 t \N \N \N {} -6908 2152 2025-03-16 15:44:14.854646-07 2025-03-16 16:27:57.619851-07 test t T4 \N f \N \N \N {} -6937 2159 2025-03-16 16:22:15.506089-07 2025-03-16 15:10:41.848628-07 test t T4 \N t \N \N \N {} -6909 2152 2025-03-16 16:12:17.885519-07 2025-03-16 16:07:41.651133-07 test f T4 \N f \N \N \N {} -6910 2153 2025-03-16 16:06:39.076718-07 2025-03-16 16:34:36.042625-07 test f T4 \N f \N \N \N {} -6911 2153 2025-03-16 16:28:04.258089-07 2025-03-16 15:53:40.235028-07 test t T4 \N f \N \N \N {} -6917 2156 2025-03-16 16:39:30.967226-07 2025-03-16 16:15:32.585871-07 benchmark f T4 \N t \N \N \N {} -6918 2156 2025-03-16 16:54:16.751464-07 2025-03-16 16:03:12.107749-07 leaderboard f T4 0.00024644342857142856 t \N \N \N {} -6920 2156 2025-03-16 14:58:37.646855-07 2025-03-16 15:33:11.455364-07 benchmark t T4 \N t \N \N \N {} -6921 2156 2025-03-16 15:16:42.101122-07 2025-03-16 16:12:37.070857-07 leaderboard t T4 0.00027644572222222224 t \N \N \N {} -6936 2159 2025-03-16 15:05:11.186583-07 2025-03-16 15:35:42.129511-07 benchmark f T4 \N f \N \N \N {} -6938 2159 2025-03-16 16:42:58.618407-07 2025-03-16 15:47:12.763026-07 benchmark t T4 \N f \N \N \N {} -6939 2161 2025-03-16 17:02:27.026204-07 2025-03-16 16:28:43.910502-07 test t T4 \N t \N \N \N {} -6940 2161 2025-03-16 16:06:50.499036-07 2025-03-16 15:50:15.342936-07 benchmark t T4 \N t \N \N \N {} -6944 2161 2025-03-16 16:05:29.503727-07 2025-03-16 16:16:37.57484-07 leaderboard f T4 0.00036897348214285714 t \N \N \N {} -6958 2165 2025-03-16 15:32:28.811938-07 2025-03-16 15:44:31.566608-07 test t T4 \N f \N \N \N {} -6959 2165 2025-03-16 15:57:30.46348-07 2025-03-16 17:11:51.584129-07 test f T4 \N f \N \N \N {} -6967 2168 2025-03-16 19:51:44.930831-07 2025-03-16 19:38:02.035729-07 test t T4 \N f \N \N \N {} -6968 2168 2025-03-16 19:59:18.445681-07 2025-03-16 20:46:51.847725-07 test f T4 \N f \N \N \N {} -6969 2169 2025-03-16 20:01:37.505709-07 2025-03-16 19:53:20.526714-07 test t T4 \N t \N \N \N {} -6970 2169 2025-03-16 18:55:53.752614-07 2025-03-16 19:43:27.875834-07 benchmark t T4 \N t \N \N \N {} -6972 2169 2025-03-16 20:01:43.044-07 2025-03-16 20:16:29.475064-07 test f T4 \N t \N \N \N {} -6973 2169 2025-03-16 20:12:25.898132-07 2025-03-16 20:12:59.112672-07 benchmark f T4 \N t \N \N \N {} -6975 2170 2025-03-16 20:55:02.810795-07 2025-03-16 19:05:21.943366-07 test f T4 \N t \N \N \N {} -6982 2171 2025-03-16 20:34:15.139307-07 2025-03-16 19:39:38.074903-07 benchmark t T4 \N t \N \N \N {} 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20:52:50.82818-07 2025-03-16 20:52:32.181629-07 test t T4 \N t \N \N \N {} -7020 2178 2025-03-16 20:56:15.870132-07 2025-03-16 21:37:33.135945-07 benchmark f T4 \N t \N \N \N {} -7021 2178 2025-03-16 20:46:26.93908-07 2025-03-16 20:15:43.470303-07 leaderboard f T4 0.00023776616 t \N \N \N {} -7022 2178 2025-03-16 20:29:51.881141-07 2025-03-16 20:49:15.652665-07 test t T4 \N t \N \N \N {} -7023 2178 2025-03-16 19:44:17.51868-07 2025-03-16 20:29:57.33345-07 benchmark t T4 \N t \N \N \N {} -7024 2178 2025-03-16 20:46:38.473464-07 2025-03-16 19:46:05.014633-07 leaderboard t T4 0.00023401815384615384 t \N \N \N {} -7025 2179 2025-03-16 20:43:42.53621-07 2025-03-16 20:37:35.619442-07 test t T4 \N t \N \N \N {} -7026 2179 2025-03-16 20:01:15.232212-07 2025-03-16 20:15:45.919775-07 benchmark t T4 \N t \N \N \N {} -7027 2179 2025-03-16 20:05:15.564236-07 2025-03-16 20:03:37.77276-07 leaderboard t T4 0.00023105845833333333 t \N \N \N {} -7028 2179 2025-03-16 21:07:58.380332-07 2025-03-16 20:38:16.446317-07 test f T4 \N t \N \N \N {} -7029 2179 2025-03-16 21:12:36.483147-07 2025-03-16 20:50:41.542771-07 benchmark f T4 \N t \N \N \N {} -7030 2179 2025-03-16 21:41:50.345063-07 2025-03-16 20:30:53.023148-07 leaderboard f T4 0.0002410815 t \N \N \N {} -7031 2180 2025-03-16 20:01:10.433977-07 2025-03-16 21:48:38.104837-07 test f T4 \N t \N \N \N {} -7032 2180 2025-03-16 20:42:55.307852-07 2025-03-16 19:56:48.747118-07 benchmark f T4 \N t \N \N \N {} -7033 2180 2025-03-16 19:54:54.43732-07 2025-03-16 21:43:31.624944-07 leaderboard f T4 0.0010320996666666666 t \N \N \N {} -7034 2180 2025-03-16 21:08:01.480549-07 2025-03-16 20:04:50.559362-07 test t T4 \N t \N \N \N {} -7035 2180 2025-03-16 20:17:30.013044-07 2025-03-16 21:24:22.031756-07 benchmark t T4 \N t \N \N \N {} -7036 2180 2025-03-16 21:41:47.373413-07 2025-03-16 21:32:11.525076-07 leaderboard t T4 0.0010306484 t \N \N \N {} -7037 2181 2025-03-16 21:22:46.486398-07 2025-03-16 20:29:47.484582-07 test f A100 \N t \N \N \N {} -7038 2181 2025-03-16 21:38:35.851119-07 2025-03-16 21:51:47.560305-07 benchmark f A100 \N t \N \N \N {} -7039 2181 2025-03-16 20:38:02.61894-07 2025-03-16 20:02:26.204495-07 leaderboard f A100 0.0000866505 t \N \N \N {} -7040 2181 2025-03-16 20:21:30.439741-07 2025-03-16 20:59:46.729245-07 test t A100 \N t \N \N \N {} -7041 2181 2025-03-16 21:46:26.568583-07 2025-03-16 21:18:37.064553-07 benchmark t A100 \N t \N \N \N {} -7042 2181 2025-03-16 21:48:37.803054-07 2025-03-16 20:30:42.439254-07 leaderboard t A100 0.00008592785365853658 t \N \N \N {} -7044 2182 2025-03-16 21:06:13.034065-07 2025-03-16 20:08:36.728568-07 benchmark f H100 \N t \N \N \N {} -7045 2182 2025-03-16 21:28:36.786376-07 2025-03-16 21:47:18.527666-07 leaderboard f H100 0.00005831765454545455 t \N \N \N {} -7046 2182 2025-03-16 20:56:24.442304-07 2025-03-16 21:33:14.384165-07 test t H100 \N t \N \N \N {} -7047 2182 2025-03-16 21:05:38.501639-07 2025-03-16 20:55:27.320926-07 benchmark t H100 \N t \N \N \N {} -7048 2182 2025-03-16 21:24:08.431353-07 2025-03-16 20:32:33.148155-07 leaderboard t H100 0.00006414326 t \N \N \N {} -7061 2185 2025-03-16 21:59:53.418742-07 2025-03-16 21:21:24.148854-07 benchmark t L4 \N t \N \N \N {} -7062 2185 2025-03-16 21:59:20.390159-07 2025-03-16 20:48:54.636456-07 leaderboard t L4 0.00011960029545454545 t \N \N \N {} -7063 2186 2025-03-16 20:23:16.497019-07 2025-03-16 21:55:58.531742-07 test t H100 \N t \N \N \N {} -7064 2186 2025-03-16 22:01:45.798895-07 2025-03-16 20:34:14.059263-07 benchmark t H100 \N t \N \N \N {} -7065 2186 2025-03-16 20:21:16.357956-07 2025-03-16 22:00:26.089828-07 leaderboard t H100 0.00008572011000000001 t \N \N \N {} -7066 2186 2025-03-16 21:34:57.985674-07 2025-03-16 21:07:45.295076-07 test f H100 \N t \N \N \N {} -7067 2186 2025-03-16 20:33:02.195088-07 2025-03-16 20:29:30.445574-07 benchmark f H100 \N t \N \N \N {} -7068 2186 2025-03-16 22:14:09.367859-07 2025-03-16 21:19:31.416324-07 leaderboard f H100 0.00007322206 t \N \N \N {} -7069 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345678901234567 1643 2025-03-15 13:08:41.987409-07 t -2132 342 kernel.py 901234567890123 1644 2025-03-15 15:40:51.471691-07 t -2133 342 impl.py 789012345678901 1645 2025-03-15 15:31:01.440834-07 t -\. - - --- --- Data for Name: user_info; Type: TABLE DATA; Schema: leaderboard; Owner: - --- - -COPY leaderboard.user_info (id, user_name) FROM stdin; -123456789012345 Alice -234567890123456 Bob -345678901234567 Charlie -456789012345678 Dave -567890123456789 Eve -678901234567890 Frank -789012345678901 Grace -890123456789012 Heidi -901234567890123 Ivan -012345678901234 James -\. - - --- --- Name: code_files_id_seq; Type: SEQUENCE SET; Schema: leaderboard; Owner: - --- - -SELECT pg_catalog.setval('leaderboard.code_files_id_seq', 1687, true); - - --- --- Name: leaderboard_id_seq; Type: SEQUENCE SET; Schema: leaderboard; Owner: - --- - -SELECT pg_catalog.setval('leaderboard.leaderboard_id_seq', 365, true); - - --- --- Name: runs_id_seq; Type: SEQUENCE SET; Schema: leaderboard; Owner: - --- - -SELECT pg_catalog.setval('leaderboard.runs_id_seq', 7078, true); - - --- --- Name: submission_id_seq; Type: SEQUENCE SET; Schema: leaderboard; Owner: - --- - -SELECT pg_catalog.setval('leaderboard.submission_id_seq', 2188, true); - - --- --- Name: code_files code_files_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.code_files - ADD CONSTRAINT code_files_pkey PRIMARY KEY (id); - - --- --- Name: gpu_type gpu_type_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.gpu_type - ADD CONSTRAINT gpu_type_pkey PRIMARY KEY (leaderboard_id, gpu_type); - - --- --- Name: leaderboard leaderboard_name_key; Type: CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.leaderboard - ADD CONSTRAINT leaderboard_name_key UNIQUE (name); - - --- --- Name: leaderboard leaderboard_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.leaderboard - ADD CONSTRAINT leaderboard_pkey PRIMARY KEY (id); - - --- --- Name: runs runs_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.runs - ADD CONSTRAINT runs_pkey PRIMARY KEY (id); - - --- --- Name: submission submission_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.submission - ADD CONSTRAINT submission_pkey PRIMARY KEY (id); - - --- --- Name: user_info user_info_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.user_info - ADD CONSTRAINT user_info_pkey PRIMARY KEY (id); - - --- --- Name: submission fk_user_info; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.submission - ADD CONSTRAINT fk_user_info FOREIGN KEY (user_id) REFERENCES leaderboard.user_info(id); - - --- --- Name: gpu_type gpu_type_leaderboard_id_fkey; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.gpu_type - ADD CONSTRAINT gpu_type_leaderboard_id_fkey FOREIGN KEY (leaderboard_id) REFERENCES leaderboard.leaderboard(id) ON DELETE CASCADE; - - --- --- Name: runs runs_submission_id_fkey; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.runs - ADD CONSTRAINT runs_submission_id_fkey FOREIGN KEY (submission_id) REFERENCES leaderboard.submission(id); - - --- --- Name: submission submission_code_id_fkey; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.submission - ADD CONSTRAINT submission_code_id_fkey FOREIGN KEY (code_id) REFERENCES leaderboard.code_files(id); - - --- --- Name: submission submission_leaderboard_id_fkey; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.submission - ADD CONSTRAINT submission_leaderboard_id_fkey FOREIGN KEY (leaderboard_id) REFERENCES leaderboard.leaderboard(id); - - --- --- PostgreSQL database dump complete --- - diff --git a/src/discord_cluster_manager.egg-info/PKG-INFO b/src/discord_cluster_manager.egg-info/PKG-INFO deleted file mode 100644 index 79bcae0c..00000000 --- a/src/discord_cluster_manager.egg-info/PKG-INFO +++ /dev/null @@ -1,408 +0,0 @@ -Metadata-Version: 2.4 -Name: discord-cluster-manager -Version: 0.1.0 -Summary: Discord bot for managing compute clusters and running kernel benchmarks -Requires-Python: >=3.10 -Description-Content-Type: text/markdown -License-File: LICENSE -Requires-Dist: PyGithub -Requires-Dist: aiohttp -Requires-Dist: discord.py -Requires-Dist: audioop-lts; python_version >= "3.13" -Requires-Dist: python-dotenv -Requires-Dist: requests -Requires-Dist: modal -Requires-Dist: psycopg2-binary -Requires-Dist: yoyo-migrations -Requires-Dist: better_profanity -Requires-Dist: PyYAML -Requires-Dist: fastapi[all] -Requires-Dist: uvicorn -Requires-Dist: jinja2 -Provides-Extra: dev -Requires-Dist: ruff; extra == "dev" -Requires-Dist: pre-commit; extra == "dev" -Requires-Dist: pytest; extra == "dev" -Requires-Dist: pytest-coverage; extra == "dev" -Dynamic: license-file - -# discord-cluster-manager - -[![nvidia-arc](https://github.com/gpu-mode/discord-cluster-manager/actions/workflows/nvidia-arc-health.yml/badge.svg)](https://github.com/pytorch-labs/discord-cluster-manager/actions/workflows/nvidia-arc-health.yml) -[![amd](https://github.com/gpu-mode/discord-cluster-manager/actions/workflows/amd-health.yml/badge.svg)](https://github.com/pytorch-labs/discord-cluster-manager/actions/workflows/amd-health.yml) - -This is the code for the Discord bot we'll be using to queue jobs to a cluster of GPUs that our generous sponsors have provided. Our goal is to be able to queue kernels that can run end to end in seconds that way things feel interactive and social. - -The key idea is that we're using Github Actions as a job scheduling engine and primarily making the Discord bot interact with the cluster via issuing Github Actions and and monitoring their status and while we're focused on having a nice user experience on discord.gg/gpumode, [we're happy to accept PRs](#local-development) that make it easier for other Discord communities to hook GPUs. - -## Table of Contents - -- [Supported Schedulers](#supported-schedulers) -- [Local Development](#local-development) - - [Clone Repository](#clone-repository) - - [Discord Bot](#discord-bot) - - [Database](#database) - - [Environment Variables](#environment-variables) - - [Verify Setup](#verify-setup) -- [Available Commands](#available-commands) -- [Using the Leaderboard](#using-the-leaderboard) - - [Creating a New Leaderboard](#creating-a-new-leaderboard) - - [Reference Code Requirements (Python)](#reference-code-requirements-python) - - [Reference Code Requirements (CUDA)](#reference-code-requirements-cuda) - - [Submitting to a Leaderboard](#submitting-to-a-leaderboard) - - [Other Available Leaderboard Commands](#other-available-leaderboard-commands) - - [GPU Kernel-Specific Commands](#gpu-kernel-specific-commands) -- [Testing the Discord Bot](#testing-the-discord-bot) -- [How to Add a New GPU to the Cluster](#how-to-add-a-new-gpu-to-the-cluster) -- [Acknowledgements](#acknowledgements) - -## Supported schedulers - -- GitHub Actions -- Modal -- Slurm (not implemented yet) - -## Local Development - -### Clone Repository - -> [!IMPORTANT] -> Do not fork this repository. Instead, directly clone this repository to your local machine. - -> [!IMPORTANT] -> Python 3.11 or higher is required. - -After, install the dependencies with `pip install -r requirements.txt`. - -### Setup Discord Bot - -To run and develop the bot locally, you need to add it to your own "staging" server. Follow the steps [here](https://discordjs.guide/preparations/setting-up-a-bot-application.html#creating-your-bot) and [here](https://discordjs.guide/preparations/adding-your-bot-to-servers.html#bot-invite-links) to create a bot application and then add it to your staging server. - -Below is a visual walk-through of the steps linked above: - -- The bot needs the `Message Content Intent` and `Server Members Intent` permissions turned on. -
- Click here for visual. - DCS_bot_perms -
- -- The bot needs `applications.commands` and `bot` scopes. - -
- Click here for visual. - Screenshot 2024-11-24 at 12 34 09 PM -
- -- Finally, generate an invite link for the bot and enter it into any browser. - -
- Click here for visual. - Screenshot 2024-11-24 at 12 44 08 PM -
- -> [!NOTE] -> Bot permissions involving threads/mentions/messages should suffice, but you can naively give it `Administrator` since it's just a test bot in your own testing Discord server. - -### Database - -The leaderboard persists information in a Postgres database. To develop locally, set Postgres up on your machine. Then start a Postgres shell with `psql`, and create a database: - -``` -$ psql -U postgres -Password for user postgres: ******** -psql (16.6 (Ubuntu 16.6-1.pgdg22.04+1)) -Type "help" for help. - -postgres=# CREATE DATABASE clusterdev; -``` - -We are using [Yoyo Migrations](https://ollycope.com/software/yoyo/) to manage tables, indexes, etc. in our database. To create tables in your local database, apply the migrations in `src/discord-cluster-manager/migrations` with the following command line: - -``` -yoyo apply src/discord-cluster-manager/migrations \ - -d postgresql://user:password@localhost/clusterdev -``` - -
- Click here for a transcript of a yoyo apply session - - $ yoyo apply . -d postgresql://user:password@localhost/clusterdev - - [20241208_01_p3yuR-initial-leaderboard-schema] - Shall I apply this migration? [Ynvdaqjk?]: y - - Selected 1 migration: - [20241208_01_p3yuR-initial-leaderboard-schema] - Apply this migration to postgresql://user:password@localhost/clusterdev [Yn]: y - Save migration configuration to yoyo.ini? - This is saved in plain text and contains your database password. - - Answering 'y' means you do not have to specify the migration source or database connection for future runs [yn]: n - -
- -Applying migrations to our staging and prod environments also happens using `yoyo apply`, just with a different database URL. - -To make changes to the structure of the database, create a new migration: - -``` -yoyo new src/discord-cluster-manager/migrations -m "short_description" -``` - -...and then edit the generated file. Please do not edit existing migration files: the existing migration files form a sort of changelog that is supposed to be immutable, and so yoyo will refuse to reapply the changes. - -We are following an expand/migrate/contract pattern to allow database migrations without downtime. When you want to make a change to the structure of the database, first determine if it is expansive or contractive. - -- _Expansive changes_ are those that have no possibility of breaking a running application. Examples include: adding a new nullable column, adding a non-null column with a default value, adding an index, adding a table, etc. -- _Contractive changes_ are those that could break a running application. Examples include: dropping a table, dropping a column, adding a not null constraint to a column, adding a unique index, etc. - -After an expansive phase, data gets migrated to the newly added elements. Code also begins using the newly added elements. This is the migration step. Finally, when all code is no longer using elements that are obsolete, these can be removed. (Or, if adding a unique or not null constraint, after checking that the data satisfies the constraint, then the constraint can be safely added.) - -Expand, migrate, and contract steps may all be written using yoyo. - -### Environment Variables - -Create a `.env` file with the following environment variables: - -- `DISCORD_DEBUG_TOKEN` : The token of the bot you want to run locally -- `DISCORD_TOKEN` : The token of the bot you want to run in production -- `DISCORD_DEBUG_CLUSTER_STAGING_ID` : The ID of the "staging" server you want to connect to -- `DISCORD_CLUSTER_STAGING_ID` : The ID of the "production" server you want to connect to -- `GITHUB_TOKEN` : A Github token with permissions to trigger workflows, for now only new branches from [discord-cluster-manager](https://github.com/gpu-mode/discord-cluster-manager) are tested, since the bot triggers workflows on your behalf -- `GITHUB_REPO` : The repository where the cluster manager is hosted. -- `GITHUB_WORKFLOW_BRANCH` : The branch to start the GitHub Actions jobs from when submitting a task. -- `DATABASE_URL` : The URL you use to connect to Postgres. -- `DISABLE_SSL` : (Optional) set if you want to disable SSL when connecting to Postgres. - -Below is where to find these environment variables: - -> [!NOTE] -> For now, you can naively set `DISCORD_DEBUG_TOKEN` and `DISCORD_DEBUG_CLUSTER_STAGING_ID` to the same values as `DISCORD_TOKEN` and `DISCORD_CLUSTER_STAGING_ID` respectively. - -- `DISCORD_DEBUG_TOKEN` or `DISCORD_TOKEN`: Found in your bot's page within the [Discord Developer Portal](https://discord.com/developers/applications/): - -
- Click here for visual. - Screenshot 2024-11-24 at 11 01 19 AM -
- -- `DISCORD_DEBUG_CLUSTER_STAGING_ID` or `DISCORD_CLUSTER_STAGING_ID`: Right-click your staging Discord server and select `Copy Server ID`: - -
- Click here for visual. - Screenshot 2024-11-24 at 10 58 27 AM -
- -- `GITHUB_TOKEN`: Found in Settings -> Developer Settings (or [here](https://github.com/settings/tokens?type=beta)). Create a new (preferably classic) personal access token with an expiration date to any day less than a year from the current date, and the scopes `repo` and `workflow`. - -
- Click here for visual. - Screenshot 2024-12-30 at 8 51 59 AM -
- -- `GITHUB_REPO`: This should be set to this repository, which is usually `gpu-mode/discord-cluster-manager`. - -- `GITHUB_WORKFLOW_BRANCH`: Usually `main` or the branch you are working from. - -- `DATABASE_URL`: This contains the connection details for your local database, and has the form `postgresql://user:password@localhost/clusterdev`. - -- `DISABLE_SSL`: Set to `1` when developing. - -### Verify Setup - -Install the kernel bot as editable using `pip install -e .` - -Run the following command to run the bot: - -``` -python src/kernelbot/main.py --debug -``` - -Then in your staging server, use the `/verifyruns` command to test basic functionalities of the bot and the `/verifydb` command to check database connectivity. - -> [!NOTE] -> To test functionality of the Modal runner, you also need to be authenticated with Modal. Modal provides free credits to get started. -> To test functionality of the GitHub runner, you may need direct access to this repo which you can ping us for. - -## Available Commands - -TODO. This is currently a work in progress. - -`/run modal ` which you can use to pick a specific gpu, right now defaults to T4 - -`/run github ` which picks one of two workflow files - -`/resync` to clear all the commands and resync them - -`/ping` to check if the bot is online - -## Using the Leaderboard - -The main purpose of the Discord bot is to allow servers to host coding competitions through Discord. -The leaderboard was designed for evaluating GPU kernels, but can be adapted easily for other -competitions. The rest of this section will mostly refer to leaderboard submissions in the context -of our GPU Kernel competition. - -> [!NOTE] -> All leaderboard commands have the prefix `/leaderboard`, and center around creating, submitting to, -> and viewing leaderboard statistics and information. - -### Creating a new Leaderboard - -``` -/leaderboard create {name: str} {deadline: str} {reference_code: .cu or .py file} -``` - -The above command creates a leaderboard named `name` that ends at `deadline`. The `reference_code` -has strict function signature requirements, and is required to contain an input generator and a -reference implementation for the desired GPU kernel. We import these functions in our evaluation -scripts for verifying leaderboard submissions and measuring runtime. In the next mini-section, we -discuss the exact requirements for the `reference_code` script. - -Each leaderboard `name` can also specify the types of hardware that users can run their kernels on. -For example, a softmax kernel on an RTX 4090 can have different performance characteristics on an -H100. After running the leaderboard creation command, a prompt will pop up where the creator can -specify the available GPUs that the leaderboard evaluates on. - -![Leaderboard GPU](assets/img/lb_gpu.png) - -#### Reference Code Requirements (Python) - -The Discord bot internally contains an `eval.py` script that handles the correctness and timing -analysis for the leaderboard. The `reference_code` that the leaderboard creator submits must have -the following function signatures with their implementations filled out. `InputType` and -`OutputType` are generics that could be a `torch.Tensor`, `List[torch.Tensor]`, etc. -depending on the reference code specifications. We leave this flexibility to the leaderboard creator. - -```python -# Reference kernel implementation. -def ref_kernel(input: InputType) -> OutputType: - # Implement me... - -# Generate a list of tensors as input to the kernel -def generate_input() -> InputType: - # Implement me... - -# Verify correctness of reference and output -def check_implementation(custom_out: OutputType, reference_out: OutputType) -> bool: - # Implement me... -``` - -#### Reference Code Requirements (CUDA) - -The Discord bot internally contains an `eval.cu` script that handles the correctness and timing -analysis for the leaderboard. The difficult of CUDA evaluation scripts is we need to explicitly -handle the typing system for tensors. The `reference.cu` that the leaderboard creator submits must have -the following function signatures with their implementations filled out: - -The main difference is we now need to define an alias for the type that the input / outputs are. A -simple and common example is a list of FP32 tensors, which can be defined using a pre-defined array of -`const int`s called `N_SIZES`, then define an array of containers, e.g. -`std::array, N_SIZES>`. - -```cuda -// User-defined type for inputs, e.g. using input_t = std::array, IN_SIZES>; -using input_t = ...; - -// User-defined type for outputs, e.g. using output_t = std::array, OUT_SIZES>; -using output_t = ...; - -// Generate random data of type input_t -input_t generate_input() { - // Implement me... -} - - -// Reference kernel host code. -output_t reference(input_t data) { - // Implement me... -} - - -// Verify correctness of reference and output -bool check_implementation(output_t out, output_t ref) { - // Implement me... -} -``` - -### Submitting to a Leaderboard - -``` -/leaderboard submit {github / modal} {leaderboard_name: str} {script: .cu or .py file} -``` - -The leaderboard submission for _Python code_ requires the following function signatures, where -`InputType` and `OutputType` are generics that could be a `torch.Tensor`, `List[torch.Tensor]`, -etc. depending on the reference code specifications. - -```python -# User kernel implementation. -def custom_kernel(input: InputType) -> OutputType: - # Implement me... -``` - -### Other Available Leaderboard Commands - -Deleting a leaderboard: - -``` -/leaderboard delete {name: str} -``` - -List all active leaderboards and which GPUs they can run on: - -``` -/leaderboard list -``` - -List all leaderboard scores (runtime) for a particular leaderboard. (currently deprecated. Doesn't -support multiple GPU types yet) - -``` -/leaderboard show {name: str} -``` - -Display all personal scores (runtime) from a specific leaderboard. - -``` -/leaderboard show-personal {name: str} -``` - -### Submitting via a CLI - -Moving forward we also allow submissions without logging in to Discord via a CLI tool we wrote in Rust https://github.com/gpu-mode/popcorn-cli - -#### GPU Kernel-specific Commands - -We plan to add support for the PyTorch profiler and CUDA NSight Compute CLI to allow users to -profile their kernels. These commands are not specific to the leaderboard, but may be helpful for -leaderboard submissions. - -## How to add a new GPU to the cluster - -If you'd like to donate a GPU to our efforts, we can make you a CI admin in Github and have you add an org level runner https://github.com/organizations/gpu-mode/settings/actions/runners - -## Acknowledgements - -- Thank you to AMD for sponsoring an MI250 node -- Thank you to NVIDIA for sponsoring an H100 node -- Thank you to Nebius for sponsoring credits and an H100 node -- Thank you Modal for credits and speedy spartup times -- Luca Antiga did something very similar for the NeurIPS LLM efficiency competition, it was great! -- Midjourney was a similar inspiration in terms of UX - -## Citation - -If you used our software please cite it as - -``` -@misc{kernelbot2025, - title={KernelBot: A Discord-based GPU Kernel Evaluation and Competition Platform}, - author={Sirovatka, Matej and Zhang, Alex and Schultheis, Erik and Horowitz, Ben and Saroufim, Mark}, - year={2025}, - month={2}, - note={Equal contribution}, - url={https://github.com/gpu-mode/discord-cluster-manager}, - publisher={GitHub} -} -``` diff --git a/src/discord_cluster_manager.egg-info/SOURCES.txt b/src/discord_cluster_manager.egg-info/SOURCES.txt deleted file mode 100644 index e6b21773..00000000 --- a/src/discord_cluster_manager.egg-info/SOURCES.txt +++ /dev/null @@ -1,59 +0,0 @@ -LICENSE -README.md -pyproject.toml -src/discord_cluster_manager.egg-info/PKG-INFO -src/discord_cluster_manager.egg-info/SOURCES.txt -src/discord_cluster_manager.egg-info/dependency_links.txt -src/discord_cluster_manager.egg-info/requires.txt -src/discord_cluster_manager.egg-info/top_level.txt -src/kernelbot/discord_reporter.py -src/kernelbot/discord_utils.py -src/kernelbot/env.py -src/kernelbot/main.py -src/kernelbot/api/__init__.py -src/kernelbot/api/api_utils.py -src/kernelbot/api/main.py -src/kernelbot/cogs/__init__.py -src/kernelbot/cogs/admin_cog.py -src/kernelbot/cogs/leaderboard_cog.py -src/kernelbot/cogs/misc_cog.py -src/kernelbot/cogs/verify_run_cog.py -src/kernelbot/ui/misc.py -src/kernelbot/ui/table.py -src/libkernelbot/__init__.py -src/libkernelbot/backend.py -src/libkernelbot/consts.py -src/libkernelbot/db_types.py -src/libkernelbot/leaderboard_db.py -src/libkernelbot/report.py -src/libkernelbot/run_eval.py -src/libkernelbot/submission.py -src/libkernelbot/task.py -src/libkernelbot/utils.py -src/libkernelbot/launchers/__init__.py -src/libkernelbot/launchers/github.py -src/libkernelbot/launchers/launcher.py -src/libkernelbot/launchers/modal.py -src/migrations/20241208_01_p3yuR-initial-leaderboard-schema.py -src/migrations/20241214_01_M62BX-drop-old-leaderboard-tables.py -src/migrations/20241221_01_54Oeg-rename-problem-table.py -src/migrations/20241222_01_ELxU5-add-gpu-types.py -src/migrations/20241224_01_Pg4FX-delete-cascade.py -src/migrations/20241226_01_ZQSOK-add_gpu_type_to_submission.py -src/migrations/20250106_01_Sgph3-add-leaderboard-creator-id.py -src/migrations/20250202_01_YYS3Q-leaderboard-rename-reference-to-task.py -src/migrations/20250221_01_GA8ro-submission-collection.py -src/migrations/20250228_01_9ANYn-submission-add-user-name.py -src/migrations/20250304_01_DzORz-collect-system-information-for-each-run.py -src/migrations/20250316_01_5oMi3-remember-forum-id.py -src/migrations/20250329_01_7VjJJ-add-a-secret-seed-column.py -src/migrations/20250406_01_ZXjWK-user-info-add-cli-id.py -src/migrations/20250412_01_l7Dra-user-info-fix-auth.py -src/migrations/20250412_02_NN9kK-user-info-cli-drop-old.py -src/migrations/20250506_01_38PkG-add-index-on-runs-runner-score.py -src/migrations/20250617_01_c5mrF-task-split.py -src/migrations/20250728_01_Q3jso-fix-code-table.py -src/migrations/20250822_01_UtXzl-website-submission.py -src/runners/github-runner.py -src/runners/modal_runner.py -src/runners/modal_runner_archs.py \ No newline at end of file diff --git a/src/discord_cluster_manager.egg-info/dependency_links.txt b/src/discord_cluster_manager.egg-info/dependency_links.txt deleted file mode 100644 index 8b137891..00000000 --- a/src/discord_cluster_manager.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/src/discord_cluster_manager.egg-info/requires.txt b/src/discord_cluster_manager.egg-info/requires.txt deleted file mode 100644 index ac2cbf9b..00000000 --- a/src/discord_cluster_manager.egg-info/requires.txt +++ /dev/null @@ -1,22 +0,0 @@ -PyGithub -aiohttp -discord.py -python-dotenv -requests -modal -psycopg2-binary -yoyo-migrations -better_profanity -PyYAML -fastapi[all] -uvicorn -jinja2 - -[:python_version >= "3.13"] -audioop-lts - -[dev] -ruff -pre-commit -pytest -pytest-coverage diff --git a/src/discord_cluster_manager.egg-info/top_level.txt b/src/discord_cluster_manager.egg-info/top_level.txt deleted file mode 100644 index e90efa46..00000000 --- a/src/discord_cluster_manager.egg-info/top_level.txt +++ /dev/null @@ -1,4 +0,0 @@ -kernelbot -libkernelbot -migrations -runners From bc5bd969ca4595739d26413aef1c60808fc04588 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 15:13:31 -0700 Subject: [PATCH 07/28] echo variables --- seed.sql | 21 --------------------- submission.py | 1 - 2 files changed, 22 deletions(-) delete mode 100644 seed.sql delete mode 100644 submission.py diff --git a/seed.sql b/seed.sql deleted file mode 100644 index 65982fa8..00000000 --- a/seed.sql +++ /dev/null @@ -1,21 +0,0 @@ -BEGIN; - -INSERT INTO leaderboard.leaderboard - (id, name, deadline, task, creator_id, forum_id, secret_seed, description) -VALUES -( - 339, - 'conv2d', - '2025-06-29 17:00:00-07', - $TASK$ -{ ... valid JSON starting with { and ending with } ... } -$TASK$, - 838132355075014667, - 1343279714277527582, - 1828004782, - $DESC$ -Multiline description goes here… -$DESC$ -); - -COMMIT; diff --git a/submission.py b/submission.py deleted file mode 100644 index 96be8b6d..00000000 --- a/submission.py +++ /dev/null @@ -1 +0,0 @@ -print("love and peace") From a0e17d128f804ef3a8ffc866bacfe958b1fecaef Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 15:16:24 -0700 Subject: [PATCH 08/28] echo variables --- src/libkernelbot/launchers/modal.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/src/libkernelbot/launchers/modal.py b/src/libkernelbot/launchers/modal.py index 6ebc92ca..ccdfea41 100644 --- a/src/libkernelbot/launchers/modal.py +++ b/src/libkernelbot/launchers/modal.py @@ -28,16 +28,6 @@ async def run_submission( logger.info(f"Starting Modal run using {func_name}") - - #elaine tesitng - await asyncio.sleep(10) - return FullResult( - success=True, - error="", - system= SystemInfo(), - runs={}, - ) - await status.push("⏳ Waiting for Modal run to finish...") result = await loop.run_in_executor( From 780ce86c83bd06ee139847fff09229c850c5e156 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 15:24:26 -0700 Subject: [PATCH 09/28] echo variables --- unit-tests/test_submission.py | 74 ----------------------------------- 1 file changed, 74 deletions(-) diff --git a/unit-tests/test_submission.py b/unit-tests/test_submission.py index b93f906b..1f2bfb7c 100644 --- a/unit-tests/test_submission.py +++ b/unit-tests/test_submission.py @@ -347,77 +347,3 @@ def test_compute_score(): mock_task.ranking_by = "WRONG" with pytest.raises(KernelBotError, match="Invalid ranking criterion WRONG"): submission.compute_score(mock_result, mock_task, 1) - - -@pytest.mark.asyncio -async def test_start_detached_run_uses_existing_mock_backend(monkeypatch, mock_backend): - db = mock_backend.db - - # Ensure the db mock has create_submission and returns a fixed id - if not hasattr(db, "create_submission"): - db.create_submission = mock.Mock(return_value=777) - else: - db.create_submission.return_value = 777 - - # Minimal submission_request: only the attributes accessed by start_detached_run - req = SimpleNamespace( - leaderboard="test_board", - file_name="hello.py", - code="print('hi')", - user_id="u-1", - user_name="alice", - ) - mode = SubmissionMode.LEADERBOARD - bg = BackgroundTasks() - - # Stub _run_submission to avoid running real work and capture the call - called = {} - async def fake_run_submission( - submission_request, - submission_mode_enum, - backend_arg, - db_arg, - sub_id_arg): - called["args"] = ( - submission_request, - submission_mode_enum, - backend_arg, db_arg, - sub_id_arg) - - monkeypatch.setattr(api_utils, "_run_submission", fake_run_submission) - - # Execute function under test - sub_id = api_utils.start_detached_run( - submission_request=req, - submission_mode_enum=mode, - backend=mock_backend, - db=db, - background_tasks=bg, - ) - - # Assert: returned id - assert sub_id == 777 - - # Assert: database context manager was entered/exited - db.__enter__.assert_called_once() - db.__exit__.assert_called_once() - - # Assert: create_submission was called with expected kwargs - db.create_submission.assert_called_once_with( - leaderboard="test_board", - file_name="hello.py", - code="print('hi')", - user_id="u-1", - time=mock.ANY, # time is dynamic - user_name="alice", - ) - - # Assert: task was enqueued into BackgroundTasks with correct function and args - assert len(bg.tasks) == 1 - task = bg.tasks[0] - assert task.func is fake_run_submission - assert task.args == (req, mode, mock_backend, db, 777) - - # verify the enqueued background task to verify the stub was called - await bg() - assert called["args"] == (req, mode, mock_backend, db, 777) From c00d167275420fdf8a83d99b4c8850637282d2f6 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 15:50:15 -0700 Subject: [PATCH 10/28] echo variables --- src/kernelbot/api/api_utils.py | 19 +++- src/kernelbot/api/main.py | 160 +++++++++++++---------------- src/libkernelbot/leaderboard_db.py | 4 +- src/libkernelbot/run_eval.py | 1 + 4 files changed, 93 insertions(+), 91 deletions(-) diff --git a/src/kernelbot/api/api_utils.py b/src/kernelbot/api/api_utils.py index 1228d7d2..44567998 100644 --- a/src/kernelbot/api/api_utils.py +++ b/src/kernelbot/api/api_utils.py @@ -1,4 +1,4 @@ -from typing import Any +from typing import Any, Optional import requests from fastapi import HTTPException, UploadFile @@ -13,7 +13,8 @@ RunResultReport, Text, ) -from libkernelbot.submission import SubmissionRequest +from libkernelbot.submission import ProcessedSubmissionRequest, SubmissionRequest, prepare_submission +from src.libkernelbot.backend import KernelBackend async def _handle_discord_oauth( @@ -157,6 +158,20 @@ async def _handle_github_oauth( return user_id, user_name +async def _run_submission( + submission: SubmissionRequest, mode: SubmissionMode, backend: KernelBackend +): + try: + req = prepare_submission(submission, backend) + except Exception as e: + raise HTTPException(status_code=400, detail=str(e)) from e + + if len(req.gpus) != 1: + raise HTTPException(status_code=400, detail="Invalid GPU type") + + reporter = MultiProgressReporterAPI() + sub_id, results = await backend.submit_full(req, mode, reporter) + return results, [rep.get_message() + "\n" + rep.long_report for rep in reporter.runs] class MultiProgressReporterAPI(MultiProgressReporter): def __init__(self): diff --git a/src/kernelbot/api/main.py b/src/kernelbot/api/main.py index c01ee917..770857b8 100644 --- a/src/kernelbot/api/main.py +++ b/src/kernelbot/api/main.py @@ -17,15 +17,16 @@ from libkernelbot.leaderboard_db import LeaderboardDB, LeaderboardRankedEntry from libkernelbot.submission import ( ProcessedSubmissionRequest, + SubmissionRequest, prepare_submission, ) from libkernelbot.utils import KernelBotError from .api_utils import ( - MultiProgressReporterAPI, _handle_discord_oauth, _handle_github_oauth, to_submit_info, + _run_submission, ) # yes, we do want ... = Depends() in function signatures @@ -33,6 +34,13 @@ app = FastAPI() +def json_serializer(obj): + """JSON serializer for objects not serializable by default json code""" + if isinstance(obj, (datetime.datetime, datetime.date, datetime.time)): + return obj.isoformat() + raise TypeError(f"Type {type(obj)} not serializable") + + backend_instance: KernelBackend = None background_submission_manager: BackgroundSubmissionManager = None @@ -295,6 +303,68 @@ async def cli_auth( "is_reset": is_reset, } +async def _stream_submission_response( + submission_request: SubmissionRequest, + submission_mode_enum: SubmissionMode, + backend: KernelBackend, +): + start_time = time.time() + task: asyncio.Task | None = None + try: + task = asyncio.create_task( + _run_submission( + submission_request, + submission_mode_enum, + backend, + ) + ) + + while not task.done(): + elapsed_time = time.time() - start_time + yield f"event: status\ndata: {json.dumps({'status': 'processing', + 'elapsed_time': round(elapsed_time, 2)}, + default=json_serializer)}\n\n" + + try: + await asyncio.wait_for(asyncio.shield(task), timeout=15.0) + except asyncio.TimeoutError: + continue + except asyncio.CancelledError: + yield f"event: error\ndata: {json.dumps( + {'status': 'error', 'detail': 'Submission cancelled'}, + default=json_serializer)}\n\n" + return + + result, reports = await task + result_data = { + "status": "success", + "results": [asdict(r) for r in result], + "reports": reports, + } + yield f"event: result\ndata: {json.dumps(result_data, default=json_serializer)}\n\n" + + except HTTPException as http_exc: + error_data = { + "status": "error", + "detail": http_exc.detail, + "status_code": http_exc.status_code, + } + yield f"event: error\ndata: {json.dumps(error_data, default=json_serializer)}\n\n" + except Exception as e: + error_type = type(e).__name__ + error_data = { + "status": "error", + "detail": f"An unexpected error occurred: {error_type}", + "raw_error": str(e), + } + yield f"event: error\ndata: {json.dumps(error_data, default=json_serializer)}\n\n" + finally: + if task and not task.done(): + task.cancel() + try: + await task + except asyncio.CancelledError: + pass @app.post("/{leaderboard_name}/{gpu_type}/{submission_mode}") async def run_submission( # noqa: C901 @@ -336,15 +406,13 @@ async def run_submission( # noqa: C901 if not req.gpus or len(req.gpus) != 1: raise HTTPException(status_code=400, detail="Invalid GPU type") - - generator = sse_stream_submission( - req=req, + generator = _stream_submission_response( + submission_request=submission_request, submission_mode_enum=submission_mode_enum, backend=backend_instance, ) return StreamingResponse(generator, media_type="text/event-stream") - @app.post("/submission/{leaderboard_name}/{gpu_type}/{submission_mode}") async def run_submission_v2( leaderboard_name: str, @@ -460,22 +528,6 @@ async def get_submission_count( status_code=500, detail=f"Error fetching submission count: {e}" ) from e - -async def _run_submission( - req: ProcessedSubmissionRequest, - mode: SubmissionMode, - backend: KernelBackend, - submission_id: Optional[int] = None, -): - reporter = MultiProgressReporterAPI() - sub_id, results = await backend.submit_full(req, mode, reporter, submission_id) - return ( - results, - [rep.get_message() + "\n" + rep.long_report for rep in reporter.runs], - sub_id, - ) - - async def start_detached_run( req: ProcessedSubmissionRequest, mode: SubmissionMode, @@ -494,69 +546,3 @@ async def start_detached_run( db.upsert_submission_job_status(sub_id, "initial", None) await manager.enqueue(req, mode, sub_id) return sub_id - - -async def sse_stream_submission( - req: ProcessedSubmissionRequest, - submission_mode_enum: SubmissionMode, - backend: KernelBackend, -): - start_time = time.time() - task: asyncio.Task | None = None - try: - task = asyncio.create_task(_run_submission(req, submission_mode_enum, backend)) - - while not task.done(): - elapsed_time = time.time() - start_time - yield ( - "event: status\n" - f"data: {json.dumps({'status': 'processing','elapsed_time': round(elapsed_time, 2)}, default=json_serializer)}\n\n" - ) - try: - await asyncio.wait_for(asyncio.shield(task), timeout=15.0) - except asyncio.TimeoutError: - continue - except asyncio.CancelledError: - yield ( - "event: error\n" - f"data: {json.dumps({'status': 'error','detail': 'Submission cancelled'}, default=json_serializer)}\n\n" - ) - return - - result, reports = await task - result_data = { - "status": "success", - "results": [asdict(r) for r in result], - "reports": reports, - } - yield "event: result\n" f"data: {json.dumps(result_data, default=json_serializer)}\n\n" - - except HTTPException as http_exc: - error_data = { - "status": "error", - "detail": http_exc.detail, - "status_code": http_exc.status_code, - } - yield "event: error\n" f"data: {json.dumps(error_data, default=json_serializer)}\n\n" - except Exception as e: - error_type = type(e).__name__ - error_data = { - "status": "error", - "detail": f"An unexpected error occurred: {error_type}", - "raw_error": str(e), - } - yield "event: error\n" f"data: {json.dumps(error_data, default=json_serializer)}\n\n" - finally: - if task and not task.done(): - task.cancel() - try: - await task - except asyncio.CancelledError: - pass - - -def json_serializer(obj): - """JSON serializer for objects not serializable by default json code""" - if isinstance(obj, (datetime.datetime, datetime.date, datetime.time)): - return obj.isoformat() - raise TypeError(f"Type {type(obj)} not serializable") diff --git a/src/libkernelbot/leaderboard_db.py b/src/libkernelbot/leaderboard_db.py index 9434845d..230d4201 100644 --- a/src/libkernelbot/leaderboard_db.py +++ b/src/libkernelbot/leaderboard_db.py @@ -200,7 +200,7 @@ def delete_leaderboard(self, leaderboard_name: str, force: bool = False): WHERE leaderboard.leaderboard.name = %s ) ); -""", + """, (leaderboard_name,), ) self.cursor.execute( @@ -291,7 +291,7 @@ def create_submission( code: str, time: datetime.datetime, user_name: str = None, - ) -> int: + ) -> Optional[int]: try: # check if we already have the code self.cursor.execute( diff --git a/src/libkernelbot/run_eval.py b/src/libkernelbot/run_eval.py index 18344dd3..442dc605 100644 --- a/src/libkernelbot/run_eval.py +++ b/src/libkernelbot/run_eval.py @@ -103,6 +103,7 @@ def _create_files(files: Optional[dict[str, str]]): return for name, content in files.items(): + print(f"Creating file {name} with type {type(content)}, {content}") assert Path(name).resolve().is_relative_to(Path.cwd()) Path(name).write_text(content) From e50bbe96021580dbf5f2481c212c7626b3c12d71 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 15:52:20 -0700 Subject: [PATCH 11/28] echo variables --- src/kernelbot/api/api_utils.py | 54 ++++++++++------------------------ 1 file changed, 16 insertions(+), 38 deletions(-) diff --git a/src/kernelbot/api/api_utils.py b/src/kernelbot/api/api_utils.py index 44567998..dffcdbbf 100644 --- a/src/kernelbot/api/api_utils.py +++ b/src/kernelbot/api/api_utils.py @@ -1,11 +1,9 @@ -from typing import Any, Optional - import requests -from fastapi import HTTPException, UploadFile +from fastapi import HTTPException from kernelbot.env import env +from libkernelbot.backend import KernelBackend from libkernelbot.consts import SubmissionMode -from libkernelbot.leaderboard_db import LeaderboardDB from libkernelbot.report import ( Log, MultiProgressReporter, @@ -13,13 +11,10 @@ RunResultReport, Text, ) -from libkernelbot.submission import ProcessedSubmissionRequest, SubmissionRequest, prepare_submission -from src.libkernelbot.backend import KernelBackend +from libkernelbot.submission import SubmissionRequest, prepare_submission -async def _handle_discord_oauth( - code: str, redirect_uri: str -) -> tuple[str, str]: +async def _handle_discord_oauth(code: str, redirect_uri: str) -> tuple[str, str]: """Handles the Discord OAuth code exchange and user info retrieval.""" client_id = env.CLI_DISCORD_CLIENT_ID client_secret = env.CLI_DISCORD_CLIENT_SECRET @@ -27,17 +22,11 @@ async def _handle_discord_oauth( user_api_url = "https://discord.com/api/users/@me" if not client_id: - raise HTTPException( - status_code=500, detail="Discord client ID not configured." - ) + raise HTTPException(status_code=500, detail="Discord client ID not configured.") if not client_secret: - raise HTTPException( - status_code=500, detail="Discord client secret not configured." - ) + raise HTTPException(status_code=500, detail="Discord client secret not configured.") if not token_url: - raise HTTPException( - status_code=500, detail="Discord token URL not configured." - ) + raise HTTPException(status_code=500, detail="Discord token URL not configured.") token_data = { "client_id": client_id, @@ -80,16 +69,13 @@ async def _handle_discord_oauth( if not user_id or not user_name: raise HTTPException( - status_code=500, - detail="Failed to retrieve user ID or username from Discord.", + status_code=500, detail="Failed to retrieve user ID or username from Discord." ) return user_id, user_name -async def _handle_github_oauth( - code: str, redirect_uri: str -) -> tuple[str, str]: +async def _handle_github_oauth(code: str, redirect_uri: str) -> tuple[str, str]: """Handles the GitHub OAuth code exchange and user info retrieval.""" client_id = env.CLI_GITHUB_CLIENT_ID client_secret = env.CLI_GITHUB_CLIENT_SECRET @@ -98,13 +84,9 @@ async def _handle_github_oauth( user_api_url = "https://api.github.com/user" if not client_id: - raise HTTPException( - status_code=500, detail="GitHub client ID not configured." - ) + raise HTTPException(status_code=500, detail="GitHub client ID not configured.") if not client_secret: - raise HTTPException( - status_code=500, detail="GitHub client secret not configured." - ) + raise HTTPException(status_code=500, detail="GitHub client secret not configured.") token_data = { "client_id": client_id, @@ -116,9 +98,7 @@ async def _handle_github_oauth( headers = {"Accept": "application/json"} # Request JSON response for token try: - token_response = requests.post( - token_url, data=token_data, headers=headers - ) + token_response = requests.post(token_url, data=token_data, headers=headers) token_response.raise_for_status() except requests.exceptions.RequestException as e: raise HTTPException( @@ -145,19 +125,17 @@ async def _handle_github_oauth( ) from e user_json = user_response.json() - user_id = str( - user_json.get("id") - ) # GitHub ID is integer, convert to string for consistency + user_id = str(user_json.get("id")) # GitHub ID is integer, convert to string for consistency user_name = user_json.get("login") # GitHub uses 'login' for username if not user_id or not user_name: raise HTTPException( - status_code=500, - detail="Failed to retrieve user ID or username from GitHub.", + status_code=500, detail="Failed to retrieve user ID or username from GitHub." ) return user_id, user_name + async def _run_submission( submission: SubmissionRequest, mode: SubmissionMode, backend: KernelBackend ): @@ -173,6 +151,7 @@ async def _run_submission( sub_id, results = await backend.submit_full(req, mode, reporter) return results, [rep.get_message() + "\n" + rep.long_report for rep in reporter.runs] + class MultiProgressReporterAPI(MultiProgressReporter): def __init__(self): self.runs = [] @@ -205,7 +184,6 @@ async def display_report(self, title: str, report: RunResultReport): self.long_report += f"\n\n## {part.header}:\n" self.long_report += f"```\n{part.content}```" - async def to_submit_info( user_info: Any, submission_mode: str, From e462fcfc2bacb0f355c0c67b31fa1dbc21fed64a Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 15:56:10 -0700 Subject: [PATCH 12/28] echo variables --- src/kernelbot/api/main.py | 53 ++++++--------------------------------- 1 file changed, 8 insertions(+), 45 deletions(-) diff --git a/src/kernelbot/api/main.py b/src/kernelbot/api/main.py index 770857b8..4fae241c 100644 --- a/src/kernelbot/api/main.py +++ b/src/kernelbot/api/main.py @@ -194,14 +194,10 @@ async def auth_init(provider: str, db_context=Depends(get_db)) -> dict: db.init_user_from_cli(state_uuid, provider) except AttributeError as e: # Catch if leaderboard_db methods don't exist - raise HTTPException( - status_code=500, detail=f"Database interface error: {e}" - ) from e + raise HTTPException(status_code=500, detail=f"Database interface error: {e}") from e except Exception as e: # Catch other potential errors during DB interaction - raise HTTPException( - status_code=500, detail=f"Failed to initialize auth in DB: {e}" - ) from e + raise HTTPException(status_code=500, detail=f"Failed to initialize auth in DB: {e}") from e return {"state": state_uuid} @@ -445,7 +441,6 @@ async def run_submission_v2( content={"id": sub_id, "status": "accepted"}, ) - @app.get("/leaderboards") async def get_leaderboards(db_context=Depends(get_db)): """An endpoint that returns all leaderboards. @@ -460,9 +455,7 @@ async def get_leaderboards(db_context=Depends(get_db)): with db_context as db: return db.get_leaderboards() except Exception as e: - raise HTTPException( - status_code=500, detail=f"Error fetching leaderboards: {e}" - ) from e + raise HTTPException(status_code=500, detail=f"Error fetching leaderboards: {e}") from e @app.get("/gpus/{leaderboard_name}") @@ -482,9 +475,7 @@ async def get_gpus(leaderboard_name: str, db_context=Depends(get_db)) -> list[st return db.get_leaderboard_gpu_types(leaderboard_name) except Exception as e: - raise HTTPException( - status_code=500, detail=f"Error fetching GPU data: {e}" - ) from e + raise HTTPException(status_code=500, detail=f"Error fetching GPU data: {e}") from e @app.get("/submissions/{leaderboard_name}/{gpu_name}") @@ -503,46 +494,18 @@ async def get_submissions( leaderboard_name, gpu_name, limit=limit, offset=offset ) except Exception as e: - raise HTTPException( - status_code=500, detail=f"Error fetching submissions: {e}" - ) from e + raise HTTPException(status_code=500, detail=f"Error fetching submissions: {e}") from e @app.get("/submission_count/{leaderboard_name}/{gpu_name}") async def get_submission_count( - leaderboard_name: str, - gpu_name: str, - user_id: str = None, - db_context=Depends(get_db), + leaderboard_name: str, gpu_name: str, user_id: str = None, db_context=Depends(get_db) ) -> dict: """Get the total count of submissions for pagination""" await simple_rate_limit() try: with db_context as db: - count = db.get_leaderboard_submission_count( - leaderboard_name, gpu_name, user_id - ) + count = db.get_leaderboard_submission_count(leaderboard_name, gpu_name, user_id) return {"count": count} except Exception as e: - raise HTTPException( - status_code=500, detail=f"Error fetching submission count: {e}" - ) from e - -async def start_detached_run( - req: ProcessedSubmissionRequest, - mode: SubmissionMode, - backend: KernelBackend, - manager: BackgroundSubmissionManager, -): - with backend.db as db: - sub_id = db.create_submission( - leaderboard=req.leaderboard, - file_name=req.file_name, - code=req.code, - user_id=req.user_id, - time=datetime.datetime.now(), - user_name=req.user_name, - ) - db.upsert_submission_job_status(sub_id, "initial", None) - await manager.enqueue(req, mode, sub_id) - return sub_id + raise HTTPException(status_code=500, detail=f"Error fetching submission count: {e}") from e From b57f8be714be9f92b2f407fd61f8d8d9c44bc31e Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 15:59:20 -0700 Subject: [PATCH 13/28] echo variables --- src/kernelbot/api/main.py | 41 ++++++++++----------------------------- 1 file changed, 10 insertions(+), 31 deletions(-) diff --git a/src/kernelbot/api/main.py b/src/kernelbot/api/main.py index 4fae241c..10ad2a52 100644 --- a/src/kernelbot/api/main.py +++ b/src/kernelbot/api/main.py @@ -111,14 +111,10 @@ async def validate_cli_header( with db_context as db: user_info = db.validate_cli_id(x_popcorn_cli_id) except Exception as e: - raise HTTPException( - status_code=500, detail=f"Database error during validation: {e}" - ) from e + raise HTTPException(status_code=500, detail=f"Database error during validation: {e}") from e if user_info is None: - raise HTTPException( - status_code=401, detail="Invalid or unauthorized X-Popcorn-Cli-Id" - ) + raise HTTPException(status_code=401, detail="Invalid or unauthorized X-Popcorn-Cli-Id") return user_info @@ -203,9 +199,7 @@ async def auth_init(provider: str, db_context=Depends(get_db)) -> dict: @app.get("/auth/cli/{auth_provider}") -async def cli_auth( - auth_provider: str, code: str, state: str, db_context=Depends(get_db) -): # noqa: C901 +async def cli_auth(auth_provider: str, code: str, state: str, db_context=Depends(get_db)): # noqa: C901 """ Handle Discord/GitHub OAuth redirect. This endpoint receives the authorization code and state parameter from the OAuth flow. @@ -222,9 +216,7 @@ async def cli_auth( ) if not code or not state: - raise HTTPException( - status_code=400, detail="Missing authorization code or state" - ) + raise HTTPException(status_code=400, detail="Missing authorization code or state") try: # Pad state if necessary for correct base64 decoding @@ -234,14 +226,10 @@ async def cli_auth( cli_id = state_data["cli_id"] is_reset = state_data.get("is_reset", False) except (json.JSONDecodeError, KeyError, Exception) as e: - raise HTTPException( - status_code=400, detail=f"Invalid state parameter: {e}" - ) from None + raise HTTPException(status_code=400, detail=f"Invalid state parameter: {e}") from None # Determine API URL (handle potential None value) - api_base_url = os.environ.get("HEROKU_APP_DEFAULT_DOMAIN_NAME") or os.getenv( - "POPCORN_API_URL" - ) + api_base_url = os.environ.get("HEROKU_APP_DEFAULT_DOMAIN_NAME") or os.getenv("POPCORN_API_URL") if not api_base_url: raise HTTPException( status_code=500, @@ -265,15 +253,10 @@ async def cli_auth( raise e except Exception as e: # Catch unexpected errors during OAuth handling - raise HTTPException( - status_code=500, detail=f"Error during {auth_provider} OAuth flow: {e}" - ) from e + raise HTTPException(status_code=500, detail=f"Error during {auth_provider} OAuth flow: {e}") from e if not user_id or not user_name: - raise HTTPException( - status_code=500, - detail="Failed to retrieve user ID or username from provider.", - ) + raise HTTPException(status_code=500,detail="Failed to retrieve user ID or username from provider.",) try: with db_context as db: @@ -283,13 +266,9 @@ async def cli_auth( db.create_user_from_cli(user_id, user_name, cli_id, auth_provider) except AttributeError as e: - raise HTTPException( - status_code=500, detail=f"Database interface error during update: {e}" - ) from e + raise HTTPException(status_code=500, detail=f"Database interface error during update: {e}") from e except Exception as e: - raise HTTPException( - status_code=400, detail=f"Database update failed: {e}" - ) from e + raise HTTPException(status_code=400, detail=f"Database update failed: {e}") from e return { "status": "success", From 4313ac528e1b049c2f03c5d4ae0a90a4059f84f0 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 16:09:11 -0700 Subject: [PATCH 14/28] echo variables --- src/kernelbot/api/api_utils.py | 3 +- src/kernelbot/api/main.py | 57 ++++++++++++++++++++++++++++------ 2 files changed, 49 insertions(+), 11 deletions(-) diff --git a/src/kernelbot/api/api_utils.py b/src/kernelbot/api/api_utils.py index dffcdbbf..12d04ba0 100644 --- a/src/kernelbot/api/api_utils.py +++ b/src/kernelbot/api/api_utils.py @@ -11,7 +11,8 @@ RunResultReport, Text, ) -from libkernelbot.submission import SubmissionRequest, prepare_submission +from libkernelbot.submission import ProcessedSubmissionRequest, SubmissionRequest, prepare_submission +from src.libkernelbot.background_submission_manager import BackgroundSubmissionManager async def _handle_discord_oauth(code: str, redirect_uri: str) -> tuple[str, str]: diff --git a/src/kernelbot/api/main.py b/src/kernelbot/api/main.py index 10ad2a52..97cf7066 100644 --- a/src/kernelbot/api/main.py +++ b/src/kernelbot/api/main.py @@ -372,15 +372,6 @@ async def run_submission( # noqa: C901 submission_request, submission_mode_enum = await to_submit_info( user_info, submission_mode, file, leaderboard_name, gpu_type, db_context ) - - # prepare submission request before the submission is started - try: - req = prepare_submission(submission_request, backend_instance) - except Exception as e: - raise HTTPException(status_code=400, detail=str(e)) from e - - if not req.gpus or len(req.gpus) != 1: - raise HTTPException(status_code=400, detail="Invalid GPU type") generator = _stream_submission_response( submission_request=submission_request, submission_mode_enum=submission_mode_enum, @@ -388,6 +379,29 @@ async def run_submission( # noqa: C901 ) return StreamingResponse(generator, media_type="text/event-stream") +async def start_detached_run( + req: ProcessedSubmissionRequest, + mode: SubmissionMode, + backend: KernelBackend, + manager: BackgroundSubmissionManager, +): + + # pre-create the submission for api returns + with backend.db as db: + sub_id = db.create_submission( + leaderboard=req.leaderboard, + file_name=req.file_name, + code=req.code, + user_id=req.user_id, + time=datetime.datetime.now(), + user_name=req.user_name, + ) + db.upsert_submission_job_status(sub_id, "initial", None) + + # put submission request in queue + await manager.enqueue(req, mode, sub_id) + return sub_id + @app.post("/submission/{leaderboard_name}/{gpu_type}/{submission_mode}") async def run_submission_v2( leaderboard_name: str, @@ -397,12 +411,30 @@ async def run_submission_v2( user_info: Annotated[dict, Depends(validate_user_header)], db_context=Depends(get_db), ) -> Any: + """An endpoint that runs a submission on a given leaderboard, runner, and GPU type. + + Requires a valid X-Popcorn-Cli-Id or X-Web-Auth-Id header. + + Args: + leaderboard_name (str): The name of the leaderboard to run the submission on. + gpu_type (str): The type of GPU to run the submission on. + submission_mode (str): The mode for the submission (test, benchmark, etc.). + file (UploadFile): The file to run the submission on. + user_id (str): The validated user ID obtained from the X-Popcorn-Cli-Id header. + Raises: + HTTPException: If the kernelbot is not initialized, or header/input is invalid. + + Returns: + JSONResponse: A JSON response containing job_id and and submission_id for the client to poll for status. + """ + await simple_rate_limit() submission_request, submission_mode_enum = await to_submit_info( user_info, submission_mode, file, leaderboard_name, gpu_type, db_context ) + # throw error if submission request is invalid try: req = prepare_submission(submission_request, backend_instance) except Exception as e: @@ -412,12 +444,17 @@ async def run_submission_v2( if not req.gpus or len(req.gpus) != 1: raise HTTPException(status_code=400, detail="Invalid GPU type") + # start the submission sub_id = await start_detached_run( req, submission_mode_enum, backend_instance, background_submission_manager ) return JSONResponse( status_code=202, - content={"id": sub_id, "status": "accepted"}, + content={ + "id": sub_id, + "jobs_id": + + "status": "accepted"}, ) @app.get("/leaderboards") From f24a2ab3a25991c553c0f4be8d91fcd34889dfda Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 16:11:06 -0700 Subject: [PATCH 15/28] echo variables --- src/libkernelbot/launchers/modal.py | 2 +- src/libkernelbot/leaderboard_db.py | 9 ++------- 2 files changed, 3 insertions(+), 8 deletions(-) diff --git a/src/libkernelbot/launchers/modal.py b/src/libkernelbot/launchers/modal.py index ccdfea41..26adfe33 100644 --- a/src/libkernelbot/launchers/modal.py +++ b/src/libkernelbot/launchers/modal.py @@ -4,7 +4,7 @@ from libkernelbot.consts import GPU, ModalGPU from libkernelbot.report import RunProgressReporter -from libkernelbot.run_eval import FullResult, SystemInfo +from libkernelbot.run_eval import FullResult from libkernelbot.utils import setup_logging from .launcher import Launcher diff --git a/src/libkernelbot/leaderboard_db.py b/src/libkernelbot/leaderboard_db.py index 230d4201..ae6991d0 100644 --- a/src/libkernelbot/leaderboard_db.py +++ b/src/libkernelbot/leaderboard_db.py @@ -5,13 +5,7 @@ import psycopg2 -from libkernelbot.db_types import ( - IdentityType, - LeaderboardItem, - LeaderboardRankedEntry, - RunItem, - SubmissionItem, -) +from libkernelbot.db_types import LeaderboardItem, LeaderboardRankedEntry, RunItem, SubmissionItem from libkernelbot.run_eval import CompileResult, RunResult, SystemInfo from libkernelbot.task import LeaderboardDefinition, LeaderboardTask from libkernelbot.utils import ( @@ -22,6 +16,7 @@ logger = setup_logging(__name__) + class LeaderboardDB: def __init__( self, host: str, database: str, user: str, password: str, port: str, url: str, ssl_mode: str From 95a1a4947d76d2a58e5d81d4aaf031599e6509ff Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 16:13:25 -0700 Subject: [PATCH 16/28] echo variables --- src/libkernelbot/leaderboard_db.py | 1 - unit-tests/test_submission.py | 5 +---- 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/src/libkernelbot/leaderboard_db.py b/src/libkernelbot/leaderboard_db.py index ae6991d0..b73a6581 100644 --- a/src/libkernelbot/leaderboard_db.py +++ b/src/libkernelbot/leaderboard_db.py @@ -348,7 +348,6 @@ def create_submission( ), ) submission_id = self.cursor.fetchone()[0] - submission_id = int(submission_id) assert submission_id is not None self.connection.commit() return submission_id diff --git a/unit-tests/test_submission.py b/unit-tests/test_submission.py index 1f2bfb7c..4e98b377 100644 --- a/unit-tests/test_submission.py +++ b/unit-tests/test_submission.py @@ -1,14 +1,11 @@ import datetime import re -from types import SimpleNamespace from unittest import mock import pytest -from fastapi import BackgroundTasks -from kernelbot.api import api_utils from libkernelbot import submission -from libkernelbot.consts import RankCriterion, SubmissionMode +from libkernelbot.consts import RankCriterion from libkernelbot.db_types import LeaderboardItem from libkernelbot.utils import KernelBotError From 7b639558f0295120c56291e3a8758c111ebfeb1e Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 16:41:25 -0700 Subject: [PATCH 17/28] echo variables --- data.sql | 9194 +++++++++++++++++ src/kernelbot/api/api_utils.py | 5 +- src/kernelbot/api/main.py | 13 +- src/libkernelbot/backend.py | 1 - .../background_submission_manager.py | 46 +- src/libkernelbot/leaderboard_db.py | 2 +- .../test_background_submission_manager.py | 2 +- 7 files changed, 9229 insertions(+), 34 deletions(-) create mode 100644 data.sql diff --git a/data.sql b/data.sql new file mode 100644 index 00000000..76a4633c --- /dev/null +++ b/data.sql @@ -0,0 +1,9194 @@ +-- +-- PostgreSQL database dump +-- + +-- Dumped from database version 16.8 (Ubuntu 16.8-1.pgdg22.04+1) +-- Dumped by pg_dump version 16.8 (Ubuntu 16.8-1.pgdg22.04+1) + +SET statement_timeout = 0; +SET lock_timeout = 0; +SET idle_in_transaction_session_timeout = 0; +SET client_encoding = 'UTF8'; +SET standard_conforming_strings = on; +SELECT pg_catalog.set_config('search_path', '', false); +SET check_function_bodies = false; +SET xmloption = content; +SET client_min_messages = warning; +SET row_security = off; + +-- +-- Name: leaderboard; Type: SCHEMA; Schema: -; Owner: - +-- + +CREATE SCHEMA leaderboard; + + +SET default_tablespace = ''; + +SET default_table_access_method = heap; + +-- +-- Name: code_files; Type: TABLE; Schema: leaderboard; Owner: - +-- + +CREATE TABLE leaderboard.code_files ( + id integer NOT NULL, + code text NOT NULL, + hash text GENERATED ALWAYS AS (encode(sha256((code)::bytea), 'hex'::text)) STORED +); + + +-- +-- Name: code_files_id_seq; Type: SEQUENCE; Schema: leaderboard; Owner: - +-- + +CREATE SEQUENCE leaderboard.code_files_id_seq + AS integer + START WITH 1 + INCREMENT BY 1 + NO MINVALUE + NO MAXVALUE + CACHE 1; + + +-- +-- Name: code_files_id_seq; Type: SEQUENCE OWNED BY; Schema: leaderboard; Owner: - +-- + +ALTER SEQUENCE leaderboard.code_files_id_seq OWNED BY leaderboard.code_files.id; + + +-- +-- Name: gpu_type; Type: TABLE; Schema: leaderboard; Owner: - +-- + +CREATE TABLE leaderboard.gpu_type ( + leaderboard_id integer NOT NULL, + gpu_type text NOT NULL +); + + +-- +-- Name: leaderboard; Type: TABLE; Schema: leaderboard; Owner: - +-- + +CREATE TABLE leaderboard.leaderboard ( + id integer NOT NULL, + name text NOT NULL, + deadline timestamp with time zone NOT NULL, + task jsonb NOT NULL, + creator_id bigint DEFAULT '-1'::integer NOT NULL, + forum_id bigint NOT NULL, + secret_seed bigint DEFAULT floor((random() * ('2147483648'::bigint)::double precision)) NOT NULL, + description text NOT NULL +); + + +-- +-- Name: leaderboard_id_seq; Type: SEQUENCE; Schema: leaderboard; Owner: - +-- + +CREATE SEQUENCE leaderboard.leaderboard_id_seq + AS integer + START WITH 1 + INCREMENT BY 1 + NO MINVALUE + NO MAXVALUE + CACHE 1; + + +-- +-- Name: leaderboard_id_seq; Type: SEQUENCE OWNED BY; Schema: leaderboard; Owner: - +-- + +ALTER SEQUENCE leaderboard.leaderboard_id_seq OWNED BY leaderboard.leaderboard.id; + + +-- +-- Name: runs; Type: TABLE; Schema: leaderboard; Owner: - +-- + +CREATE TABLE leaderboard.runs ( + id integer NOT NULL, + submission_id integer NOT NULL, + start_time timestamp with time zone NOT NULL, + end_time timestamp with time zone NOT NULL, + mode text NOT NULL, + secret boolean NOT NULL, + runner text NOT NULL, + score numeric, + passed boolean NOT NULL, + compilation jsonb, + meta jsonb, + result jsonb, + system_info jsonb NOT NULL +); + + +-- +-- Name: runs_id_seq; Type: SEQUENCE; Schema: leaderboard; Owner: - +-- + +CREATE SEQUENCE leaderboard.runs_id_seq + AS integer + START WITH 1 + INCREMENT BY 1 + NO MINVALUE + NO MAXVALUE + CACHE 1; + + +-- +-- Name: runs_id_seq; Type: SEQUENCE OWNED BY; Schema: leaderboard; Owner: - +-- + +ALTER SEQUENCE leaderboard.runs_id_seq OWNED BY leaderboard.runs.id; + + +-- +-- Name: submission; Type: TABLE; Schema: leaderboard; Owner: - +-- + +CREATE TABLE leaderboard.submission ( + id integer NOT NULL, + leaderboard_id integer NOT NULL, + file_name text NOT NULL, + user_id text NOT NULL, + code_id integer NOT NULL, + submission_time timestamp with time zone NOT NULL, + done boolean DEFAULT false +); + + +-- +-- Name: submission_id_seq; Type: SEQUENCE; Schema: leaderboard; Owner: - +-- + +CREATE SEQUENCE leaderboard.submission_id_seq + AS integer + START WITH 1 + INCREMENT BY 1 + NO MINVALUE + NO MAXVALUE + CACHE 1; + + +-- +-- Name: submission_id_seq; Type: SEQUENCE OWNED BY; Schema: leaderboard; Owner: - +-- + +ALTER SEQUENCE leaderboard.submission_id_seq OWNED BY leaderboard.submission.id; + + +-- +-- Name: user_info; Type: TABLE; Schema: leaderboard; Owner: - +-- + +CREATE TABLE leaderboard.user_info ( + id text NOT NULL, + user_name text +); + + +-- +-- Name: code_files id; Type: DEFAULT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.code_files ALTER COLUMN id SET DEFAULT nextval('leaderboard.code_files_id_seq'::regclass); + + +-- +-- Name: leaderboard id; Type: DEFAULT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.leaderboard ALTER COLUMN id SET DEFAULT nextval('leaderboard.leaderboard_id_seq'::regclass); + + +-- +-- Name: runs id; Type: DEFAULT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.runs ALTER COLUMN id SET DEFAULT nextval('leaderboard.runs_id_seq'::regclass); + + +-- +-- Name: submission id; Type: DEFAULT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.submission ALTER COLUMN id SET DEFAULT nextval('leaderboard.submission_id_seq'::regclass); + + +-- +-- Data for Name: code_files; Type: TABLE DATA; Schema: leaderboard; Owner: - +-- + +COPY leaderboard.code_files (id, code) FROM stdin; +13 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +14 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +24 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +25 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +39 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +40 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +41 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +211 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +53 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +56 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +83 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +102 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +101 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1608 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +105 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +125 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +126 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +127 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +147 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +159 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +905 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +146 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1459 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +153 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +161 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +907 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +172 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +184 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +806 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +290 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +807 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1274 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +929 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +372 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +373 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1275 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +392 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1276 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1278 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +423 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1361 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +429 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +435 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +442 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +445 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1362 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1363 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1364 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1365 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1366 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1367 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1368 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1369 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1370 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +507 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +572 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +513 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +521 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +524 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +526 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +534 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +535 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1371 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +578 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1372 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1373 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1374 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1375 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1376 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +709 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +716 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +717 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1377 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1378 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +743 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +744 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1379 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1390 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1391 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1393 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +793 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +794 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +890 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +891 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +820 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +821 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1553 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1554 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1555 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1556 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1601 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1049 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1078 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1135 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1117 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1143 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1144 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1146 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1149 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1161 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1192 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1602 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1603 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1604 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1605 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1606 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1609 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1610 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1611 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1612 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1613 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1614 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1615 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1616 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1617 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +22 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1270 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1277 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1299 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1300 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1301 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1322 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1321 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1324 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1353 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1354 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1392 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1410 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1422 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1411 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +298 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +299 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +300 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +301 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +23 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1539 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1543 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +26 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1588 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1607 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +302 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +303 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +29 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +30 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1661 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1663 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +304 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +305 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +31 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +32 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +33 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +15 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +306 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +16 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +291 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +292 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +293 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +294 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +279 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +295 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +296 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +307 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +353 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +354 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +355 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +356 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +357 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +358 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +379 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +380 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +381 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +382 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +383 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +384 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +385 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +386 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +387 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +388 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +389 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +390 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +391 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +72 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +970 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +393 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +5 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +394 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +395 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +396 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +397 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +400 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +401 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +402 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +17 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +18 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +19 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +20 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +21 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +34 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +35 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +36 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +37 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +38 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +46 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +54 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +55 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +58 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +59 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +60 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +403 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +404 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +405 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +406 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +407 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +408 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +409 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +410 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +411 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +412 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +413 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +414 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +308 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +415 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +417 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +418 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +421 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +425 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +309 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +426 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +427 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +431 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +433 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +432 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +310 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +437 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +438 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +439 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +440 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +61 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +62 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +63 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +64 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +65 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +71 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +66 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +67 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +68 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +69 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +70 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1644 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +962 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +447 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +448 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +449 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +451 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +452 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +453 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +454 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +455 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +456 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1645 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +457 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +458 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +459 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +460 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +461 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +462 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +463 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +464 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +465 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +466 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +467 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +468 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +469 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +470 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +471 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +472 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +473 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +474 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +475 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +476 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +477 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +478 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +479 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +480 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +482 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +483 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +484 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +485 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +486 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +487 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +488 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +490 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +489 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +491 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +492 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +493 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +495 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +497 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +499 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +500 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +976 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +536 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +537 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +538 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +539 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +540 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +541 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +542 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +543 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +544 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +545 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +546 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +547 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +548 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +549 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +550 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +551 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +552 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +553 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +554 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +555 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +556 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +557 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +558 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +559 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +560 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +561 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +562 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +563 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +564 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +565 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +566 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +567 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +568 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +569 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +570 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +963 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +571 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +573 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +574 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +575 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +576 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +577 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +245 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +579 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +580 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +581 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +582 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +583 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +584 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +585 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +586 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +587 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +588 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +589 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +590 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +591 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +592 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +594 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +595 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +596 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +597 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +598 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +599 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +600 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +601 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +603 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +604 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +605 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +606 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +607 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +608 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +609 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +611 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +610 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +613 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +612 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +614 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +615 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +616 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +617 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +618 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +619 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +620 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +621 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +622 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +623 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +624 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +625 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +626 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +627 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +628 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +629 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +630 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +636 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +631 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +632 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +633 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +634 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +635 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +637 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +638 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +639 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +640 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +641 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +642 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +643 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +644 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +645 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +646 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +648 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +647 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +649 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +650 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +651 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +652 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +653 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +654 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +656 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +655 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +657 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +658 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +659 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +660 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +661 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +662 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +663 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +664 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +665 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +666 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +668 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +667 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +669 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +670 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +671 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +672 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +673 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +674 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +675 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +676 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +677 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +11 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +73 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +74 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +75 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +76 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +678 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +679 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +680 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +681 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +682 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +683 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +684 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +685 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +686 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +687 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +688 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +689 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +691 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +690 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +692 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +693 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +694 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +696 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +695 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +697 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +698 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +700 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +964 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +971 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +972 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +699 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +701 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +702 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +703 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +704 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +705 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +722 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +706 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +707 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +708 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +710 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +723 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +711 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +712 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +714 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +718 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +721 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +724 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +725 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +726 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +727 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +728 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +729 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +730 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +731 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +732 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +733 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +734 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +735 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +736 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +737 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +738 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +77 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +78 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +79 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +80 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +82 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +81 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +6 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +85 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +84 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +86 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +87 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +89 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +90 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +92 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +93 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +95 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +96 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +97 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +98 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +100 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +745 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +746 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +747 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +748 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +749 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +750 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +751 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +752 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +753 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +754 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +755 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +756 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +757 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +758 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +759 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +760 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +761 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +762 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +965 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +763 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +764 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +765 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +766 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +767 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +768 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +769 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +770 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +771 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +772 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +773 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +774 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +775 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +130 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +776 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +777 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +778 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +103 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +106 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +107 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +108 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +109 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +110 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +111 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +114 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +115 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +132 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +116 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +117 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +119 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +120 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +121 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +122 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +123 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +124 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +129 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +131 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +134 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +954 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +968 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +779 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +780 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +781 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +783 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +784 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +785 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +786 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +805 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +787 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +788 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +803 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +802 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +148 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +150 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +151 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +152 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +154 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +156 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +157 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +158 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +804 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +808 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +809 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +810 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +811 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +812 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +813 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +814 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +815 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +816 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +817 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +818 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +819 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +822 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +826 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +827 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +828 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +829 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +830 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +831 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +832 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +833 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +834 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +835 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +836 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +837 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +838 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +839 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +840 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +841 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +842 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +843 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +844 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +845 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +846 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +847 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +848 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +849 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +850 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +851 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +852 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +854 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +853 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +855 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +857 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +856 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +859 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +858 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +860 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +861 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +862 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +863 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +864 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +160 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +782 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +865 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +866 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +867 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +868 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +869 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +870 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +871 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +872 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +873 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +874 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +875 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +876 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +877 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +878 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +879 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +880 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +881 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +882 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +883 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +966 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +884 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +885 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +886 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +887 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +888 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +889 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +892 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +893 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +894 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +895 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +896 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +897 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +898 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +899 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +900 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +901 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +902 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +903 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +904 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +906 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +179 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +908 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +909 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +910 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +911 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +912 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +913 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +914 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +915 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +916 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +917 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +918 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +919 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +920 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +921 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +922 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +923 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +924 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +164 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +165 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +166 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +167 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +168 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +169 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +925 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +926 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +927 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +928 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +930 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +931 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +932 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +934 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +935 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +936 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +937 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +938 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +939 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +940 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +941 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +942 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +943 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +944 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +945 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +946 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +947 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +948 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +949 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +950 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +951 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +952 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +953 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +955 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +956 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +957 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +974 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +958 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +959 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +960 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +961 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +967 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +973 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +975 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +978 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +979 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +980 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +991 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +992 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +993 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +994 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +995 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +996 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +997 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +998 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +999 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1000 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1001 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1002 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1003 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1004 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1005 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1006 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1007 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1008 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1009 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1010 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1011 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1012 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1013 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1014 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1015 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1016 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +170 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +171 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +173 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +174 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +176 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +177 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +178 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1017 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1018 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1019 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1020 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1021 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1022 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1023 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1024 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1025 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1027 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1028 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1029 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1030 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1031 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1032 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1033 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1034 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1035 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1036 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1037 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1038 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1039 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1040 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1041 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1042 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1043 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1044 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1045 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1046 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1047 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1048 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1050 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1051 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1052 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1053 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1054 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1055 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1056 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1057 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1058 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1059 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1060 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1061 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1062 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1079 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1080 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1081 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1082 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1083 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1084 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1085 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +181 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +182 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +185 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +186 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +187 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +188 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +190 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +191 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +192 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +193 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +194 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +195 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +196 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1086 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1087 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1088 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1089 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1090 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1091 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1092 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1093 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1094 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1095 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1096 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1101 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1102 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1103 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1104 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1105 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1106 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1107 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1108 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1109 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1110 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1111 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1112 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1113 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1114 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1115 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1116 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1118 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1119 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1120 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1121 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1124 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1125 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1126 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1127 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1128 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1129 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +197 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1130 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1131 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1132 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1133 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1134 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1136 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1140 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1141 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1151 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1152 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1153 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1154 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1155 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1156 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1157 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1158 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1159 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1162 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1163 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1166 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1167 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1168 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1169 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1170 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1171 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1172 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1173 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1174 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1175 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1176 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +213 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1177 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1178 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1179 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1180 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1181 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1182 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1183 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1184 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1185 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1186 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1187 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1188 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1189 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1190 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1191 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1193 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1194 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1195 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1196 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1197 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1198 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1199 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1200 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1201 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1202 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +225 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1203 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +198 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +199 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1267 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1268 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1269 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1271 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1272 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1279 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1280 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1281 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1282 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1283 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1284 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1285 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1286 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1287 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1288 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1289 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1290 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1291 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1292 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1293 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1294 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1295 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1296 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1297 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1298 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1323 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1355 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1356 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1357 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1358 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1359 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1360 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +244 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1380 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1381 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1383 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1384 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1385 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1386 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1387 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1388 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1389 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1394 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1395 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1396 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1397 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +200 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +201 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +202 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +203 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +204 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +205 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +206 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +207 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +208 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +209 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +210 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +212 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1412 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1413 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1414 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1415 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1416 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1417 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1418 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +216 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1419 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1420 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1421 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1423 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +217 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1424 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1453 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1454 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1455 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +218 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1456 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1457 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1458 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +331 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1273 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1460 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1461 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1462 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1463 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +219 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1464 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1465 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1466 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1467 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +220 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1468 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1469 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1470 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1471 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +221 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1472 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1473 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1474 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1475 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +222 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1476 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1477 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1478 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1479 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +223 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1480 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1481 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1482 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +214 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +215 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +224 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1483 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1487 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1488 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1489 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1490 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1491 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1497 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1498 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1545 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1546 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1499 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1500 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1501 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1502 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1547 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1548 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1503 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1504 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1505 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1506 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1549 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1550 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1507 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1509 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1510 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1511 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +226 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1512 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1513 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1514 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1515 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +227 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1516 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1517 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1518 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1519 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +228 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1520 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1521 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1522 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1523 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +229 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1524 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1525 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1526 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1527 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +230 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1528 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1529 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1530 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1531 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +231 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1532 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1533 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1534 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1535 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +232 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1536 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1537 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1538 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1540 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1541 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1542 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1544 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +233 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +234 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +235 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +236 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +237 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +238 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +239 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +240 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +241 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +242 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +243 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1551 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1552 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1557 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1558 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1559 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1560 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1561 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1562 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1563 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1564 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1565 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1566 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +10 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1567 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1568 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1569 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1570 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1571 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1572 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1573 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1574 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1575 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1576 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1577 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1578 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +246 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +247 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +248 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +249 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +250 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +251 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +252 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +253 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +254 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +255 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +256 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +257 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +969 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +977 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +258 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +259 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +260 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1579 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1580 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1581 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1582 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1583 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1584 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1585 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1586 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1587 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1589 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1590 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1591 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1592 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1593 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1594 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1595 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1596 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1597 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1598 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1599 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1600 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +311 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +312 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +313 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +314 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +315 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +316 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +317 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +318 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +319 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +320 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +321 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +322 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +323 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +324 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +325 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +326 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +327 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +328 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +329 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +330 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1618 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1619 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1620 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1621 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +332 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +333 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +334 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +335 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +336 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +337 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +338 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +339 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +340 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +341 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1622 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1623 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1624 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1625 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1626 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1627 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1628 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1629 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1630 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1631 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1632 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1633 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1634 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1646 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1647 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1648 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1649 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1650 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1651 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1652 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1653 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1654 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1655 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +1656 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n +359 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1657 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1658 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1659 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1660 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1664 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1665 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1667 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1669 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +343 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1670 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1671 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1672 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1673 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +344 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +1674 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1675 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1676 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1677 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +345 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1678 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1679 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1680 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1681 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +346 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1682 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1683 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1684 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1685 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +342 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +347 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +348 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +349 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +350 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +351 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +352 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1686 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1687 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n +1635 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1636 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1637 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1642 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1638 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +261 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +262 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +263 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +264 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +265 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +266 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n +267 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +268 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +269 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +270 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +271 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +272 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +273 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +274 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +275 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +276 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +277 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +278 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +280 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +281 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +282 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +283 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +284 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +285 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +286 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +287 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +288 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +289 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +297 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1643 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +360 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +361 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +362 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +363 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +364 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +365 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +366 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +367 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +368 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +369 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +370 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +371 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +376 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +377 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +378 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +593 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1639 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n +1640 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +1641 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n +\. + + +-- +-- Data for Name: gpu_type; Type: TABLE DATA; Schema: leaderboard; Owner: - +-- + +COPY leaderboard.gpu_type (leaderboard_id, gpu_type) FROM stdin; +339 H100 +339 A100 +339 T4 +339 L4 +340 H100 +340 A100 +340 T4 +340 L4 +341 H100 +341 A100 +341 T4 +341 L4 +342 H100 +342 A100 +342 T4 +342 L4 +343 H100 +343 A100 +343 T4 +343 L4 +\. + + +-- +-- Data for Name: leaderboard; Type: TABLE DATA; Schema: leaderboard; Owner: - +-- + +COPY leaderboard.leaderboard (id, name, deadline, task, creator_id, forum_id, secret_seed, description) FROM stdin; +339 conv2d 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "from typing import TypedDict, TypeVar, Tuple\\nimport torch\\n\\ninput_t = TypeVar(\\"input_t\\", bound=Tuple[torch.Tensor, torch.Tensor])\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor)\\n\\n\\nclass TestSpec(TypedDict):\\n size: int\\n kernelsize: int\\n channels: int\\n batch: int\\n seed: int ", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "from utils import make_match_reference\\nimport torch\\nimport torch.nn.functional as F\\nfrom task import input_t, output_t\\n\\nclass DisableCuDNNTF32:\\n def __init__(self):\\n self.allow_tf32 = torch.backends.cudnn.allow_tf32\\n self.deterministic = torch.backends.cudnn.deterministic\\n pass\\n\\n def __enter__(self):\\n torch.backends.cudnn.allow_tf32 = False\\n torch.backends.cudnn.deterministic = True\\n return self\\n\\n def __exit__(self, exc_type, exc_value, traceback):\\n torch.backends.cudnn.allow_tf32 = self.allow_tf32\\n torch.backends.cudnn.deterministic = self.deterministic\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n \\"\\"\\"\\n Reference implementation of 2D convolution using PyTorch.\\n Args:\\n data: Tuple of (input tensor, kernel tensor)\\n Returns:\\n Output tensor after convolution\\n \\"\\"\\"\\n with DisableCuDNNTF32():\\n input_tensor, kernel = data\\n return F.conv2d(\\n input_tensor, \\n kernel,\\n\\n # No padding and no striding\\n # TODO: Can revisit this in future problems\\n stride=1,\\n padding=0\\n )\\n\\n\\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\\n \\"\\"\\"\\n Generates random input and kernel tensors.\\n Returns:\\n Tuple of (input tensor, kernel tensor)\\n \\"\\"\\"\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n \\n # Generate input tensor: [batch, in_channels, height, width]\\n input_tensor = torch.randn(\\n batch, channels, size, size,\\n device='cuda', \\n dtype=torch.float32, \\n generator=gen\\n ).contiguous()\\n \\n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\\n # Here we use same number of output channels as input channels for simplicity\\n kernel = torch.randn(\\n channels, channels, kernelsize, kernelsize,\\n device='cuda',\\n dtype=torch.float32,\\n generator=gen\\n ).contiguous()\\n \\n return (input_tensor, kernel)\\n\\n\\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)", "submission.py": "@SUBMISSION@"}, "tests": [{"seed": 4242, "size": 32, "batch": 1, "channels": 16, "kernelsize": 4}, {"seed": 5236, "size": 32, "batch": 2, "channels": 16, "kernelsize": 4}, {"seed": 1001, "size": 64, "batch": 1, "channels": 32, "kernelsize": 4}, {"seed": 5531, "size": 64, "batch": 2, "channels": 32, "kernelsize": 8}, {"seed": 9173, "size": 128, "batch": 1, "channels": 64, "kernelsize": 8}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"seed": 54352, "size": 128, "batch": 4, "channels": 64, "kernelsize": 8}, {"seed": 93246, "size": 128, "batch": 4, "channels": 64, "kernelsize": 16}, {"seed": 6256, "size": 256, "batch": 2, "channels": 128, "kernelsize": 16}, {"seed": 8841, "size": 256, "batch": 2, "channels": 128, "kernelsize": 16}, {"seed": 6252, "size": 256, "batch": 1, "channels": 128, "kernelsize": 32}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279714277527582 1828004782 Implement a 2D convolution kernel that matches the reference implementation.\nThe kernel should perform 2D convolution with the given specifications\nWe will benchmark different sizes, kernel sizes, channels and batch sizes but they will all \nbe even numbers with the exception of batch size which can sometimes be 1\nWe assume no padding and striding and instead vary the size of the input and kernel,\nnumber of channels, and batch size.\n\nInput: Tuple of (input_tensor, kernel)\n - input_tensor: 4D tensor of shape (batch, channels, height, width) with arbitrary values\n - kernel: 4D tensor of shape (channels, channels, kernelsize, kernelsize) with arbitrary values\nOutput: 4D tensor of shape (batch, channels, height-kernelsize+1, width-kernelsize+1) with convolved values\n +340 grayscale 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "from typing import TypedDict, TypeVar\\nimport torch\\n\\ninput_t = TypeVar(\\"input_t\\", bound=torch.Tensor) # Input will be (H, W, 3) RGB tensor\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor) # Output will be (H, W) grayscale tensor\\n\\nclass TestSpec(TypedDict):\\n size: int # Size of the square image (H=W)\\n seed: int ", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "from utils import make_match_reference\\nimport torch\\nfrom task import input_t, output_t\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n \\"\\"\\"\\n Reference implementation of RGB to grayscale conversion using PyTorch.\\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\\n \\n Args:\\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\\n Returns:\\n Grayscale tensor of shape (H, W) with values in [0, 1]\\n \\"\\"\\"\\n # Standard RGB to Grayscale coefficients\\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \\n device=data.device, \\n dtype=data.dtype)\\n return torch.sum(data * weights, dim=-1)\\n\\n\\ndef generate_input(size: int, seed: int) -> input_t:\\n \\"\\"\\"\\n Generates random RGB image tensor of specified size.\\n Returns:\\n Tensor of shape (size, size, 3) with values in [0, 1]\\n \\"\\"\\"\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n return torch.rand(size, size, 3, \\n device='cuda', \\n dtype=torch.float32, \\n generator=gen).contiguous()\\n\\n\\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\\n", "submission.py": "@SUBMISSION@"}, "tests": [{"seed": 1001, "size": 128}, {"seed": 5531, "size": 256}, {"seed": 9173, "size": 512}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"seed": 54352, "size": 512}, {"seed": 93246, "size": 1024}, {"seed": 6256, "size": 2048}, {"seed": 8841, "size": 4096}, {"seed": 6252, "size": 8192}, {"seed": 54352, "size": 16384}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279718383878165 1067465556 Implement an RGB to grayscale conversion kernel that matches the reference implementation.\nThe kernel should convert square RGB images with even sizes to grayscale using the standard coefficients:\nY = 0.2989 R + 0.5870 G + 0.1140 B\n\nInput: RGB tensor of shape (H, W, 3) with values in [0, 1]\nOutput: Grayscale tensor of shape (H, W) with values in [0, 1]\n +341 histogram 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "from typing import TypedDict, TypeVar\\nimport torch\\n\\ninput_t = TypeVar(\\"input_t\\", bound=torch.Tensor)\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor)\\n\\nclass TestSpec(TypedDict):\\n size: int\\n seed: int\\n contention: int\\n\\n", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "from utils import verbose_allequal\\nimport torch\\nfrom task import input_t, output_t\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n \\"\\"\\"\\n Reference implementation of histogram using PyTorch.\\n Args:\\n data: tensor of shape (size,)\\n Returns:\\n Tensor containing bin counts\\n \\"\\"\\"\\n # Count values in each bin\\n return torch.bincount(data, minlength=256)\\n\\n\\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\\n \\"\\"\\"\\n Generates random input tensor for histogram.\\n\\n Args:\\n size: Size of the input tensor (must be multiple of 16)\\n contention: float in [0, 100], specifying the percentage of identical values\\n seed: Random seed\\n Returns:\\n The input tensor with values in [0, 255]\\n \\"\\"\\"\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n \\n # Generate integer values between 0 and 256\\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\\n\\n # make one value appear quite often, increasing the chance for atomic contention\\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\\n data[evil_loc] = evil_value\\n\\n return data.contiguous()\\n\\n\\ndef check_implementation(data, output):\\n expected = ref_kernel(data)\\n reasons = verbose_allequal(output, expected)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n", "submission.py": "@SUBMISSION@"}, "tests": [{"seed": 9991, "size": 5120, "contention": 10}, {"seed": 2105, "size": 7840, "contention": 10}, {"seed": 9999, "size": 30080, "contention": 10}, {"seed": 4254, "size": 30080, "contention": 90}, {"seed": 1212, "size": 100000, "contention": 10}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"seed": 6252, "size": 1310720, "contention": 10}, {"seed": 8841, "size": 2621440, "contention": 10}, {"seed": 3411, "size": 2621440, "contention": 40}, {"seed": 8753, "size": 2621440, "contention": 90}, {"seed": 6252, "size": 5242880, "contention": 10}, {"seed": 8841, "size": 10485760, "contention": 10}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279720996929577 186241933 Implement a histogram kernel that counts the number of elements falling into each bin across the specified range.\nThe minimum and maximum values of the range are fixed to 0 and 100 respectively.\nAll sizes are multiples of 16 and the number of bins is set to the size of the input tensor divided by 16.\n\nInput:\n - data: a tensor of shape (size,)\n +342 matmul 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "import torch\\nfrom typing import TypeVar, TypedDict\\n\\ninput_t = TypeVar(\\"input_t\\", bound=tuple[torch.Tensor, torch.Tensor])\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor)\\n\\nclass TestSpec(TypedDict):\\n m: int\\n n: int\\n k: int\\n seed: int\\n", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "import torch\\nfrom task import input_t, output_t\\nfrom utils import make_match_reference\\n\\n\\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\\n a.uniform_(0, 1, generator=gen)\\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\\n b.uniform_(0, 1, generator=gen)\\n return (a, b)\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n a, b = data\\n return a @ b\\n\\n\\ncheck_implementation = make_match_reference(ref_kernel)\\n", "submission.py": "@SUBMISSION@"}, "tests": [{"k": 64, "m": 64, "n": 64, "seed": 53124}, {"k": 128, "m": 128, "n": 128, "seed": 3321}, {"k": 256, "m": 256, "n": 256, "seed": 1200}, {"k": 32, "m": 32, "n": 512, "seed": 32523}, {"k": 64, "m": 64, "n": 1024, "seed": 4327}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"k": 128, "m": 128, "n": 128, "seed": 43214}, {"k": 256, "m": 256, "n": 256, "seed": 423011}, {"k": 512, "m": 512, "n": 512, "seed": 123456}, {"k": 1024, "m": 1024, "n": 1024, "seed": 1029}, {"k": 2048, "m": 2048, "n": 2048, "seed": 75342}, {"k": 1024, "m": 1024, "n": 1536, "seed": 321}, {"k": 2048, "m": 2048, "n": 3072, "seed": 32412}, {"k": 4096, "m": 4096, "n": 5120, "seed": 123456}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279724021026939 818466997 Implement a custom matmul function that matches the reference implementation.\nThe function should handle a tuple of input tensors and apply matmul\nThe shapes of all outer and inner dimensions of tensors are multiples of 16\n +343 prefixsum 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "from typing import TypedDict, TypeVar\\nimport torch\\n\\ninput_t = TypeVar(\\"input_t\\", bound=torch.Tensor)\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor)\\n\\nclass TestSpec(TypedDict):\\n size: int\\n seed: int ", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "from utils import match_reference\\nimport torch\\nfrom task import input_t, output_t\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n \\"\\"\\"\\n Reference implementation of inclusive prefix sum using PyTorch.\\n Args:\\n data: Input tensor to compute prefix sum on\\n Returns:\\n Tensor containing the inclusive prefix sum\\n \\"\\"\\"\\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\\n\\n\\ndef generate_input(size: int, seed: int) -> input_t:\\n \\"\\"\\"\\n Generates random input tensor.\\n Returns:\\n Tensor to compute prefix sum on\\n \\"\\"\\"\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\\n\\n\\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\\n# The tolerance is scaled by the square root of the input size\\ndef check_implementation(data: input_t, output: output_t) -> str:\\n # Then get the size for scaling the tolerance\\n n = data.numel()\\n \\n scale_factor = n ** 0.5 # Square root of input size\\n rtol = 1e-5 * scale_factor\\n atol = 1e-5 * scale_factor\\n\\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\\n", "submission.py": "@SUBMISSION@"}, "tests": [{"seed": 4242, "size": 1023}, {"seed": 5236, "size": 1024}, {"seed": 1001, "size": 1025}, {"seed": 5531, "size": 2048}, {"seed": 9173, "size": 4096}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"seed": 12345, "size": 262144}, {"seed": 67890, "size": 524288}, {"seed": 13579, "size": 1048576}, {"seed": 24680, "size": 2097152}, {"seed": 35791, "size": 4194304}, {"seed": 46802, "size": 8388608}, {"seed": 57913, "size": 16777216}, {"seed": 68024, "size": 33554432}, {"seed": 79135, "size": 67108864}, {"seed": 80246, "size": 134217728}, {"seed": 91357, "size": 268435456}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279728202874890 2028323019 Implement an inclusive prefix sum (scan) kernel that matches the reference implementation.\nThe kernel should compute the cumulative sum of all elements up to each position.\nBecause of numerical instability, the tolerance is scaled by the square root of the input size.\n\nInput:\n- `data`: A 1D tensor of size `n`\nOutput:\n- `output`: A 1D tensor of size `n`\n +\. + + +-- +-- Data for Name: runs; Type: TABLE DATA; Schema: leaderboard; Owner: - +-- + +COPY leaderboard.runs (id, submission_id, start_time, end_time, mode, secret, runner, score, passed, compilation, meta, result, system_info) FROM stdin; +124 19 2025-02-23 10:35:45.16001-08 2025-02-23 09:11:28.790124-08 test f H100 \N t \N \N \N {} +125 19 2025-02-23 10:03:43.960257-08 2025-02-23 10:50:24.236204-08 benchmark f H100 \N t \N \N \N {} +126 19 2025-02-23 09:41:46.565827-08 2025-02-23 09:15:24.849129-08 leaderboard f H100 0.006118796 t \N \N \N {} +204 36 2025-02-23 12:17:55.397587-08 2025-02-23 12:19:38.507867-08 benchmark f A100 \N t \N \N \N {} +946 278 2025-02-24 14:44:53.850686-08 2025-02-24 14:00:11.854228-08 test f L4 \N t \N \N \N {} +205 36 2025-02-23 12:02:10.362107-08 2025-02-23 11:12:04.114552-08 benchmark f L4 \N t \N \N \N {} +206 36 2025-02-23 11:00:03.586181-08 2025-02-23 11:24:17.467616-08 benchmark f T4 \N t \N \N \N {} +207 37 2025-02-23 11:17:23.516624-08 2025-02-23 11:31:53.132068-08 test t H100 \N t \N \N \N {} +5225 1609 2025-03-06 07:56:36.142637-08 2025-03-06 06:19:05.979588-08 benchmark f T4 \N f \N \N \N {} +208 37 2025-02-23 11:26:57.890844-08 2025-02-23 11:52:24.509388-08 benchmark t H100 \N t \N \N \N {} +209 37 2025-02-23 12:35:01.552626-08 2025-02-23 11:29:30.650285-08 leaderboard t H100 0.012353844333333334 t \N \N \N {} +210 37 2025-02-23 11:15:04.963959-08 2025-02-23 11:05:51.028191-08 test f A100 \N t \N \N \N {} +3564 1210 2025-02-28 11:17:29.229928-08 2025-02-28 10:53:05.57324-08 leaderboard t A100 0.0030784606666666666 t \N \N \N {} +211 37 2025-02-23 12:02:46.792172-08 2025-02-23 11:06:28.086792-08 benchmark f A100 \N t \N \N \N {} +212 37 2025-02-23 12:10:16.775337-08 2025-02-23 11:11:59.763695-08 leaderboard f A100 0.018901413333333332 t \N \N \N {} +213 37 2025-02-23 11:16:17.316289-08 2025-02-23 11:02:40.657232-08 test f L4 \N t \N \N \N {} +214 37 2025-02-23 11:03:49.41155-08 2025-02-23 11:59:30.243303-08 benchmark f L4 \N t \N \N \N {} +215 37 2025-02-23 12:39:40.437807-08 2025-02-23 11:20:57.604522-08 leaderboard f L4 0.018757093833333332 t \N \N \N {} +217 37 2025-02-23 12:34:49.874051-08 2025-02-23 11:12:01.835801-08 benchmark t T4 \N t \N \N \N {} +218 37 2025-02-23 12:40:43.031117-08 2025-02-23 11:29:51.197423-08 leaderboard t T4 0.03179523833333333 t \N \N \N {} +219 37 2025-02-23 11:14:46.019525-08 2025-02-23 11:54:59.698254-08 test t A100 \N t \N \N \N {} +221 37 2025-02-23 11:25:33.405411-08 2025-02-23 12:18:02.508921-08 leaderboard t A100 0.018848922333333334 t \N \N \N {} +222 37 2025-02-23 11:58:16.947847-08 2025-02-23 12:21:51.3522-08 test t L4 \N t \N \N \N {} +223 37 2025-02-23 10:58:25.479183-08 2025-02-23 11:47:34.593766-08 benchmark t L4 \N t \N \N \N {} +224 37 2025-02-23 10:49:41.805569-08 2025-02-23 11:56:17.24445-08 leaderboard t L4 0.01939468366666667 t \N \N \N {} +226 37 2025-02-23 12:22:30.275578-08 2025-02-23 11:48:36.047752-08 benchmark f T4 \N t \N \N \N {} +227 37 2025-02-23 12:40:05.515776-08 2025-02-23 10:58:56.313103-08 leaderboard f T4 0.030903404 t \N \N \N {} +228 37 2025-02-23 11:04:15.184419-08 2025-02-23 11:00:17.817738-08 test f H100 \N t \N \N \N {} +229 37 2025-02-23 12:35:53.114693-08 2025-02-23 10:57:25.119918-08 benchmark f H100 \N t \N \N \N {} +230 37 2025-02-23 11:23:12.875657-08 2025-02-23 10:51:41.667717-08 leaderboard f H100 0.012353275333333334 t \N \N \N {} +231 38 2025-02-23 11:50:25.46655-08 2025-02-23 11:45:34.668208-08 test f T4 \N f \N \N \N {} +232 39 2025-02-23 12:32:41.932731-08 2025-02-23 10:59:56.084677-08 test f T4 \N f \N \N \N {} +259 54 2025-02-23 13:17:30.33764-08 2025-02-23 13:46:28.669109-08 test t T4 \N t \N \N \N {} +233 40 2025-02-23 11:29:02.58055-08 2025-02-23 12:36:20.107203-08 test f T4 \N f \N \N \N {} +234 41 2025-02-23 11:36:32.996867-08 2025-02-23 12:51:08.480576-08 test f T4 \N f \N \N \N {} +240 47 2025-02-23 11:50:55.177428-08 2025-02-23 12:30:28.807619-08 test f T4 \N f \N \N \N {} +241 48 2025-02-23 13:23:28.109642-08 2025-02-23 11:57:04.295155-08 test f T4 \N f \N \N \N {} +242 49 2025-02-23 11:53:10.397813-08 2025-02-23 12:03:03.783486-08 test f T4 \N f \N \N \N {} +1276 427 2025-02-24 22:41:37.072571-08 2025-02-24 23:30:11.670458-08 test t H100 \N t \N \N \N {} +246 53 2025-02-23 13:05:55.652535-08 2025-02-23 13:14:14.362137-08 benchmark f T4 \N t \N \N \N {} +254 54 2025-02-23 11:58:10.69175-08 2025-02-23 12:34:56.201415-08 benchmark f L4 \N t \N \N \N {} +255 54 2025-02-23 12:05:59.80503-08 2025-02-23 12:05:35.639007-08 leaderboard f L4 0.01764701466666667 t \N \N \N {} +256 54 2025-02-23 13:16:11.769925-08 2025-02-23 13:35:56.018887-08 test f A100 \N t \N \N \N {} +257 54 2025-02-23 12:06:54.552569-08 2025-02-23 12:27:58.339221-08 benchmark f A100 \N t \N \N \N {} +258 54 2025-02-23 11:52:48.189654-08 2025-02-23 13:04:58.541644-08 leaderboard f A100 0.0032710223333333333 t \N \N \N {} +260 54 2025-02-23 13:00:18.36019-08 2025-02-23 12:08:18.086439-08 benchmark t T4 \N t \N \N \N {} +261 54 2025-02-23 12:48:01.153003-08 2025-02-23 13:08:30.274831-08 leaderboard t T4 0.017266412 t \N \N \N {} +262 54 2025-02-23 12:11:01.579205-08 2025-02-23 11:58:34.491578-08 test t H100 \N t \N \N \N {} +263 54 2025-02-23 13:16:28.788531-08 2025-02-23 13:00:17.556954-08 benchmark t H100 \N t \N \N \N {} +264 54 2025-02-23 12:04:25.422389-08 2025-02-23 13:13:19.248136-08 leaderboard t H100 0.001458645875 t \N \N \N {} +265 54 2025-02-23 12:31:12.419543-08 2025-02-23 13:09:45.606598-08 test f T4 \N t \N \N \N {} +266 54 2025-02-23 12:40:46.985253-08 2025-02-23 13:34:24.451784-08 benchmark f T4 \N t \N \N \N {} +267 54 2025-02-23 13:25:58.537497-08 2025-02-23 12:54:43.007543-08 leaderboard f T4 0.01723258033333333 t \N \N \N {} +269 54 2025-02-23 12:20:20.316186-08 2025-02-23 13:13:12.18701-08 benchmark t L4 \N t \N \N \N {} +270 54 2025-02-23 13:11:14.571589-08 2025-02-23 12:58:35.784449-08 leaderboard t L4 0.017926125272727272 t \N \N \N {} +271 54 2025-02-23 12:33:04.814186-08 2025-02-23 12:38:40.126485-08 test f H100 \N t \N \N \N {} +272 54 2025-02-23 13:50:03.91953-08 2025-02-23 13:25:15.447661-08 benchmark f H100 \N t \N \N \N {} +273 54 2025-02-23 13:10:29.552929-08 2025-02-23 13:26:17.441886-08 leaderboard f H100 0.0010422076999999999 t \N \N \N {} +274 55 2025-02-23 13:01:35.147956-08 2025-02-23 13:02:44.097619-08 test f T4 \N t \N \N \N {} +275 56 2025-02-23 12:44:20.029345-08 2025-02-23 14:21:48.402951-08 test f T4 \N f \N \N \N {} +276 57 2025-02-23 12:54:37.579254-08 2025-02-23 13:41:28.569725-08 test f T4 \N t \N \N \N {} +314 77 2025-02-23 14:22:31.876974-08 2025-02-23 13:58:03.675484-08 test t H100 \N t \N \N \N {} +345 91 2025-02-23 18:28:50.540756-08 2025-02-23 18:30:32.69747-08 test f H100 \N f \N \N \N {} +277 58 2025-02-23 14:27:11.52301-08 2025-02-23 12:39:45.802969-08 benchmark f H100 \N t \N \N \N {} +278 59 2025-02-23 14:36:38.592486-08 2025-02-23 14:35:07.207052-08 test f T4 \N f \N \N \N {} +279 60 2025-02-23 13:55:19.127155-08 2025-02-23 14:12:12.400517-08 benchmark f T4 \N f \N \N \N {} +280 61 2025-02-23 13:18:07.578575-08 2025-02-23 14:55:51.403557-08 test f T4 \N f \N \N \N {} +320 78 2025-02-23 14:11:00.190481-08 2025-02-23 15:16:48.880961-08 test f H100 \N t \N \N \N {} +321 78 2025-02-23 14:03:20.036503-08 2025-02-23 14:23:37.916867-08 benchmark f H100 \N t \N \N \N {} +322 78 2025-02-23 15:36:20.056643-08 2025-02-23 15:34:57.100941-08 leaderboard f H100 0.001428631625 t \N \N \N {} +324 79 2025-02-23 15:02:01.218849-08 2025-02-23 15:47:08.188349-08 benchmark f H100 \N t \N \N \N {} +325 79 2025-02-23 14:41:07.330032-08 2025-02-23 15:38:15.89614-08 leaderboard f H100 0.0014191957142857144 t \N \N \N {} +326 79 2025-02-23 14:41:13.012481-08 2025-02-23 15:16:33.618694-08 test t H100 \N t \N \N \N {} +328 79 2025-02-23 14:27:28.406633-08 2025-02-23 15:30:01.543516-08 leaderboard t H100 0.0014211721666666666 t \N \N \N {} +329 80 2025-02-23 15:07:57.673532-08 2025-02-23 14:03:31.284187-08 test t H100 \N t \N \N \N {} +330 80 2025-02-23 13:57:07.60172-08 2025-02-23 13:55:16.031154-08 benchmark t H100 \N t \N \N \N {} +331 80 2025-02-23 14:45:52.647508-08 2025-02-23 14:51:00.631073-08 leaderboard t H100 0.0014186101666666668 t \N \N \N {} +333 80 2025-02-23 15:21:15.153944-08 2025-02-23 14:32:10.166808-08 benchmark f H100 \N t \N \N \N {} +334 80 2025-02-23 15:11:41.57439-08 2025-02-23 14:59:52.063767-08 leaderboard f H100 0.0014430232 t \N \N \N {} +4353 1425 2025-03-01 19:49:30.705637-08 2025-03-01 19:52:46.274107-08 leaderboard t A100 0.003362815 t \N \N \N {} +341 87 2025-02-23 18:32:19.084216-08 2025-02-23 18:24:30.655121-08 test f L4 \N f \N \N \N {} +342 88 2025-02-23 18:14:30.40463-08 2025-02-23 17:59:25.451214-08 test f H100 \N f \N \N \N {} +343 89 2025-02-23 17:38:34.140327-08 2025-02-23 17:53:30.915891-08 test f H100 \N f \N \N \N {} +344 90 2025-02-23 18:21:47.199663-08 2025-02-23 17:59:54.220257-08 test f H100 \N f \N \N \N {} +409 115 2025-02-23 19:35:25.852763-08 2025-02-23 18:57:39.125632-08 test f T4 \N t \N \N \N {} +575 162 2025-02-24 04:19:29.964217-08 2025-02-24 03:34:20.053499-08 benchmark t L4 \N t \N \N \N {} +346 92 2025-02-23 17:54:16.477409-08 2025-02-23 18:50:22.606457-08 test f H100 \N f \N \N \N {} +347 93 2025-02-23 18:25:21.117234-08 2025-02-23 18:07:32.76191-08 test t H100 \N t \N \N \N {} +348 93 2025-02-23 18:44:24.03074-08 2025-02-23 17:50:35.187274-08 benchmark t H100 \N t \N \N \N {} +349 93 2025-02-23 18:52:30.803751-08 2025-02-23 18:08:51.653673-08 leaderboard t H100 0.011944822666666665 t \N \N \N {} +350 93 2025-02-23 17:33:45.219692-08 2025-02-23 18:09:19.103073-08 test f H100 \N t \N \N \N {} +127 19 2025-02-23 09:52:43.386488-08 2025-02-23 09:04:44.918935-08 test t H100 \N t \N \N \N {} +128 19 2025-02-23 10:19:49.22057-08 2025-02-23 10:18:08.314956-08 benchmark t H100 \N t \N \N \N {} +129 19 2025-02-23 09:19:50.98444-08 2025-02-23 09:12:23.909584-08 leaderboard t H100 0.006109189 t \N \N \N {} +351 93 2025-02-23 18:02:58.08203-08 2025-02-23 19:27:48.608126-08 benchmark f H100 \N t \N \N \N {} +352 93 2025-02-23 17:35:41.478368-08 2025-02-23 17:43:26.596456-08 leaderboard f H100 0.0121398512 t \N \N \N {} +353 94 2025-02-23 18:01:28.888692-08 2025-02-23 18:25:10.085679-08 test t H100 \N t \N \N \N {} +355 94 2025-02-23 19:24:29.747081-08 2025-02-23 17:41:44.084312-08 leaderboard t H100 0.011895692333333334 t \N \N \N {} +356 94 2025-02-23 18:29:05.574286-08 2025-02-23 19:30:40.100513-08 test f H100 \N t \N \N \N {} +357 94 2025-02-23 19:00:06.086828-08 2025-02-23 19:27:29.302545-08 benchmark f H100 \N t \N \N \N {} +360 96 2025-02-23 18:52:27.921108-08 2025-02-23 19:32:10.278467-08 benchmark f H100 \N t \N \N \N {} +362 97 2025-02-23 18:43:11.382309-08 2025-02-23 18:32:53.162246-08 benchmark t H100 \N t \N \N \N {} +363 97 2025-02-23 18:08:22.457997-08 2025-02-23 18:01:35.677657-08 leaderboard t H100 0.0015710386666666668 t \N \N \N {} +364 97 2025-02-23 18:00:30.642191-08 2025-02-23 19:31:22.809425-08 test f H100 \N t \N \N \N {} +365 97 2025-02-23 19:12:44.951064-08 2025-02-23 17:52:24.96366-08 benchmark f H100 \N t \N \N \N {} +366 97 2025-02-23 18:34:04.98843-08 2025-02-23 19:32:44.684641-08 leaderboard f H100 0.0015707563333333333 t \N \N \N {} +370 99 2025-02-23 19:47:48.997469-08 2025-02-23 19:43:46.393992-08 leaderboard t H100 0.0014419603333333333 t \N \N \N {} +419 123 2025-02-23 20:03:41.74647-08 2025-02-23 19:48:21.795119-08 test f L4 \N t \N \N \N {} +372 99 2025-02-23 19:38:48.275786-08 2025-02-23 18:15:04.56295-08 benchmark f H100 \N t \N \N \N {} +373 99 2025-02-23 19:12:46.215077-08 2025-02-23 18:59:54.371176-08 leaderboard f H100 0.001431599 t \N \N \N {} +374 100 2025-02-23 19:55:52.569939-08 2025-02-23 19:27:50.142717-08 benchmark f H100 \N t \N \N \N {} +375 101 2025-02-23 18:47:52.05672-08 2025-02-23 18:49:29.177973-08 benchmark f H100 \N f \N \N \N {} +376 103 2025-02-23 19:50:51.240681-08 2025-02-23 19:03:51.348191-08 benchmark f T4 \N t \N \N \N {} +402 107 2025-02-23 19:56:20.721626-08 2025-02-23 19:44:53.872926-08 test f H100 \N f \N \N \N {} +381 104 2025-02-23 18:11:03.43025-08 2025-02-23 18:08:57.029995-08 test t A100 \N t \N \N \N {} +382 104 2025-02-23 18:46:23.802647-08 2025-02-23 18:27:06.148219-08 benchmark t A100 \N t \N \N \N {} +383 104 2025-02-23 20:01:50.376892-08 2025-02-23 19:53:06.069857-08 leaderboard t A100 0.0017416949090909091 t \N \N \N {} +384 104 2025-02-23 18:19:47.990746-08 2025-02-23 19:28:16.772556-08 test f A100 \N t \N \N \N {} +386 104 2025-02-23 18:12:18.644326-08 2025-02-23 19:34:19.590964-08 leaderboard f A100 0.001762813 t \N \N \N {} +387 104 2025-02-23 19:49:03.622017-08 2025-02-23 18:55:44.844842-08 test t H100 \N t \N \N \N {} +388 104 2025-02-23 19:24:20.239368-08 2025-02-23 19:08:05.533919-08 benchmark t H100 \N t \N \N \N {} +389 104 2025-02-23 20:02:50.775118-08 2025-02-23 19:19:38.596389-08 leaderboard t H100 0.001051997 t \N \N \N {} +390 104 2025-02-23 19:55:47.925265-08 2025-02-23 18:30:53.620534-08 test f T4 \N t \N \N \N {} +391 104 2025-02-23 19:19:44.079247-08 2025-02-23 19:10:46.577196-08 benchmark f T4 \N t \N \N \N {} +392 104 2025-02-23 19:52:00.493107-08 2025-02-23 19:03:38.169538-08 leaderboard f T4 0.009289433666666666 t \N \N \N {} +393 104 2025-02-23 18:24:57.464605-08 2025-02-23 20:02:29.61335-08 test f L4 \N t \N \N \N {} +582 167 2025-02-24 03:48:55.014484-08 2025-02-24 05:19:01.867475-08 test f H100 \N f \N \N \N {} +3574 1220 2025-02-28 13:45:47.445988-08 2025-02-28 13:55:19.067931-08 benchmark f A100 \N t \N \N \N {} +605 172 2025-02-24 05:12:35.084465-08 2025-02-24 03:53:41.553176-08 leaderboard f T4 0.017926890833333334 t \N \N \N {} +607 172 2025-02-24 05:35:11.303477-08 2025-02-24 04:35:10.682311-08 benchmark f L4 \N t \N \N \N {} +608 172 2025-02-24 05:14:02.764614-08 2025-02-24 05:32:57.132512-08 leaderboard f L4 0.017603741333333332 t \N \N \N {} +616 174 2025-02-24 04:08:35.184962-08 2025-02-24 04:11:19.105749-08 benchmark f A100 \N t \N \N \N {} +617 174 2025-02-24 04:45:50.307802-08 2025-02-24 04:49:07.034774-08 leaderboard f A100 0.003163628 t \N \N \N {} +618 174 2025-02-24 05:05:10.997982-08 2025-02-24 04:21:07.467177-08 test t H100 \N t \N \N \N {} +619 174 2025-02-24 04:49:07.394184-08 2025-02-24 05:36:35.84385-08 benchmark t H100 \N t \N \N \N {} +620 174 2025-02-24 03:56:43.871806-08 2025-02-24 05:06:06.267042-08 leaderboard t H100 0.0014537886666666667 t \N \N \N {} +621 174 2025-02-24 04:11:26.341666-08 2025-02-24 05:44:14.091621-08 test t A100 \N t \N \N \N {} +622 174 2025-02-24 05:03:36.072136-08 2025-02-24 04:51:58.063144-08 benchmark t A100 \N t \N \N \N {} +623 174 2025-02-24 04:20:01.163633-08 2025-02-24 03:57:19.043219-08 leaderboard t A100 0.0031645186666666666 t \N \N \N {} +690 217 2025-02-24 07:33:26.518857-08 2025-02-24 07:07:15.672567-08 test f A100 \N f \N \N \N {} +630 174 2025-02-24 04:46:32.413079-08 2025-02-24 05:37:46.353976-08 test f T4 \N t \N \N \N {} +631 174 2025-02-24 04:08:35.774251-08 2025-02-24 04:13:23.290244-08 benchmark f T4 \N t \N \N \N {} +632 174 2025-02-24 05:26:47.831146-08 2025-02-24 05:29:05.035813-08 leaderboard f T4 0.02047257833333333 t \N \N \N {} +3277 1144 2025-02-28 05:40:20.632509-08 2025-02-28 06:23:09.782086-08 leaderboard f A100 0.0030818813333333334 t \N \N \N {} +680 211 2025-02-24 06:16:43.935048-08 2025-02-24 05:24:09.037312-08 benchmark t H100 \N t \N \N \N {} +681 211 2025-02-24 06:20:50.131308-08 2025-02-24 06:19:33.50419-08 leaderboard t H100 0.00343199925 t \N \N \N {} +682 211 2025-02-24 06:04:20.742817-08 2025-02-24 06:51:37.437888-08 test f H100 \N t \N \N \N {} +725 231 2025-02-24 08:33:34.394553-08 2025-02-24 07:52:30.592827-08 test f T4 \N t \N \N \N {} +683 211 2025-02-24 05:48:41.277846-08 2025-02-24 06:20:29.451233-08 benchmark f H100 \N t \N \N \N {} +684 211 2025-02-24 05:40:28.316963-08 2025-02-24 05:24:29.585484-08 leaderboard f H100 0.0034547503333333335 t \N \N \N {} +685 212 2025-02-24 07:16:11.487533-08 2025-02-24 05:49:31.110402-08 benchmark f H100 \N t \N \N \N {} +707 225 2025-02-24 07:34:13.86609-08 2025-02-24 09:25:38.673749-08 leaderboard t A100 0.010140606949999999 t \N \N \N {} +713 229 2025-02-24 08:36:51.707477-08 2025-02-24 09:19:35.205825-08 leaderboard t A100 0.004042308666666667 t \N \N \N {} +715 229 2025-02-24 08:59:14.990632-08 2025-02-24 08:40:06.19623-08 benchmark f A100 \N t \N \N \N {} +1002 289 2025-02-24 16:17:52.085682-08 2025-02-24 15:49:52.549542-08 leaderboard t T4 0.017548657333333332 t \N \N \N {} +1004 289 2025-02-24 15:49:21.702779-08 2025-02-24 15:56:44.290148-08 benchmark f H100 \N t \N \N \N {} +1005 289 2025-02-24 16:30:57.115003-08 2025-02-24 16:53:11.026432-08 leaderboard f H100 0.0014277393999999998 t \N \N \N {} +1006 289 2025-02-24 17:08:54.259849-08 2025-02-24 16:01:18.083591-08 test f T4 \N t \N \N \N {} +1012 289 2025-02-24 15:50:44.700014-08 2025-02-24 16:13:26.646399-08 test t L4 \N t \N \N \N {} +2411 812 2025-02-26 11:55:00.140651-08 2025-02-26 12:25:31.245914-08 test t T4 \N f \N \N \N {} +1474 491 2025-02-25 04:38:47.881649-08 2025-02-25 06:09:17.695829-08 test f A100 \N t \N \N \N {} +1477 492 2025-02-25 05:11:27.321383-08 2025-02-25 05:35:34.773378-08 test t H100 \N t \N \N \N {} +1507 504 2025-02-25 07:20:39.230369-08 2025-02-25 07:49:03.830451-08 test f A100 \N t \N \N \N {} +1018 290 2025-02-24 17:13:55.431038-08 2025-02-24 16:41:27.422869-08 test f A100 \N f \N \N \N {} +1019 290 2025-02-24 16:59:40.514483-08 2025-02-24 15:54:21.408908-08 test t H100 \N f \N \N \N {} +1020 290 2025-02-24 16:35:12.120375-08 2025-02-24 17:02:25.401972-08 test t A100 \N f \N \N \N {} +1329 443 2025-02-25 01:04:39.585597-08 2025-02-25 02:46:51.917328-08 benchmark f A100 \N t \N \N \N {} +1330 443 2025-02-25 01:21:21.17285-08 2025-02-25 02:34:21.089915-08 leaderboard f A100 0.0031880928 t \N \N \N {} +1032 291 2025-02-24 17:02:48.537924-08 2025-02-24 16:42:54.273152-08 test t H100 \N t \N \N \N {} +1033 291 2025-02-24 17:18:28.789252-08 2025-02-24 17:23:51.01374-08 benchmark t H100 \N t \N \N \N {} +6109 1847 2025-03-11 00:39:56.642025-07 2025-03-11 00:31:13.424537-07 benchmark f A100 \N f \N \N \N {} +1034 291 2025-02-24 17:27:21.544186-08 2025-02-24 16:17:02.165474-08 leaderboard t H100 0.0014878653333333333 t \N \N \N {} +1035 291 2025-02-24 16:34:37.159795-08 2025-02-24 16:06:45.714048-08 test t L4 \N t \N \N \N {} +1047 291 2025-02-24 17:36:54.22773-08 2025-02-24 17:03:45.859127-08 test f L4 \N t \N \N \N {} +1480 492 2025-02-25 06:19:11.982018-08 2025-02-25 06:17:28.318413-08 test f H100 \N t \N \N \N {} +1036 291 2025-02-24 17:54:34.398457-08 2025-02-24 16:41:28.568693-08 benchmark t L4 \N t \N \N \N {} +1037 291 2025-02-24 16:38:12.362104-08 2025-02-24 16:19:38.183305-08 leaderboard t L4 0.017247674666666667 t \N \N \N {} +1038 291 2025-02-24 16:24:02.713537-08 2025-02-24 16:41:50.09925-08 test f H100 \N t \N \N \N {} +1039 291 2025-02-24 17:42:26.593225-08 2025-02-24 17:17:44.711243-08 benchmark f H100 \N t \N \N \N {} +1040 291 2025-02-24 17:17:51.955383-08 2025-02-24 17:24:28.626898-08 leaderboard f H100 0.001511652 t \N \N \N {} +1379 462 2025-02-25 04:30:12.6402-08 2025-02-25 03:17:09.15611-08 benchmark f H100 \N t \N \N \N {} +1042 291 2025-02-24 17:37:57.142995-08 2025-02-24 17:18:33.682684-08 benchmark t T4 \N t \N \N \N {} +1043 291 2025-02-24 17:05:51.220243-08 2025-02-24 17:10:08.13623-08 leaderboard t T4 0.017371874333333332 t \N \N \N {} +1044 291 2025-02-24 17:06:11.858432-08 2025-02-24 16:33:50.751909-08 test f T4 \N t \N \N \N {} +1045 291 2025-02-24 16:16:44.982001-08 2025-02-24 16:31:49.425344-08 benchmark f T4 \N t \N \N \N {} +1046 291 2025-02-24 17:12:14.548228-08 2025-02-24 16:41:40.82905-08 leaderboard f T4 0.017367958 t \N \N \N {} +1380 463 2025-02-25 03:45:25.261332-08 2025-02-25 04:20:54.913035-08 benchmark f H100 \N t \N \N \N {} +2380 802 2025-02-26 12:30:03.429377-08 2025-02-26 11:34:34.394213-08 benchmark f A100 \N f \N \N \N {} +1048 291 2025-02-24 16:19:24.170092-08 2025-02-24 17:06:26.862153-08 benchmark f L4 \N t \N \N \N {} +1049 291 2025-02-24 15:56:27.762751-08 2025-02-24 17:05:19.405224-08 leaderboard f L4 0.017399107333333334 t \N \N \N {} +1050 292 2025-02-24 17:52:15.055926-08 2025-02-24 17:44:34.698152-08 test f A100 \N t \N \N \N {} +1051 292 2025-02-24 16:22:47.842013-08 2025-02-24 17:43:41.94016-08 benchmark f A100 \N t \N \N \N {} +1052 292 2025-02-24 17:08:31.329041-08 2025-02-24 16:04:23.906272-08 leaderboard f A100 0.0031465863333333334 t \N \N \N {} +1381 464 2025-02-25 04:30:12.200923-08 2025-02-25 04:46:17.323875-08 test f H100 \N t \N \N \N {} +1510 504 2025-02-25 07:50:24.939578-08 2025-02-25 07:14:47.363201-08 test t A100 \N t \N \N \N {} +1053 292 2025-02-24 17:46:58.491665-08 2025-02-24 17:43:14.530104-08 test t A100 \N t \N \N \N {} +1054 292 2025-02-24 16:28:07.677678-08 2025-02-24 17:17:05.064075-08 benchmark t A100 \N t \N \N \N {} +1055 292 2025-02-24 17:20:28.611387-08 2025-02-24 16:25:56.736329-08 leaderboard t A100 0.003155266 t \N \N \N {} +1056 292 2025-02-24 16:22:44.412346-08 2025-02-24 16:35:31.252286-08 test t L4 \N t \N \N \N {} +1057 292 2025-02-24 17:51:06.9878-08 2025-02-24 16:57:56.1698-08 benchmark t L4 \N t \N \N \N {} +1384 464 2025-02-25 04:01:57.357806-08 2025-02-25 04:32:07.486918-08 test t H100 \N t \N \N \N {} +1485 495 2025-02-25 07:02:18.828551-08 2025-02-25 05:31:13.315806-08 test t A100 \N f \N \N \N {} +1058 292 2025-02-24 16:12:14.175483-08 2025-02-24 16:19:24.281562-08 leaderboard t L4 0.017181366666666666 t \N \N \N {} +1059 292 2025-02-24 16:45:08.313765-08 2025-02-24 16:24:12.371964-08 test f H100 \N t \N \N \N {} +1060 292 2025-02-24 16:57:23.927277-08 2025-02-24 17:45:53.131411-08 benchmark f H100 \N t \N \N \N {} +1387 465 2025-02-25 03:17:52.9791-08 2025-02-25 04:50:46.975041-08 test t H100 \N t \N \N \N {} +1063 292 2025-02-24 17:43:12.741558-08 2025-02-24 16:54:56.492661-08 benchmark t T4 \N t \N \N \N {} +1064 292 2025-02-24 16:36:30.649022-08 2025-02-24 16:31:15.555557-08 leaderboard t T4 0.016662345666666665 t \N \N \N {} +6110 1848 2025-03-11 02:24:40.439817-07 2025-03-11 01:49:27.022226-07 benchmark f A100 \N f \N \N \N {} +1065 292 2025-02-24 17:01:31.487312-08 2025-02-24 16:08:17.982794-08 test f T4 \N t \N \N \N {} +1066 292 2025-02-24 17:34:24.686206-08 2025-02-24 16:15:54.00558-08 benchmark f T4 \N t \N \N \N {} +1067 292 2025-02-24 16:25:45.909582-08 2025-02-24 17:35:16.325804-08 leaderboard f T4 0.016692271666666664 t \N \N \N {} +1089 300 2025-02-24 17:15:39.013548-08 2025-02-24 18:07:57.788624-08 test f A100 \N t \N \N \N {} +1390 465 2025-02-25 04:07:56.363414-08 2025-02-25 03:08:55.772199-08 test f H100 \N t \N \N \N {} +1069 292 2025-02-24 16:33:24.005735-08 2025-02-24 17:00:37.977793-08 benchmark f L4 \N t \N \N \N {} +1070 292 2025-02-24 16:11:38.035638-08 2025-02-24 16:35:28.298125-08 leaderboard f L4 0.01725526833333333 t \N \N \N {} +1072 292 2025-02-24 17:49:21.420511-08 2025-02-24 17:39:52.392614-08 benchmark t H100 \N t \N \N \N {} +1073 292 2025-02-24 17:39:03.403534-08 2025-02-24 16:41:35.838976-08 leaderboard t H100 0.00143393225 t \N \N \N {} +1074 293 2025-02-24 16:28:54.883242-08 2025-02-24 17:35:23.368271-08 test f H100 \N t \N \N \N {} +1075 294 2025-02-24 17:12:03.460856-08 2025-02-24 17:11:25.091501-08 benchmark f H100 \N t \N \N \N {} +1076 295 2025-02-24 17:45:00.760378-08 2025-02-24 18:03:30.527064-08 test f H100 \N t \N \N \N {} +3899 1300 2025-03-01 04:59:30.357371-08 2025-03-01 05:19:06.000963-08 benchmark f H100 \N t \N \N \N {} +1083 297 2025-02-24 18:12:12.906661-08 2025-02-24 16:31:51.879538-08 test f A100 \N t \N \N \N {} +1087 300 2025-02-24 17:00:42.01574-08 2025-02-24 17:08:59.120585-08 benchmark t A100 \N t \N \N \N {} +1088 300 2025-02-24 16:54:53.189438-08 2025-02-24 17:03:28.128475-08 leaderboard t A100 0.014111941 t \N \N \N {} +1090 300 2025-02-24 18:18:13.575515-08 2025-02-24 17:44:59.217622-08 benchmark f A100 \N t \N \N \N {} +1101 304 2025-02-24 17:40:45.55189-08 2025-02-24 17:01:05.496887-08 test t H100 \N t \N \N \N {} +1102 304 2025-02-24 18:50:24.116927-08 2025-02-24 18:27:17.991007-08 benchmark t H100 \N t \N \N \N {} +1103 304 2025-02-24 18:56:45.352747-08 2025-02-24 17:39:36.014601-08 leaderboard t H100 0.0036164646666666665 t \N \N \N {} +1104 304 2025-02-24 17:14:31.119701-08 2025-02-24 18:21:08.729398-08 test f T4 \N t \N \N \N {} +1108 304 2025-02-24 17:14:00.044903-08 2025-02-24 17:35:39.490324-08 benchmark t T4 \N t \N \N \N {} +1109 304 2025-02-24 18:01:06.49365-08 2025-02-24 17:46:19.984475-08 leaderboard t T4 0.02827727433333333 t \N \N \N {} +1110 304 2025-02-24 18:55:51.538374-08 2025-02-24 18:30:10.634801-08 test f H100 \N t \N \N \N {} +1112 304 2025-02-24 17:35:18.104482-08 2025-02-24 17:19:54.790912-08 leaderboard f H100 0.003638923 t \N \N \N {} +1115 304 2025-02-24 17:17:06.648489-08 2025-02-24 18:55:20.002642-08 leaderboard t L4 0.027880181666666667 t \N \N \N {} +1116 304 2025-02-24 18:01:36.171802-08 2025-02-24 17:39:40.210678-08 test f L4 \N t \N \N \N {} +1117 304 2025-02-24 17:16:05.293543-08 2025-02-24 17:09:24.002204-08 benchmark f L4 \N t \N \N \N {} +1149 322 2025-02-24 20:09:50.646883-08 2025-02-24 20:09:27.027041-08 benchmark f A100 \N t \N \N \N {} +1150 323 2025-02-24 19:44:17.381273-08 2025-02-24 18:40:51.316233-08 test f H100 \N f \N \N \N {} +1513 505 2025-02-25 06:40:15.410176-08 2025-02-25 07:37:48.715184-08 test f A100 \N t \N \N \N {} +1151 324 2025-02-24 18:48:04.557654-08 2025-02-24 19:42:55.295313-08 benchmark f A100 \N t \N \N \N {} +1152 325 2025-02-24 19:59:18.76177-08 2025-02-24 18:45:50.840944-08 benchmark f A100 \N f \N \N \N {} +1153 326 2025-02-24 18:52:24.053553-08 2025-02-24 19:29:33.646058-08 benchmark f A100 \N t \N \N \N {} +1154 327 2025-02-24 20:15:12.831932-08 2025-02-24 18:46:28.80301-08 benchmark f A100 \N t \N \N \N {} +1165 333 2025-02-24 19:59:10.567094-08 2025-02-24 19:43:21.331396-08 benchmark f A100 \N f \N \N \N {} +1156 329 2025-02-24 20:00:37.855323-08 2025-02-24 19:49:02.655715-08 test f H100 \N t \N \N \N {} +1157 329 2025-02-24 19:16:48.278346-08 2025-02-24 18:35:28.873095-08 benchmark f H100 \N t \N \N \N {} +1158 329 2025-02-24 19:19:35.133313-08 2025-02-24 19:39:48.264665-08 leaderboard f H100 0.0015907006666666668 t \N \N \N {} +1159 329 2025-02-24 20:06:20.184176-08 2025-02-24 19:17:10.273748-08 test t H100 \N t \N \N \N {} +1395 467 2025-02-25 04:01:56.308441-08 2025-02-25 03:40:45.970012-08 test f A100 \N f \N \N \N {} +1162 330 2025-02-24 20:15:53.048254-08 2025-02-24 19:40:58.672546-08 benchmark f A100 \N t \N \N \N {} +1163 331 2025-02-24 19:39:33.765253-08 2025-02-24 20:18:32.015656-08 benchmark f A100 \N f \N \N \N {} +1164 332 2025-02-24 19:57:11.008009-08 2025-02-24 19:45:28.206315-08 test f A100 \N f \N \N \N {} +1396 468 2025-02-25 03:56:27.738846-08 2025-02-25 04:21:51.589822-08 test f A100 \N t \N \N \N {} +1170 338 2025-02-24 20:00:52.228565-08 2025-02-24 19:06:41.353851-08 benchmark f A100 \N f \N \N \N {} +1185 353 2025-02-24 20:23:22.920373-08 2025-02-24 20:31:23.388252-08 benchmark f A100 \N f \N \N \N {} +1172 340 2025-02-24 20:30:32.647454-08 2025-02-24 19:20:53.458575-08 benchmark f A100 \N t \N \N \N {} +1173 341 2025-02-24 19:18:26.427276-08 2025-02-24 20:25:12.965728-08 benchmark f A100 \N t \N \N \N {} +1174 342 2025-02-24 19:16:50.848989-08 2025-02-24 20:26:36.909914-08 benchmark f A100 \N f \N \N \N {} +1399 468 2025-02-25 03:54:55.679208-08 2025-02-25 04:15:06.87232-08 test t A100 \N t \N \N \N {} +1516 505 2025-02-25 07:18:07.594758-08 2025-02-25 07:03:51.966033-08 test t A100 \N t \N \N \N {} +1178 346 2025-02-24 20:46:35.583472-08 2025-02-24 19:47:02.016449-08 benchmark f A100 \N f \N \N \N {} +1179 347 2025-02-24 20:01:38.196152-08 2025-02-24 19:54:49.601721-08 benchmark f A100 \N f \N \N \N {} +1402 469 2025-02-25 03:40:45.982253-08 2025-02-25 03:31:06.961948-08 test f A100 \N t \N \N \N {} +1182 350 2025-02-24 19:20:16.154751-08 2025-02-24 20:48:25.71245-08 benchmark f A100 \N t \N \N \N {} +1183 351 2025-02-24 20:45:34.464741-08 2025-02-24 20:18:14.635844-08 benchmark f H100 \N t \N \N \N {} +1184 352 2025-02-24 20:01:05.338551-08 2025-02-24 20:52:19.857137-08 test f A100 \N f \N \N \N {} +1405 469 2025-02-25 04:58:27.346875-08 2025-02-25 04:51:26.433509-08 test t A100 \N t \N \N \N {} +1519 506 2025-02-25 06:47:13.929749-08 2025-02-25 06:42:22.498667-08 test t H100 \N t \N \N \N {} +1188 358 2025-02-24 19:37:31.122358-08 2025-02-24 20:18:57.150066-08 benchmark f A100 \N f \N \N \N {} +1189 360 2025-02-24 21:06:56.386392-08 2025-02-24 19:27:17.523239-08 benchmark f A100 \N f \N \N \N {} +1190 363 2025-02-24 20:45:31.277208-08 2025-02-24 20:01:17.753967-08 benchmark f A100 \N f \N \N \N {} +1408 470 2025-02-25 03:59:58.320768-08 2025-02-25 04:28:37.828048-08 test t A100 \N t \N \N \N {} +1193 365 2025-02-24 19:43:43.570899-08 2025-02-24 19:39:23.906226-08 benchmark f A100 \N t \N \N \N {} +6140 1882 2025-03-11 10:55:44.95308-07 2025-03-11 11:05:57.417109-07 test t T4 \N f \N \N \N {} +1194 365 2025-02-24 20:08:47.222036-08 2025-02-24 19:30:56.207701-08 leaderboard f A100 0.01920636041666667 t \N \N \N {} +1195 365 2025-02-24 19:51:58.749971-08 2025-02-24 19:45:07.726733-08 test t A100 \N t \N \N \N {} +1196 365 2025-02-24 19:38:48.837266-08 2025-02-24 19:34:14.850955-08 benchmark t A100 \N t \N \N \N {} +1197 365 2025-02-24 21:18:06.454034-08 2025-02-24 20:41:46.182401-08 leaderboard t A100 0.023418701 t \N \N \N {} +1411 470 2025-02-25 03:23:48.300357-08 2025-02-25 04:14:39.867996-08 test f A100 \N t \N \N \N {} +1201 369 2025-02-24 19:57:06.809954-08 2025-02-24 19:38:21.922123-08 benchmark f A100 \N f \N \N \N {} +1202 370 2025-02-24 20:42:28.895887-08 2025-02-24 20:44:35.166507-08 benchmark f A100 \N f \N \N \N {} +1205 373 2025-02-24 20:31:46.327908-08 2025-02-24 19:52:32.554037-08 benchmark f A100 \N f \N \N \N {} +1251 412 2025-02-24 21:58:13.257224-08 2025-02-24 20:59:40.847628-08 benchmark f A100 \N t \N \N \N {} +1252 413 2025-02-24 21:02:53.363826-08 2025-02-24 21:32:32.418367-08 test f A100 \N t \N \N \N {} +1253 413 2025-02-24 21:45:44.88428-08 2025-02-24 22:03:57.186711-08 benchmark f A100 \N t \N \N \N {} +1254 413 2025-02-24 22:46:22.934333-08 2025-02-24 22:52:04.198254-08 leaderboard f A100 0.0008381501 t \N \N \N {} +1261 416 2025-02-24 22:58:19.495213-08 2025-02-24 21:04:11.500619-08 benchmark f H100 \N t \N \N \N {} +1262 416 2025-02-24 21:17:50.838915-08 2025-02-24 22:04:53.900724-08 leaderboard f H100 0.00182382483 t \N \N \N {} +1267 418 2025-02-24 23:14:01.289913-08 2025-02-24 23:08:07.113865-08 test f H100 \N f \N \N \N {} +1268 419 2025-02-24 22:02:19.904283-08 2025-02-24 22:45:33.136654-08 test f H100 \N f \N \N \N {} +1269 420 2025-02-24 22:11:27.988828-08 2025-02-24 22:20:38.794633-08 test f L4 \N f \N \N \N {} +1270 421 2025-02-24 22:44:39.054132-08 2025-02-24 23:23:37.763687-08 test f H100 \N f \N \N \N {} +1285 431 2025-02-25 00:40:59.712906-08 2025-02-25 00:18:36.054996-08 benchmark f A100 \N t \N \N \N {} +1286 432 2025-02-25 00:31:05.092152-08 2025-02-25 00:20:51.202659-08 test f A100 \N f \N \N \N {} +1287 432 2025-02-25 00:49:38.550792-08 2025-02-25 00:46:23.249092-08 test t A100 \N f \N \N \N {} +1288 433 2025-02-25 00:12:52.435038-08 2025-02-25 00:43:33.913224-08 test t A100 \N f \N \N \N {} +1289 433 2025-02-25 00:41:52.056466-08 2025-02-25 01:46:10.51578-08 test f A100 \N f \N \N \N {} +1290 434 2025-02-25 02:07:55.489435-08 2025-02-25 00:38:51.721901-08 test f A100 \N f \N \N \N {} +1300 437 2025-02-25 01:42:35.344955-08 2025-02-25 00:18:58.076587-08 test f A100 \N t \N \N \N {} +1301 438 2025-02-25 00:52:08.957799-08 2025-02-25 01:01:30.640546-08 test f A100 \N t \N \N \N {} +1306 438 2025-02-25 02:17:28.708296-08 2025-02-25 00:43:42.436098-08 leaderboard t A100 0.00319022725 t \N \N \N {} +1307 439 2025-02-25 00:35:26.857477-08 2025-02-25 00:40:59.59712-08 test t A100 \N t \N \N \N {} +1312 439 2025-02-25 02:25:43.593928-08 2025-02-25 00:33:11.310668-08 leaderboard f A100 0.018465732104166667 t \N \N \N {} +1313 440 2025-02-25 00:40:22.163117-08 2025-02-25 02:26:51.813453-08 test t A100 \N f \N \N \N {} +1345 447 2025-02-25 01:34:33.910168-08 2025-02-25 03:08:17.172342-08 test f A100 \N t \N \N \N {} +1314 440 2025-02-25 01:49:18.063405-08 2025-02-25 01:27:01.624065-08 test f A100 \N f \N \N \N {} +1315 441 2025-02-25 02:02:15.063829-08 2025-02-25 01:35:47.494284-08 test t A100 \N t \N \N \N {} +1316 441 2025-02-25 01:37:10.608604-08 2025-02-25 01:46:29.023721-08 benchmark t A100 \N f \N \N \N {} +1335 445 2025-02-25 02:43:54.759243-08 2025-02-25 01:44:21.434113-08 leaderboard f A100 0.0030836436666666664 t \N \N \N {} +1336 445 2025-02-25 02:54:21.021597-08 2025-02-25 01:51:35.634885-08 test t A100 \N t \N \N \N {} +1346 448 2025-02-25 02:33:47.252483-08 2025-02-25 02:35:35.888545-08 test f H100 \N t \N \N \N {} +1434 476 2025-02-25 04:00:22.852373-08 2025-02-25 04:04:45.593426-08 test t A100 \N f \N \N \N {} +1339 446 2025-02-25 02:36:06.477421-08 2025-02-25 03:07:01.814655-08 test f H100 \N t \N \N \N {} +1340 446 2025-02-25 01:30:46.736236-08 2025-02-25 01:35:56.032794-08 benchmark f H100 \N t \N \N \N {} +1341 446 2025-02-25 02:50:19.546464-08 2025-02-25 03:05:51.854793-08 leaderboard f H100 0.001413967 t \N \N \N {} +1342 446 2025-02-25 01:57:52.59555-08 2025-02-25 03:11:12.62416-08 test t H100 \N t \N \N \N {} +1343 446 2025-02-25 02:54:39.59271-08 2025-02-25 01:59:29.726735-08 benchmark t H100 \N t \N \N \N {} +1344 446 2025-02-25 02:39:25.860644-08 2025-02-25 02:13:36.717111-08 leaderboard t H100 0.001406711 t \N \N \N {} +1347 448 2025-02-25 02:43:41.717721-08 2025-02-25 03:26:33.446347-08 benchmark f H100 \N t \N \N \N {} +1348 448 2025-02-25 02:24:25.941676-08 2025-02-25 01:44:32.522029-08 leaderboard f H100 0.0014466804444444445 t \N \N \N {} +1349 448 2025-02-25 02:46:47.800926-08 2025-02-25 02:27:20.46965-08 test t H100 \N t \N \N \N {} +1350 448 2025-02-25 03:06:45.439062-08 2025-02-25 02:01:51.335675-08 benchmark t H100 \N t \N \N \N {} +1351 448 2025-02-25 03:30:09.49455-08 2025-02-25 03:12:41.990012-08 leaderboard t H100 0.0014756132222222221 t \N \N \N {} +1352 449 2025-02-25 03:33:12.39421-08 2025-02-25 02:23:52.923798-08 benchmark f H100 \N f \N \N \N {} +1435 476 2025-02-25 04:55:28.037485-08 2025-02-25 03:57:08.314649-08 test f A100 \N f \N \N \N {} +1555 513 2025-02-25 07:41:24.90562-08 2025-02-25 07:02:07.563164-08 test t A100 \N t \N \N \N {} +2909 1016 2025-02-27 00:36:25.1576-08 2025-02-27 00:50:15.121869-08 benchmark f L4 \N t \N \N \N {} +2910 1016 2025-02-27 00:33:26.148508-08 2025-02-27 01:43:46.236307-08 leaderboard f L4 0.017180370666666667 t \N \N \N {} +3041 1088 2025-02-27 14:56:34.384661-08 2025-02-27 15:37:21.179849-08 benchmark f A100 \N t \N \N \N {} +3369 1163 2025-02-28 10:09:00.6989-08 2025-02-28 09:38:40.385253-08 leaderboard t T4 0.01692768366666667 t \N \N \N {} +1400 468 2025-02-25 04:45:59.134192-08 2025-02-25 03:14:40.329073-08 benchmark t A100 \N t \N \N \N {} +1401 468 2025-02-25 03:36:58.192612-08 2025-02-25 04:35:55.042696-08 leaderboard t A100 0.003100401 t \N \N \N {} +2919 1023 2025-02-27 02:46:56.557824-08 2025-02-27 03:30:51.397045-08 test t A100 \N t \N \N \N {} +2920 1023 2025-02-27 02:54:26.545327-08 2025-02-27 03:57:32.864122-08 benchmark t A100 \N t \N \N \N {} +2921 1023 2025-02-27 04:31:43.865249-08 2025-02-27 03:08:56.104014-08 leaderboard t A100 0.00009803284313725489 t \N \N \N {} +3120 1110 2025-02-28 01:35:44.444997-08 2025-02-28 03:00:13.974579-08 leaderboard t A100 0.00310022 t \N \N \N {} +3559 1210 2025-02-28 11:35:52.32336-08 2025-02-28 10:56:38.662439-08 test f A100 \N t \N \N \N {} +1403 469 2025-02-25 04:31:28.4595-08 2025-02-25 03:33:06.99222-08 benchmark f A100 \N t \N \N \N {} +2381 803 2025-02-26 11:33:33.487376-08 2025-02-26 12:57:20.418702-08 benchmark f A100 \N f \N \N \N {} +2427 833 2025-02-26 13:42:17.786461-08 2025-02-26 12:30:47.6523-08 benchmark f H100 \N t \N \N \N {} +3009 1074 2025-02-27 13:15:17.144176-08 2025-02-27 12:29:52.339984-08 leaderboard t A100 0.008153932666666667 t \N \N \N {} +2952 1039 2025-02-27 09:30:20.903906-08 2025-02-27 09:13:37.115048-08 benchmark f A100 \N t \N \N \N {} +3010 1074 2025-02-27 13:59:33.48938-08 2025-02-27 13:27:19.116278-08 test f A100 \N t \N \N \N {} +3011 1074 2025-02-27 14:02:47.636628-08 2025-02-27 13:32:31.614474-08 benchmark f A100 \N t \N \N \N {} +3012 1074 2025-02-27 13:15:20.480402-08 2025-02-27 12:20:44.401517-08 leaderboard f A100 0.008157396666666667 t \N \N \N {} +3085 1104 2025-02-28 01:59:18.600676-08 2025-02-28 02:22:56.347954-08 test f A100 \N t \N \N \N {} +3094 1106 2025-02-28 02:25:58.750131-08 2025-02-28 03:06:28.944884-08 test t A100 \N t \N \N \N {} +3100 1107 2025-02-28 01:26:20.879799-08 2025-02-28 02:25:12.920428-08 test f A100 \N t \N \N \N {} +1478 492 2025-02-25 04:23:20.449898-08 2025-02-25 04:58:19.421573-08 benchmark t H100 \N t \N \N \N {} +1479 492 2025-02-25 04:51:19.751745-08 2025-02-25 05:39:17.407689-08 leaderboard t H100 0.0014122803333333333 t \N \N \N {} +2953 1040 2025-02-27 09:36:18.815464-08 2025-02-27 08:40:35.471157-08 test f A100 \N t \N \N \N {} +2954 1040 2025-02-27 08:26:37.101243-08 2025-02-27 09:01:30.532817-08 benchmark f A100 \N t \N \N \N {} +2955 1040 2025-02-27 09:41:18.891429-08 2025-02-27 08:04:00.37954-08 leaderboard f A100 0.003193513 t \N \N \N {} +3013 1075 2025-02-27 13:11:51.024355-08 2025-02-27 13:17:55.679639-08 test f A100 \N f \N \N \N {} +3103 1107 2025-02-28 03:07:10.741712-08 2025-02-28 02:19:44.946765-08 test t A100 \N t \N \N \N {} +3225 1127 2025-02-28 05:28:19.551054-08 2025-02-28 05:56:14.735613-08 leaderboard f A100 0.0030895356666666663 t \N \N \N {} +1481 492 2025-02-25 04:54:31.873992-08 2025-02-25 04:22:13.298402-08 benchmark f H100 \N t \N \N \N {} +1482 492 2025-02-25 06:14:51.357942-08 2025-02-25 06:18:57.491972-08 leaderboard f H100 0.001430011 t \N \N \N {} +1524 506 2025-02-25 06:33:04.067626-08 2025-02-25 07:23:14.188914-08 leaderboard f H100 0.0014538050909090909 t \N \N \N {} +1525 507 2025-02-25 07:34:25.667422-08 2025-02-25 06:18:53.214067-08 test t A100 \N f \N \N \N {} +1526 507 2025-02-25 08:04:00.305064-08 2025-02-25 07:53:47.234207-08 test f A100 \N f \N \N \N {} +1527 508 2025-02-25 06:51:53.729029-08 2025-02-25 06:49:15.739902-08 benchmark f A100 \N f \N \N \N {} +1528 509 2025-02-25 07:57:51.169726-08 2025-02-25 08:03:52.639663-08 benchmark f A100 \N t \N \N \N {} +3160 1117 2025-02-28 01:57:36.630356-08 2025-02-28 02:15:23.444492-08 test t A100 \N t \N \N \N {} +1531 512 2025-02-25 07:27:21.471591-08 2025-02-25 07:32:23.967466-08 test t A100 \N t \N \N \N {} +130 20 2025-02-23 09:47:46.962051-08 2025-02-23 10:49:22.323496-08 test f H100 \N t \N \N \N {} +1532 512 2025-02-25 07:07:12.686135-08 2025-02-25 06:51:42.064245-08 benchmark t A100 \N t \N \N \N {} +1533 512 2025-02-25 07:35:12.160809-08 2025-02-25 06:23:12.621053-08 leaderboard t A100 0.0033605953333333337 t \N \N \N {} +1534 512 2025-02-25 07:40:57.831066-08 2025-02-25 07:21:40.9266-08 test f A100 \N t \N \N \N {} +1535 512 2025-02-25 07:49:49.262012-08 2025-02-25 07:10:31.438503-08 benchmark f A100 \N t \N \N \N {} +1536 512 2025-02-25 06:32:25.921122-08 2025-02-25 07:25:06.894207-08 leaderboard f A100 0.0033895406666666667 t \N \N \N {} +1537 512 2025-02-25 06:24:52.920212-08 2025-02-25 06:36:53.942491-08 test t H100 \N t \N \N \N {} +1538 512 2025-02-25 06:30:33.771888-08 2025-02-25 07:19:58.296436-08 benchmark t H100 \N t \N \N \N {} +1558 513 2025-02-25 07:20:20.352133-08 2025-02-25 07:48:40.674871-08 test f A100 \N t \N \N \N {} +3263 1142 2025-02-28 06:26:36.269643-08 2025-02-28 06:34:10.748084-08 test f A100 \N t \N \N \N {} +2020 694 2025-02-25 14:25:32.099879-08 2025-02-25 14:05:01.976545-08 test f H100 \N f \N \N \N {} +1576 516 2025-02-25 07:10:56.831342-08 2025-02-25 07:01:47.567348-08 test f A100 \N t \N \N \N {} +1577 516 2025-02-25 08:23:01.443137-08 2025-02-25 07:25:44.603998-08 benchmark f A100 \N t \N \N \N {} +1578 516 2025-02-25 08:13:13.657148-08 2025-02-25 07:31:55.453583-08 leaderboard f A100 0.0031859834 t \N \N \N {} +1580 518 2025-02-25 08:07:50.629405-08 2025-02-25 07:17:48.243079-08 benchmark f T4 \N t \N \N \N {} +1582 519 2025-02-25 07:03:06.655132-08 2025-02-25 07:54:35.221308-08 benchmark t H100 \N t \N \N \N {} +1583 519 2025-02-25 08:20:50.0298-08 2025-02-25 08:34:10.546647-08 leaderboard t H100 0.0014114776666666667 t \N \N \N {} +1584 519 2025-02-25 08:01:59.762518-08 2025-02-25 08:40:03.53953-08 test f H100 \N t \N \N \N {} +1585 519 2025-02-25 06:48:35.700356-08 2025-02-25 07:22:01.937122-08 benchmark f H100 \N t \N \N \N {} +1586 519 2025-02-25 07:48:14.082024-08 2025-02-25 07:23:02.722501-08 leaderboard f H100 0.0014109543333333332 t \N \N \N {} +1587 520 2025-02-25 06:50:50.074767-08 2025-02-25 06:52:07.567587-08 test f A100 \N t \N \N \N {} +1588 521 2025-02-25 07:04:03.01671-08 2025-02-25 08:36:16.326265-08 benchmark f A100 \N t \N \N \N {} +1603 532 2025-02-25 08:01:48.392638-08 2025-02-25 07:55:14.350257-08 test t A100 \N t \N \N \N {} +1604 532 2025-02-25 08:04:01.52357-08 2025-02-25 08:48:53.380739-08 benchmark t A100 \N t \N \N \N {} +1605 532 2025-02-25 08:07:29.187473-08 2025-02-25 07:30:38.019107-08 leaderboard t A100 0.0030833873333333335 t \N \N \N {} +1606 533 2025-02-25 07:13:57.228578-08 2025-02-25 09:02:45.282064-08 test t H100 \N t \N \N \N {} +1607 533 2025-02-25 08:46:17.241842-08 2025-02-25 08:46:34.390308-08 benchmark t H100 \N t \N \N \N {} +1608 533 2025-02-25 07:15:43.847706-08 2025-02-25 07:17:56.713485-08 leaderboard t H100 0.0014808887368421052 t \N \N \N {} +1614 536 2025-02-25 07:40:05.156643-08 2025-02-25 08:50:38.71485-08 benchmark f H100 \N t \N \N \N {} +1615 537 2025-02-25 08:20:34.71729-08 2025-02-25 08:00:07.524271-08 test f A100 \N t \N \N \N {} +1626 539 2025-02-25 08:31:18.319762-08 2025-02-25 07:36:37.562316-08 benchmark t H100 \N t \N \N \N {} +1627 539 2025-02-25 07:45:22.622109-08 2025-02-25 07:26:16.545192-08 leaderboard t H100 0.0014020573333333333 t \N \N \N {} +1628 540 2025-02-25 08:39:12.579698-08 2025-02-25 08:21:54.313518-08 test f H100 \N t \N \N \N {} +1629 540 2025-02-25 09:06:18.861595-08 2025-02-25 09:06:08.76981-08 benchmark f H100 \N t \N \N \N {} +1630 540 2025-02-25 07:42:40.881925-08 2025-02-25 09:24:44.220023-08 leaderboard f H100 0.0014151046666666667 t \N \N \N {} +1631 540 2025-02-25 09:11:16.420021-08 2025-02-25 08:59:13.13714-08 test t H100 \N t \N \N \N {} +1683 557 2025-02-25 08:58:37.853947-08 2025-02-25 09:17:12.880606-08 test f A100 \N t \N \N \N {} +1642 543 2025-02-25 09:29:08.780743-08 2025-02-25 07:49:54.770844-08 benchmark t A100 \N t \N \N \N {} +1643 543 2025-02-25 08:08:43.291503-08 2025-02-25 07:45:38.670153-08 leaderboard t A100 0.003091805 t \N \N \N {} +1644 543 2025-02-25 08:16:39.851047-08 2025-02-25 09:04:53.563147-08 test f A100 \N t \N \N \N {} +1645 543 2025-02-25 08:33:33.890389-08 2025-02-25 09:25:02.242877-08 benchmark f A100 \N t \N \N \N {} +1647 544 2025-02-25 08:10:30.369306-08 2025-02-25 07:43:45.870981-08 test f A100 \N f \N \N \N {} +1648 544 2025-02-25 07:53:54.16523-08 2025-02-25 08:33:04.35208-08 test t A100 \N f \N \N \N {} +1649 544 2025-02-25 08:33:21.506269-08 2025-02-25 08:07:38.323301-08 test t T4 \N f \N \N \N {} +1650 544 2025-02-25 09:15:52.74522-08 2025-02-25 08:12:39.641754-08 test f T4 \N f \N \N \N {} +1651 545 2025-02-25 08:25:31.259943-08 2025-02-25 08:02:07.523474-08 test f A100 \N t \N \N \N {} +1976 679 2025-02-25 12:53:34.216219-08 2025-02-25 13:15:06.928515-08 test t T4 \N t \N \N \N {} +1710 571 2025-02-25 09:19:18.026341-08 2025-02-25 09:52:30.405634-08 test t T4 \N f \N \N \N {} +1711 571 2025-02-25 08:33:43.295723-08 2025-02-25 10:06:35.053909-08 test f T4 \N f \N \N \N {} +1726 579 2025-02-25 10:12:23.270121-08 2025-02-25 09:22:50.331776-08 test f A100 \N f \N \N \N {} +1734 585 2025-02-25 08:29:31.038561-08 2025-02-25 08:26:58.367928-08 test f A100 \N f \N \N \N {} +1735 583 2025-02-25 08:35:16.843418-08 2025-02-25 09:05:45.380553-08 test t H100 \N t \N \N \N {} +1736 583 2025-02-25 10:11:22.418499-08 2025-02-25 09:25:54.155768-08 benchmark t H100 \N t \N \N \N {} +1737 583 2025-02-25 08:51:06.601652-08 2025-02-25 09:10:23.575069-08 leaderboard t H100 0.0000772660625 t \N \N \N {} +2810 981 2025-02-26 23:47:22.315813-08 2025-02-26 23:04:15.576922-08 leaderboard f H100 0.00149895725 t \N \N \N {} +3817 1285 2025-03-01 03:36:02.152194-08 2025-03-01 03:19:46.959566-08 test f A100 \N t \N \N \N {} +4806 1487 2025-03-02 14:19:46.759466-08 2025-03-02 12:30:42.83975-08 test f T4 \N t \N \N \N {} +1784 613 2025-02-25 11:14:58.236656-08 2025-02-25 10:52:55.040195-08 test f A100 \N t \N \N \N {} +1785 614 2025-02-25 10:45:46.882457-08 2025-02-25 10:39:52.677212-08 benchmark f H100 \N t \N \N \N {} +2355 791 2025-02-26 08:55:33.742418-08 2025-02-26 07:47:49.701265-08 test f H100 \N f \N \N \N {} +1786 615 2025-02-25 10:56:29.193673-08 2025-02-25 09:37:14.897702-08 benchmark f H100 \N f \N \N \N {} +1787 616 2025-02-25 10:39:54.716283-08 2025-02-25 10:38:23.837561-08 test f A100 \N t \N \N \N {} +1788 616 2025-02-25 10:53:22.333552-08 2025-02-25 10:30:34.088241-08 benchmark f A100 \N t \N \N \N {} +1789 616 2025-02-25 11:11:25.986403-08 2025-02-25 10:31:45.03736-08 leaderboard f A100 0.000205736 t \N \N \N {} +1790 616 2025-02-25 09:41:53.090321-08 2025-02-25 10:12:39.131059-08 test t A100 \N t \N \N \N {} +1796 616 2025-02-25 09:49:54.963655-08 2025-02-25 11:04:32.101396-08 test t T4 \N t \N \N \N {} +2775 966 2025-02-26 20:12:59.070745-08 2025-02-26 19:39:08.158654-08 leaderboard f A100 0.014324531 t \N \N \N {} +1792 616 2025-02-25 10:01:38.707534-08 2025-02-25 11:11:50.587762-08 leaderboard t A100 0.00017068230769230768 t \N \N \N {} +1793 616 2025-02-25 10:03:00.704862-08 2025-02-25 09:45:03.774932-08 test f H100 \N t \N \N \N {} +1794 616 2025-02-25 10:53:30.74314-08 2025-02-25 11:05:56.056638-08 benchmark f H100 \N t \N \N \N {} +1795 616 2025-02-25 11:17:45.875715-08 2025-02-25 10:39:10.578077-08 leaderboard f H100 0.00012224712903225807 t \N \N \N {} +2356 791 2025-02-26 07:29:55.883929-08 2025-02-26 08:47:28.665781-08 test t H100 \N f \N \N \N {} +1798 616 2025-02-25 09:55:41.682206-08 2025-02-25 10:56:48.765016-08 leaderboard t T4 0.000549977 t \N \N \N {} +1799 616 2025-02-25 09:35:29.329871-08 2025-02-25 10:19:26.68501-08 test f T4 \N t \N \N \N {} +1800 616 2025-02-25 10:37:31.462732-08 2025-02-25 10:39:06.329009-08 benchmark f T4 \N t \N \N \N {} +1801 616 2025-02-25 10:49:46.717275-08 2025-02-25 09:34:27.810838-08 leaderboard f T4 0.0007055456666666667 t \N \N \N {} +2357 792 2025-02-26 09:14:17.578297-08 2025-02-26 08:39:13.532029-08 test t H100 \N f \N \N \N {} +1803 616 2025-02-25 10:02:17.879749-08 2025-02-25 11:05:44.711116-08 benchmark t H100 \N t \N \N \N {} +1804 616 2025-02-25 11:14:14.091418-08 2025-02-25 10:00:08.773034-08 leaderboard t H100 0.00012245449019607842 t \N \N \N {} +1805 616 2025-02-25 10:57:07.382054-08 2025-02-25 10:21:18.841312-08 test f L4 \N t \N \N \N {} +1806 616 2025-02-25 11:25:11.990406-08 2025-02-25 10:09:51.776687-08 benchmark f L4 \N t \N \N \N {} +2358 792 2025-02-26 08:54:16.016762-08 2025-02-26 08:39:38.92267-08 test f H100 \N f \N \N \N {} +1810 616 2025-02-25 11:00:52.381329-08 2025-02-25 11:23:28.247671-08 leaderboard t L4 0.00021845146153846154 t \N \N \N {} +1811 617 2025-02-25 10:55:46.819723-08 2025-02-25 10:55:23.275903-08 benchmark f H100 \N f \N \N \N {} +1812 618 2025-02-25 10:21:57.018191-08 2025-02-25 10:55:18.464595-08 benchmark f A100 \N t \N \N \N {} +2370 797 2025-02-26 10:53:12.943723-08 2025-02-26 09:33:24.539346-08 test f A100 \N f \N \N \N {} +1816 620 2025-02-25 10:05:33.623324-08 2025-02-25 10:33:30.515341-08 leaderboard t A100 0.00309566 t \N \N \N {} +1817 620 2025-02-25 11:21:18.896922-08 2025-02-25 10:03:28.563169-08 test f A100 \N t \N \N \N {} +1818 620 2025-02-25 11:29:14.645796-08 2025-02-25 09:47:07.54021-08 benchmark f A100 \N t \N \N \N {} +1819 620 2025-02-25 10:07:21.891596-08 2025-02-25 11:09:15.984274-08 leaderboard f A100 0.0030797906666666665 t \N \N \N {} +2415 817 2025-02-26 12:03:48.353023-08 2025-02-26 12:27:23.72571-08 benchmark f H100 \N f \N \N \N {} +1821 621 2025-02-25 09:54:58.090582-08 2025-02-25 11:30:23.907082-08 benchmark f A100 \N t \N \N \N {} +138 23 2025-02-23 11:54:58.067969-08 2025-02-23 12:08:04.435299-08 test f A100 \N t \N \N \N {} +147 23 2025-02-23 11:38:07.488367-08 2025-02-23 11:53:30.778579-08 test t T4 \N t \N \N \N {} +156 23 2025-02-23 10:30:22.436806-08 2025-02-23 10:14:59.268017-08 test f L4 \N t \N \N \N {} +1353 450 2025-02-25 02:01:19.511091-08 2025-02-25 01:50:23.554828-08 benchmark f H100 \N f \N \N \N {} +1822 621 2025-02-25 10:17:37.410428-08 2025-02-25 10:36:03.237286-08 leaderboard f A100 0.00011739522222222221 t \N \N \N {} +4809 1487 2025-03-02 13:45:28.37586-08 2025-03-02 12:40:42.351849-08 benchmark t L4 \N f \N \N \N {} +2226 725 2025-02-25 14:54:19.929968-08 2025-02-25 15:47:46.340338-08 benchmark f A100 \N t \N \N \N {} +2228 727 2025-02-25 20:35:49.008461-08 2025-02-25 21:27:48.607545-08 test f H100 \N f \N \N \N {} +2230 729 2025-02-25 21:17:44.501223-08 2025-02-25 20:34:01.289359-08 test f H100 \N f \N \N \N {} +2234 733 2025-02-25 21:21:37.259904-08 2025-02-25 22:02:04.25938-08 benchmark f H100 \N t \N \N \N {} +2235 734 2025-02-25 22:33:25.794507-08 2025-02-25 22:37:59.655817-08 test f H100 \N f \N \N \N {} +4810 1487 2025-03-02 14:06:07.69494-08 2025-03-02 14:05:29.275996-08 test f L4 \N t \N \N \N {} +2237 736 2025-02-25 23:19:21.988232-08 2025-02-25 23:45:39.636835-08 benchmark f A100 \N t \N \N \N {} +2238 737 2025-02-25 23:00:41.307289-08 2025-02-25 23:14:23.811188-08 benchmark f A100 \N t \N \N \N {} +2239 738 2025-02-25 23:48:44.965108-08 2025-02-25 23:42:33.619927-08 benchmark f A100 \N f \N \N \N {} +2240 739 2025-02-25 23:19:53.648595-08 2025-02-25 23:40:49.676196-08 benchmark f A100 \N f \N \N \N {} +2272 766 2025-02-26 04:44:38.752744-08 2025-02-26 03:37:57.159249-08 test f A100 \N t \N \N \N {} +2244 743 2025-02-26 00:35:45.969202-08 2025-02-26 00:32:30.806442-08 benchmark f A100 \N f \N \N \N {} +3587 1229 2025-02-28 14:32:10.95012-08 2025-02-28 14:44:22.261204-08 test f H100 \N t \N \N \N {} +2246 745 2025-02-26 00:37:33.931264-08 2025-02-26 00:40:26.964338-08 test f H100 \N f \N \N \N {} +2247 746 2025-02-26 01:55:19.252796-08 2025-02-26 00:36:07.797743-08 test f H100 \N f \N \N \N {} +2248 747 2025-02-26 01:15:55.844133-08 2025-02-26 01:53:31.828353-08 test f H100 \N f \N \N \N {} +2250 749 2025-02-26 00:25:17.403977-08 2025-02-26 00:24:56.829005-08 benchmark f H100 \N t \N \N \N {} +2256 755 2025-02-26 02:22:45.950588-08 2025-02-26 02:07:54.180517-08 benchmark f H100 \N f \N \N \N {} +2257 756 2025-02-26 03:00:14.310747-08 2025-02-26 02:46:46.937635-08 benchmark f H100 \N t \N \N \N {} +2258 757 2025-02-26 01:10:17.629744-08 2025-02-26 01:41:31.08645-08 benchmark f H100 \N t \N \N \N {} +2263 762 2025-02-26 02:18:34.242484-08 2025-02-26 02:55:39.021981-08 test t H100 \N t \N \N \N {} +2291 770 2025-02-26 04:07:08.486179-08 2025-02-26 03:34:41.389378-08 test f A100 \N t \N \N \N {} +2264 762 2025-02-26 02:58:11.327721-08 2025-02-26 03:46:33.348626-08 benchmark t H100 \N t \N \N \N {} +2266 762 2025-02-26 02:37:45.731387-08 2025-02-26 03:44:38.02645-08 test f H100 \N t \N \N \N {} +2267 762 2025-02-26 03:39:25.066238-08 2025-02-26 02:14:19.098169-08 benchmark f H100 \N t \N \N \N {} +2268 762 2025-02-26 02:23:03.127111-08 2025-02-26 02:36:35.552634-08 leaderboard f H100 0.0014381206666666667 t \N \N \N {} +2269 763 2025-02-26 02:45:57.978469-08 2025-02-26 02:49:10.932866-08 benchmark f H100 \N f \N \N \N {} +2270 765 2025-02-26 04:02:02.067979-08 2025-02-26 03:15:12.820422-08 test f A100 \N f \N \N \N {} +2271 765 2025-02-26 03:46:07.247621-08 2025-02-26 03:33:23.013529-08 test t A100 \N f \N \N \N {} +2273 766 2025-02-26 04:26:50.535168-08 2025-02-26 04:37:20.190686-08 benchmark f A100 \N t \N \N \N {} +2276 766 2025-02-26 03:57:01.450513-08 2025-02-26 04:19:48.460438-08 benchmark t A100 \N t \N \N \N {} +2277 766 2025-02-26 04:23:34.32179-08 2025-02-26 04:37:21.779651-08 leaderboard t A100 0.0008035725 t \N \N \N {} +2278 767 2025-02-26 04:59:10.716734-08 2025-02-26 04:56:31.615231-08 test f A100 \N t \N \N \N {} +2279 767 2025-02-26 04:04:53.620256-08 2025-02-26 03:40:43.285038-08 benchmark f A100 \N t \N \N \N {} +2811 981 2025-02-26 23:20:55.588139-08 2025-02-26 23:02:56.11656-08 test t H100 \N t \N \N \N {} +3820 1285 2025-03-01 02:46:38.245389-08 2025-03-01 02:36:19.519197-08 test t A100 \N t \N \N \N {} +2283 767 2025-02-26 03:48:47.510918-08 2025-02-26 04:12:24.590357-08 leaderboard t A100 0.0008112885 t \N \N \N {} +2285 769 2025-02-26 03:32:55.537334-08 2025-02-26 04:57:18.17064-08 test t A100 \N t \N \N \N {} +2286 769 2025-02-26 03:54:10.03714-08 2025-02-26 03:53:44.323045-08 benchmark t A100 \N t \N \N \N {} +2287 769 2025-02-26 04:13:17.84753-08 2025-02-26 04:35:13.277401-08 leaderboard t A100 0.0008150356 t \N \N \N {} +2288 769 2025-02-26 03:53:13.370089-08 2025-02-26 03:34:52.714707-08 test f A100 \N t \N \N \N {} +2289 769 2025-02-26 04:13:58.330006-08 2025-02-26 03:53:06.926282-08 benchmark f A100 \N t \N \N \N {} +2290 769 2025-02-26 04:11:02.675153-08 2025-02-26 04:13:02.665399-08 leaderboard f A100 0.0008101136666666666 t \N \N \N {} +2292 770 2025-02-26 03:47:42.434385-08 2025-02-26 03:50:45.275328-08 benchmark f A100 \N t \N \N \N {} +2293 770 2025-02-26 03:53:00.59453-08 2025-02-26 04:00:41.911017-08 leaderboard f A100 0.0008105805 t \N \N \N {} +2294 770 2025-02-26 04:01:53.351812-08 2025-02-26 03:50:07.775814-08 test t A100 \N t \N \N \N {} +2295 770 2025-02-26 03:11:56.96532-08 2025-02-26 04:01:17.104616-08 benchmark t A100 \N t \N \N \N {} +2296 770 2025-02-26 03:30:49.241367-08 2025-02-26 04:03:05.950494-08 leaderboard t A100 0.00080491525 t \N \N \N {} +2297 771 2025-02-26 04:33:09.624984-08 2025-02-26 04:02:12.069329-08 benchmark f A100 \N t \N \N \N {} +2298 772 2025-02-26 04:59:38.93896-08 2025-02-26 04:38:25.02308-08 benchmark f A100 \N t \N \N \N {} +2299 773 2025-02-26 04:20:34.328694-08 2025-02-26 03:54:56.809911-08 benchmark f A100 \N t \N \N \N {} +2304 774 2025-02-26 03:57:56.984318-08 2025-02-26 04:38:02.103282-08 benchmark t A100 \N t \N \N \N {} +2305 774 2025-02-26 03:44:38.635658-08 2025-02-26 05:02:38.874989-08 leaderboard t A100 0.00311144 t \N \N \N {} +2306 775 2025-02-26 04:12:31.181055-08 2025-02-26 04:59:52.882918-08 benchmark f A100 \N t \N \N \N {} +2307 776 2025-02-26 04:20:56.019237-08 2025-02-26 03:52:11.190759-08 benchmark f A100 \N t \N \N \N {} +2329 784 2025-02-26 07:42:05.262849-08 2025-02-26 07:36:19.042812-08 test f A100 \N f \N \N \N {} +2311 778 2025-02-26 03:30:09.435566-08 2025-02-26 04:06:02.956045-08 leaderboard t A100 0.003092609 t \N \N \N {} +2312 778 2025-02-26 05:23:38.077187-08 2025-02-26 03:53:55.424873-08 test f A100 \N t \N \N \N {} +2326 783 2025-02-26 08:05:37.792029-08 2025-02-26 08:12:28.335942-08 test f A100 \N t \N \N \N {} +2327 783 2025-02-26 08:39:42.131724-08 2025-02-26 08:09:32.991225-08 benchmark f A100 \N t \N \N \N {} +2328 783 2025-02-26 08:30:37.407938-08 2025-02-26 07:35:49.357033-08 leaderboard f A100 0.003227151 t \N \N \N {} +2331 785 2025-02-26 07:27:47.482468-08 2025-02-26 08:17:53.94964-08 test f A100 \N t \N \N \N {} +2332 785 2025-02-26 07:37:11.087311-08 2025-02-26 08:15:07.866294-08 benchmark f A100 \N t \N \N \N {} +2333 785 2025-02-26 07:59:24.368247-08 2025-02-26 08:20:21.480809-08 leaderboard f A100 0.004367405666666667 t \N \N \N {} +2337 786 2025-02-26 08:22:04.365539-08 2025-02-26 08:24:47.375813-08 test f A100 \N f \N \N \N {} +2338 786 2025-02-26 08:35:45.345849-08 2025-02-26 08:14:30.033798-08 test t A100 \N f \N \N \N {} +2339 787 2025-02-26 07:22:18.968697-08 2025-02-26 08:22:46.178651-08 test t A100 \N f \N \N \N {} +2346 789 2025-02-26 07:08:50.231065-08 2025-02-26 08:49:16.966065-08 test f A100 \N t \N \N \N {} +3844 1289 2025-03-01 02:58:23.301247-08 2025-03-01 03:47:22.156604-08 test t A100 \N t \N \N \N {} +2344 789 2025-02-26 08:30:43.202837-08 2025-02-26 08:53:02.953547-08 benchmark t A100 \N t \N \N \N {} +2345 789 2025-02-26 08:54:31.507554-08 2025-02-26 08:18:29.611102-08 leaderboard t A100 0.00322034475 t \N \N \N {} +2349 790 2025-02-26 07:27:15.296498-08 2025-02-26 07:45:31.770943-08 test t H100 \N t \N \N \N {} +2350 790 2025-02-26 07:27:09.22196-08 2025-02-26 08:40:58.669769-08 benchmark t H100 \N t \N \N \N {} +2351 790 2025-02-26 07:38:50.506814-08 2025-02-26 07:39:57.059049-08 leaderboard t H100 0.0015428555 t \N \N \N {} +2352 790 2025-02-26 07:14:10.99536-08 2025-02-26 08:11:26.301088-08 test f H100 \N t \N \N \N {} +2353 790 2025-02-26 07:55:02.506428-08 2025-02-26 08:30:47.285542-08 benchmark f H100 \N t \N \N \N {} +2354 790 2025-02-26 08:58:21.825582-08 2025-02-26 08:36:23.311282-08 leaderboard f H100 0.00153994575 t \N \N \N {} +2362 794 2025-02-26 09:16:23.731727-08 2025-02-26 09:45:35.701403-08 test f A100 \N f \N \N \N {} +2363 794 2025-02-26 10:05:30.642869-08 2025-02-26 10:06:11.654779-08 test f H100 \N f \N \N \N {} +2364 794 2025-02-26 08:54:28.852181-08 2025-02-26 10:36:05.947891-08 test f L4 \N f \N \N \N {} +2369 796 2025-02-26 09:04:59.633587-08 2025-02-26 08:58:58.997794-08 test f H100 \N f \N \N \N {} +3269 1143 2025-02-28 05:49:04.471481-08 2025-02-28 05:33:52.950669-08 test t A100 \N t \N \N \N {} +3272 1143 2025-02-28 07:24:05.395179-08 2025-02-28 06:21:30.549804-08 test f A100 \N t \N \N \N {} +2389 806 2025-02-26 11:59:47.526877-08 2025-02-26 13:27:08.118395-08 benchmark f H100 \N t \N \N \N {} +131 21 2025-02-23 10:54:12.413903-08 2025-02-23 11:03:24.456859-08 benchmark f H100 \N t \N \N \N {} +132 22 2025-02-23 11:18:52.406962-08 2025-02-23 11:21:40.34417-08 test f H100 \N t \N \N \N {} +133 22 2025-02-23 11:10:07.454357-08 2025-02-23 11:27:41.266157-08 benchmark f H100 \N t \N \N \N {} +134 22 2025-02-23 11:11:08.332204-08 2025-02-23 09:56:47.514659-08 leaderboard f H100 0.007636646 t \N \N \N {} +135 22 2025-02-23 11:15:29.967609-08 2025-02-23 10:36:22.679826-08 test t H100 \N t \N \N \N {} +136 22 2025-02-23 10:30:49.187464-08 2025-02-23 10:54:58.140967-08 benchmark t H100 \N t \N \N \N {} +137 22 2025-02-23 10:01:17.414414-08 2025-02-23 10:57:17.950859-08 leaderboard t H100 0.007676687 t \N \N \N {} +6139 1882 2025-03-11 10:43:46.751248-07 2025-03-11 11:39:34.207773-07 test f T4 \N f \N \N \N {} +139 23 2025-02-23 11:00:36.681319-08 2025-02-23 11:20:38.884145-08 benchmark f A100 \N t \N \N \N {} +140 23 2025-02-23 11:47:46.303484-08 2025-02-23 10:27:15.204326-08 leaderboard f A100 0.010159300357142857 t \N \N \N {} +141 23 2025-02-23 11:55:55.792415-08 2025-02-23 11:05:18.699548-08 test t A100 \N t \N \N \N {} +142 23 2025-02-23 10:45:14.3074-08 2025-02-23 10:59:33.198898-08 benchmark t A100 \N t \N \N \N {} +143 23 2025-02-23 11:17:34.462052-08 2025-02-23 10:26:24.612757-08 leaderboard t A100 0.010135129449999999 t \N \N \N {} +144 23 2025-02-23 10:09:53.746-08 2025-02-23 11:09:01.710035-08 test f H100 \N t \N \N \N {} +145 23 2025-02-23 11:23:51.944416-08 2025-02-23 11:35:42.525714-08 benchmark f H100 \N t \N \N \N {} +146 23 2025-02-23 11:15:27.676586-08 2025-02-23 12:03:10.988718-08 leaderboard f H100 0.006125231666666667 t \N \N \N {} +148 23 2025-02-23 11:13:16.041704-08 2025-02-23 11:50:47.350172-08 benchmark t T4 \N t \N \N \N {} +149 23 2025-02-23 11:28:20.433165-08 2025-02-23 10:28:06.298908-08 leaderboard t T4 0.048027397 t \N \N \N {} +150 23 2025-02-23 11:18:42.410803-08 2025-02-23 10:31:01.372052-08 test t H100 \N t \N \N \N {} +151 23 2025-02-23 11:58:30.811996-08 2025-02-23 11:21:35.60515-08 benchmark t H100 \N t \N \N \N {} +152 23 2025-02-23 11:11:00.271834-08 2025-02-23 11:00:53.563642-08 leaderboard t H100 0.006109236666666667 t \N \N \N {} +153 23 2025-02-23 10:26:41.850092-08 2025-02-23 10:39:34.799916-08 test f T4 \N t \N \N \N {} +2390 807 2025-02-26 12:40:07.882315-08 2025-02-26 13:29:00.67918-08 benchmark f A100 \N t \N \N \N {} +2391 808 2025-02-26 12:44:03.203064-08 2025-02-26 12:20:13.654261-08 benchmark f H100 \N t \N \N \N {} +2392 809 2025-02-26 11:46:24.444642-08 2025-02-26 13:37:16.547993-08 benchmark f H100 \N t \N \N \N {} +2393 811 2025-02-26 11:51:23.557392-08 2025-02-26 12:37:24.914401-08 benchmark f H100 \N f \N \N \N {} +4811 1487 2025-03-02 13:46:45.410776-08 2025-03-02 13:08:51.056877-08 benchmark f L4 \N f \N \N \N {} +3847 1290 2025-03-01 04:03:32.270689-08 2025-03-01 02:32:19.138733-08 test f A100 \N t \N \N \N {} +2394 810 2025-02-26 13:25:37.482593-08 2025-02-26 12:59:08.470332-08 benchmark f H100 \N f \N \N \N {} +2395 812 2025-02-26 13:33:28.597174-08 2025-02-26 12:57:21.570896-08 test f H100 \N t \N \N \N {} +2396 812 2025-02-26 12:34:20.532251-08 2025-02-26 12:31:27.053945-08 benchmark f H100 \N t \N \N \N {} +2397 812 2025-02-26 12:19:54.717591-08 2025-02-26 12:10:14.47041-08 leaderboard f H100 0.0032161647200000002 t \N \N \N {} +2398 812 2025-02-26 13:18:48.216215-08 2025-02-26 11:52:47.39111-08 test f A100 \N t \N \N \N {} +2401 812 2025-02-26 12:08:17.632371-08 2025-02-26 12:08:36.634538-08 test t A100 \N t \N \N \N {} +2402 812 2025-02-26 13:29:27.61967-08 2025-02-26 13:25:55.587367-08 benchmark t A100 \N t \N \N \N {} +2403 812 2025-02-26 13:21:36.142662-08 2025-02-26 12:50:20.677716-08 leaderboard t A100 0.00698412748 t \N \N \N {} +2404 812 2025-02-26 13:13:30.239955-08 2025-02-26 12:39:13.840154-08 test f L4 \N f \N \N \N {} +2405 812 2025-02-26 12:25:57.063676-08 2025-02-26 12:24:58.010138-08 test t H100 \N t \N \N \N {} +2410 812 2025-02-26 12:20:36.785307-08 2025-02-26 12:48:06.635821-08 test f T4 \N f \N \N \N {} +2435 839 2025-02-26 14:18:21.87914-08 2025-02-26 14:18:15.815334-08 benchmark t H100 \N t \N \N \N {} +2436 839 2025-02-26 14:10:30.590633-08 2025-02-26 14:11:00.415518-08 leaderboard t H100 0.0014024583333333332 t \N \N \N {} +2441 845 2025-02-26 14:15:04.422652-08 2025-02-26 14:13:19.625716-08 test t L4 \N t \N \N \N {} +2447 852 2025-02-26 14:16:39.200008-08 2025-02-26 13:25:34.941047-08 benchmark f A100 \N t \N \N \N {} +2442 845 2025-02-26 13:50:40.358584-08 2025-02-26 13:07:51.38911-08 benchmark t L4 \N t \N \N \N {} +2451 856 2025-02-26 12:43:06.254876-08 2025-02-26 13:32:18.217747-08 benchmark f A100 \N t \N \N \N {} +2452 858 2025-02-26 13:00:26.844507-08 2025-02-26 14:38:31.646993-08 test f T4 \N f \N \N \N {} +2453 859 2025-02-26 14:36:40.012139-08 2025-02-26 13:10:56.467322-08 benchmark f H100 \N t \N \N \N {} +2455 860 2025-02-26 13:23:16.833722-08 2025-02-26 14:41:34.259251-08 benchmark f A100 \N t \N \N \N {} +2462 864 2025-02-26 13:11:46.57133-08 2025-02-26 14:39:28.851851-08 test f T4 \N f \N \N \N {} +2463 865 2025-02-26 13:11:01.636695-08 2025-02-26 14:32:14.927023-08 test f A100 \N t \N \N \N {} +2464 865 2025-02-26 13:30:02.44975-08 2025-02-26 14:19:35.875255-08 benchmark f A100 \N t \N \N \N {} +2465 865 2025-02-26 13:09:06.850344-08 2025-02-26 14:04:10.639691-08 leaderboard f A100 0.0068088155999999995 t \N \N \N {} +2466 865 2025-02-26 14:08:58.695846-08 2025-02-26 12:57:46.38922-08 test t A100 \N t \N \N \N {} +3385 1166 2025-02-28 10:20:27.28906-08 2025-02-28 10:10:11.161724-08 leaderboard t L4 0.017129881333333333 t \N \N \N {} +2481 870 2025-02-26 13:59:09.266137-08 2025-02-26 13:07:02.894219-08 leaderboard f H100 0.001470785 t \N \N \N {} +2820 983 2025-02-26 23:04:13.026351-08 2025-02-26 23:16:23.959588-08 test f T4 \N t \N \N \N {} +2508 872 2025-02-26 13:21:09.133466-08 2025-02-26 13:53:51.09567-08 benchmark f A100 \N t \N \N \N {} +2509 872 2025-02-26 14:26:03.179438-08 2025-02-26 13:48:36.711846-08 leaderboard f A100 0.003223094 t \N \N \N {} +3286 1147 2025-02-28 07:23:54.538307-08 2025-02-28 06:54:57.696014-08 benchmark f L4 \N t \N \N \N {} +2511 872 2025-02-26 13:29:54.390732-08 2025-02-26 14:56:13.502248-08 benchmark f H100 \N t \N \N \N {} +2512 872 2025-02-26 14:00:24.384-08 2025-02-26 14:52:47.576219-08 leaderboard f H100 0.0014829895714285714 t \N \N \N {} +2513 872 2025-02-26 14:08:21.032655-08 2025-02-26 14:48:45.337774-08 test f T4 \N t \N \N \N {} +2514 872 2025-02-26 13:43:03.545363-08 2025-02-26 13:27:16.423676-08 benchmark f T4 \N t \N \N \N {} +2515 872 2025-02-26 14:53:04.043695-08 2025-02-26 14:31:15.017571-08 leaderboard f T4 0.016977648666666668 t \N \N \N {} +3297 1148 2025-02-28 05:56:14.732913-08 2025-02-28 06:35:38.237057-08 benchmark t T4 \N t \N \N \N {} +2516 872 2025-02-26 13:59:35.326154-08 2025-02-26 14:28:54.006791-08 test t T4 \N t \N \N \N {} +2517 872 2025-02-26 14:10:22.48064-08 2025-02-26 13:05:44.349918-08 benchmark t T4 \N t \N \N \N {} +2518 872 2025-02-26 13:38:47.072409-08 2025-02-26 13:18:53.541371-08 leaderboard t T4 0.016951106 t \N \N \N {} +2519 872 2025-02-26 14:41:30.759683-08 2025-02-26 13:51:40.998977-08 test f L4 \N t \N \N \N {} +2520 872 2025-02-26 13:15:09.29921-08 2025-02-26 13:07:54.264512-08 benchmark f L4 \N t \N \N \N {} +3181 1120 2025-02-28 02:42:01.210008-08 2025-02-28 02:43:08.325982-08 test t A100 \N t \N \N \N {} +154 23 2025-02-23 11:57:05.139267-08 2025-02-23 11:09:15.68083-08 benchmark f T4 \N t \N \N \N {} +180 27 2025-02-23 10:28:14.91215-08 2025-02-23 11:48:21.706378-08 test t T4 \N t \N \N \N {} +2521 872 2025-02-26 14:43:07.037109-08 2025-02-26 14:45:02.826917-08 leaderboard f L4 0.017294490333333332 t \N \N \N {} +2522 872 2025-02-26 14:50:44.169022-08 2025-02-26 14:42:28.113826-08 test t L4 \N t \N \N \N {} +2523 872 2025-02-26 13:56:52.894596-08 2025-02-26 13:28:48.739095-08 benchmark t L4 \N t \N \N \N {} +2527 875 2025-02-26 14:23:38.227767-08 2025-02-26 13:30:18.365385-08 test t L4 \N t \N \N \N {} +3589 1229 2025-02-28 13:21:19.11789-08 2025-02-28 14:31:58.395368-08 leaderboard f H100 0.0011892495 t \N \N \N {} +2532 875 2025-02-26 14:59:13.072405-08 2025-02-26 13:27:06.641704-08 leaderboard f H100 0.001465262 t \N \N \N {} +2533 875 2025-02-26 14:47:12.530795-08 2025-02-26 13:13:21.249202-08 test f A100 \N t \N \N \N {} +2534 875 2025-02-26 13:48:48.551741-08 2025-02-26 13:28:31.146854-08 benchmark f A100 \N t \N \N \N {} +2535 875 2025-02-26 14:39:27.031045-08 2025-02-26 13:25:27.404117-08 leaderboard f A100 0.0032578983333333335 t \N \N \N {} +3312 1150 2025-02-28 07:23:23.622707-08 2025-02-28 06:09:54.030373-08 benchmark f H100 \N t \N \N \N {} +2537 875 2025-02-26 14:32:13.549304-08 2025-02-26 14:39:07.302029-08 benchmark t A100 \N t \N \N \N {} +2548 875 2025-02-26 14:56:04.001163-08 2025-02-26 13:42:23.904365-08 test f L4 \N t \N \N \N {} +2549 875 2025-02-26 14:18:16.659538-08 2025-02-26 13:23:18.958744-08 benchmark f L4 \N t \N \N \N {} +2550 875 2025-02-26 14:22:09.462839-08 2025-02-26 14:54:20.343262-08 leaderboard f L4 0.01742031 t \N \N \N {} +2551 877 2025-02-26 13:57:57.39587-08 2025-02-26 13:12:36.270032-08 test f L4 \N t \N \N \N {} +2573 877 2025-02-26 13:16:45.52631-08 2025-02-26 14:52:12.270412-08 test t T4 \N t \N \N \N {} +3399 1172 2025-02-28 10:30:05.610744-08 2025-02-28 09:16:34.458793-08 test f L4 \N f \N \N \N {} +2553 877 2025-02-26 13:10:29.354581-08 2025-02-26 14:37:38.257685-08 leaderboard f L4 0.01741081066666667 t \N \N \N {} +2554 877 2025-02-26 13:58:10.825604-08 2025-02-26 14:00:37.261867-08 test t L4 \N t \N \N \N {} +2555 877 2025-02-26 13:48:17.730063-08 2025-02-26 14:50:07.298178-08 benchmark t L4 \N t \N \N \N {} +2556 877 2025-02-26 13:50:16.824546-08 2025-02-26 14:44:02.236307-08 leaderboard t L4 0.017350969 t \N \N \N {} +3353 1157 2025-02-28 07:00:17.816299-08 2025-02-28 08:05:40.861-08 test t A100 \N t \N \N \N {} +2559 877 2025-02-26 14:12:21.70114-08 2025-02-26 14:49:53.732127-08 benchmark f A100 \N t \N \N \N {} +2560 877 2025-02-26 13:13:01.61476-08 2025-02-26 14:52:53.675129-08 leaderboard f A100 0.00324053 t \N \N \N {} +2561 877 2025-02-26 14:17:12.652893-08 2025-02-26 14:33:51.585787-08 test f H100 \N t \N \N \N {} +3356 1157 2025-02-28 07:54:22.893197-08 2025-02-28 07:34:43.645784-08 test f A100 \N t \N \N \N {} +2563 877 2025-02-26 13:56:06.528311-08 2025-02-26 15:06:14.803986-08 leaderboard f H100 0.001457541 t \N \N \N {} +2564 877 2025-02-26 14:47:01.068968-08 2025-02-26 14:04:16.625491-08 test t H100 \N t \N \N \N {} +2565 877 2025-02-26 13:58:40.178869-08 2025-02-26 15:00:37.290202-08 benchmark t H100 \N t \N \N \N {} +2566 877 2025-02-26 14:22:11.994956-08 2025-02-26 14:57:41.541904-08 leaderboard t H100 0.00144549375 t \N \N \N {} +3359 1158 2025-02-28 06:54:49.380912-08 2025-02-28 07:18:43.60823-08 test f T4 \N f \N \N \N {} +3859 1292 2025-03-01 03:27:40.255676-08 2025-03-01 02:36:03.541477-08 test f A100 \N t \N \N \N {} +2568 877 2025-02-26 14:12:07.205591-08 2025-02-26 13:51:47.845088-08 benchmark t A100 \N t \N \N \N {} +2569 877 2025-02-26 13:57:24.028157-08 2025-02-26 14:47:07.033256-08 leaderboard t A100 0.0032292653333333334 t \N \N \N {} +2570 877 2025-02-26 14:16:41.930867-08 2025-02-26 13:09:12.487972-08 test f T4 \N t \N \N \N {} +2571 877 2025-02-26 14:36:17.713292-08 2025-02-26 14:26:39.900745-08 benchmark f T4 \N t \N \N \N {} +2572 877 2025-02-26 15:02:18.460758-08 2025-02-26 14:58:57.369637-08 leaderboard f T4 0.01811966666666667 t \N \N \N {} +3406 1174 2025-02-28 09:14:03.047294-08 2025-02-28 09:16:54.531135-08 leaderboard t L4 0.017114292333333333 t \N \N \N {} +2575 877 2025-02-26 13:12:49.74724-08 2025-02-26 14:37:18.913817-08 leaderboard t T4 0.01813283333333333 t \N \N \N {} +2576 878 2025-02-26 13:16:27.296503-08 2025-02-26 14:27:29.207894-08 benchmark f T4 \N f \N \N \N {} +2577 879 2025-02-26 14:34:25.671098-08 2025-02-26 13:45:30.958405-08 benchmark f T4 \N t \N \N \N {} +2578 881 2025-02-26 13:39:45.596644-08 2025-02-26 14:01:02.933021-08 test f T4 \N f \N \N \N {} +2579 881 2025-02-26 14:04:26.768148-08 2025-02-26 13:45:26.952914-08 test f L4 \N f \N \N \N {} +3408 1175 2025-02-28 10:29:09.88226-08 2025-02-28 09:14:06.803313-08 test f L4 \N f \N \N \N {} +3862 1292 2025-03-01 03:25:13.486827-08 2025-03-01 02:53:16.099437-08 test t A100 \N t \N \N \N {} +2581 882 2025-02-26 13:33:10.645023-08 2025-02-26 13:55:42.912415-08 test f L4 \N t \N \N \N {} +2582 883 2025-02-26 14:19:49.686171-08 2025-02-26 14:38:27.107218-08 benchmark f L4 \N t \N \N \N {} +2583 883 2025-02-26 13:51:19.440545-08 2025-02-26 14:09:42.760292-08 benchmark f H100 \N t \N \N \N {} +2584 883 2025-02-26 14:39:09.643525-08 2025-02-26 13:31:21.985198-08 benchmark f A100 \N t \N \N \N {} +3418 1178 2025-02-28 10:35:09.967262-08 2025-02-28 11:06:54.18143-08 test f L4 \N f \N \N \N {} +3865 1293 2025-03-01 02:35:22.489765-08 2025-03-01 04:14:08.608387-08 test f A100 \N t \N \N \N {} +2587 884 2025-02-26 13:22:21.621679-08 2025-02-26 13:44:27.093032-08 benchmark f A100 \N t \N \N \N {} +2588 884 2025-02-26 14:52:31.919133-08 2025-02-26 13:38:05.904058-08 leaderboard f A100 0.003262141 t \N \N \N {} +2589 885 2025-02-26 13:31:31.720937-08 2025-02-26 13:36:09.135751-08 test f H100 \N t \N \N \N {} +2607 884 2025-02-26 15:03:02.758478-08 2025-02-26 14:21:28.592486-08 test t T4 \N t \N \N \N {} +3419 1178 2025-02-28 09:14:52.41289-08 2025-02-28 09:52:57.968186-08 test t L4 \N f \N \N \N {} +2592 884 2025-02-26 14:47:15.785668-08 2025-02-26 14:38:49.447224-08 test f L4 \N t \N \N \N {} +2062 700 2025-02-25 13:08:14.95001-08 2025-02-25 15:03:46.466768-08 test f A100 \N f \N \N \N {} +4223 1394 2025-03-01 14:57:35.411891-08 2025-03-01 15:35:59.507097-08 benchmark t A100 \N t \N \N \N {} +4247 1402 2025-03-01 17:04:55.972494-08 2025-03-01 17:47:24.376431-08 test t H100 \N t \N \N \N {} +4228 1395 2025-03-01 17:18:07.852776-08 2025-03-01 15:48:31.55626-08 benchmark f A100 \N t \N \N \N {} +4229 1396 2025-03-01 16:29:41.366025-08 2025-03-01 17:24:30.840608-08 test f H100 \N t \N \N \N {} +4717 1477 2025-03-02 08:12:18.55485-08 2025-03-02 06:22:18.597758-08 test f A100 \N t \N \N \N {} +4231 1398 2025-03-01 17:25:09.232586-08 2025-03-01 17:18:31.325671-08 test t A100 \N t \N \N \N {} +4232 1398 2025-03-01 16:28:07.719269-08 2025-03-01 15:58:31.010676-08 benchmark t A100 \N t \N \N \N {} +4233 1398 2025-03-01 15:43:44.691938-08 2025-03-01 17:19:39.809228-08 leaderboard t A100 0.0031178923333333333 t \N \N \N {} +4234 1398 2025-03-01 16:39:23.354655-08 2025-03-01 15:51:38.701412-08 test f H100 \N t \N \N \N {} +4253 1403 2025-03-01 17:26:34.106395-08 2025-03-01 17:42:47.155658-08 test t H100 \N t \N \N \N {} +4236 1398 2025-03-01 16:06:23.791952-08 2025-03-01 17:26:44.856408-08 leaderboard f H100 0.0014146966666666668 t \N \N \N {} +4237 1398 2025-03-01 15:36:12.31163-08 2025-03-01 15:33:41.234566-08 test t H100 \N t \N \N \N {} +4238 1398 2025-03-01 16:56:17.454711-08 2025-03-01 17:19:38.557978-08 benchmark t H100 \N t \N \N \N {} +4239 1398 2025-03-01 16:47:15.049127-08 2025-03-01 16:11:47.093143-08 leaderboard t H100 0.0014016626666666667 t \N \N \N {} +4378 1432 2025-03-01 22:51:39.742514-08 2025-03-01 22:23:37.282462-08 benchmark f T4 \N t \N \N \N {} +4242 1399 2025-03-01 16:22:07.45004-08 2025-03-01 16:01:06.543886-08 benchmark f A100 \N f \N \N \N {} +4243 1400 2025-03-01 17:07:07.130708-08 2025-03-01 16:24:06.325488-08 benchmark f H100 \N t \N \N \N {} +4244 1400 2025-03-01 17:29:39.977791-08 2025-03-01 17:02:08.939787-08 benchmark f A100 \N f \N \N \N {} +4346 1425 2025-03-01 18:21:27.980051-08 2025-03-01 19:51:56.286091-08 benchmark t L4 \N t \N \N \N {} +4347 1425 2025-03-01 18:37:42.52065-08 2025-03-01 19:22:48.263204-08 leaderboard t L4 0.01815886933333333 t \N \N \N {} +4348 1425 2025-03-01 18:57:46.285303-08 2025-03-01 20:03:56.603557-08 test f A100 \N t \N \N \N {} +4349 1425 2025-03-01 19:34:06.836256-08 2025-03-01 19:17:45.64278-08 benchmark f A100 \N t \N \N \N {} +4350 1425 2025-03-01 19:38:27.662128-08 2025-03-01 18:55:37.052703-08 leaderboard f A100 0.0033818667999999997 t \N \N \N {} +4352 1425 2025-03-01 18:49:23.803275-08 2025-03-01 19:23:11.101795-08 benchmark t A100 \N t \N \N \N {} +4357 1425 2025-03-01 19:41:41.606451-08 2025-03-01 18:23:09.857404-08 test t H100 \N t \N \N \N {} +4358 1425 2025-03-01 20:09:08.19207-08 2025-03-01 19:19:21.809996-08 benchmark t H100 \N t \N \N \N {} +4359 1425 2025-03-01 19:59:58.903988-08 2025-03-01 19:30:42.092627-08 leaderboard t H100 0.0015028730833333333 t \N \N \N {} +4361 1425 2025-03-01 18:31:13.881994-08 2025-03-01 18:26:52.74436-08 benchmark f T4 \N t \N \N \N {} +4362 1425 2025-03-01 20:12:38.573176-08 2025-03-01 19:33:01.456733-08 leaderboard f T4 0.01936384925 t \N \N \N {} +4363 1425 2025-03-01 19:42:14.310793-08 2025-03-01 18:41:20.723626-08 test f L4 \N t \N \N \N {} +4389 1440 2025-03-02 00:03:48.502169-08 2025-03-01 23:51:41.439127-08 test f A100 \N f \N \N \N {} +4390 1440 2025-03-02 00:04:14.366803-08 2025-03-01 22:49:12.463788-08 test f L4 \N f \N \N \N {} +4391 1440 2025-03-02 00:19:06.804988-08 2025-03-01 22:28:37.519544-08 test t T4 \N f \N \N \N {} +155 23 2025-02-23 11:57:27.550237-08 2025-02-23 11:56:29.734882-08 leaderboard f T4 0.048176454 t \N \N \N {} +157 23 2025-02-23 11:12:21.468647-08 2025-02-23 10:43:21.975416-08 benchmark f L4 \N t \N \N \N {} +4392 1440 2025-03-01 22:48:12.01349-08 2025-03-01 23:58:44.155125-08 test f T4 \N f \N \N \N {} +4877 1500 2025-03-03 10:04:01.91414-08 2025-03-03 08:30:53.107105-08 test f A100 \N t \N \N \N {} +4394 1440 2025-03-01 22:21:22.693848-08 2025-03-01 22:51:41.663873-08 test t H100 \N f \N \N \N {} +4395 1441 2025-03-01 23:58:46.89894-08 2025-03-01 23:53:44.591271-08 test f H100 \N f \N \N \N {} +4396 1441 2025-03-01 22:28:23.815345-08 2025-03-01 23:22:03.532582-08 test t L4 \N f \N \N \N {} +4397 1441 2025-03-01 23:53:44.616031-08 2025-03-01 23:38:33.596759-08 test t A100 \N f \N \N \N {} +4884 1505 2025-03-03 12:56:34.606368-08 2025-03-03 12:53:18.783025-08 test t A100 \N f \N \N \N {} +4399 1441 2025-03-01 23:03:04.456378-08 2025-03-02 00:06:04.885856-08 test f T4 \N f \N \N \N {} +4400 1441 2025-03-01 23:24:40.888023-08 2025-03-01 22:59:35.615921-08 test t H100 \N f \N \N \N {} +4401 1441 2025-03-02 00:02:25.303815-08 2025-03-01 22:57:40.957396-08 test t T4 \N f \N \N \N {} +4402 1441 2025-03-01 22:31:27.972284-08 2025-03-02 00:14:26.267738-08 test f L4 \N f \N \N \N {} +5107 1565 2025-03-04 18:00:00.576218-08 2025-03-04 18:32:07.019701-08 test f A100 \N t \N \N \N {} +4411 1443 2025-03-01 23:33:39.745676-08 2025-03-02 00:11:02.528027-08 test f L4 \N t \N \N \N {} +4412 1443 2025-03-02 01:02:06.237344-08 2025-03-01 23:53:38.020406-08 benchmark f L4 \N t \N \N \N {} +4413 1443 2025-03-02 00:45:38.730106-08 2025-03-02 00:36:30.242811-08 leaderboard f L4 0.009132959666666666 t \N \N \N {} +4418 1443 2025-03-01 23:15:31.653046-08 2025-03-02 00:20:24.722124-08 benchmark t A100 \N t \N \N \N {} +4419 1443 2025-03-01 23:11:05.373713-08 2025-03-02 00:24:01.197231-08 leaderboard t A100 0.002639421 t \N \N \N {} +4420 1443 2025-03-02 00:51:28.235424-08 2025-03-01 23:57:03.643851-08 test f A100 \N t \N \N \N {} +4421 1443 2025-03-02 00:14:40.806085-08 2025-03-02 00:04:05.289386-08 benchmark f A100 \N t \N \N \N {} +4422 1443 2025-03-01 23:14:33.6022-08 2025-03-02 00:33:33.808058-08 leaderboard f A100 0.0022384285652173913 t \N \N \N {} +4426 1443 2025-03-02 00:44:05.280092-08 2025-03-02 00:15:14.019635-08 test t H100 \N t \N \N \N {} +4427 1443 2025-03-01 23:59:15.284401-08 2025-03-02 00:58:48.422971-08 benchmark t H100 \N t \N \N \N {} +4428 1443 2025-03-02 00:11:17.701257-08 2025-03-01 23:26:04.825333-08 leaderboard t H100 0.00150569175 t \N \N \N {} +4429 1443 2025-03-01 23:25:46.014191-08 2025-03-02 00:05:37.324677-08 test f T4 \N t \N \N \N {} +2776 965 2025-02-26 21:06:31.43475-08 2025-02-26 19:53:09.101962-08 test t A100 \N t \N \N \N {} +4482 1445 2025-03-01 23:50:49.419996-08 2025-03-02 00:57:24.575893-08 leaderboard t L4 0.009311355333333333 t \N \N \N {} +4483 1446 2025-03-01 23:44:24.497614-08 2025-03-02 00:35:56.595882-08 test f A100 \N t \N \N \N {} +4484 1447 2025-03-01 23:27:17.550566-08 2025-03-02 01:12:31.419414-08 test f H100 \N t \N \N \N {} +4485 1447 2025-03-02 00:26:50.566555-08 2025-03-02 00:25:59.636108-08 benchmark f H100 \N f \N \N \N {} +4487 1447 2025-03-01 23:39:57.57287-08 2025-03-02 00:18:06.18322-08 benchmark t A100 \N f \N \N \N {} +2045 697 2025-02-25 13:02:11.887058-08 2025-02-25 13:35:15.729017-08 test f T4 \N f \N \N \N {} +2047 698 2025-02-25 14:51:14.379308-08 2025-02-25 14:49:23.452588-08 test f T4 \N t \N \N \N {} +2791 974 2025-02-26 20:28:12.007924-08 2025-02-26 21:26:15.641402-08 leaderboard t A100 0.003154194 t \N \N \N {} +3509 1201 2025-02-28 11:18:27.367438-08 2025-02-28 11:48:52.303198-08 test f A100 \N t \N \N \N {} +4498 1447 2025-03-01 23:16:26.107227-08 2025-03-02 00:47:04.910932-08 test t L4 \N t \N \N \N {} +4499 1447 2025-03-02 00:01:54.884825-08 2025-03-01 23:38:49.704891-08 benchmark t L4 \N f \N \N \N {} +4891 1507 2025-03-03 12:40:00.041984-08 2025-03-03 13:14:35.944127-08 test t A100 \N t \N \N \N {} +4504 1448 2025-03-01 23:24:02.182135-08 2025-03-01 23:40:44.693368-08 benchmark t A100 \N t \N \N \N {} +4505 1448 2025-03-02 00:40:33.995417-08 2025-03-02 00:56:55.060033-08 leaderboard t A100 0.00524246372 t \N \N \N {} +4506 1449 2025-03-02 00:45:08.313002-08 2025-03-02 00:16:16.737716-08 test t H100 \N t \N \N \N {} +4507 1449 2025-03-01 23:29:42.540346-08 2025-03-01 23:39:28.57237-08 benchmark t H100 \N t \N \N \N {} +4508 1449 2025-03-02 01:19:24.4957-08 2025-03-01 23:51:56.634988-08 leaderboard t H100 0.0033840856666666665 t \N \N \N {} +4509 1449 2025-03-02 00:12:06.024757-08 2025-03-02 01:10:54.396657-08 test f H100 \N t \N \N \N {} +4510 1449 2025-03-02 01:18:17.542284-08 2025-03-02 00:19:32.525502-08 benchmark f H100 \N t \N \N \N {} +4511 1449 2025-03-01 23:57:24.368485-08 2025-03-02 00:13:37.996217-08 leaderboard f H100 0.00341148875 t \N \N \N {} +4512 1450 2025-03-02 00:45:11.616534-08 2025-03-02 00:48:49.228002-08 test t H100 \N t \N \N \N {} +4513 1450 2025-03-01 23:40:22.279655-08 2025-03-02 00:00:22.363114-08 benchmark t H100 \N t \N \N \N {} +4899 1508 2025-03-03 13:37:04.374969-08 2025-03-03 13:09:34.935444-08 leaderboard t H100 0.007641404666666667 t \N \N \N {} +4517 1450 2025-03-02 00:03:53.293716-08 2025-03-02 00:25:33.705236-08 leaderboard t L4 0.009105309 t \N \N \N {} +4518 1450 2025-03-02 00:49:38.190529-08 2025-03-02 00:22:20.570554-08 test f A100 \N t \N \N \N {} +4519 1450 2025-03-02 00:11:10.810461-08 2025-03-02 01:03:13.770329-08 benchmark f A100 \N t \N \N \N {} +4520 1450 2025-03-02 01:08:07.36635-08 2025-03-02 00:17:23.477428-08 leaderboard f A100 0.0018727816666666668 t \N \N \N {} +4524 1450 2025-03-02 01:17:14.573469-08 2025-03-02 00:17:34.147369-08 test t T4 \N t \N \N \N {} +4525 1450 2025-03-02 00:43:27.056954-08 2025-03-01 23:43:04.43667-08 benchmark t T4 \N t \N \N \N {} +4526 1450 2025-03-02 00:53:05.519328-08 2025-03-02 00:06:06.503136-08 leaderboard t T4 0.009121147333333334 t \N \N \N {} +4527 1450 2025-03-01 23:54:26.363976-08 2025-03-02 01:19:17.019076-08 test t A100 \N t \N \N \N {} +4528 1450 2025-03-02 01:18:02.912247-08 2025-03-01 23:49:49.929737-08 benchmark t A100 \N t \N \N \N {} +4529 1450 2025-03-01 23:35:24.197844-08 2025-03-02 01:11:23.44255-08 leaderboard t A100 0.0017849657777777778 t \N \N \N {} +4530 1450 2025-03-02 00:54:36.280862-08 2025-03-01 23:35:47.462129-08 test f T4 \N t \N \N \N {} +4531 1450 2025-03-02 01:00:08.84832-08 2025-03-02 00:06:08.090762-08 benchmark f T4 \N t \N \N \N {} +4532 1450 2025-03-01 23:55:16.426583-08 2025-03-02 00:04:54.110158-08 leaderboard f T4 0.009106582333333333 t \N \N \N {} +4533 1450 2025-03-02 01:11:28.694794-08 2025-03-02 00:06:59.909067-08 test f L4 \N t \N \N \N {} +4534 1450 2025-03-02 00:11:02.404367-08 2025-03-02 01:17:42.734039-08 benchmark f L4 \N t \N \N \N {} +6713 2081 2025-03-15 02:46:39.538367-07 2025-03-15 03:50:32.068412-07 test t A100 \N t \N \N \N {} +4537 1451 2025-03-02 00:52:04.139941-08 2025-03-02 00:11:49.100971-08 benchmark t H100 \N t \N \N \N {} +4538 1451 2025-03-02 00:51:58.06123-08 2025-03-02 00:02:39.242368-08 leaderboard t H100 0.001079664 t \N \N \N {} +4539 1451 2025-03-02 00:06:02.121158-08 2025-03-02 00:00:09.264632-08 test f H100 \N t \N \N \N {} +4540 1451 2025-03-02 00:23:19.306421-08 2025-03-02 00:51:05.732006-08 benchmark f H100 \N t \N \N \N {} +4541 1451 2025-03-01 23:45:53.396285-08 2025-03-02 01:03:28.077189-08 leaderboard f H100 0.0010786196666666668 t \N \N \N {} +4626 1459 2025-03-02 06:58:07.444981-08 2025-03-02 06:44:02.224251-08 test t A100 \N t \N \N \N {} +4627 1459 2025-03-02 06:21:17.548115-08 2025-03-02 06:15:21.551657-08 benchmark t A100 \N t \N \N \N {} +4628 1459 2025-03-02 07:19:36.161701-08 2025-03-02 06:24:21.742016-08 leaderboard t A100 0.0030925023333333336 t \N \N \N {} +4629 1459 2025-03-02 05:52:24.785964-08 2025-03-02 05:52:16.34119-08 test f A100 \N t \N \N \N {} +4630 1459 2025-03-02 06:37:13.167125-08 2025-03-02 05:59:45.399577-08 benchmark f A100 \N t \N \N \N {} +4631 1459 2025-03-02 05:50:19.68331-08 2025-03-02 05:29:11.389842-08 leaderboard f A100 0.003070814 t \N \N \N {} +4635 1460 2025-03-02 07:14:27.918333-08 2025-03-02 05:44:05.344296-08 test f A100 \N t \N \N \N {} +4636 1460 2025-03-02 06:43:52.831925-08 2025-03-02 07:13:44.23175-08 benchmark f A100 \N t \N \N \N {} +4637 1460 2025-03-02 06:16:46.167909-08 2025-03-02 06:00:38.936259-08 leaderboard f A100 0.0030692296666666665 t \N \N \N {} +4638 1461 2025-03-02 06:03:46.169582-08 2025-03-02 06:31:43.401529-08 test t A100 \N t \N \N \N {} +4639 1461 2025-03-02 06:35:00.966361-08 2025-03-02 05:55:09.655918-08 benchmark t A100 \N t \N \N \N {} +4640 1461 2025-03-02 05:53:37.416513-08 2025-03-02 06:54:43.324146-08 leaderboard t A100 0.003078079 t \N \N \N {} +4644 1462 2025-03-02 06:49:33.296679-08 2025-03-02 06:48:19.278536-08 test f A100 \N t \N \N \N {} +4645 1462 2025-03-02 05:51:40.609401-08 2025-03-02 05:54:32.467256-08 benchmark f A100 \N t \N \N \N {} +4646 1462 2025-03-02 07:10:09.012521-08 2025-03-02 06:39:52.753547-08 leaderboard f A100 0.0030936686666666667 t \N \N \N {} +4647 1462 2025-03-02 06:21:07.074149-08 2025-03-02 06:19:05.973852-08 test t A100 \N t \N \N \N {} +4648 1462 2025-03-02 06:22:56.946678-08 2025-03-02 06:32:18.964173-08 benchmark t A100 \N t \N \N \N {} +4649 1462 2025-03-02 06:57:59.888395-08 2025-03-02 06:30:20.934248-08 leaderboard t A100 0.0030776453333333335 t \N \N \N {} +4652 1463 2025-03-02 06:00:11.005396-08 2025-03-02 05:45:35.797748-08 leaderboard t A100 0.0030704546666666666 t \N \N \N {} +4654 1463 2025-03-02 07:23:29.318291-08 2025-03-02 06:15:05.549905-08 benchmark f A100 \N t \N \N \N {} +158 23 2025-02-23 10:46:27.961353-08 2025-02-23 10:54:07.890706-08 leaderboard f L4 0.043783646333333336 t \N \N \N {} +159 23 2025-02-23 11:24:25.855339-08 2025-02-23 11:24:36.858525-08 test t L4 \N t \N \N \N {} +160 23 2025-02-23 11:23:10.687318-08 2025-02-23 10:51:27.096721-08 benchmark t L4 \N t \N \N \N {} +161 23 2025-02-23 10:17:17.903963-08 2025-02-23 11:20:35.401154-08 leaderboard t L4 0.04378879433333334 t \N \N \N {} +162 24 2025-02-23 11:04:17.242792-08 2025-02-23 12:05:18.328255-08 test f T4 \N f \N \N \N {} +163 25 2025-02-23 11:29:26.781421-08 2025-02-23 11:38:33.754711-08 test f H100 \N t \N \N \N {} +164 25 2025-02-23 11:46:38.661863-08 2025-02-23 11:39:19.931063-08 test f A100 \N t \N \N \N {} +165 25 2025-02-23 11:44:46.673883-08 2025-02-23 12:23:21.640415-08 test f T4 \N t \N \N \N {} +4655 1463 2025-03-02 05:44:44.87667-08 2025-03-02 06:39:01.814288-08 leaderboard f A100 0.003075862 t \N \N \N {} +4656 1464 2025-03-02 06:24:06.602618-08 2025-03-02 06:25:40.742865-08 test t A100 \N t \N \N \N {} +4657 1464 2025-03-02 06:25:44.512665-08 2025-03-02 06:07:00.018787-08 benchmark t A100 \N t \N \N \N {} +4658 1464 2025-03-02 07:24:38.102391-08 2025-03-02 07:03:30.28672-08 leaderboard t A100 0.0030908703333333335 t \N \N \N {} +4662 1465 2025-03-02 06:45:59.862943-08 2025-03-02 06:27:15.188637-08 test f A100 \N t \N \N \N {} +4663 1465 2025-03-02 06:12:13.396238-08 2025-03-02 06:13:12.261244-08 benchmark f A100 \N t \N \N \N {} +4664 1465 2025-03-02 05:44:57.969997-08 2025-03-02 07:15:25.843464-08 leaderboard f A100 0.0031012606666666665 t \N \N \N {} +4665 1465 2025-03-02 07:22:35.937837-08 2025-03-02 07:15:34.451037-08 test t A100 \N t \N \N \N {} +4666 1465 2025-03-02 05:41:53.096577-08 2025-03-02 06:59:11.932007-08 benchmark t A100 \N t \N \N \N {} +4667 1465 2025-03-02 07:22:50.191152-08 2025-03-02 06:20:27.38764-08 leaderboard t A100 0.0031609141666666665 t \N \N \N {} +4671 1466 2025-03-02 06:48:49.912925-08 2025-03-02 06:36:05.088369-08 test f A100 \N t \N \N \N {} +4672 1466 2025-03-02 07:00:40.126712-08 2025-03-02 07:05:41.314194-08 benchmark f A100 \N t \N \N \N {} +4673 1466 2025-03-02 07:03:50.192296-08 2025-03-02 06:21:41.476403-08 leaderboard f A100 0.0030736896666666664 t \N \N \N {} +4674 1467 2025-03-02 07:34:10.787696-08 2025-03-02 07:29:26.411023-08 test t A100 \N t \N \N \N {} +4675 1467 2025-03-02 06:56:49.136222-08 2025-03-02 06:41:21.315023-08 benchmark t A100 \N t \N \N \N {} +4807 1487 2025-03-02 12:30:33.450976-08 2025-03-02 13:35:13.770124-08 benchmark f T4 \N f \N \N \N {} +4693 1470 2025-03-02 06:12:06.283994-08 2025-03-02 06:51:42.391905-08 benchmark t A100 \N t \N \N \N {} +4694 1470 2025-03-02 06:33:53.917995-08 2025-03-02 07:33:01.914128-08 leaderboard t A100 0.004008321333333334 t \N \N \N {} +4698 1471 2025-03-02 07:29:03.787601-08 2025-03-02 06:41:20.283599-08 test f A100 \N t \N \N \N {} +4699 1471 2025-03-02 05:48:12.197045-08 2025-03-02 06:12:43.42567-08 benchmark f A100 \N t \N \N \N {} +4700 1471 2025-03-02 06:56:34.879652-08 2025-03-02 07:44:48.551731-08 leaderboard f A100 0.0031009833333333335 t \N \N \N {} +4701 1471 2025-03-02 06:30:38.128029-08 2025-03-02 07:27:12.350551-08 test t A100 \N t \N \N \N {} +4702 1471 2025-03-02 07:44:43.71905-08 2025-03-02 07:20:57.056431-08 benchmark t A100 \N t \N \N \N {} +4703 1471 2025-03-02 05:52:40.876763-08 2025-03-02 06:18:11.15908-08 leaderboard t A100 0.003077995 t \N \N \N {} +4708 1476 2025-03-02 07:04:38.833526-08 2025-03-02 06:22:07.053844-08 test t A100 \N t \N \N \N {} +4711 1476 2025-03-02 06:32:53.010168-08 2025-03-02 08:13:38.448364-08 test f A100 \N t \N \N \N {} +4712 1476 2025-03-02 07:07:03.075398-08 2025-03-02 06:39:50.920146-08 benchmark f A100 \N t \N \N \N {} +4713 1476 2025-03-02 07:29:16.863809-08 2025-03-02 08:09:32.549873-08 leaderboard f A100 0.003081995 t \N \N \N {} +4714 1477 2025-03-02 08:12:43.521171-08 2025-03-02 07:24:21.691154-08 test t A100 \N t \N \N \N {} +4715 1477 2025-03-02 08:12:18.641473-08 2025-03-02 08:16:06.720104-08 benchmark t A100 \N t \N \N \N {} +4716 1477 2025-03-02 07:47:17.510849-08 2025-03-02 06:38:36.379617-08 leaderboard t A100 0.0030823416666666665 t \N \N \N {} +4720 1478 2025-03-02 06:43:15.887395-08 2025-03-02 07:01:09.324629-08 test f A100 \N t \N \N \N {} +4721 1478 2025-03-02 06:19:24.519466-08 2025-03-02 07:02:12.8161-08 benchmark f A100 \N t \N \N \N {} +4722 1478 2025-03-02 07:24:47.923184-08 2025-03-02 06:22:30.821228-08 leaderboard f A100 0.0030851883333333336 t \N \N \N {} +4723 1478 2025-03-02 06:21:50.565071-08 2025-03-02 06:38:29.276371-08 test t A100 \N t \N \N \N {} +4724 1478 2025-03-02 07:55:40.137891-08 2025-03-02 08:07:32.643622-08 benchmark t A100 \N t \N \N \N {} +4725 1478 2025-03-02 08:04:19.082753-08 2025-03-02 06:39:54.506806-08 leaderboard t A100 0.0030785193333333336 t \N \N \N {} +4728 1479 2025-03-02 08:14:46.908587-08 2025-03-02 07:03:37.792816-08 leaderboard f A100 0.0030867813333333334 t \N \N \N {} +4729 1479 2025-03-02 06:45:56.767886-08 2025-03-02 08:16:10.271116-08 test t A100 \N t \N \N \N {} +4730 1479 2025-03-02 07:53:43.730007-08 2025-03-02 06:49:41.785807-08 benchmark t A100 \N t \N \N \N {} +4731 1479 2025-03-02 07:11:52.419771-08 2025-03-02 07:23:15.061576-08 leaderboard t A100 0.0031140623333333335 t \N \N \N {} +4732 1480 2025-03-02 07:09:43.554747-08 2025-03-02 06:58:05.009739-08 test t A100 \N t \N \N \N {} +4733 1480 2025-03-02 07:52:32.541906-08 2025-03-02 07:38:29.663474-08 benchmark t A100 \N t \N \N \N {} +4734 1480 2025-03-02 07:44:10.008433-08 2025-03-02 08:17:23.134924-08 leaderboard t A100 0.0030920546666666666 t \N \N \N {} +4738 1481 2025-03-02 07:04:34.701596-08 2025-03-02 07:21:13.788761-08 test f A100 \N t \N \N \N {} +4739 1481 2025-03-02 06:49:36.00208-08 2025-03-02 07:31:10.469625-08 benchmark f A100 \N t \N \N \N {} +4740 1481 2025-03-02 07:47:10.568157-08 2025-03-02 08:09:45.89037-08 leaderboard f A100 0.003085828 t \N \N \N {} +4741 1481 2025-03-02 06:48:53.469551-08 2025-03-02 07:03:55.679567-08 test t A100 \N t \N \N \N {} +4742 1481 2025-03-02 06:31:13.795781-08 2025-03-02 06:49:28.737931-08 benchmark t A100 \N t \N \N \N {} +4743 1481 2025-03-02 08:15:11.812141-08 2025-03-02 06:37:00.374019-08 leaderboard t A100 0.0030922536666666664 t \N \N \N {} +4744 1482 2025-03-02 07:43:03.723058-08 2025-03-02 07:35:15.071326-08 test f T4 \N f \N \N \N {} +4745 1483 2025-03-02 07:31:56.372241-08 2025-03-02 08:05:39.164579-08 test f T4 \N t \N \N \N {} +4746 1484 2025-03-02 08:35:02.123221-08 2025-03-02 07:56:53.596064-08 test f T4 \N t \N \N \N {} +4747 1484 2025-03-02 07:06:16.362073-08 2025-03-02 07:58:54.271757-08 benchmark f T4 \N t \N \N \N {} +4748 1484 2025-03-02 07:48:44.592479-08 2025-03-02 06:54:14.32109-08 leaderboard f T4 0.07661455133333332 t \N \N \N {} +4749 1484 2025-03-02 08:43:32.528981-08 2025-03-02 07:50:46.63591-08 test t T4 \N t \N \N \N {} +4750 1484 2025-03-02 07:57:18.393313-08 2025-03-02 07:47:01.612778-08 benchmark t T4 \N t \N \N \N {} +4751 1484 2025-03-02 07:42:07.868143-08 2025-03-02 08:02:04.953244-08 leaderboard t T4 0.07684545833333332 t \N \N \N {} +6735 2086 2025-03-15 04:33:46.257775-07 2025-03-15 04:41:06.722991-07 test t A100 \N t \N \N \N {} +4755 1485 2025-03-02 11:37:55.155684-08 2025-03-02 12:30:52.129299-08 test t A100 \N t \N \N \N {} +4756 1485 2025-03-02 11:54:47.977319-08 2025-03-02 11:49:51.955954-08 benchmark t A100 \N t \N \N \N {} +4757 1485 2025-03-02 12:35:43.476034-08 2025-03-02 13:11:14.518517-08 leaderboard t A100 0.0017045449166666667 t \N \N \N {} +4758 1485 2025-03-02 12:19:44.247676-08 2025-03-02 12:41:13.158847-08 test f H100 \N t \N \N \N {} +4762 1485 2025-03-02 13:26:36.984272-08 2025-03-02 13:09:50.663429-08 benchmark t H100 \N t \N \N \N {} +4763 1485 2025-03-02 11:57:05.793115-08 2025-03-02 12:51:08.493618-08 leaderboard t H100 0.0010300953333333333 t \N \N \N {} +4765 1485 2025-03-02 13:18:29.547704-08 2025-03-02 11:53:12.069821-08 benchmark f T4 \N t \N \N \N {} +4766 1485 2025-03-02 12:34:18.620178-08 2025-03-02 12:01:46.474428-08 leaderboard f T4 0.013093859 t \N \N \N {} +4770 1485 2025-03-02 11:28:06.060656-08 2025-03-02 12:33:39.575643-08 test t L4 \N t \N \N \N {} +4771 1485 2025-03-02 12:38:32.300929-08 2025-03-02 12:35:21.086611-08 benchmark t L4 \N t \N \N \N {} +4772 1485 2025-03-02 11:38:38.993577-08 2025-03-02 12:03:06.955576-08 leaderboard t L4 0.009067796666666666 t \N \N \N {} +4782 1486 2025-03-02 13:23:24.636755-08 2025-03-02 13:02:48.120735-08 leaderboard t L4 0.01330954 t \N \N \N {} +4783 1486 2025-03-02 11:52:32.464719-08 2025-03-02 11:51:42.704104-08 test t T4 \N t \N \N \N {} +4784 1486 2025-03-02 12:54:34.061003-08 2025-03-02 13:23:05.121934-08 benchmark t T4 \N f \N \N \N {} +4785 1486 2025-03-02 13:03:39.801457-08 2025-03-02 13:50:21.033648-08 test t H100 \N t \N \N \N {} +4786 1486 2025-03-02 13:02:54.20138-08 2025-03-02 13:03:49.524039-08 benchmark t H100 \N t \N \N \N {} +4787 1486 2025-03-02 13:37:42.944284-08 2025-03-02 13:04:38.530285-08 leaderboard t H100 0.014144686666666666 t \N \N \N {} +166 25 2025-02-23 11:29:44.547374-08 2025-02-23 11:45:47.371586-08 test f L4 \N t \N \N \N {} +6741 2087 2025-03-15 03:27:43.798435-07 2025-03-15 04:38:01.97182-07 test t A100 \N t \N \N \N {} +4789 1486 2025-03-02 13:07:40.863559-08 2025-03-02 12:56:43.445817-08 benchmark f T4 \N f \N \N \N {} +4790 1486 2025-03-02 11:54:48.604048-08 2025-03-02 13:06:33.970638-08 test f H100 \N t \N \N \N {} +4791 1486 2025-03-02 13:12:20.061089-08 2025-03-02 13:06:35.664868-08 benchmark f H100 \N t \N \N \N {} +4792 1486 2025-03-02 12:46:36.012816-08 2025-03-02 13:51:06.73015-08 leaderboard f H100 0.014137576333333334 t \N \N \N {} +4793 1486 2025-03-02 11:56:43.712458-08 2025-03-02 12:12:52.126902-08 test f L4 \N t \N \N \N {} +4794 1486 2025-03-02 12:57:19.285267-08 2025-03-02 13:40:00.434892-08 benchmark f L4 \N t \N \N \N {} +6744 2087 2025-03-15 03:03:55.336213-07 2025-03-15 04:21:27.642744-07 test f A100 \N t \N \N \N {} +4796 1487 2025-03-02 13:56:03.952211-08 2025-03-02 14:17:06.867401-08 test t H100 \N t \N \N \N {} +2058 700 2025-02-25 13:35:50.559263-08 2025-02-25 14:57:49.798923-08 test t A100 \N f \N \N \N {} +4944 1522 2025-03-03 14:24:33.347813-08 2025-03-03 14:26:01.708268-08 test t H100 \N t \N \N \N {} +4946 1522 2025-03-03 15:27:11.77608-08 2025-03-03 14:42:48.21655-08 leaderboard t H100 0.00110717921 t \N \N \N {} +4948 1524 2025-03-03 14:06:57.97848-08 2025-03-03 15:53:35.131501-08 test f A100 \N t \N \N \N {} +4949 1524 2025-03-03 14:35:09.882957-08 2025-03-03 14:20:11.280882-08 benchmark f A100 \N t \N \N \N {} +6769 2097 2025-03-15 04:27:49.490385-07 2025-03-15 03:27:02.099035-07 test f A100 \N t \N \N \N {} +4957 1524 2025-03-03 14:23:59.058716-08 2025-03-03 15:25:20.637415-08 test f H100 \N t \N \N \N {} +4958 1524 2025-03-03 14:03:34.405543-08 2025-03-03 14:18:35.717577-08 benchmark f H100 \N t \N \N \N {} +4959 1524 2025-03-03 14:09:29.703995-08 2025-03-03 15:30:42.011483-08 leaderboard f H100 0.00140554825 t \N \N \N {} +5255 1613 2025-03-06 07:12:38.56695-08 2025-03-06 08:33:42.642582-08 leaderboard f L4 0.01713484 t \N \N \N {} +4981 1533 2025-03-03 23:32:30.171269-08 2025-03-03 22:37:21.705286-08 benchmark f L4 \N f \N \N \N {} +5018 1541 2025-03-04 09:09:22.632315-08 2025-03-04 09:22:15.724438-08 test f T4 \N f \N \N \N {} +4982 1533 2025-03-03 22:41:27.291617-08 2025-03-03 22:26:36.666409-08 test t L4 \N t \N \N \N {} +4983 1533 2025-03-03 22:10:42.942356-08 2025-03-03 23:52:58.998599-08 benchmark t L4 \N f \N \N \N {} +4984 1534 2025-03-03 22:26:12.301319-08 2025-03-03 22:26:15.332411-08 test t A100 \N t \N \N \N {} +4985 1534 2025-03-03 22:36:43.072753-08 2025-03-03 23:30:03.866041-08 benchmark t A100 \N t \N \N \N {} +4986 1534 2025-03-03 22:14:27.365325-08 2025-03-03 23:53:50.401219-08 leaderboard t A100 0.001491775 t \N \N \N {} +4997 1534 2025-03-03 23:13:33.43142-08 2025-03-03 22:46:25.592457-08 benchmark t H100 \N t \N \N \N {} +4998 1534 2025-03-03 23:34:45.888365-08 2025-03-03 22:22:19.090656-08 leaderboard t H100 0.001050694 t \N \N \N {} +4999 1534 2025-03-03 23:33:14.778803-08 2025-03-03 23:11:24.883939-08 test t T4 \N t \N \N \N {} +5004 1534 2025-03-03 22:52:44.48133-08 2025-03-03 22:20:58.315866-08 leaderboard f T4 0.013578895 t \N \N \N {} +5005 1534 2025-03-03 22:45:45.446291-08 2025-03-03 23:14:25.105517-08 test t L4 \N t \N \N \N {} +5006 1534 2025-03-03 23:46:05.132215-08 2025-03-03 23:00:38.449509-08 benchmark t L4 \N t \N \N \N {} +5007 1534 2025-03-03 23:25:45.105749-08 2025-03-03 22:52:16.954981-08 leaderboard t L4 0.00906405 t \N \N \N {} +5022 1545 2025-03-04 14:58:30.960919-08 2025-03-04 14:52:33.273284-08 test t A100 \N t \N \N \N {} +5023 1545 2025-03-04 14:51:10.938222-08 2025-03-04 15:28:30.503521-08 benchmark t A100 \N t \N \N \N {} +5074 1551 2025-03-04 16:47:22.476681-08 2025-03-04 17:10:56.60229-08 test f H100 \N t \N \N \N {} +5024 1545 2025-03-04 13:51:12.047062-08 2025-03-04 14:35:53.536936-08 leaderboard t A100 0.00004836930555555555 t \N \N \N {} +5025 1545 2025-03-04 14:21:17.803519-08 2025-03-04 15:31:14.61315-08 test f A100 \N t \N \N \N {} +5026 1545 2025-03-04 13:52:48.051537-08 2025-03-04 15:31:03.104837-08 benchmark f A100 \N t \N \N \N {} +5028 1545 2025-03-04 15:34:09.122729-08 2025-03-04 14:42:01.686509-08 test t H100 \N t \N \N \N {} +5034 1545 2025-03-04 14:29:25.07288-08 2025-03-04 14:33:25.56239-08 test t T4 \N t \N \N \N {} +5029 1545 2025-03-04 15:35:11.114668-08 2025-03-04 14:50:01.633693-08 benchmark t H100 \N t \N \N \N {} +5030 1545 2025-03-04 14:15:23.548104-08 2025-03-04 14:34:05.166616-08 leaderboard t H100 0.00004719919 t \N \N \N {} +5031 1545 2025-03-04 13:53:27.796904-08 2025-03-04 13:50:34.783512-08 test f H100 \N t \N \N \N {} +5157 1571 2025-03-04 23:08:49.976878-08 2025-03-04 22:42:28.36004-08 benchmark f A100 \N t \N \N \N {} +5158 1571 2025-03-04 22:35:15.21-08 2025-03-04 22:34:20.752683-08 leaderboard f A100 0.0014883393333333334 t \N \N \N {} +5159 1571 2025-03-04 23:12:18.343342-08 2025-03-04 23:12:10.298764-08 test f H100 \N t \N \N \N {} +5160 1571 2025-03-04 22:52:31.849847-08 2025-03-04 22:15:55.305164-08 benchmark f H100 \N t \N \N \N {} +5161 1571 2025-03-04 21:54:25.033079-08 2025-03-04 23:19:13.810944-08 leaderboard f H100 0.0010518306666666667 t \N \N \N {} +5162 1571 2025-03-04 23:26:27.704888-08 2025-03-04 21:45:35.062949-08 test t H100 \N t \N \N \N {} +2066 702 2025-02-25 14:01:35.67269-08 2025-02-25 14:13:03.658618-08 test f H100 \N t \N \N \N {} +2422 827 2025-02-26 13:49:48.149073-08 2025-02-26 13:42:33.751386-08 benchmark f H100 \N f \N \N \N {} +5622 1716 2025-03-09 10:46:42.607602-07 2025-03-09 09:07:56.188107-07 benchmark t T4 \N f \N \N \N {} +5623 1716 2025-03-09 09:31:22.907917-07 2025-03-09 09:56:01.467636-07 test f T4 \N t \N \N \N {} +5624 1716 2025-03-09 10:16:02.064243-07 2025-03-09 10:32:48.331759-07 benchmark f T4 \N f \N \N \N {} +5625 1717 2025-03-09 10:13:52.307292-07 2025-03-09 09:42:55.90134-07 test f T4 \N t \N \N \N {} +5626 1717 2025-03-09 10:41:28.462205-07 2025-03-09 10:30:33.37398-07 benchmark f T4 \N t \N \N \N {} +5627 1717 2025-03-09 09:58:14.43763-07 2025-03-09 09:29:59.555675-07 leaderboard f T4 0.01689148266666667 t \N \N \N {} +5628 1717 2025-03-09 08:54:15.401227-07 2025-03-09 09:01:20.665735-07 test t T4 \N t \N \N \N {} +5633 1718 2025-03-09 10:16:16.764055-07 2025-03-09 08:57:06.957134-07 leaderboard t T4 0.02135399933333333 t \N \N \N {} +5660 1723 2025-03-09 10:03:33.814323-07 2025-03-09 10:04:32.576403-07 leaderboard f T4 0.016605525666666666 t \N \N \N {} +5661 1723 2025-03-09 10:47:11.568642-07 2025-03-09 09:35:57.762614-07 test t T4 \N t \N \N \N {} +5662 1723 2025-03-09 10:00:24.122137-07 2025-03-09 09:14:18.616382-07 benchmark t T4 \N t \N \N \N {} +5683 1726 2025-03-09 09:19:42.062291-07 2025-03-09 09:47:18.455329-07 benchmark f L4 \N t \N \N \N {} +5684 1726 2025-03-09 09:35:37.887021-07 2025-03-09 10:36:27.46401-07 leaderboard f L4 0.017259138 t \N \N \N {} +5685 1727 2025-03-09 10:09:36.558925-07 2025-03-09 09:36:26.692942-07 test f L4 \N t \N \N \N {} +5692 1728 2025-03-09 09:54:55.125187-07 2025-03-09 09:30:43.343027-07 benchmark f A100 \N t \N \N \N {} +5693 1728 2025-03-09 10:44:16.533423-07 2025-03-09 10:12:57.412213-07 leaderboard f A100 0.0026361306666666663 t \N \N \N {} +5694 1729 2025-03-09 10:18:27.24795-07 2025-03-09 11:00:29.021145-07 test t H100 \N t \N \N \N {} +5780 1750 2025-03-09 22:31:41.177478-07 2025-03-09 22:32:40.296083-07 leaderboard t H100 0.0015017373333333333 t \N \N \N {} +5783 1751 2025-03-09 22:33:46.42896-07 2025-03-09 22:29:10.948791-07 leaderboard t L4 0.017186112666666666 t \N \N \N {} +5789 1752 2025-03-09 22:09:13.460918-07 2025-03-09 22:30:06.841849-07 leaderboard f T4 0.016641214666666668 t \N \N \N {} +5791 1752 2025-03-09 21:25:43.431271-07 2025-03-09 22:03:39.205218-07 benchmark t T4 \N t \N \N \N {} +5792 1752 2025-03-09 22:06:34.639731-07 2025-03-09 22:03:04.943777-07 leaderboard t T4 0.016605053666666664 t \N \N \N {} +5890 1783 2025-03-10 11:42:15.635873-07 2025-03-10 12:18:31.181049-07 test f T4 \N f \N \N \N {} +5891 1784 2025-03-10 12:42:17.820815-07 2025-03-10 12:42:44.878659-07 test f T4 \N f \N \N \N {} +5892 1784 2025-03-10 13:03:34.982484-07 2025-03-10 11:10:33.398792-07 test t T4 \N f \N \N \N {} +6112 1851 2025-03-11 02:01:12.667219-07 2025-03-11 01:10:58.721364-07 test f A100 \N f \N \N \N {} +5899 1788 2025-03-10 12:51:42.965452-07 2025-03-10 11:53:10.996306-07 test t T4 \N f \N \N \N {} +5900 1788 2025-03-10 11:22:13.20187-07 2025-03-10 11:32:35.572673-07 test f T4 \N f \N \N \N {} +5901 1789 2025-03-10 13:02:58.074539-07 2025-03-10 13:17:21.023123-07 test f T4 \N f \N \N \N {} +5902 1789 2025-03-10 12:01:52.164022-07 2025-03-10 11:27:33.462883-07 test t T4 \N f \N \N \N {} +5903 1790 2025-03-10 11:26:13.302143-07 2025-03-10 12:50:52.591159-07 test t T4 \N f \N \N \N {} +5915 1796 2025-03-10 12:15:59.801508-07 2025-03-10 13:02:17.771902-07 test t T4 \N f \N \N \N {} +5916 1796 2025-03-10 12:57:44.974492-07 2025-03-10 12:53:59.035234-07 test f T4 \N f \N \N \N {} +5944 1809 2025-03-10 13:50:04.063532-07 2025-03-10 14:02:36.910964-07 test t A100 \N t \N \N \N {} +5917 1797 2025-03-10 12:47:21.898424-07 2025-03-10 12:35:53.990658-07 test f T4 \N f \N \N \N {} +5918 1797 2025-03-10 11:54:01.992015-07 2025-03-10 13:25:49.093367-07 test t T4 \N f \N \N \N {} +5919 1798 2025-03-10 12:02:54.007464-07 2025-03-10 12:00:35.023294-07 test f T4 \N f \N \N \N {} +5920 1798 2025-03-10 11:53:48.460819-07 2025-03-10 12:54:41.600572-07 test t T4 \N f \N \N \N {} +5921 1799 2025-03-10 12:01:32.892869-07 2025-03-10 12:01:20.064834-07 test f T4 \N f \N \N \N {} +5945 1809 2025-03-10 13:30:52.742-07 2025-03-10 13:35:00.53151-07 benchmark t A100 \N t \N \N \N {} +2067 702 2025-02-25 13:08:25.421143-08 2025-02-25 13:34:01.386083-08 benchmark f H100 \N t \N \N \N {} +2068 702 2025-02-25 13:42:25.465302-08 2025-02-25 13:31:29.0514-08 leaderboard f H100 0.001931559125 t \N \N \N {} +167 26 2025-02-23 12:21:33.030404-08 2025-02-23 11:41:55.949378-08 benchmark f A100 \N t \N \N \N {} +168 26 2025-02-23 11:54:48.8754-08 2025-02-23 11:19:18.094078-08 benchmark f H100 \N t \N \N \N {} +169 26 2025-02-23 11:53:04.673811-08 2025-02-23 11:52:00.921431-08 benchmark f T4 \N t \N \N \N {} +170 26 2025-02-23 10:45:00.538357-08 2025-02-23 11:33:31.309144-08 benchmark f L4 \N t \N \N \N {} +5969 1818 2025-03-10 13:34:50.62569-07 2025-03-10 13:50:19.381297-07 leaderboard f T4 0.007938620666666667 t \N \N \N {} +5988 1821 2025-03-10 14:19:10.235543-07 2025-03-10 13:00:17.011132-07 test t L4 \N t \N \N \N {} +5972 1818 2025-03-10 14:20:22.971921-07 2025-03-10 13:57:46.364461-07 leaderboard t T4 0.006741959333333333 t \N \N \N {} +5973 1819 2025-03-10 14:09:36.845256-07 2025-03-10 12:47:23.183159-07 test f A100 \N t \N \N \N {} +5974 1819 2025-03-10 13:27:31.914549-07 2025-03-10 13:38:50.356656-07 benchmark f A100 \N t \N \N \N {} +5975 1819 2025-03-10 13:22:24.682951-07 2025-03-10 12:52:47.777634-07 leaderboard f A100 0.00066186475 t \N \N \N {} +5976 1820 2025-03-10 13:05:57.916285-07 2025-03-10 14:10:04.245483-07 test f H100 \N t \N \N \N {} +5977 1820 2025-03-10 13:06:16.389801-07 2025-03-10 13:56:50.012127-07 benchmark f H100 \N t \N \N \N {} +5980 1820 2025-03-10 14:27:30.646317-07 2025-03-10 14:22:26.85464-07 benchmark t H100 \N t \N \N \N {} +5981 1820 2025-03-10 13:34:45.928055-07 2025-03-10 14:10:03.141068-07 leaderboard t H100 0.00025382670689655173 t \N \N \N {} +5982 1821 2025-03-10 13:18:05.58891-07 2025-03-10 12:57:07.627811-07 test f L4 \N t \N \N \N {} +5983 1821 2025-03-10 13:10:21.68803-07 2025-03-10 12:59:33.456792-07 benchmark f L4 \N t \N \N \N {} +5984 1821 2025-03-10 14:27:34.743682-07 2025-03-10 13:17:42.837991-07 leaderboard f L4 0.0023442443333333337 t \N \N \N {} +5985 1819 2025-03-10 14:00:17.259921-07 2025-03-10 13:54:42.382815-07 test t A100 \N t \N \N \N {} +5986 1819 2025-03-10 13:40:20.642276-07 2025-03-10 13:23:25.385771-07 benchmark t A100 \N t \N \N \N {} +5991 1822 2025-03-10 13:01:51.312859-07 2025-03-10 13:23:25.247008-07 test f A100 \N t \N \N \N {} +5992 1822 2025-03-10 13:52:54.966043-07 2025-03-10 13:40:58.589221-07 benchmark f A100 \N t \N \N \N {} +5993 1822 2025-03-10 13:20:20.347858-07 2025-03-10 13:01:28.199599-07 leaderboard f A100 0.011864730666666667 t \N \N \N {} +5994 1822 2025-03-10 14:40:44.674945-07 2025-03-10 13:28:53.37983-07 test t A100 \N t \N \N \N {} +5995 1822 2025-03-10 14:21:29.232684-07 2025-03-10 13:52:16.353634-07 benchmark t A100 \N t \N \N \N {} +5996 1822 2025-03-10 13:03:56.315257-07 2025-03-10 13:23:43.023609-07 leaderboard t A100 0.011399361 t \N \N \N {} +6003 1824 2025-03-10 14:26:38.384984-07 2025-03-10 14:09:13.727909-07 test f L4 \N t \N \N \N {} +6004 1824 2025-03-10 13:12:15.653503-07 2025-03-10 14:52:55.190975-07 benchmark f L4 \N t \N \N \N {} +6005 1824 2025-03-10 13:13:37.254193-07 2025-03-10 13:18:36.888686-07 leaderboard f L4 0.07897275766666667 t \N \N \N {} +6011 1825 2025-03-10 13:09:55.890405-07 2025-03-10 13:48:11.961778-07 leaderboard t T4 0.07677698866666667 t \N \N \N {} +6013 1825 2025-03-10 14:28:08.622984-07 2025-03-10 14:46:31.402484-07 benchmark f T4 \N t \N \N \N {} +6014 1825 2025-03-10 14:21:16.246646-07 2025-03-10 13:28:59.68858-07 leaderboard f T4 0.07667353866666667 t \N \N \N {} +6095 1844 2025-03-10 22:07:21.414-07 2025-03-10 23:17:28.15254-07 benchmark t A100 \N t \N \N \N {} +6107 1846 2025-03-10 22:00:08.835784-07 2025-03-10 22:00:11.368406-07 benchmark f L4 \N t \N \N \N {} +6108 1846 2025-03-10 22:23:46.695176-07 2025-03-10 23:57:02.247288-07 leaderboard f L4 0.3110419582857143 t \N \N \N {} +6655 2057 2025-03-14 12:30:26.103998-07 2025-03-14 12:52:39.669941-07 benchmark f A100 \N t \N \N \N {} +6661 2058 2025-03-14 13:23:30.755804-07 2025-03-14 13:50:09.852519-07 leaderboard f A100 0.002474787 t \N \N \N {} +6672 2064 2025-03-14 15:30:52.385494-07 2025-03-14 14:26:29.449376-07 test t T4 \N f \N \N \N {} +6673 2064 2025-03-14 15:46:19.901898-07 2025-03-14 14:34:56.062065-07 test f T4 \N f \N \N \N {} +6693 2074 2025-03-14 16:21:36.880226-07 2025-03-14 17:05:48.053303-07 test f T4 \N f \N \N \N {} +6889 2142 2025-03-16 10:09:32.257151-07 2025-03-16 11:37:21.891364-07 test t H100 \N t \N \N \N {} +6694 2075 2025-03-14 16:49:43.41812-07 2025-03-14 16:35:30.091773-07 test f T4 \N f \N \N \N {} +6890 2142 2025-03-16 10:05:39.67375-07 2025-03-16 11:46:49.850371-07 benchmark t H100 \N f \N \N \N {} +6708 2080 2025-03-15 03:13:35.894439-07 2025-03-15 04:06:56.714414-07 benchmark f A100 \N t \N \N \N {} +6709 2080 2025-03-15 03:36:07.318078-07 2025-03-15 04:02:50.2109-07 leaderboard f A100 0.0025194633333333336 t \N \N \N {} +6711 2081 2025-03-15 03:52:50.095799-07 2025-03-15 03:57:17.540486-07 benchmark f A100 \N t \N \N \N {} +6721 2082 2025-03-15 02:47:16.092609-07 2025-03-15 02:57:12.920931-07 leaderboard f A100 0.0025854276666666667 t \N \N \N {} +6804 2105 2025-03-15 04:42:04.98069-07 2025-03-15 03:45:38.84867-07 benchmark f A100 \N t \N \N \N {} +6806 2107 2025-03-15 04:57:27.096967-07 2025-03-15 05:03:18.110884-07 benchmark f A100 \N t \N \N \N {} +6807 2108 2025-03-15 03:40:00.092892-07 2025-03-15 05:03:01.447944-07 benchmark f A100 \N t \N \N \N {} +6808 2109 2025-03-15 05:29:02.178375-07 2025-03-15 04:43:10.854188-07 benchmark f A100 \N t \N \N \N {} +6891 2142 2025-03-16 10:03:19.487989-07 2025-03-16 10:15:21.78931-07 test f H100 \N t \N \N \N {} +6816 2117 2025-03-15 09:06:01.891683-07 2025-03-15 09:51:02.921922-07 benchmark f A100 \N t \N \N \N {} +6893 2143 2025-03-16 12:26:21.451192-07 2025-03-16 11:55:44.286131-07 test f T4 \N f \N \N \N {} +6823 2120 2025-03-15 10:10:54.860558-07 2025-03-15 09:57:15.216128-07 benchmark t A100 \N t \N \N \N {} +6868 2128 2025-03-15 13:36:39.60935-07 2025-03-15 12:30:47.242903-07 test f T4 \N f \N \N \N {} +6869 2129 2025-03-15 12:55:04.568241-07 2025-03-15 13:27:08.036947-07 test t T4 \N f \N \N \N {} +6870 2129 2025-03-15 13:43:39.360608-07 2025-03-15 13:40:42.286936-07 test f T4 \N f \N \N \N {} +6871 2130 2025-03-15 12:56:13.688421-07 2025-03-15 13:31:59.222715-07 test t T4 \N f \N \N \N {} +6872 2130 2025-03-15 13:37:07.488274-07 2025-03-15 14:00:52.365959-07 test f T4 \N f \N \N \N {} +6874 2131 2025-03-15 14:10:20.038407-07 2025-03-15 13:06:30.274573-07 test t T4 \N f \N \N \N {} +6875 2132 2025-03-15 13:03:08.713745-07 2025-03-15 14:16:20.509728-07 test f T4 \N f \N \N \N {} +6876 2132 2025-03-15 12:46:33.250206-07 2025-03-15 14:16:03.803892-07 test t T4 \N f \N \N \N {} +6877 2133 2025-03-15 14:13:55.89837-07 2025-03-15 14:32:53.847522-07 test f T4 \N f \N \N \N {} +6878 2133 2025-03-15 13:49:17.874309-07 2025-03-15 13:42:01.322803-07 test t T4 \N f \N \N \N {} +6884 2137 2025-03-16 09:35:46.366813-07 2025-03-16 09:58:28.560263-07 test f H100 \N f \N \N \N {} +6941 2161 2025-03-16 15:54:46.742477-07 2025-03-16 16:55:32.003397-07 leaderboard t T4 0.00036617938461538463 t \N \N \N {} +6942 2161 2025-03-16 15:48:02.479891-07 2025-03-16 15:54:40.057512-07 test f T4 \N t \N \N \N {} +6943 2161 2025-03-16 16:14:50.39459-07 2025-03-16 16:50:14.655957-07 benchmark f T4 \N t \N \N \N {} +6945 2162 2025-03-16 15:42:59.335687-07 2025-03-16 16:32:27.860049-07 test t T4 \N t \N \N \N {} +6946 2162 2025-03-16 15:21:34.523769-07 2025-03-16 15:44:43.801402-07 benchmark t T4 \N t \N \N \N {} +6947 2162 2025-03-16 15:24:00.324625-07 2025-03-16 16:09:43.79608-07 leaderboard t T4 0.00028399790999999997 t \N \N \N {} +6948 2162 2025-03-16 15:07:29.788194-07 2025-03-16 16:45:42.575251-07 test f T4 \N t \N \N \N {} +6949 2162 2025-03-16 16:58:35.480846-07 2025-03-16 15:40:08.469048-07 benchmark f T4 \N t \N \N \N {} +2083 702 2025-02-25 13:37:08.890367-08 2025-02-25 14:31:19.469976-08 leaderboard f T4 0.019992128 t \N \N \N {} +2829 984 2025-02-26 23:44:55.909365-08 2025-02-26 23:59:03.36721-08 test f L4 \N t \N \N \N {} +2843 988 2025-02-27 00:16:23.988573-08 2025-02-26 23:57:40.2744-08 leaderboard f H100 0.00153285175 t \N \N \N {} +171 27 2025-02-23 10:45:11.479148-08 2025-02-23 11:31:42.340162-08 test t A100 \N t \N \N \N {} +172 27 2025-02-23 11:43:09.385926-08 2025-02-23 11:39:14.096637-08 benchmark t A100 \N t \N \N \N {} +220 37 2025-02-23 11:24:37.144954-08 2025-02-23 12:36:11.010727-08 benchmark t A100 \N t \N \N \N {} +173 27 2025-02-23 12:14:48.113644-08 2025-02-23 10:35:05.48636-08 leaderboard t A100 0.014219568666666666 t \N \N \N {} +174 27 2025-02-23 11:52:06.861806-08 2025-02-23 12:06:16.599311-08 test f A100 \N t \N \N \N {} +175 27 2025-02-23 11:14:31.938617-08 2025-02-23 10:50:54.199169-08 benchmark f A100 \N t \N \N \N {} +176 27 2025-02-23 10:55:35.824906-08 2025-02-23 11:40:46.262363-08 leaderboard f A100 0.014177354333333335 t \N \N \N {} +177 27 2025-02-23 10:54:29.927447-08 2025-02-23 11:20:37.982222-08 test f H100 \N t \N \N \N {} +178 27 2025-02-23 11:15:34.972209-08 2025-02-23 12:22:50.553085-08 benchmark f H100 \N t \N \N \N {} +179 27 2025-02-23 11:27:38.137008-08 2025-02-23 12:17:23.904583-08 leaderboard f H100 0.00628814 t \N \N \N {} +181 27 2025-02-23 12:09:44.028933-08 2025-02-23 11:26:46.80693-08 benchmark t T4 \N t \N \N \N {} +182 27 2025-02-23 11:56:25.478243-08 2025-02-23 10:36:01.655206-08 leaderboard t T4 0.07672247533333333 t \N \N \N {} +183 27 2025-02-23 11:51:05.559443-08 2025-02-23 10:39:36.577419-08 test f L4 \N t \N \N \N {} +184 27 2025-02-23 11:41:30.376423-08 2025-02-23 10:37:16.305101-08 benchmark f L4 \N t \N \N \N {} +185 27 2025-02-23 10:35:57.229029-08 2025-02-23 10:39:52.072211-08 leaderboard f L4 0.07915553666666666 t \N \N \N {} +186 27 2025-02-23 11:10:53.767744-08 2025-02-23 10:48:50.231998-08 test f T4 \N t \N \N \N {} +187 27 2025-02-23 12:02:00.098937-08 2025-02-23 11:08:01.038877-08 benchmark f T4 \N t \N \N \N {} +188 27 2025-02-23 11:11:25.926517-08 2025-02-23 10:30:09.448745-08 leaderboard f T4 0.076629145 t \N \N \N {} +189 27 2025-02-23 11:53:45.306343-08 2025-02-23 12:16:41.267247-08 test t L4 \N t \N \N \N {} +190 27 2025-02-23 11:28:09.310531-08 2025-02-23 10:48:48.140068-08 benchmark t L4 \N t \N \N \N {} +191 27 2025-02-23 12:08:23.202643-08 2025-02-23 11:17:51.689748-08 leaderboard t L4 0.07890172333333333 t \N \N \N {} +192 27 2025-02-23 10:44:11.726542-08 2025-02-23 11:02:53.940073-08 test t H100 \N t \N \N \N {} +193 27 2025-02-23 12:19:32.856515-08 2025-02-23 10:47:43.576992-08 benchmark t H100 \N t \N \N \N {} +194 27 2025-02-23 12:21:29.202301-08 2025-02-23 11:46:31.676892-08 leaderboard t H100 0.006306255 t \N \N \N {} +195 28 2025-02-23 11:13:19.67724-08 2025-02-23 10:39:48.64665-08 test f T4 \N f \N \N \N {} +196 29 2025-02-23 10:49:56.579251-08 2025-02-23 11:05:27.758434-08 test f T4 \N f \N \N \N {} +197 30 2025-02-23 12:27:41.798772-08 2025-02-23 12:02:37.751314-08 test f T4 \N f \N \N \N {} +198 31 2025-02-23 11:37:04.951166-08 2025-02-23 12:07:49.407209-08 test f T4 \N f \N \N \N {} +199 32 2025-02-23 11:58:09.474874-08 2025-02-23 10:43:38.089712-08 test f T4 \N t \N \N \N {} +200 33 2025-02-23 11:22:39.540982-08 2025-02-23 11:42:22.336345-08 benchmark f T4 \N t \N \N \N {} +201 34 2025-02-23 12:04:23.683096-08 2025-02-23 10:52:18.627224-08 test f T4 \N t \N \N \N {} +1622 539 2025-02-25 07:52:47.641192-08 2025-02-25 08:52:28.657841-08 test f H100 \N t \N \N \N {} +202 35 2025-02-23 12:24:22.817189-08 2025-02-23 10:54:19.203754-08 benchmark f T4 \N t \N \N \N {} +216 37 2025-02-23 11:14:38.746659-08 2025-02-23 11:03:54.206188-08 test t T4 \N t \N \N \N {} +243 50 2025-02-23 13:18:19.125258-08 2025-02-23 13:13:38.565546-08 test f T4 \N f \N \N \N {} +244 51 2025-02-23 13:01:35.786926-08 2025-02-23 12:24:04.575677-08 test f T4 \N f \N \N \N {} +245 52 2025-02-23 12:24:37.291631-08 2025-02-23 12:23:14.530709-08 test f T4 \N t \N \N \N {} +268 54 2025-02-23 12:25:21.411502-08 2025-02-23 12:05:19.559567-08 test t L4 \N t \N \N \N {} +247 53 2025-02-23 12:25:27.998153-08 2025-02-23 12:00:47.080967-08 benchmark f A100 \N t \N \N \N {} +248 53 2025-02-23 12:39:45.238062-08 2025-02-23 12:53:50.087404-08 benchmark f L4 \N t \N \N \N {} +249 53 2025-02-23 13:45:33.218295-08 2025-02-23 12:33:06.247895-08 benchmark f H100 \N t \N \N \N {} +250 54 2025-02-23 13:11:45.380271-08 2025-02-23 12:54:03.319507-08 test t A100 \N t \N \N \N {} +251 54 2025-02-23 12:41:48.180775-08 2025-02-23 12:18:18.458891-08 benchmark t A100 \N t \N \N \N {} +252 54 2025-02-23 13:47:01.02295-08 2025-02-23 13:12:03.617869-08 leaderboard t A100 0.00318012725 t \N \N \N {} +253 54 2025-02-23 12:36:35.151783-08 2025-02-23 12:27:40.807586-08 test f L4 \N t \N \N \N {} +3565 1211 2025-02-28 12:01:14.621003-08 2025-02-28 12:23:14.001408-08 test f H100 \N t \N \N \N {} +291 67 2025-02-23 13:52:22.554731-08 2025-02-23 15:04:28.302779-08 benchmark f H100 \N t \N \N \N {} +292 68 2025-02-23 14:13:32.52352-08 2025-02-23 13:39:23.211145-08 test f H100 \N t \N \N \N {} +294 70 2025-02-23 13:48:10.257644-08 2025-02-23 15:04:21.69425-08 test f H100 \N t \N \N \N {} +295 70 2025-02-23 13:45:01.008267-08 2025-02-23 15:19:04.053139-08 benchmark f H100 \N t \N \N \N {} +296 70 2025-02-23 14:08:32.500158-08 2025-02-23 13:34:17.476377-08 leaderboard f H100 0.0014198665 t \N \N \N {} +297 70 2025-02-23 14:22:14.947889-08 2025-02-23 14:43:43.399906-08 test t H100 \N t \N \N \N {} +395 104 2025-02-23 19:37:48.159657-08 2025-02-23 19:42:22.47971-08 leaderboard f L4 0.009219998333333333 t \N \N \N {} +396 104 2025-02-23 19:29:57.556657-08 2025-02-23 19:14:44.962181-08 test t T4 \N t \N \N \N {} +397 104 2025-02-23 19:06:51.535467-08 2025-02-23 19:09:13.584955-08 benchmark t T4 \N t \N \N \N {} +398 104 2025-02-23 19:08:03.666634-08 2025-02-23 18:31:14.289034-08 leaderboard t T4 0.009337150333333334 t \N \N \N {} +399 104 2025-02-23 18:20:58.004675-08 2025-02-23 18:12:39.887533-08 test t L4 \N t \N \N \N {} +400 104 2025-02-23 18:37:28.598432-08 2025-02-23 19:04:08.192125-08 benchmark t L4 \N t \N \N \N {} +401 104 2025-02-23 18:23:08.135641-08 2025-02-23 19:25:04.431404-08 leaderboard t L4 0.009286899666666666 t \N \N \N {} +440 137 2025-02-23 19:19:33.088698-08 2025-02-23 19:28:34.03025-08 test t A100 \N t \N \N \N {} +405 110 2025-02-23 19:36:40.687466-08 2025-02-23 19:24:36.001868-08 test f H100 \N f \N \N \N {} +406 111 2025-02-23 19:22:44.96918-08 2025-02-23 18:57:09.743491-08 test f L4 \N f \N \N \N {} +407 113 2025-02-23 18:49:58.235949-08 2025-02-23 19:26:22.885286-08 test f L4 \N f \N \N \N {} +408 112 2025-02-23 19:16:17.884334-08 2025-02-23 20:17:51.435513-08 test f H100 \N f \N \N \N {} +4354 1425 2025-03-01 20:13:46.966029-08 2025-03-01 18:52:04.589122-08 test t T4 \N t \N \N \N {} +465 144 2025-02-24 00:55:32.36629-08 2025-02-24 01:02:07.010747-08 leaderboard f A100 0.0031325836666666663 t \N \N \N {} +466 145 2025-02-24 00:32:22.702838-08 2025-02-24 01:20:22.072236-08 test f L4 \N t \N \N \N {} +467 145 2025-02-24 00:52:22.990735-08 2025-02-24 00:23:20.150431-08 benchmark f L4 \N t \N \N \N {} +468 145 2025-02-24 01:17:15.371174-08 2025-02-24 00:43:01.83229-08 leaderboard f L4 0.01690829933333333 t \N \N \N {} +472 145 2025-02-24 00:20:39.930346-08 2025-02-24 01:30:42.571148-08 test t L4 \N t \N \N \N {} +473 145 2025-02-24 01:15:01.028642-08 2025-02-24 01:45:10.923025-08 benchmark t L4 \N t \N \N \N {} +474 145 2025-02-24 01:13:01.276238-08 2025-02-24 02:03:18.528002-08 leaderboard t L4 0.016441613666666667 t \N \N \N {} +475 145 2025-02-24 00:23:20.873525-08 2025-02-24 00:29:22.877398-08 test t A100 \N t \N \N \N {} +476 145 2025-02-24 01:36:08.741349-08 2025-02-24 01:17:03.910006-08 benchmark t A100 \N t \N \N \N {} +477 145 2025-02-24 01:59:25.837709-08 2025-02-24 01:17:11.163833-08 leaderboard t A100 0.00310126 t \N \N \N {} +478 145 2025-02-24 02:01:13.502168-08 2025-02-24 01:09:26.58412-08 test t H100 \N t \N \N \N {} +298 70 2025-02-23 13:31:54.273224-08 2025-02-23 14:34:44.74164-08 benchmark t H100 \N t \N \N \N {} +299 70 2025-02-23 13:44:07.386708-08 2025-02-23 15:06:34.650489-08 leaderboard t H100 0.001028769 t \N \N \N {} +323 79 2025-02-23 15:26:33.721086-08 2025-02-23 15:20:07.296257-08 test f H100 \N t \N \N \N {} +486 145 2025-02-24 01:01:38.175295-08 2025-02-24 01:58:42.065024-08 test t T4 \N t \N \N \N {} +769 244 2025-02-24 11:02:11.786679-08 2025-02-24 09:19:00.797899-08 test f A100 \N t \N \N \N {} +479 145 2025-02-24 00:22:39.260481-08 2025-02-24 00:31:46.970049-08 benchmark t H100 \N t \N \N \N {} +480 145 2025-02-24 01:01:44.442094-08 2025-02-24 00:11:35.7708-08 leaderboard t H100 0.00142161375 t \N \N \N {} +481 145 2025-02-24 00:37:45.034628-08 2025-02-24 00:06:19.291315-08 test f H100 \N t \N \N \N {} +482 145 2025-02-24 01:23:58.17948-08 2025-02-24 00:43:49.157244-08 benchmark f H100 \N t \N \N \N {} +483 145 2025-02-24 00:09:29.066055-08 2025-02-24 00:27:00.430461-08 leaderboard f H100 0.00140814175 t \N \N \N {} +484 145 2025-02-24 02:01:37.341833-08 2025-02-24 01:22:53.107149-08 test f T4 \N t \N \N \N {} +541 157 2025-02-24 03:23:47.844322-08 2025-02-24 03:43:51.69252-08 test f A100 \N t \N \N \N {} +558 159 2025-02-24 03:52:44.512606-08 2025-02-24 05:07:17.070819-08 test f H100 \N t \N \N \N {} +542 157 2025-02-24 04:17:48.694412-08 2025-02-24 04:12:41.545476-08 benchmark f A100 \N t \N \N \N {} +549 157 2025-02-24 04:20:20.945717-08 2025-02-24 03:23:10.699715-08 leaderboard t T4 0.023143011666666668 t \N \N \N {} +1491 498 2025-02-25 07:26:44.389059-08 2025-02-25 06:14:45.396909-08 test f A100 \N f \N \N \N {} +550 157 2025-02-24 04:41:06.56919-08 2025-02-24 03:16:56.08599-08 test t A100 \N t \N \N \N {} +551 157 2025-02-24 04:37:58.696168-08 2025-02-24 03:37:13.752989-08 benchmark t A100 \N t \N \N \N {} +552 157 2025-02-24 03:31:21.654659-08 2025-02-24 04:02:58.352237-08 leaderboard t A100 0.006896261545454546 t \N \N \N {} +553 157 2025-02-24 05:03:09.664072-08 2025-02-24 05:13:14.483324-08 test t L4 \N f \N \N \N {} +554 157 2025-02-24 04:29:42.313161-08 2025-02-24 05:11:38.142222-08 test f L4 \N t \N \N \N {} +555 157 2025-02-24 04:36:10.687082-08 2025-02-24 03:31:24.036485-08 benchmark f L4 \N t \N \N \N {} +556 157 2025-02-24 03:38:18.277868-08 2025-02-24 03:25:50.851837-08 leaderboard f L4 0.018886963333333333 t \N \N \N {} +557 158 2025-02-24 04:36:41.191894-08 2025-02-24 04:18:45.993597-08 test f H100 \N t \N \N \N {} +1492 498 2025-02-25 06:34:51.735413-08 2025-02-25 05:41:39.626728-08 test t A100 \N f \N \N \N {} +559 160 2025-02-24 04:47:33.735469-08 2025-02-24 03:48:26.300529-08 test f H100 \N t \N \N \N {} +572 162 2025-02-24 05:00:12.670271-08 2025-02-24 05:05:35.448889-08 leaderboard f T4 0.018292196666666666 t \N \N \N {} +573 162 2025-02-24 03:26:01.433346-08 2025-02-24 03:56:51.262514-08 test f L4 \N f \N \N \N {} +574 162 2025-02-24 04:51:39.961542-08 2025-02-24 04:59:26.105146-08 test t L4 \N t \N \N \N {} +5259 1613 2025-03-06 06:50:43.258546-08 2025-03-06 08:19:11.165198-08 test f T4 \N t \N \N \N {} +600 172 2025-02-24 04:53:48.285554-08 2025-02-24 05:23:18.845114-08 test f A100 \N t \N \N \N {} +581 166 2025-02-24 03:35:47.72346-08 2025-02-24 03:38:46.586413-08 test f H100 \N f \N \N \N {} +606 172 2025-02-24 03:42:34.249272-08 2025-02-24 05:40:44.184189-08 test f L4 \N t \N \N \N {} +311 77 2025-02-23 14:12:30.101398-08 2025-02-23 15:24:57.946248-08 test f H100 \N t \N \N \N {} +312 77 2025-02-23 15:19:37.391023-08 2025-02-23 14:11:23.026317-08 benchmark f H100 \N t \N \N \N {} +313 77 2025-02-23 15:05:19.0137-08 2025-02-23 15:11:53.57026-08 leaderboard f H100 0.001450680857142857 t \N \N \N {} +315 77 2025-02-23 13:53:12.61876-08 2025-02-23 13:59:11.207858-08 benchmark t H100 \N t \N \N \N {} +316 77 2025-02-23 14:48:41.323897-08 2025-02-23 15:13:01.5905-08 leaderboard t H100 0.00145937425 t \N \N \N {} +317 78 2025-02-23 14:02:23.760311-08 2025-02-23 13:57:30.080076-08 test t H100 \N t \N \N \N {} +716 229 2025-02-24 08:35:17.736081-08 2025-02-24 07:54:05.005879-08 leaderboard f A100 0.004042775333333333 t \N \N \N {} +717 230 2025-02-24 09:22:18.875039-08 2025-02-24 08:00:26.274996-08 test f A100 \N t \N \N \N {} +1135 316 2025-02-24 20:02:44.708551-08 2025-02-24 18:21:09.94372-08 leaderboard t A100 0.011420428666666666 t \N \N \N {} +718 230 2025-02-24 08:33:59.269854-08 2025-02-24 07:51:14.292412-08 benchmark f A100 \N t \N \N \N {} +720 230 2025-02-24 09:24:41.117156-08 2025-02-24 08:14:51.47456-08 test t A100 \N t \N \N \N {} +721 230 2025-02-24 09:25:59.168682-08 2025-02-24 07:38:18.264898-08 benchmark t A100 \N t \N \N \N {} +722 230 2025-02-24 08:13:38.930592-08 2025-02-24 07:37:12.954266-08 leaderboard t A100 0.0032000705 t \N \N \N {} +723 231 2025-02-24 09:17:26.030333-08 2025-02-24 08:18:23.337179-08 test f H100 \N t \N \N \N {} +724 231 2025-02-24 09:02:18.510228-08 2025-02-24 08:27:34.118305-08 test f A100 \N t \N \N \N {} +1136 316 2025-02-24 18:52:30.044102-08 2025-02-24 19:36:46.191371-08 test f A100 \N t \N \N \N {} +726 231 2025-02-24 08:34:48.003809-08 2025-02-24 08:40:27.717239-08 test f L4 \N f \N \N \N {} +732 233 2025-02-24 09:29:37.727181-08 2025-02-24 08:02:29.392295-08 benchmark f A100 \N t \N \N \N {} +733 233 2025-02-24 08:20:40.863772-08 2025-02-24 07:58:20.853653-08 leaderboard f A100 0.0031231213333333336 t \N \N \N {} +734 233 2025-02-24 08:49:10.944512-08 2025-02-24 08:12:13.280323-08 test f H100 \N t \N \N \N {} +735 233 2025-02-24 08:30:07.553648-08 2025-02-24 08:58:56.261174-08 benchmark f H100 \N t \N \N \N {} +1354 451 2025-02-25 02:04:40.095144-08 2025-02-25 03:49:19.013675-08 benchmark f H100 \N t \N \N \N {} +736 233 2025-02-24 08:27:18.705589-08 2025-02-24 09:10:53.230123-08 leaderboard f H100 0.002061958 t \N \N \N {} +741 233 2025-02-24 08:13:06.765051-08 2025-02-24 07:57:46.536533-08 benchmark f T4 \N t \N \N \N {} +742 233 2025-02-24 09:28:14.411862-08 2025-02-24 07:55:13.882323-08 leaderboard f T4 0.004024178 t \N \N \N {} +743 233 2025-02-24 08:45:35.726194-08 2025-02-24 07:49:16.0139-08 test t H100 \N t \N \N \N {} +744 233 2025-02-24 08:30:17.598827-08 2025-02-24 08:45:38.138191-08 benchmark t H100 \N t \N \N \N {} +745 233 2025-02-24 08:29:13.376851-08 2025-02-24 08:41:38.50256-08 leaderboard t H100 0.0024627566666666667 t \N \N \N {} +318 78 2025-02-23 14:02:29.033294-08 2025-02-23 14:26:37.663496-08 benchmark t H100 \N t \N \N \N {} +319 78 2025-02-23 15:24:09.465856-08 2025-02-23 14:31:24.442508-08 leaderboard t H100 0.001455489 t \N \N \N {} +327 79 2025-02-23 15:44:13.226491-08 2025-02-23 13:56:26.731737-08 benchmark t H100 \N t \N \N \N {} +336 82 2025-02-23 17:25:08.638515-08 2025-02-23 17:57:21.345045-08 test f H100 \N f \N \N \N {} +337 83 2025-02-23 18:00:31.70052-08 2025-02-23 17:58:50.769159-08 test f H100 \N f \N \N \N {} +338 84 2025-02-23 18:06:57.258257-08 2025-02-23 16:51:18.239842-08 test f H100 \N f \N \N \N {} +339 85 2025-02-23 17:10:24.770217-08 2025-02-23 17:18:24.35571-08 test f H100 \N f \N \N \N {} +768 244 2025-02-24 10:02:14.922389-08 2025-02-24 10:56:52.559337-08 leaderboard t A100 0.0032697126666666665 t \N \N \N {} +340 86 2025-02-23 17:42:30.9851-08 2025-02-23 18:05:44.756059-08 test f H100 \N f \N \N \N {} +3566 1211 2025-02-28 12:19:44.674046-08 2025-02-28 11:20:06.595714-08 benchmark f H100 \N t \N \N \N {} +1356 453 2025-02-25 02:50:36.296171-08 2025-02-25 03:40:22.203999-08 benchmark f H100 \N t \N \N \N {} +777 250 2025-02-24 10:13:18.705553-08 2025-02-24 11:57:51.322708-08 test f A100 \N t \N \N \N {} +778 250 2025-02-24 11:03:04.113706-08 2025-02-24 11:50:05.562623-08 benchmark f A100 \N t \N \N \N {} +779 250 2025-02-24 11:49:40.71682-08 2025-02-24 10:20:18.789178-08 leaderboard f A100 0.0032043086666666666 t \N \N \N {} +780 250 2025-02-24 11:38:14.616988-08 2025-02-24 11:50:19.652707-08 test t A100 \N t \N \N \N {} +781 250 2025-02-24 11:06:57.166985-08 2025-02-24 11:37:32.323803-08 benchmark t A100 \N t \N \N \N {} +1358 456 2025-02-25 03:04:57.340562-08 2025-02-25 03:41:50.970915-08 benchmark f H100 \N t \N \N \N {} +782 250 2025-02-24 11:40:30.959556-08 2025-02-24 11:55:45.22426-08 leaderboard t A100 0.0031807153333333333 t \N \N \N {} +783 251 2025-02-24 10:28:34.15622-08 2025-02-24 10:26:21.513069-08 test t A100 \N t \N \N \N {} +784 251 2025-02-24 11:22:43.230478-08 2025-02-24 10:17:13.237357-08 benchmark t A100 \N t \N \N \N {} +785 251 2025-02-24 11:01:06.012176-08 2025-02-24 10:42:38.296715-08 leaderboard t A100 0.003201019 t \N \N \N {} +354 94 2025-02-23 18:07:11.918125-08 2025-02-23 18:49:09.9145-08 benchmark t H100 \N t \N \N \N {} +361 97 2025-02-23 19:03:55.153785-08 2025-02-23 19:25:28.656928-08 test t H100 \N t \N \N \N {} +367 98 2025-02-23 19:12:25.166912-08 2025-02-23 18:43:49.363313-08 benchmark f H100 \N t \N \N \N {} +368 99 2025-02-23 18:24:18.547946-08 2025-02-23 19:38:43.869117-08 test t H100 \N t \N \N \N {} +369 99 2025-02-23 19:55:02.527597-08 2025-02-23 19:45:12.210798-08 benchmark t H100 \N t \N \N \N {} +371 99 2025-02-23 19:05:59.158414-08 2025-02-23 18:08:37.209856-08 test f H100 \N t \N \N \N {} +377 102 2025-02-23 18:16:40.647606-08 2025-02-23 18:22:04.894771-08 benchmark f H100 \N t \N \N \N {} +378 104 2025-02-23 18:44:39.804161-08 2025-02-23 20:02:52.428781-08 test f H100 \N t \N \N \N {} +379 104 2025-02-23 18:34:28.483515-08 2025-02-23 19:39:00.450206-08 benchmark f H100 \N t \N \N \N {} +380 104 2025-02-23 19:35:24.292215-08 2025-02-23 20:07:53.402422-08 leaderboard f H100 0.0010334816666666667 t \N \N \N {} +385 104 2025-02-23 19:53:09.908295-08 2025-02-23 19:18:59.271408-08 benchmark f A100 \N t \N \N \N {} +394 104 2025-02-23 19:10:09.186446-08 2025-02-23 18:43:18.809569-08 benchmark f L4 \N t \N \N \N {} +403 108 2025-02-23 19:55:38.938073-08 2025-02-23 20:17:37.031218-08 test f H100 \N f \N \N \N {} +404 109 2025-02-23 19:08:16.246023-08 2025-02-23 19:56:30.430723-08 test f H100 \N f \N \N \N {} +410 114 2025-02-23 20:00:48.18605-08 2025-02-23 19:19:29.420657-08 test f H100 \N f \N \N \N {} +411 116 2025-02-23 19:34:58.202046-08 2025-02-23 20:01:11.73353-08 benchmark f T4 \N t \N \N \N {} +412 118 2025-02-23 20:37:38.267216-08 2025-02-23 19:02:18.39604-08 benchmark f T4 \N f \N \N \N {} +413 117 2025-02-23 19:24:46.341973-08 2025-02-23 18:57:45.646571-08 test f A100 \N f \N \N \N {} +414 117 2025-02-23 18:57:59.477452-08 2025-02-23 20:15:43.624804-08 test t A100 \N f \N \N \N {} +415 119 2025-02-23 18:57:34.156909-08 2025-02-23 18:54:25.73194-08 benchmark f T4 \N f \N \N \N {} +416 120 2025-02-23 20:05:08.814298-08 2025-02-23 18:59:37.20305-08 test f L4 \N t \N \N \N {} +418 122 2025-02-23 20:50:03.177865-08 2025-02-23 20:14:16.240173-08 test f L4 \N t \N \N \N {} +786 251 2025-02-24 10:26:17.480514-08 2025-02-24 10:27:14.231102-08 test t T4 \N t \N \N \N {} +792 251 2025-02-24 11:19:29.761457-08 2025-02-24 10:12:45.329053-08 test t H100 \N t \N \N \N {} +1444 484 2025-02-25 05:32:24.532424-08 2025-02-25 05:46:04.733398-08 test t A100 \N t \N \N \N {} +788 251 2025-02-24 10:59:47.100658-08 2025-02-24 11:38:00.698912-08 leaderboard t T4 0.017558684 t \N \N \N {} +789 251 2025-02-24 12:04:49.533693-08 2025-02-24 10:27:37.844537-08 test f A100 \N t \N \N \N {} +790 251 2025-02-24 12:04:40.357942-08 2025-02-24 10:51:07.619523-08 benchmark f A100 \N t \N \N \N {} +791 251 2025-02-24 11:22:21.198065-08 2025-02-24 11:52:00.418781-08 leaderboard f A100 0.003236336 t \N \N \N {} +811 253 2025-02-24 11:06:37.307391-08 2025-02-24 11:40:28.537535-08 test t A100 \N t \N \N \N {} +1447 484 2025-02-25 05:28:22.571964-08 2025-02-25 04:07:59.842135-08 test f A100 \N t \N \N \N {} +793 251 2025-02-24 11:52:18.452547-08 2025-02-24 11:37:31.847226-08 benchmark t H100 \N t \N \N \N {} +794 251 2025-02-24 10:55:56.967992-08 2025-02-24 12:02:37.363415-08 leaderboard t H100 0.001464676 t \N \N \N {} +795 251 2025-02-24 11:55:37.519662-08 2025-02-24 10:47:45.331086-08 test f H100 \N t \N \N \N {} +796 251 2025-02-24 10:51:49.810385-08 2025-02-24 10:25:51.454365-08 benchmark f H100 \N t \N \N \N {} +797 251 2025-02-24 11:15:30.778494-08 2025-02-24 11:00:25.052673-08 leaderboard f H100 0.0014206696 t \N \N \N {} +825 258 2025-02-24 13:00:16.497406-08 2025-02-24 13:34:23.466302-08 leaderboard t A100 0.0032076514 t \N \N \N {} +420 124 2025-02-23 20:02:27.111264-08 2025-02-23 20:04:53.203923-08 test f L4 \N t \N \N \N {} +422 126 2025-02-23 20:06:43.793931-08 2025-02-23 20:32:18.759967-08 test f L4 \N t \N \N \N {} +423 127 2025-02-23 20:24:10.490624-08 2025-02-23 20:17:20.346438-08 test f L4 \N f \N \N \N {} +425 129 2025-02-23 20:46:15.037395-08 2025-02-23 19:07:35.835051-08 test f L4 \N f \N \N \N {} +1450 485 2025-02-25 04:19:06.14904-08 2025-02-25 04:39:46.798027-08 test t A100 \N t \N \N \N {} +800 251 2025-02-24 10:59:21.599538-08 2025-02-24 10:25:36.051807-08 leaderboard f T4 0.01756872166666667 t \N \N \N {} +801 251 2025-02-24 10:42:44.855208-08 2025-02-24 10:38:35.745986-08 test f L4 \N t \N \N \N {} +802 251 2025-02-24 11:52:41.634738-08 2025-02-24 11:17:02.406538-08 benchmark f L4 \N t \N \N \N {} +826 258 2025-02-24 12:08:48.726753-08 2025-02-24 13:40:11.091506-08 test f A100 \N t \N \N \N {} +1453 485 2025-02-25 04:53:38.828316-08 2025-02-25 04:03:40.596895-08 test f A100 \N t \N \N \N {} +806 251 2025-02-24 10:12:06.628686-08 2025-02-24 10:32:34.248863-08 leaderboard t L4 0.017363646666666666 t \N \N \N {} +807 252 2025-02-24 10:34:25.534395-08 2025-02-24 12:10:21.1189-08 test f A100 \N f \N \N \N {} +808 252 2025-02-24 12:08:19.909711-08 2025-02-24 11:07:28.545277-08 test t A100 \N f \N \N \N {} +809 253 2025-02-24 10:56:51.671658-08 2025-02-24 12:19:25.430319-08 test f A100 \N t \N \N \N {} +810 253 2025-02-24 12:24:27.607242-08 2025-02-24 11:11:09.097006-08 benchmark f A100 \N f \N \N \N {} +821 256 2025-02-24 13:32:00.37212-08 2025-02-24 12:39:04.454955-08 test f A100 \N f \N \N \N {} +822 257 2025-02-24 11:59:03.654568-08 2025-02-24 12:25:11.023194-08 test f H100 \N t \N \N \N {} +823 258 2025-02-24 13:11:25.747263-08 2025-02-24 12:51:12.133715-08 test t A100 \N t \N \N \N {} +824 258 2025-02-24 12:23:35.99065-08 2025-02-24 12:54:48.794104-08 benchmark t A100 \N t \N \N \N {} +828 258 2025-02-24 12:28:03.710997-08 2025-02-24 12:02:38.320312-08 leaderboard f A100 0.00319396075 t \N \N \N {} +830 259 2025-02-24 13:38:53.547783-08 2025-02-24 12:05:16.740651-08 benchmark f H100 \N t \N \N \N {} +831 259 2025-02-24 12:19:41.162515-08 2025-02-24 13:02:34.045891-08 leaderboard f H100 0.0014907706363636365 t \N \N \N {} +832 259 2025-02-24 13:34:25.778466-08 2025-02-24 12:32:27.73762-08 test t H100 \N t \N \N \N {} +833 259 2025-02-24 11:57:09.771187-08 2025-02-24 13:17:24.239643-08 benchmark t H100 \N t \N \N \N {} +834 259 2025-02-24 12:53:22.94161-08 2025-02-24 13:02:51.615542-08 leaderboard t H100 0.0015141807894736843 t \N \N \N {} +837 261 2025-02-24 14:33:31.764087-08 2025-02-24 14:09:19.937583-08 benchmark f H100 \N t \N \N \N {} +838 261 2025-02-24 13:29:16.943379-08 2025-02-24 14:28:28.491199-08 benchmark f A100 \N t \N \N \N {} +965 286 2025-02-24 15:17:07.072445-08 2025-02-24 15:23:57.396796-08 test f A100 \N t \N \N \N {} +843 262 2025-02-24 14:18:36.348524-08 2025-02-24 14:09:28.031494-08 leaderboard f H100 0.00158875175 t \N \N \N {} +1456 486 2025-02-25 05:57:12.50863-08 2025-02-25 04:24:28.062012-08 test f A100 \N t \N \N \N {} +850 262 2025-02-24 13:53:27.308737-08 2025-02-24 14:16:34.938488-08 test f L4 \N t \N \N \N {} +851 262 2025-02-24 12:43:25.694004-08 2025-02-24 14:30:18.247948-08 benchmark f L4 \N t \N \N \N {} +852 262 2025-02-24 14:41:37.221242-08 2025-02-24 14:15:54.675901-08 leaderboard f L4 0.01720207333333333 t \N \N \N {} +853 262 2025-02-24 13:03:41.972489-08 2025-02-24 13:01:38.374088-08 test f A100 \N t \N \N \N {} +854 262 2025-02-24 14:31:55.855835-08 2025-02-24 13:01:31.443949-08 benchmark f A100 \N t \N \N \N {} +1359 457 2025-02-25 02:33:43.299256-08 2025-02-25 02:58:46.454768-08 benchmark f H100 \N t \N \N \N {} +860 262 2025-02-24 14:13:52.225617-08 2025-02-24 14:17:22.296848-08 test t H100 \N t \N \N \N {} +1360 458 2025-02-25 04:21:39.8674-08 2025-02-25 03:31:46.64471-08 benchmark f H100 \N t \N \N \N {} +866 264 2025-02-24 14:18:15.875273-08 2025-02-24 14:25:54.32018-08 test t A100 \N f \N \N \N {} +426 130 2025-02-23 19:07:19.532288-08 2025-02-23 19:27:05.035489-08 test f L4 \N f \N \N \N {} +427 131 2025-02-23 19:48:04.004883-08 2025-02-23 20:55:59.519835-08 test f L4 \N f \N \N \N {} +428 132 2025-02-23 19:43:42.154861-08 2025-02-23 19:58:51.977474-08 test f L4 \N t \N \N \N {} +430 134 2025-02-23 19:41:20.008317-08 2025-02-23 19:11:47.214628-08 test f L4 \N t \N \N \N {} +431 135 2025-02-23 20:41:38.742341-08 2025-02-23 20:10:24.488802-08 test t A100 \N t \N \N \N {} +867 264 2025-02-24 13:47:02.310423-08 2025-02-24 14:19:30.07954-08 test f A100 \N f \N \N \N {} +868 265 2025-02-24 13:25:41.120417-08 2025-02-24 13:19:04.569194-08 test f A100 \N f \N \N \N {} +869 265 2025-02-24 14:09:21.007191-08 2025-02-24 14:19:43.13391-08 test t A100 \N f \N \N \N {} +5676 1725 2025-03-09 09:53:25.9676-07 2025-03-09 10:54:50.998675-07 test t H100 \N t \N \N \N {} +1361 459 2025-02-25 02:30:56.124266-08 2025-02-25 02:51:08.772443-08 test t A100 \N t \N \N \N {} +871 266 2025-02-24 13:56:37.656623-08 2025-02-24 13:34:36.431618-08 test f H100 \N f \N \N \N {} +872 267 2025-02-24 13:27:30.522948-08 2025-02-24 14:25:22.159132-08 test f A100 \N t \N \N \N {} +873 267 2025-02-24 14:06:05.758615-08 2025-02-24 14:17:03.596666-08 benchmark f A100 \N t \N \N \N {} +874 267 2025-02-24 14:47:02.145718-08 2025-02-24 14:26:33.636732-08 leaderboard f A100 0.0032893633333333336 t \N \N \N {} +875 267 2025-02-24 14:02:32.569668-08 2025-02-24 13:04:10.687457-08 test t A100 \N t \N \N \N {} +876 267 2025-02-24 14:20:17.088913-08 2025-02-24 13:03:00.990794-08 benchmark t A100 \N t \N \N \N {} +877 267 2025-02-24 13:03:37.085609-08 2025-02-24 14:42:26.626312-08 leaderboard t A100 0.0032918306666666667 t \N \N \N {} +878 268 2025-02-24 13:32:17.727867-08 2025-02-24 14:42:15.893182-08 test f H100 \N t \N \N \N {} +879 268 2025-02-24 13:46:35.691848-08 2025-02-24 14:33:17.187051-08 benchmark f H100 \N t \N \N \N {} +880 268 2025-02-24 13:09:44.706773-08 2025-02-24 14:40:37.527146-08 leaderboard f H100 0.00148714025 t \N \N \N {} +881 268 2025-02-24 14:38:11.050645-08 2025-02-24 14:37:44.308056-08 test t H100 \N t \N \N \N {} +972 288 2025-02-24 16:59:51.753306-08 2025-02-24 16:32:43.265594-08 test t A100 \N t \N \N \N {} +889 270 2025-02-24 13:23:28.860312-08 2025-02-24 15:14:01.508532-08 benchmark f H100 \N t \N \N \N {} +890 270 2025-02-24 14:06:21.369663-08 2025-02-24 13:44:06.555384-08 leaderboard f H100 0.00633421 t \N \N \N {} +891 270 2025-02-24 15:11:19.3884-08 2025-02-24 14:22:05.663402-08 test t H100 \N t \N \N \N {} +892 270 2025-02-24 13:34:23.965761-08 2025-02-24 15:03:36.056155-08 benchmark t H100 \N t \N \N \N {} +897 271 2025-02-24 13:23:42.310773-08 2025-02-24 13:47:13.289788-08 benchmark f T4 \N t \N \N \N {} +432 135 2025-02-23 19:22:15.455814-08 2025-02-23 19:58:43.024263-08 benchmark t A100 \N t \N \N \N {} +433 135 2025-02-23 19:46:23.596065-08 2025-02-23 21:02:09.718637-08 leaderboard t A100 0.011416369 t \N \N \N {} +434 135 2025-02-23 20:11:32.421186-08 2025-02-23 20:54:46.274828-08 test f A100 \N t \N \N \N {} +435 135 2025-02-23 20:18:13.731355-08 2025-02-23 20:15:53.15599-08 benchmark f A100 \N t \N \N \N {} +436 135 2025-02-23 20:00:20.118277-08 2025-02-23 20:51:44.559411-08 leaderboard f A100 0.010131549619047619 t \N \N \N {} +437 137 2025-02-23 19:49:28.16914-08 2025-02-23 20:57:31.153582-08 test f A100 \N t \N \N \N {} +438 137 2025-02-23 20:09:57.38955-08 2025-02-23 20:26:38.729961-08 benchmark f A100 \N t \N \N \N {} +439 137 2025-02-23 19:51:53.206453-08 2025-02-23 20:31:39.322151-08 leaderboard f A100 0.010931205 t \N \N \N {} +441 137 2025-02-23 20:15:20.626379-08 2025-02-23 20:57:29.810715-08 benchmark t A100 \N t \N \N \N {} +442 137 2025-02-23 20:45:29.846555-08 2025-02-23 19:10:45.121007-08 leaderboard t A100 0.010102002266666667 t \N \N \N {} +576 162 2025-02-24 04:19:34.761817-08 2025-02-24 05:01:13.840464-08 leaderboard t L4 0.017179168666666668 t \N \N \N {} +577 163 2025-02-24 04:01:18.525271-08 2025-02-24 04:17:48.618121-08 test f T4 \N f \N \N \N {} +898 272 2025-02-24 14:03:42.781647-08 2025-02-24 13:55:34.397369-08 test t H100 \N t \N \N \N {} +899 272 2025-02-24 13:44:04.949972-08 2025-02-24 13:28:20.378369-08 benchmark t H100 \N t \N \N \N {} +900 272 2025-02-24 13:45:21.588357-08 2025-02-24 15:00:10.446354-08 leaderboard t H100 0.001380456 t \N \N \N {} +901 272 2025-02-24 14:59:05.351263-08 2025-02-24 15:02:57.541551-08 test f A100 \N t \N \N \N {} +3584 1228 2025-02-28 13:43:38.707851-08 2025-02-28 14:06:32.301701-08 test t A100 \N t \N \N \N {} +902 272 2025-02-24 13:41:50.302661-08 2025-02-24 14:48:59.498164-08 benchmark f A100 \N t \N \N \N {} +903 272 2025-02-24 13:24:49.213705-08 2025-02-24 13:38:04.997177-08 leaderboard f A100 0.0030942173333333334 t \N \N \N {} +904 272 2025-02-24 14:29:42.928168-08 2025-02-24 13:26:18.774967-08 test t A100 \N t \N \N \N {} +905 272 2025-02-24 14:12:54.933528-08 2025-02-24 14:48:10.033573-08 benchmark t A100 \N t \N \N \N {} +906 272 2025-02-24 14:06:49.466739-08 2025-02-24 14:26:14.363403-08 leaderboard t A100 0.0030895613333333334 t \N \N \N {} +907 272 2025-02-24 13:27:26.40339-08 2025-02-24 14:22:30.451832-08 test f T4 \N t \N \N \N {} +908 272 2025-02-24 13:50:22.416422-08 2025-02-24 14:23:23.865231-08 benchmark f T4 \N t \N \N \N {} +909 272 2025-02-24 15:02:51.400167-08 2025-02-24 14:45:31.55137-08 leaderboard f T4 0.016812591 t \N \N \N {} +910 272 2025-02-24 14:54:42.275644-08 2025-02-24 14:02:35.113798-08 test t T4 \N t \N \N \N {} +911 272 2025-02-24 14:14:55.576574-08 2025-02-24 13:55:39.639099-08 benchmark t T4 \N t \N \N \N {} +912 272 2025-02-24 14:30:30.596529-08 2025-02-24 15:11:06.343919-08 leaderboard t T4 0.016815438 t \N \N \N {} +913 272 2025-02-24 14:03:43.587782-08 2025-02-24 13:32:24.372553-08 test f H100 \N t \N \N \N {} +914 272 2025-02-24 13:58:45.530798-08 2025-02-24 14:37:34.053628-08 benchmark f H100 \N t \N \N \N {} +915 272 2025-02-24 13:44:39.285633-08 2025-02-24 14:28:08.305027-08 leaderboard f H100 0.0013929076666666668 t \N \N \N {} +916 273 2025-02-24 13:46:49.172996-08 2025-02-24 14:08:28.506582-08 test f H100 \N t \N \N \N {} +917 273 2025-02-24 13:44:56.476334-08 2025-02-24 14:40:36.885261-08 benchmark f H100 \N t \N \N \N {} +918 273 2025-02-24 14:57:37.29992-08 2025-02-24 14:13:39.653275-08 leaderboard f H100 0.00629756 t \N \N \N {} +919 272 2025-02-24 14:32:30.167492-08 2025-02-24 14:11:08.41029-08 test t L4 \N t \N \N \N {} +920 272 2025-02-24 13:47:24.679318-08 2025-02-24 13:20:10.828648-08 benchmark t L4 \N t \N \N \N {} +921 272 2025-02-24 15:14:24.435268-08 2025-02-24 15:12:42.536442-08 leaderboard t L4 0.016873888666666666 t \N \N \N {} +923 272 2025-02-24 14:50:59.333942-08 2025-02-24 14:12:16.270455-08 benchmark f L4 \N t \N \N \N {} +924 272 2025-02-24 14:47:47.523473-08 2025-02-24 13:44:27.360148-08 leaderboard f L4 0.016924354 t \N \N \N {} +925 273 2025-02-24 15:11:48.660708-08 2025-02-24 13:29:55.704446-08 test t H100 \N t \N \N \N {} +926 273 2025-02-24 15:15:14.766004-08 2025-02-24 13:19:47.409296-08 benchmark t H100 \N t \N \N \N {} +927 273 2025-02-24 14:10:49.544286-08 2025-02-24 14:57:42.082587-08 leaderboard t H100 0.004302405666666667 t \N \N \N {} +984 288 2025-02-24 15:26:18.349293-08 2025-02-24 15:44:04.650422-08 test f A100 \N t \N \N \N {} +928 274 2025-02-24 15:05:47.967562-08 2025-02-24 15:17:26.160717-08 test f H100 \N f \N \N \N {} +929 274 2025-02-24 15:04:47.728115-08 2025-02-24 14:57:41.957077-08 test t H100 \N f \N \N \N {} +3585 1228 2025-02-28 14:17:57.496054-08 2025-02-28 13:50:52.43144-08 benchmark t A100 \N t \N \N \N {} +935 278 2025-02-24 14:28:49.91519-08 2025-02-24 15:19:34.617051-08 benchmark t A100 \N t \N \N \N {} +936 278 2025-02-24 13:44:17.851045-08 2025-02-24 15:26:06.123862-08 leaderboard t A100 0.0031084026666666664 t \N \N \N {} +937 278 2025-02-24 15:01:10.720121-08 2025-02-24 14:26:25.903037-08 test f A100 \N t \N \N \N {} +990 288 2025-02-24 16:29:43.864283-08 2025-02-24 16:06:04.412307-08 test f L4 \N t \N \N \N {} +938 278 2025-02-24 15:22:13.9455-08 2025-02-24 15:34:30.906411-08 benchmark f A100 \N t \N \N \N {} +939 278 2025-02-24 15:34:14.43871-08 2025-02-24 14:09:55.104192-08 leaderboard f A100 0.003099416 t \N \N \N {} +449 138 2025-02-23 20:17:26.595429-08 2025-02-23 20:35:29.611944-08 benchmark f L4 \N t \N \N \N {} +451 139 2025-02-23 20:51:10.6817-08 2025-02-23 20:38:48.437704-08 test f A100 \N t \N \N \N {} +452 139 2025-02-23 19:49:38.747753-08 2025-02-23 19:40:42.834492-08 benchmark f A100 \N t \N \N \N {} +940 278 2025-02-24 15:09:41.101654-08 2025-02-24 13:40:05.003362-08 test f H100 \N t \N \N \N {} +941 278 2025-02-24 14:12:31.025675-08 2025-02-24 15:21:52.442215-08 benchmark f H100 \N t \N \N \N {} +942 278 2025-02-24 15:26:06.113726-08 2025-02-24 14:54:47.505262-08 leaderboard f H100 0.0013924253333333332 t \N \N \N {} +943 278 2025-02-24 13:41:17.222805-08 2025-02-24 14:40:55.819332-08 test t H100 \N t \N \N \N {} +944 278 2025-02-24 14:51:33.706683-08 2025-02-24 15:02:31.7023-08 benchmark t H100 \N t \N \N \N {} +945 278 2025-02-24 15:15:53.225485-08 2025-02-24 13:41:18.489963-08 leaderboard t H100 0.001391712 t \N \N \N {} +947 278 2025-02-24 13:47:07.517676-08 2025-02-24 14:35:18.503122-08 benchmark f L4 \N t \N \N \N {} +948 278 2025-02-24 15:32:37.786282-08 2025-02-24 14:47:49.400993-08 leaderboard f L4 0.016921183 t \N \N \N {} +950 278 2025-02-24 14:52:40.705932-08 2025-02-24 15:12:22.348896-08 benchmark t L4 \N t \N \N \N {} +951 278 2025-02-24 15:20:16.824802-08 2025-02-24 14:51:53.886502-08 leaderboard t L4 0.01689634233333333 t \N \N \N {} +952 278 2025-02-24 13:42:04.283901-08 2025-02-24 13:50:01.376078-08 test t T4 \N t \N \N \N {} +959 280 2025-02-24 14:37:57.453433-08 2025-02-24 13:55:35.067117-08 benchmark f A100 \N t \N \N \N {} +960 281 2025-02-24 15:29:16.351075-08 2025-02-24 15:15:34.238127-08 benchmark f A100 \N f \N \N \N {} +996 289 2025-02-24 17:05:53.711415-08 2025-02-24 16:12:17.469633-08 leaderboard f A100 0.0031874743333333336 t \N \N \N {} +961 282 2025-02-24 14:54:40.402838-08 2025-02-24 15:07:51.744951-08 benchmark f A100 \N t \N \N \N {} +1326 443 2025-02-25 01:31:19.548777-08 2025-02-25 02:21:57.556426-08 benchmark t A100 \N t \N \N \N {} +969 286 2025-02-24 14:27:36.98298-08 2025-02-24 14:24:18.873791-08 benchmark t A100 \N t \N \N \N {} +970 286 2025-02-24 15:45:02.969062-08 2025-02-24 15:20:58.131836-08 leaderboard t A100 0.003574085 t \N \N \N {} +971 287 2025-02-24 16:45:49.221907-08 2025-02-24 15:11:06.695278-08 benchmark f A100 \N t \N \N \N {} +1364 459 2025-02-25 03:07:07.507086-08 2025-02-25 03:28:33.611205-08 test f A100 \N t \N \N \N {} +973 288 2025-02-24 16:57:39.832425-08 2025-02-24 16:14:19.158825-08 benchmark t A100 \N t \N \N \N {} +974 288 2025-02-24 16:17:36.67277-08 2025-02-24 15:25:36.315446-08 leaderboard t A100 0.0035733863333333336 t \N \N \N {} +976 288 2025-02-24 16:12:37.835505-08 2025-02-24 16:32:25.940514-08 benchmark t H100 \N t \N \N \N {} +977 288 2025-02-24 16:02:16.632285-08 2025-02-24 15:30:08.973921-08 leaderboard t H100 0.0015792905 t \N \N \N {} +1367 460 2025-02-25 02:54:18.569745-08 2025-02-25 03:04:44.660402-08 test f H100 \N t \N \N \N {} +1462 487 2025-02-25 05:11:56.373844-08 2025-02-25 05:12:25.148355-08 test t H100 \N t \N \N \N {} +979 288 2025-02-24 16:39:20.43363-08 2025-02-24 16:31:03.690589-08 benchmark f H100 \N t \N \N \N {} +980 288 2025-02-24 15:39:09.650488-08 2025-02-24 15:08:31.848197-08 leaderboard f H100 0.001654801 t \N \N \N {} +981 288 2025-02-24 16:32:20.98845-08 2025-02-24 15:27:23.058332-08 test f T4 \N t \N \N \N {} +982 288 2025-02-24 15:11:31.029521-08 2025-02-24 15:54:36.042517-08 benchmark f T4 \N t \N \N \N {} +983 288 2025-02-24 15:57:27.675389-08 2025-02-24 16:16:35.300427-08 leaderboard f T4 0.01727558566666667 t \N \N \N {} +1370 460 2025-02-25 03:37:37.587036-08 2025-02-25 03:12:01.251999-08 test t H100 \N t \N \N \N {} +1465 487 2025-02-25 05:28:37.945849-08 2025-02-25 05:31:06.395877-08 test f H100 \N t \N \N \N {} +985 288 2025-02-24 16:00:46.319439-08 2025-02-24 16:11:47.492733-08 benchmark f A100 \N t \N \N \N {} +986 288 2025-02-24 16:16:37.007915-08 2025-02-24 16:47:35.551783-08 leaderboard f A100 0.0035907183333333333 t \N \N \N {} +987 288 2025-02-24 15:53:31.716837-08 2025-02-24 15:52:50.195056-08 test t T4 \N t \N \N \N {} +988 288 2025-02-24 15:24:51.892743-08 2025-02-24 15:37:05.023212-08 benchmark t T4 \N t \N \N \N {} +989 288 2025-02-24 15:27:07.594575-08 2025-02-24 16:56:10.768764-08 leaderboard t T4 0.017142831666666667 t \N \N \N {} +1373 461 2025-02-25 03:02:32.607966-08 2025-02-25 02:46:59.676745-08 test t H100 \N t \N \N \N {} +1469 489 2025-02-25 06:10:25.611874-08 2025-02-25 04:53:36.581953-08 test f A100 \N t \N \N \N {} +991 288 2025-02-24 16:15:35.403958-08 2025-02-24 15:42:05.66197-08 benchmark f L4 \N t \N \N \N {} +992 288 2025-02-24 15:44:25.515087-08 2025-02-24 16:58:38.135537-08 leaderboard f L4 0.017213298666666668 t \N \N \N {} +453 139 2025-02-23 20:25:09.33463-08 2025-02-23 21:00:42.630896-08 leaderboard f A100 2.3280243876666664 t \N \N \N {} +454 139 2025-02-23 19:54:51.244923-08 2025-02-23 20:51:32.367147-08 test t A100 \N t \N \N \N {} +455 139 2025-02-23 19:45:18.073547-08 2025-02-23 20:41:22.97686-08 benchmark t A100 \N t \N \N \N {} +456 139 2025-02-23 21:07:14.882985-08 2025-02-23 20:12:14.513014-08 leaderboard t A100 2.2617255652727275 t \N \N \N {} +458 142 2025-02-24 01:14:42.946921-08 2025-02-24 00:35:39.452004-08 test f A100 \N f \N \N \N {} +459 143 2025-02-24 01:11:00.689386-08 2025-02-24 00:41:21.530234-08 test f A100 \N t \N \N \N {} +460 144 2025-02-24 00:41:59.723288-08 2025-02-24 00:27:53.246315-08 test t A100 \N t \N \N \N {} +461 144 2025-02-24 00:21:52.786601-08 2025-02-24 01:33:07.398183-08 benchmark t A100 \N t \N \N \N {} +462 144 2025-02-24 01:54:02.801471-08 2025-02-24 01:19:41.256152-08 leaderboard t A100 0.003105596 t \N \N \N {} +463 144 2025-02-24 00:31:47.833871-08 2025-02-24 01:26:28.316452-08 test f A100 \N t \N \N \N {} +464 144 2025-02-24 01:15:21.059343-08 2025-02-24 01:40:04.362432-08 benchmark f A100 \N t \N \N \N {} +469 145 2025-02-24 00:21:16.048547-08 2025-02-24 01:12:36.574185-08 test f A100 \N t \N \N \N {} +470 145 2025-02-24 00:17:52.83432-08 2025-02-24 01:49:29.222442-08 benchmark f A100 \N t \N \N \N {} +471 145 2025-02-24 01:06:14.871957-08 2025-02-24 01:44:46.357832-08 leaderboard f A100 0.0030890706666666665 t \N \N \N {} +485 145 2025-02-24 01:20:54.849052-08 2025-02-24 01:08:55.25546-08 benchmark f T4 \N f \N \N \N {} +487 145 2025-02-24 00:17:54.793622-08 2025-02-24 00:31:22.4894-08 benchmark t T4 \N f \N \N \N {} +488 148 2025-02-24 03:09:35.205519-08 2025-02-24 03:27:23.971276-08 benchmark f A100 \N t \N \N \N {} +489 149 2025-02-24 03:30:55.449817-08 2025-02-24 03:37:56.567594-08 test f H100 \N t \N \N \N {} +490 150 2025-02-24 04:21:26.450468-08 2025-02-24 04:24:44.221828-08 test f H100 \N f \N \N \N {} +491 150 2025-02-24 04:05:31.466828-08 2025-02-24 03:04:56.25643-08 test t H100 \N t \N \N \N {} +993 288 2025-02-24 16:22:57.509895-08 2025-02-24 16:11:34.646111-08 test t L4 \N f \N \N \N {} +994 289 2025-02-24 16:42:29.124469-08 2025-02-24 16:15:50.980733-08 test f A100 \N t \N \N \N {} +995 289 2025-02-24 16:03:41.521804-08 2025-02-24 15:55:40.302003-08 benchmark f A100 \N t \N \N \N {} +997 289 2025-02-24 15:54:37.535241-08 2025-02-24 15:25:33.427509-08 test t A100 \N t \N \N \N {} +1327 443 2025-02-25 02:11:01.004727-08 2025-02-25 02:37:11.369093-08 leaderboard t A100 0.003228157 t \N \N \N {} +1328 443 2025-02-25 02:29:43.574743-08 2025-02-25 01:45:12.561001-08 test f A100 \N t \N \N \N {} +998 289 2025-02-24 15:30:32.784103-08 2025-02-24 15:14:53.659942-08 benchmark t A100 \N t \N \N \N {} +492 150 2025-02-24 03:09:36.145036-08 2025-02-24 03:37:47.928735-08 benchmark t H100 \N t \N \N \N {} +493 150 2025-02-24 03:50:14.789495-08 2025-02-24 04:18:31.651743-08 leaderboard t H100 0.00032365892307692306 t \N \N \N {} +494 151 2025-02-24 03:23:37.243425-08 2025-02-24 03:24:21.097123-08 test t H100 \N t \N \N \N {} +495 151 2025-02-24 04:50:10.450163-08 2025-02-24 03:28:44.586591-08 benchmark t H100 \N t \N \N \N {} +496 151 2025-02-24 04:56:39.642072-08 2025-02-24 04:24:35.925272-08 leaderboard t H100 0.00029347884745762714 t \N \N \N {} +497 151 2025-02-24 03:19:04.263395-08 2025-02-24 04:48:57.333637-08 test f H100 \N t \N \N \N {} +498 151 2025-02-24 04:44:55.465081-08 2025-02-24 04:38:58.27472-08 benchmark f H100 \N t \N \N \N {} +499 151 2025-02-24 04:22:14.523605-08 2025-02-24 03:36:00.958698-08 leaderboard f H100 0.0003125368620689655 t \N \N \N {} +5247 1613 2025-03-06 06:38:02.469655-08 2025-03-06 08:12:20.172654-08 test f H100 \N t \N \N \N {} +1322 442 2025-02-25 00:41:54.545147-08 2025-02-25 02:36:02.144792-08 test t T4 \N t \N \N \N {} +999 289 2025-02-24 15:25:33.457905-08 2025-02-24 16:27:05.872169-08 leaderboard t A100 0.0031989713333333337 t \N \N \N {} +1000 289 2025-02-24 16:48:53.415372-08 2025-02-24 16:21:48.670188-08 test t T4 \N t \N \N \N {} +1001 289 2025-02-24 17:09:32.511952-08 2025-02-24 15:33:31.453203-08 benchmark t T4 \N t \N \N \N {} +1376 461 2025-02-25 03:14:56.330738-08 2025-02-25 02:37:49.658574-08 test f H100 \N t \N \N \N {} +1471 491 2025-02-25 05:29:20.665807-08 2025-02-25 05:15:22.315477-08 test t A100 \N t \N \N \N {} +532 154 2025-02-24 04:48:16.131212-08 2025-02-24 04:44:31.007006-08 test f H100 \N t \N \N \N {} +3573 1218 2025-02-28 13:45:23.407437-08 2025-02-28 12:25:46.532879-08 test f H100 \N t \N \N \N {} +1118 304 2025-02-24 18:18:52.599968-08 2025-02-24 18:09:21.428872-08 leaderboard f L4 0.027837713666666666 t \N \N \N {} +2417 820 2025-02-26 12:21:06.882191-08 2025-02-26 13:15:22.606597-08 benchmark f H100 \N t \N \N \N {} +1123 306 2025-02-24 17:10:46.114629-08 2025-02-24 18:46:22.129619-08 benchmark f L4 \N t \N \N \N {} +1124 307 2025-02-24 17:18:47.489571-08 2025-02-24 17:33:10.036339-08 benchmark f H100 \N f \N \N \N {} +1129 312 2025-02-24 18:14:08.836383-08 2025-02-24 19:05:16.466962-08 test f T4 \N t \N \N \N {} +1130 313 2025-02-24 18:32:21.793683-08 2025-02-24 18:56:10.553395-08 test f T4 \N f \N \N \N {} +1131 314 2025-02-24 19:53:32.767284-08 2025-02-24 18:20:01.673708-08 test f A100 \N t \N \N \N {} +1132 315 2025-02-24 19:41:33.984587-08 2025-02-24 19:22:46.368176-08 test f A100 \N f \N \N \N {} +1133 316 2025-02-24 19:51:34.421459-08 2025-02-24 18:58:50.828608-08 test t A100 \N t \N \N \N {} +1134 316 2025-02-24 19:10:22.774186-08 2025-02-24 18:51:45.619893-08 benchmark t A100 \N t \N \N \N {} +1137 316 2025-02-24 19:55:11.126516-08 2025-02-24 18:17:41.336484-08 benchmark f A100 \N t \N \N \N {} +1138 316 2025-02-24 19:34:48.644475-08 2025-02-24 18:14:08.964849-08 leaderboard f A100 0.01013649895 t \N \N \N {} +1139 317 2025-02-24 19:01:55.707471-08 2025-02-24 19:26:25.81366-08 test f A100 \N f \N \N \N {} +1140 318 2025-02-24 18:34:52.552066-08 2025-02-24 19:48:38.716235-08 benchmark f A100 \N t \N \N \N {} +1141 319 2025-02-24 19:23:43.21952-08 2025-02-24 18:51:42.032684-08 test f A100 \N t \N \N \N {} +1142 320 2025-02-24 19:25:23.832847-08 2025-02-24 18:33:34.811732-08 benchmark f A100 \N t \N \N \N {} +1143 321 2025-02-24 20:03:32.364119-08 2025-02-24 18:41:04.100608-08 test f A100 \N t \N \N \N {} +1148 321 2025-02-24 19:35:02.480415-08 2025-02-24 19:46:33.415377-08 leaderboard t A100 0.008000332333333334 t \N \N \N {} +533 154 2025-02-24 04:30:12.737914-08 2025-02-24 03:41:45.719226-08 benchmark f H100 \N t \N \N \N {} +534 154 2025-02-24 04:26:39.743431-08 2025-02-24 03:49:38.668231-08 leaderboard f H100 0.00028956029310344826 t \N \N \N {} +1652 545 2025-02-25 09:28:59.147977-08 2025-02-25 08:22:34.616891-08 benchmark f A100 \N t \N \N \N {} +1653 545 2025-02-25 09:29:22.402709-08 2025-02-25 09:43:20.480827-08 leaderboard f A100 0.0031917851666666665 t \N \N \N {} +1654 545 2025-02-25 09:23:36.561531-08 2025-02-25 09:22:56.481202-08 test t A100 \N t \N \N \N {} +1655 545 2025-02-25 09:14:47.319988-08 2025-02-25 09:09:28.866828-08 benchmark t A100 \N t \N \N \N {} +1665 549 2025-02-25 09:27:34.175531-08 2025-02-25 08:31:10.590161-08 test t H100 \N t \N \N \N {} +2774 966 2025-02-26 20:38:42.929598-08 2025-02-26 19:33:54.323653-08 benchmark f A100 \N t \N \N \N {} +1671 550 2025-02-25 08:02:54.544859-08 2025-02-25 09:33:33.645968-08 test t H100 \N t \N \N \N {} +1672 550 2025-02-25 08:20:15.110251-08 2025-02-25 09:46:59.895856-08 benchmark t H100 \N t \N \N \N {} +1673 550 2025-02-25 08:16:05.976617-08 2025-02-25 08:56:29.376989-08 leaderboard t H100 0.0015150700833333332 t \N \N \N {} +1674 550 2025-02-25 09:23:16.872377-08 2025-02-25 09:42:17.075687-08 test f H100 \N t \N \N \N {} +1675 550 2025-02-25 08:00:39.040555-08 2025-02-25 08:14:08.930921-08 benchmark f H100 \N t \N \N \N {} +1677 551 2025-02-25 09:24:55.210117-08 2025-02-25 08:31:50.387183-08 benchmark f H100 \N t \N \N \N {} +4802 1487 2025-03-02 13:11:42.877171-08 2025-03-02 12:36:48.868582-08 test t A100 \N t \N \N \N {} +1704 565 2025-02-25 10:02:10.859525-08 2025-02-25 10:07:50.65938-08 test f A100 \N f \N \N \N {} +1705 566 2025-02-25 08:18:19.009138-08 2025-02-25 09:21:13.615277-08 test f A100 \N f \N \N \N {} +1706 566 2025-02-25 08:59:25.06759-08 2025-02-25 08:45:34.409254-08 test t A100 \N f \N \N \N {} +1708 570 2025-02-25 10:07:15.647675-08 2025-02-25 08:36:37.22994-08 test f T4 \N f \N \N \N {} +1709 570 2025-02-25 09:55:08.132128-08 2025-02-25 08:32:55.552216-08 test t T4 \N f \N \N \N {} +535 154 2025-02-24 04:07:21.29398-08 2025-02-24 04:13:20.791686-08 test t H100 \N t \N \N \N {} +536 154 2025-02-24 03:48:34.48216-08 2025-02-24 03:16:51.711753-08 benchmark t H100 \N t \N \N \N {} +537 154 2025-02-24 04:32:16.672746-08 2025-02-24 03:57:43.518406-08 leaderboard t H100 0.0002925063181818182 t \N \N \N {} +538 155 2025-02-24 04:35:32.646526-08 2025-02-24 05:04:46.599926-08 test f L4 \N f \N \N \N {} +539 155 2025-02-24 04:18:31.344638-08 2025-02-24 04:55:25.858103-08 test f T4 \N f \N \N \N {} +540 156 2025-02-24 03:43:29.839126-08 2025-02-24 03:21:47.390495-08 test f T4 \N t \N \N \N {} +543 157 2025-02-24 04:59:50.4166-08 2025-02-24 04:19:05.690038-08 leaderboard f A100 0.007824704333333333 t \N \N \N {} +544 157 2025-02-24 03:39:23.735651-08 2025-02-24 04:15:01.411276-08 test f T4 \N t \N \N \N {} +545 157 2025-02-24 05:04:10.421675-08 2025-02-24 04:14:54.241718-08 benchmark f T4 \N t \N \N \N {} +546 157 2025-02-24 04:57:33.857732-08 2025-02-24 04:22:04.003536-08 leaderboard f T4 0.022980689 t \N \N \N {} +547 157 2025-02-24 05:07:45.788613-08 2025-02-24 05:08:16.466658-08 test t T4 \N t \N \N \N {} +548 157 2025-02-24 03:17:36.228955-08 2025-02-24 05:13:27.73913-08 benchmark t T4 \N t \N \N \N {} +561 162 2025-02-24 05:06:05.251848-08 2025-02-24 03:48:29.825948-08 test f A100 \N t \N \N \N {} +562 162 2025-02-24 04:57:26.287277-08 2025-02-24 04:27:02.699784-08 benchmark f A100 \N t \N \N \N {} +563 162 2025-02-24 03:32:11.303899-08 2025-02-24 04:54:23.971009-08 leaderboard f A100 0.003460327 t \N \N \N {} +564 162 2025-02-24 04:54:00.853848-08 2025-02-24 04:59:22.66851-08 test t A100 \N t \N \N \N {} +565 162 2025-02-24 03:49:00.261076-08 2025-02-24 04:32:14.986087-08 benchmark t A100 \N t \N \N \N {} +566 162 2025-02-24 05:24:04.211942-08 2025-02-24 04:24:57.431418-08 leaderboard t A100 0.0034634703333333333 t \N \N \N {} +567 162 2025-02-24 04:05:26.770481-08 2025-02-24 03:41:41.45303-08 test t T4 \N t \N \N \N {} +1823 621 2025-02-25 09:37:22.310956-08 2025-02-25 10:04:30.945593-08 test f H100 \N t \N \N \N {} +1824 621 2025-02-25 09:48:47.475736-08 2025-02-25 10:36:25.122648-08 benchmark f H100 \N t \N \N \N {} +2416 818 2025-02-26 13:08:10.332573-08 2025-02-26 12:27:32.298697-08 benchmark f H100 \N f \N \N \N {} +2631 885 2025-02-26 14:51:45.736554-08 2025-02-26 14:47:20.105491-08 test f T4 \N t \N \N \N {} +1829 621 2025-02-25 09:36:01.751668-08 2025-02-25 10:06:26.482401-08 test t H100 \N t \N \N \N {} +1835 621 2025-02-25 10:55:06.644298-08 2025-02-25 10:16:25.436989-08 test t T4 \N t \N \N \N {} +1831 621 2025-02-25 09:36:43.309023-08 2025-02-25 09:39:20.008693-08 leaderboard t H100 0.00007977966666666668 t \N \N \N {} +1832 621 2025-02-25 09:37:12.560705-08 2025-02-25 11:23:33.885332-08 test f T4 \N t \N \N \N {} +1833 621 2025-02-25 10:54:16.770926-08 2025-02-25 10:13:23.913495-08 benchmark f T4 \N t \N \N \N {} +1834 621 2025-02-25 10:45:06.995356-08 2025-02-25 11:28:55.043321-08 leaderboard f T4 0.0003147605 t \N \N \N {} +1885 637 2025-02-25 11:14:01.886924-08 2025-02-25 10:33:34.875247-08 test t A100 \N t \N \N \N {} +3795 1280 2025-03-01 03:06:18.765637-08 2025-03-01 02:10:41.553886-08 test f A100 \N t \N \N \N {} +1836 621 2025-02-25 09:59:07.958405-08 2025-02-25 11:28:20.518166-08 benchmark t T4 \N t \N \N \N {} +1837 621 2025-02-25 10:14:38.896693-08 2025-02-25 10:18:05.012617-08 leaderboard t T4 0.00028145 t \N \N \N {} +568 162 2025-02-24 04:07:53.242047-08 2025-02-24 04:45:46.701262-08 benchmark t T4 \N t \N \N \N {} +569 162 2025-02-24 04:57:58.483951-08 2025-02-24 03:47:41.879786-08 leaderboard t T4 0.018000088 t \N \N \N {} +570 162 2025-02-24 04:07:02.728359-08 2025-02-24 03:30:28.466692-08 test f T4 \N t \N \N \N {} +571 162 2025-02-24 04:55:55.111211-08 2025-02-24 03:47:05.632536-08 benchmark f T4 \N t \N \N \N {} +578 164 2025-02-24 03:34:30.302743-08 2025-02-24 03:44:44.420625-08 test t H100 \N f \N \N \N {} +579 164 2025-02-24 04:40:03.835891-08 2025-02-24 05:14:49.678388-08 test f H100 \N f \N \N \N {} +580 165 2025-02-24 03:46:14.412089-08 2025-02-24 03:30:10.462639-08 test f T4 \N f \N \N \N {} +583 168 2025-02-24 03:40:59.436525-08 2025-02-24 04:13:33.24366-08 test f T4 \N f \N \N \N {} +584 169 2025-02-24 04:11:41.64701-08 2025-02-24 04:29:29.162765-08 test f T4 \N f \N \N \N {} +585 170 2025-02-24 03:50:54.355136-08 2025-02-24 04:32:48.353854-08 test f T4 \N f \N \N \N {} +609 172 2025-02-24 04:16:21.994773-08 2025-02-24 05:27:31.23999-08 test t L4 \N t \N \N \N {} +1839 621 2025-02-25 10:11:09.972425-08 2025-02-25 11:10:01.691566-08 benchmark f L4 \N t \N \N \N {} +1840 621 2025-02-25 10:32:35.014208-08 2025-02-25 10:14:06.170474-08 leaderboard f L4 0.00015338506896551724 t \N \N \N {} +1891 641 2025-02-25 12:30:16.797873-08 2025-02-25 12:50:15.869754-08 test t A100 \N t \N \N \N {} +3798 1280 2025-03-01 02:58:08.249313-08 2025-03-01 02:10:02.417766-08 test t A100 \N t \N \N \N {} +1844 622 2025-02-25 10:39:39.253611-08 2025-02-25 10:37:50.127615-08 benchmark f H100 \N t \N \N \N {} +1845 623 2025-02-25 11:18:57.219265-08 2025-02-25 10:30:49.939246-08 benchmark f H100 \N t \N \N \N {} +1851 626 2025-02-25 11:37:40.953324-08 2025-02-25 10:18:34.146973-08 test t H100 \N t \N \N \N {} +1911 658 2025-02-25 13:07:44.459666-08 2025-02-25 12:36:55.345563-08 test f L4 \N t \N \N \N {} +1854 627 2025-02-25 11:09:31.179796-08 2025-02-25 09:46:07.024596-08 test f H100 \N t \N \N \N {} +1855 627 2025-02-25 10:50:06.45193-08 2025-02-25 10:00:12.679191-08 benchmark f H100 \N t \N \N \N {} +1856 627 2025-02-25 11:18:03.678053-08 2025-02-25 09:50:41.832265-08 leaderboard f H100 0.001456563 t \N \N \N {} +1857 627 2025-02-25 10:15:45.419667-08 2025-02-25 09:46:39.110459-08 test t H100 \N t \N \N \N {} +1858 627 2025-02-25 11:37:33.070625-08 2025-02-25 10:23:48.04595-08 benchmark t H100 \N t \N \N \N {} +1859 627 2025-02-25 10:14:25.240727-08 2025-02-25 10:53:04.749149-08 leaderboard t H100 0.0014359026000000002 t \N \N \N {} +1860 628 2025-02-25 10:38:15.088855-08 2025-02-25 11:33:14.17158-08 benchmark f A100 \N t \N \N \N {} +1861 629 2025-02-25 11:24:33.838768-08 2025-02-25 11:45:59.052869-08 test f T4 \N t \N \N \N {} +1862 629 2025-02-25 10:18:02.87223-08 2025-02-25 10:01:30.670626-08 benchmark f T4 \N t \N \N \N {} +1863 629 2025-02-25 10:09:20.677026-08 2025-02-25 09:56:35.921709-08 leaderboard f T4 0.02022982983333333 t \N \N \N {} +2418 821 2025-02-26 12:32:06.906595-08 2025-02-26 12:30:33.821366-08 benchmark f H100 \N f \N \N \N {} +1872 635 2025-02-25 10:44:47.83768-08 2025-02-25 11:13:43.163043-08 leaderboard f A100 0.0031772633333333336 t \N \N \N {} +1873 635 2025-02-25 11:15:16.010215-08 2025-02-25 11:53:54.827853-08 test t A100 \N t \N \N \N {} +1875 635 2025-02-25 11:43:28.20069-08 2025-02-25 10:13:38.45603-08 leaderboard t A100 0.0032446378333333336 t \N \N \N {} +1877 636 2025-02-25 11:17:18.590101-08 2025-02-25 10:55:34.300914-08 benchmark f H100 \N t \N \N \N {} +1878 636 2025-02-25 11:59:53.786933-08 2025-02-25 11:34:35.925027-08 leaderboard f H100 0.0015205423333333333 t \N \N \N {} +588 172 2025-02-24 03:58:37.431798-08 2025-02-24 05:14:46.271612-08 test t A100 \N t \N \N \N {} +589 172 2025-02-24 05:31:54.505019-08 2025-02-24 04:23:39.720037-08 benchmark t A100 \N t \N \N \N {} +590 172 2025-02-24 03:42:59.187635-08 2025-02-24 04:26:15.997691-08 leaderboard t A100 0.003166644 t \N \N \N {} +591 172 2025-02-24 05:33:24.628818-08 2025-02-24 05:27:53.609663-08 test f H100 \N t \N \N \N {} +624 174 2025-02-24 04:00:03.344427-08 2025-02-24 04:30:10.212094-08 test f H100 \N t \N \N \N {} +592 172 2025-02-24 04:51:20.046359-08 2025-02-24 04:45:20.807066-08 benchmark f H100 \N t \N \N \N {} +1879 636 2025-02-25 10:21:35.338595-08 2025-02-25 10:41:36.288859-08 test t H100 \N t \N \N \N {} +1880 636 2025-02-25 11:47:02.581075-08 2025-02-25 11:37:54.178586-08 benchmark t H100 \N t \N \N \N {} +1881 636 2025-02-25 11:55:10.637717-08 2025-02-25 10:36:44.890626-08 leaderboard t H100 0.0014829484285714286 t \N \N \N {} +1886 637 2025-02-25 12:00:57.953939-08 2025-02-25 10:55:31.592722-08 benchmark t A100 \N t \N \N \N {} +1887 637 2025-02-25 11:56:19.406489-08 2025-02-25 11:35:39.598186-08 leaderboard t A100 0.003213315 t \N \N \N {} +1888 638 2025-02-25 12:00:43.449685-08 2025-02-25 12:07:45.096661-08 benchmark f A100 \N t \N \N \N {} +2420 823 2025-02-26 13:00:06.805549-08 2025-02-26 12:21:44.75151-08 benchmark f H100 \N t \N \N \N {} +1895 641 2025-02-25 12:54:01.264518-08 2025-02-25 11:46:30.101684-08 benchmark f A100 \N t \N \N \N {} +1896 641 2025-02-25 13:38:23.446635-08 2025-02-25 13:36:59.184489-08 leaderboard f A100 0.0030864853333333335 t \N \N \N {} +1897 642 2025-02-25 13:27:14.029692-08 2025-02-25 12:51:21.781335-08 test f T4 \N f \N \N \N {} +1912 658 2025-02-25 13:03:54.537257-08 2025-02-25 13:47:58.322026-08 benchmark f L4 \N t \N \N \N {} +1898 643 2025-02-25 13:22:18.087617-08 2025-02-25 11:54:21.931015-08 benchmark f A100 \N t \N \N \N {} +2421 825 2025-02-26 13:18:04.197511-08 2025-02-26 13:05:21.334106-08 benchmark f H100 \N t \N \N \N {} +1904 650 2025-02-25 12:31:28.402471-08 2025-02-25 13:39:04.170868-08 benchmark f A100 \N t \N \N \N {} +1905 651 2025-02-25 13:24:04.41766-08 2025-02-25 13:44:49.042952-08 benchmark f A100 \N t \N \N \N {} +1914 658 2025-02-25 13:39:12.203771-08 2025-02-25 12:35:51.134173-08 test t L4 \N t \N \N \N {} +1906 652 2025-02-25 13:04:03.79534-08 2025-02-25 12:26:24.162075-08 benchmark f A100 \N t \N \N \N {} +1907 654 2025-02-25 12:31:53.004451-08 2025-02-25 12:56:24.053648-08 test f T4 \N f \N \N \N {} +1908 653 2025-02-25 12:56:54.374943-08 2025-02-25 12:02:39.31621-08 benchmark f A100 \N t \N \N \N {} +1909 656 2025-02-25 13:47:06.574062-08 2025-02-25 13:06:17.287316-08 benchmark f A100 \N t \N \N \N {} +1910 657 2025-02-25 12:05:55.108287-08 2025-02-25 12:31:57.728641-08 benchmark f A100 \N t \N \N \N {} +3804 1281 2025-03-01 03:15:18.618566-08 2025-03-01 03:09:21.435376-08 test t A100 \N t \N \N \N {} +1915 658 2025-02-25 12:52:49.663863-08 2025-02-25 12:15:36.754739-08 benchmark t L4 \N t \N \N \N {} +1916 658 2025-02-25 13:46:15.680818-08 2025-02-25 12:14:55.778199-08 leaderboard t L4 0.017479398 t \N \N \N {} +593 172 2025-02-24 04:00:11.157726-08 2025-02-24 04:23:52.92541-08 leaderboard f H100 0.001457972 t \N \N \N {} +594 172 2025-02-24 04:24:57.48744-08 2025-02-24 05:39:56.243248-08 test t H100 \N t \N \N \N {} +595 172 2025-02-24 04:05:36.140858-08 2025-02-24 04:45:49.663541-08 benchmark t H100 \N t \N \N \N {} +596 172 2025-02-24 04:03:24.134887-08 2025-02-24 04:07:50.692444-08 leaderboard t H100 0.0014493626666666668 t \N \N \N {} +597 172 2025-02-24 04:22:04.734823-08 2025-02-24 05:20:24.419522-08 test t T4 \N t \N \N \N {} +598 172 2025-02-24 04:54:26.09647-08 2025-02-24 04:37:32.588527-08 benchmark t T4 \N t \N \N \N {} +599 172 2025-02-24 05:20:32.672091-08 2025-02-24 04:22:02.580439-08 leaderboard t T4 0.01753550875 t \N \N \N {} +601 172 2025-02-24 04:01:40.262757-08 2025-02-24 04:31:18.294476-08 benchmark f A100 \N t \N \N \N {} +602 172 2025-02-24 04:53:49.361583-08 2025-02-24 04:48:49.182764-08 leaderboard f A100 0.0031660506666666664 t \N \N \N {} +603 172 2025-02-24 03:52:06.072469-08 2025-02-24 03:59:33.848814-08 test f T4 \N t \N \N \N {} +604 172 2025-02-24 03:42:48.462511-08 2025-02-24 05:26:31.44727-08 benchmark f T4 \N t \N \N \N {} +610 172 2025-02-24 05:04:05.242805-08 2025-02-24 03:44:40.846459-08 benchmark t L4 \N t \N \N \N {} +611 172 2025-02-24 04:35:15.919214-08 2025-02-24 05:11:46.93162-08 leaderboard t L4 0.017569029 t \N \N \N {} +612 173 2025-02-24 05:24:50.95809-08 2025-02-24 04:11:33.537819-08 test f H100 \N f \N \N \N {} +613 173 2025-02-24 03:51:03.148107-08 2025-02-24 05:38:19.375618-08 test t H100 \N f \N \N \N {} +614 175 2025-02-24 04:40:13.642706-08 2025-02-24 05:40:34.3719-08 test f T4 \N f \N \N \N {} +615 174 2025-02-24 05:40:44.406358-08 2025-02-24 03:57:34.265608-08 test f A100 \N t \N \N \N {} +625 174 2025-02-24 05:28:15.27384-08 2025-02-24 04:58:32.636743-08 benchmark f H100 \N t \N \N \N {} +626 174 2025-02-24 04:27:19.875646-08 2025-02-24 05:38:11.378119-08 leaderboard f H100 0.00146166825 t \N \N \N {} +627 174 2025-02-24 04:57:35.414356-08 2025-02-24 05:31:36.9668-08 test t T4 \N t \N \N \N {} +628 174 2025-02-24 04:45:34.255845-08 2025-02-24 04:05:43.860604-08 benchmark t T4 \N t \N \N \N {} +629 174 2025-02-24 05:27:09.661991-08 2025-02-24 04:50:39.24896-08 leaderboard t T4 0.0208113558 t \N \N \N {} +1499 502 2025-02-25 06:47:02.590704-08 2025-02-25 06:27:19.767132-08 test f T4 \N t \N \N \N {} +633 174 2025-02-24 04:12:12.277241-08 2025-02-24 05:10:27.381931-08 test t L4 \N t \N \N \N {} +634 174 2025-02-24 05:00:36.484962-08 2025-02-24 04:04:08.518337-08 benchmark t L4 \N t \N \N \N {} +635 174 2025-02-24 04:34:32.791262-08 2025-02-24 04:57:05.719322-08 leaderboard t L4 0.017792222333333333 t \N \N \N {} +636 174 2025-02-24 04:08:01.422936-08 2025-02-24 05:26:59.018927-08 test f L4 \N t \N \N \N {} +637 174 2025-02-24 05:17:03.979995-08 2025-02-24 04:45:28.587227-08 benchmark f L4 \N t \N \N \N {} +638 174 2025-02-24 04:27:22.118036-08 2025-02-24 03:57:14.285261-08 leaderboard f L4 0.017829622 t \N \N \N {} +3276 1144 2025-02-28 06:55:15.001553-08 2025-02-28 05:46:00.750654-08 benchmark f A100 \N t \N \N \N {} +1961 670 2025-02-25 12:57:56.587732-08 2025-02-25 13:49:48.593819-08 benchmark f A100 \N t \N \N \N {} +642 177 2025-02-24 05:07:49.574927-08 2025-02-24 04:45:31.62306-08 test f T4 \N f \N \N \N {} +1962 671 2025-02-25 13:46:47.86222-08 2025-02-25 14:02:39.534938-08 benchmark f A100 \N t \N \N \N {} +1963 672 2025-02-25 14:00:04.67254-08 2025-02-25 13:25:13.842123-08 test t H100 \N t \N \N \N {} +1964 672 2025-02-25 12:36:59.057138-08 2025-02-25 14:02:22.120333-08 benchmark t H100 \N t \N \N \N {} +1965 672 2025-02-25 12:29:52.01167-08 2025-02-25 14:05:06.656703-08 leaderboard t H100 0.003437508 t \N \N \N {} +1966 672 2025-02-25 12:39:24.055726-08 2025-02-25 13:00:24.611271-08 test f H100 \N t \N \N \N {} +1967 672 2025-02-25 12:58:49.474139-08 2025-02-25 13:26:41.827259-08 benchmark f H100 \N t \N \N \N {} +1968 672 2025-02-25 13:26:33.936094-08 2025-02-25 12:40:21.065589-08 leaderboard f H100 0.003485398 t \N \N \N {} +2215 724 2025-02-25 14:37:54.567252-08 2025-02-25 14:43:57.444662-08 leaderboard t A100 0.010253208460000001 t \N \N \N {} +2216 724 2025-02-25 15:33:44.119406-08 2025-02-25 15:13:09.534917-08 test t H100 \N t \N \N \N {} +2253 752 2025-02-26 00:42:28.558196-08 2025-02-26 02:01:39.061078-08 benchmark f H100 \N f \N \N \N {} +2218 724 2025-02-25 15:32:48.937419-08 2025-02-25 15:25:26.740188-08 leaderboard t H100 0.00353085645 t \N \N \N {} +2219 724 2025-02-25 15:11:42.946931-08 2025-02-25 14:44:35.593924-08 test f A100 \N t \N \N \N {} +2220 724 2025-02-25 14:34:20.966564-08 2025-02-25 15:36:27.340882-08 benchmark f A100 \N t \N \N \N {} +2221 724 2025-02-25 15:58:30.291254-08 2025-02-25 14:42:07.237481-08 leaderboard f A100 0.0107430945 t \N \N \N {} +691 217 2025-02-24 07:04:53.233308-08 2025-02-24 08:10:24.296185-08 test t A100 \N f \N \N \N {} +5663 1723 2025-03-09 10:05:43.141436-07 2025-03-09 10:57:02.505632-07 leaderboard t T4 0.016583018 t \N \N \N {} +2313 778 2025-02-26 03:54:39.554659-08 2025-02-26 04:48:44.686597-08 benchmark f A100 \N t \N \N \N {} +2314 778 2025-02-26 03:35:24.258305-08 2025-02-26 04:57:01.237295-08 leaderboard f A100 0.0030904886666666665 t \N \N \N {} +2315 781 2025-02-26 08:10:42.313296-08 2025-02-26 08:01:03.24323-08 test t A100 \N t \N \N \N {} +2330 784 2025-02-26 08:48:24.701263-08 2025-02-26 06:56:52.366196-08 test t A100 \N f \N \N \N {} +2318 781 2025-02-26 08:17:57.941797-08 2025-02-26 07:48:29.179932-08 test f A100 \N t \N \N \N {} +2319 781 2025-02-26 08:04:49.559876-08 2025-02-26 07:59:49.9534-08 benchmark f A100 \N t \N \N \N {} +2320 781 2025-02-26 07:21:32.163845-08 2025-02-26 08:26:16.891069-08 leaderboard f A100 0.0031749516666666664 t \N \N \N {} +2321 782 2025-02-26 07:07:41.841142-08 2025-02-26 08:08:18.275768-08 test f T4 \N f \N \N \N {} +2322 782 2025-02-26 08:40:25.972436-08 2025-02-26 07:46:04.302799-08 test t T4 \N f \N \N \N {} +2323 783 2025-02-26 08:25:20.355357-08 2025-02-26 07:28:44.30517-08 test t A100 \N t \N \N \N {} +2334 785 2025-02-26 08:50:29.968504-08 2025-02-26 08:16:16.86245-08 test t A100 \N t \N \N \N {} +3266 1142 2025-02-28 05:40:58.845992-08 2025-02-28 06:20:23.84623-08 test t A100 \N t \N \N \N {} +692 218 2025-02-24 07:35:12.909281-08 2025-02-24 08:32:29.712533-08 test f T4 \N f \N \N \N {} +3856 1291 2025-03-01 03:58:25.966221-08 2025-03-01 02:58:47.903585-08 test f A100 \N t \N \N \N {} +2500 871 2025-02-26 14:21:21.727765-08 2025-02-26 14:48:13.334885-08 test f T4 \N t \N \N \N {} +2501 872 2025-02-26 14:53:44.720161-08 2025-02-26 14:09:15.911305-08 test t A100 \N t \N \N \N {} +2502 872 2025-02-26 14:29:17.86941-08 2025-02-26 14:42:23.807854-08 benchmark t A100 \N t \N \N \N {} +2504 872 2025-02-26 14:21:53.085089-08 2025-02-26 13:33:28.796563-08 test t H100 \N t \N \N \N {} +3396 1171 2025-02-28 08:52:34.344836-08 2025-02-28 10:12:06.257868-08 test t L4 \N f \N \N \N {} +663 194 2025-02-24 05:41:36.621215-08 2025-02-24 06:09:54.974777-08 test f T4 \N t \N \N \N {} +664 195 2025-02-24 06:08:45.709175-08 2025-02-24 05:08:22.138404-08 benchmark f H100 \N f \N \N \N {} +666 197 2025-02-24 06:56:56.061652-08 2025-02-24 06:22:23.208702-08 benchmark f H100 \N f \N \N \N {} +667 198 2025-02-24 06:34:23.314709-08 2025-02-24 05:20:01.543454-08 benchmark f H100 \N f \N \N \N {} +668 199 2025-02-24 04:59:42.957047-08 2025-02-24 05:30:49.743808-08 benchmark f H100 \N f \N \N \N {} +2598 885 2025-02-26 14:21:31.605809-08 2025-02-26 13:26:53.536131-08 test t L4 \N t \N \N \N {} +2599 885 2025-02-26 14:56:23.588162-08 2025-02-26 13:44:20.657543-08 benchmark t L4 \N t \N \N \N {} +2600 885 2025-02-26 14:30:27.342225-08 2025-02-26 14:48:35.398787-08 leaderboard t L4 0.017775642666666668 t \N \N \N {} +2601 884 2025-02-26 14:45:15.947305-08 2025-02-26 14:55:41.866073-08 test t H100 \N t \N \N \N {} +2613 884 2025-02-26 13:25:03.130497-08 2025-02-26 13:21:04.408924-08 test f T4 \N t \N \N \N {} +2604 884 2025-02-26 15:11:47.082796-08 2025-02-26 14:52:15.530642-08 test t A100 \N t \N \N \N {} +2605 884 2025-02-26 13:27:46.343561-08 2025-02-26 14:55:24.020372-08 benchmark t A100 \N t \N \N \N {} +2606 884 2025-02-26 14:44:05.568836-08 2025-02-26 13:52:58.061404-08 leaderboard t A100 0.0032559906666666666 t \N \N \N {} +2621 885 2025-02-26 14:23:14.309831-08 2025-02-26 13:26:20.529447-08 leaderboard t A100 0.00319686775 t \N \N \N {} +3421 1179 2025-02-28 10:14:03.171361-08 2025-02-28 10:16:52.20393-08 test t L4 \N f \N \N \N {} +2610 884 2025-02-26 15:13:57.604086-08 2025-02-26 13:38:33.967499-08 test f H100 \N t \N \N \N {} +2611 884 2025-02-26 14:21:20.575521-08 2025-02-26 14:28:32.061414-08 benchmark f H100 \N t \N \N \N {} +2612 884 2025-02-26 14:43:21.793286-08 2025-02-26 15:00:54.664103-08 leaderboard f H100 0.0014509983333333332 t \N \N \N {} +2622 885 2025-02-26 14:42:43.496715-08 2025-02-26 14:25:56.942011-08 test f L4 \N t \N \N \N {} +3428 1183 2025-02-28 10:08:45.167126-08 2025-02-28 11:18:19.555595-08 test t L4 \N f \N \N \N {} +2614 884 2025-02-26 15:08:00.153692-08 2025-02-26 14:14:23.980237-08 benchmark f T4 \N t \N \N \N {} +2615 884 2025-02-26 14:02:53.674937-08 2025-02-26 14:12:29.617332-08 leaderboard f T4 0.01843677733333333 t \N \N \N {} +2616 885 2025-02-26 13:36:00.637075-08 2025-02-26 13:52:46.766673-08 test t H100 \N t \N \N \N {} +2617 885 2025-02-26 14:50:02.881007-08 2025-02-26 15:06:57.135832-08 benchmark t H100 \N t \N \N \N {} +2618 885 2025-02-26 13:35:29.138108-08 2025-02-26 15:14:08.351625-08 leaderboard t H100 0.0014837716 t \N \N \N {} +2619 885 2025-02-26 13:51:02.414525-08 2025-02-26 14:11:16.314329-08 test t A100 \N t \N \N \N {} +2620 885 2025-02-26 14:21:00.02625-08 2025-02-26 13:34:27.254132-08 benchmark t A100 \N t \N \N \N {} +2625 885 2025-02-26 13:29:27.081756-08 2025-02-26 14:06:55.891402-08 test f A100 \N t \N \N \N {} +2626 885 2025-02-26 14:36:19.92372-08 2025-02-26 14:31:38.799097-08 benchmark f A100 \N t \N \N \N {} +2627 885 2025-02-26 13:30:35.621947-08 2025-02-26 14:27:23.062497-08 leaderboard f A100 0.003209451 t \N \N \N {} +2628 885 2025-02-26 14:10:08.722188-08 2025-02-26 15:18:05.698046-08 test t T4 \N t \N \N \N {} +2629 885 2025-02-26 14:59:49.529515-08 2025-02-26 13:34:26.486834-08 benchmark t T4 \N t \N \N \N {} +2630 885 2025-02-26 14:58:52.752015-08 2025-02-26 14:53:03.340521-08 leaderboard t T4 0.017220341666666666 t \N \N \N {} +2634 886 2025-02-26 14:08:14.352049-08 2025-02-26 13:46:49.236868-08 benchmark f H100 \N t \N \N \N {} +670 201 2025-02-24 05:45:11.842551-08 2025-02-24 06:28:30.253378-08 benchmark f H100 \N f \N \N \N {} +704 225 2025-02-24 08:56:12.740421-08 2025-02-24 08:34:20.349342-08 leaderboard f A100 0.011330630333333334 t \N \N \N {} +1439 480 2025-02-25 04:48:27.37121-08 2025-02-25 05:01:05.017307-08 test t A100 \N f \N \N \N {} +671 203 2025-02-24 06:24:20.549783-08 2025-02-24 05:24:34.368331-08 test f H100 \N f \N \N \N {} +673 205 2025-02-24 07:04:18.018562-08 2025-02-24 05:35:41.546773-08 test f T4 \N t \N \N \N {} +674 206 2025-02-24 06:06:45.212354-08 2025-02-24 07:06:51.567687-08 benchmark f T4 \N t \N \N \N {} +676 208 2025-02-24 06:06:40.542676-08 2025-02-24 06:41:12.566535-08 test f H100 \N t \N \N \N {} +705 225 2025-02-24 08:55:10.930728-08 2025-02-24 07:46:29.223577-08 test t A100 \N t \N \N \N {} +1502 502 2025-02-25 07:17:02.787-08 2025-02-25 07:28:38.183565-08 test t T4 \N t \N \N \N {} +677 209 2025-02-24 05:52:20.627547-08 2025-02-24 06:50:43.221253-08 benchmark f H100 \N t \N \N \N {} +678 210 2025-02-24 05:28:59.266404-08 2025-02-24 06:40:36.860883-08 benchmark f H100 \N t \N \N \N {} +679 211 2025-02-24 05:59:18.290632-08 2025-02-24 05:31:04.882898-08 test t H100 \N t \N \N \N {} +6187 1904 2025-03-11 16:30:03.874833-07 2025-03-11 16:23:04.032961-07 test f T4 \N f \N \N \N {} +2635 887 2025-02-26 14:23:20.516781-08 2025-02-26 13:30:42.901197-08 benchmark f H100 \N t \N \N \N {} +2636 888 2025-02-26 15:08:09.895226-08 2025-02-26 15:02:44.771912-08 benchmark f H100 \N t \N \N \N {} +2637 889 2025-02-26 14:20:26.49628-08 2025-02-26 14:31:54.196513-08 benchmark f H100 \N t \N \N \N {} +3429 1183 2025-02-28 10:30:24.045825-08 2025-02-28 09:58:43.334761-08 test f L4 \N f \N \N \N {} +2658 917 2025-02-26 16:04:22.640816-08 2025-02-26 15:50:33.550145-08 test t L4 \N t \N \N \N {} +2642 894 2025-02-26 15:22:35.57726-08 2025-02-26 15:19:16.529445-08 benchmark f H100 \N t \N \N \N {} +2643 895 2025-02-26 14:19:06.387984-08 2025-02-26 13:54:35.774098-08 benchmark f H100 \N t \N \N \N {} +2644 896 2025-02-26 14:32:56.05166-08 2025-02-26 15:21:02.107636-08 benchmark f H100 \N t \N \N \N {} +2645 897 2025-02-26 14:43:32.97254-08 2025-02-26 15:35:10.004299-08 benchmark f H100 \N t \N \N \N {} +3642 1244 2025-02-28 23:55:30.080534-08 2025-03-01 00:02:11.688916-08 test f A100 \N t \N \N \N {} +2648 900 2025-02-26 14:55:59.543208-08 2025-02-26 15:16:42.738329-08 benchmark f H100 \N f \N \N \N {} +2649 902 2025-02-26 15:19:37.018566-08 2025-02-26 14:33:09.313223-08 benchmark f H100 \N t \N \N \N {} +2650 905 2025-02-26 15:06:58.156118-08 2025-02-26 15:39:57.941001-08 benchmark f H100 \N t \N \N \N {} +3868 1293 2025-03-01 03:27:42.833831-08 2025-03-01 02:55:20.805453-08 test t A100 \N t \N \N \N {} +2653 910 2025-02-26 15:22:55.54094-08 2025-02-26 15:29:37.757077-08 benchmark f H100 \N f \N \N \N {} +2654 912 2025-02-26 15:40:15.529449-08 2025-02-26 14:30:04.45723-08 test f H100 \N t \N \N \N {} +2655 913 2025-02-26 15:41:19.632645-08 2025-02-26 15:25:04.114543-08 benchmark f H100 \N t \N \N \N {} +2656 914 2025-02-26 15:28:36.303476-08 2025-02-26 14:41:59.161801-08 benchmark f A100 \N t \N \N \N {} +686 213 2025-02-24 06:35:37.280408-08 2025-02-24 07:07:31.745156-08 benchmark f H100 \N t \N \N \N {} +687 214 2025-02-24 06:45:56.939468-08 2025-02-24 06:25:32.523917-08 benchmark f H100 \N t \N \N \N {} +693 219 2025-02-24 09:02:17.179137-08 2025-02-24 08:31:55.588104-08 test f T4 \N f \N \N \N {} +694 220 2025-02-24 07:57:20.923442-08 2025-02-24 08:06:53.334965-08 test f T4 \N f \N \N \N {} +6861 2127 2025-03-15 09:30:21.684959-07 2025-03-15 10:10:14.889721-07 test f A100 \N t \N \N \N {} +2657 915 2025-02-26 15:22:50.97253-08 2025-02-26 14:13:32.883512-08 benchmark f H100 \N f \N \N \N {} +2659 917 2025-02-26 15:59:10.277444-08 2025-02-26 16:06:48.272166-08 benchmark t L4 \N t \N \N \N {} +2660 917 2025-02-26 15:22:27.480576-08 2025-02-26 15:49:28.223669-08 leaderboard t L4 0.017139778 t \N \N \N {} +2661 917 2025-02-26 14:50:12.413294-08 2025-02-26 14:36:02.481211-08 test t A100 \N t \N \N \N {} +2662 917 2025-02-26 14:49:43.883464-08 2025-02-26 15:40:03.528298-08 benchmark t A100 \N t \N \N \N {} +695 221 2025-02-24 08:04:03.997077-08 2025-02-24 09:06:50.538681-08 test f T4 \N f \N \N \N {} +696 222 2025-02-24 08:59:10.271492-08 2025-02-24 07:29:33.102147-08 benchmark f A100 \N f \N \N \N {} +697 222 2025-02-24 08:07:36.901531-08 2025-02-24 07:47:31.363673-08 benchmark f T4 \N f \N \N \N {} +1440 480 2025-02-25 04:05:00.096466-08 2025-02-25 05:05:26.708798-08 test f A100 \N f \N \N \N {} +698 222 2025-02-24 08:43:36.056535-08 2025-02-24 08:16:52.237368-08 benchmark f L4 \N f \N \N \N {} +699 222 2025-02-24 08:22:16.816685-08 2025-02-24 08:37:10.387978-08 benchmark f H100 \N f \N \N \N {} +700 223 2025-02-24 09:06:11.706145-08 2025-02-24 09:03:29.685017-08 benchmark f A100 \N f \N \N \N {} +701 224 2025-02-24 08:10:04.943823-08 2025-02-24 08:28:43.870189-08 test f T4 \N f \N \N \N {} +702 225 2025-02-24 07:56:55.117396-08 2025-02-24 08:48:49.945346-08 test f A100 \N t \N \N \N {} +703 225 2025-02-24 07:41:19.688526-08 2025-02-24 07:41:37.470123-08 benchmark f A100 \N t \N \N \N {} +706 225 2025-02-24 08:37:11.189015-08 2025-02-24 09:05:40.321522-08 benchmark t A100 \N t \N \N \N {} +708 226 2025-02-24 09:25:07.198881-08 2025-02-24 07:35:03.936398-08 benchmark f A100 \N f \N \N \N {} +709 227 2025-02-24 08:24:29.897497-08 2025-02-24 09:20:50.161506-08 benchmark f A100 \N t \N \N \N {} +710 228 2025-02-24 08:50:24.79349-08 2025-02-24 08:55:39.391024-08 test f T4 \N t \N \N \N {} +711 229 2025-02-24 08:11:00.669556-08 2025-02-24 09:13:50.969935-08 test t A100 \N t \N \N \N {} +1111 304 2025-02-24 17:03:32.591659-08 2025-02-24 17:41:47.088082-08 benchmark f H100 \N t \N \N \N {} +712 229 2025-02-24 08:28:09.285744-08 2025-02-24 09:27:17.171225-08 benchmark t A100 \N t \N \N \N {} +2663 917 2025-02-26 15:02:41.87638-08 2025-02-26 15:04:14.826114-08 leaderboard t A100 0.002618883 t \N \N \N {} +3474 1194 2025-02-28 10:51:23.270197-08 2025-02-28 10:28:38.739112-08 benchmark f A100 \N f \N \N \N {} +714 229 2025-02-24 09:20:32.013179-08 2025-02-24 09:10:19.888175-08 test f A100 \N t \N \N \N {} +719 230 2025-02-24 07:43:26.148361-08 2025-02-24 09:27:29.034748-08 leaderboard f A100 0.003160102 t \N \N \N {} +728 233 2025-02-24 07:51:31.813355-08 2025-02-24 07:55:17.330164-08 test t A100 \N t \N \N \N {} +729 233 2025-02-24 07:58:04.52096-08 2025-02-24 09:14:58.005432-08 benchmark t A100 \N t \N \N \N {} +730 233 2025-02-24 09:01:40.583243-08 2025-02-24 08:17:02.267771-08 leaderboard t A100 0.0031330916666666665 t \N \N \N {} +1443 483 2025-02-25 04:08:48.170684-08 2025-02-25 04:18:24.374905-08 test f A100 \N f \N \N \N {} +731 233 2025-02-24 08:17:49.25495-08 2025-02-24 09:15:06.490991-08 test f A100 \N t \N \N \N {} +5675 1724 2025-03-09 09:09:34.316424-07 2025-03-09 09:56:58.297658-07 leaderboard t A100 0.002777401 t \N \N \N {} +2664 917 2025-02-26 15:33:18.523093-08 2025-02-26 14:23:26.27638-08 test f T4 \N t \N \N \N {} +2665 917 2025-02-26 14:41:08.119956-08 2025-02-26 14:40:45.367615-08 benchmark f T4 \N t \N \N \N {} +2666 917 2025-02-26 14:19:24.006278-08 2025-02-26 15:07:56.17656-08 leaderboard f T4 0.017432490666666668 t \N \N \N {} +2667 917 2025-02-26 14:50:03.208616-08 2025-02-26 14:15:31.2517-08 test f H100 \N t \N \N \N {} +2668 917 2025-02-26 15:41:58.425053-08 2025-02-26 14:23:19.930763-08 benchmark f H100 \N t \N \N \N {} +3184 1121 2025-02-28 04:45:28.941252-08 2025-02-28 03:32:18.191207-08 test f A100 \N t \N \N \N {} +2669 917 2025-02-26 15:11:08.928473-08 2025-02-26 15:58:37.434081-08 leaderboard f H100 0.0014553213333333332 t \N \N \N {} +2670 917 2025-02-26 15:28:35.839236-08 2025-02-26 15:52:24.038032-08 test f A100 \N t \N \N \N {} +2671 917 2025-02-26 14:52:23.197769-08 2025-02-26 15:34:23.428279-08 benchmark f A100 \N t \N \N \N {} +2672 917 2025-02-26 14:12:48.444376-08 2025-02-26 14:28:23.028924-08 leaderboard f A100 0.003219043 t \N \N \N {} +2673 917 2025-02-26 14:12:35.128126-08 2025-02-26 14:43:26.451849-08 test t T4 \N t \N \N \N {} +737 233 2025-02-24 09:16:03.275201-08 2025-02-24 09:11:08.635071-08 test t T4 \N t \N \N \N {} +738 233 2025-02-24 08:28:01.859065-08 2025-02-24 09:22:10.211632-08 benchmark t T4 \N t \N \N \N {} +739 233 2025-02-24 09:08:18.555299-08 2025-02-24 09:02:46.143397-08 leaderboard t T4 0.0036530893333333336 t \N \N \N {} +740 233 2025-02-24 08:49:10.05294-08 2025-02-24 08:07:07.637237-08 test f T4 \N t \N \N \N {} +776 249 2025-02-24 10:24:50.908208-08 2025-02-24 11:51:41.650348-08 test f A100 \N t \N \N \N {} +3898 1300 2025-03-01 04:41:21.824886-08 2025-03-01 04:35:22.271324-08 benchmark f A100 \N t \N \N \N {} +747 235 2025-02-24 09:17:44.247963-08 2025-02-24 09:28:51.71791-08 test f A100 \N f \N \N \N {} +748 235 2025-02-24 09:36:38.154313-08 2025-02-24 09:52:27.287797-08 test t A100 \N f \N \N \N {} +749 236 2025-02-24 09:03:52.762503-08 2025-02-24 09:18:03.476551-08 test f A100 \N t \N \N \N {} +750 237 2025-02-24 09:27:00.648875-08 2025-02-24 09:17:20.060425-08 test t A100 \N t \N \N \N {} +758 241 2025-02-24 10:35:45.406451-08 2025-02-24 10:49:56.940266-08 test f A100 \N f \N \N \N {} +2679 917 2025-02-26 15:58:31.502323-08 2025-02-26 15:05:12.236739-08 test f L4 \N t \N \N \N {} +3475 1196 2025-02-28 10:58:00.781332-08 2025-02-28 11:31:50.239552-08 test t A100 \N t \N \N \N {} +2675 917 2025-02-26 15:10:31.598044-08 2025-02-26 15:45:21.966872-08 leaderboard t T4 0.017423966 t \N \N \N {} +2676 917 2025-02-26 15:37:23.354452-08 2025-02-26 14:45:19.799848-08 test t H100 \N t \N \N \N {} +2677 917 2025-02-26 15:32:46.804737-08 2025-02-26 14:42:52.925066-08 benchmark t H100 \N t \N \N \N {} +2678 917 2025-02-26 15:26:19.888166-08 2025-02-26 15:42:16.647691-08 leaderboard t H100 0.0014749786666666668 t \N \N \N {} +3478 1196 2025-02-28 11:49:03.159248-08 2025-02-28 11:21:38.865185-08 test f A100 \N t \N \N \N {} +3871 1294 2025-03-01 03:25:39.575831-08 2025-03-01 03:47:44.417542-08 test f A100 \N t \N \N \N {} +2680 917 2025-02-26 14:12:15.287164-08 2025-02-26 14:56:49.444837-08 benchmark f L4 \N t \N \N \N {} +2681 917 2025-02-26 14:13:57.106393-08 2025-02-26 14:37:54.475639-08 leaderboard f L4 0.017202002666666667 t \N \N \N {} +2682 919 2025-02-26 15:07:06.306277-08 2025-02-26 14:43:54.993315-08 test f T4 \N t \N \N \N {} +2683 918 2025-02-26 14:37:18.145006-08 2025-02-26 14:58:27.554341-08 test f H100 \N f \N \N \N {} +2684 920 2025-02-26 15:32:10.569826-08 2025-02-26 15:21:11.603029-08 benchmark f H100 \N f \N \N \N {} +2690 925 2025-02-26 15:23:42.15559-08 2025-02-26 16:09:05.490772-08 leaderboard t A100 0.003207825 t \N \N \N {} +2685 921 2025-02-26 15:18:14.224991-08 2025-02-26 15:44:27.451168-08 benchmark f H100 \N f \N \N \N {} +2687 923 2025-02-26 15:03:01.412592-08 2025-02-26 15:06:11.143738-08 benchmark f H100 \N t \N \N \N {} +2688 925 2025-02-26 16:08:56.665897-08 2025-02-26 15:00:21.657629-08 test t A100 \N t \N \N \N {} +2689 925 2025-02-26 16:07:17.241061-08 2025-02-26 14:31:28.824618-08 benchmark t A100 \N t \N \N \N {} +751 237 2025-02-24 10:32:35.667519-08 2025-02-24 09:04:14.834137-08 benchmark t A100 \N t \N \N \N {} +752 237 2025-02-24 10:42:58.851055-08 2025-02-24 09:40:55.016006-08 leaderboard t A100 0.0033126033333333335 t \N \N \N {} +753 237 2025-02-24 10:22:09.7399-08 2025-02-24 10:48:42.661894-08 test f A100 \N t \N \N \N {} +754 237 2025-02-24 09:37:37.615117-08 2025-02-24 10:02:09.097882-08 benchmark f A100 \N t \N \N \N {} +755 237 2025-02-24 10:36:07.704848-08 2025-02-24 10:23:31.161901-08 leaderboard f A100 0.003277688 t \N \N \N {} +756 238 2025-02-24 09:28:50.167162-08 2025-02-24 09:49:32.625124-08 test f A100 \N f \N \N \N {} +759 242 2025-02-24 10:36:29.62006-08 2025-02-24 10:53:18.167093-08 test f A100 \N t \N \N \N {} +3485 1197 2025-02-28 11:18:37.224946-08 2025-02-28 11:49:48.964789-08 test f A100 \N t \N \N \N {} +3874 1294 2025-03-01 03:31:01.665765-08 2025-03-01 04:31:31.171928-08 test t A100 \N t \N \N \N {} +760 242 2025-02-24 09:47:29.285286-08 2025-02-24 10:30:48.947657-08 benchmark f A100 \N t \N \N \N {} +761 242 2025-02-24 10:47:46.257274-08 2025-02-24 10:32:35.015062-08 leaderboard f A100 0.0032635085 t \N \N \N {} +762 242 2025-02-24 10:33:54.353655-08 2025-02-24 09:20:51.991089-08 test t A100 \N t \N \N \N {} +763 242 2025-02-24 10:41:28.131991-08 2025-02-24 10:23:01.349186-08 benchmark t A100 \N t \N \N \N {} +764 242 2025-02-24 10:31:24.13368-08 2025-02-24 11:10:39.141474-08 leaderboard t A100 0.0032074475454545452 t \N \N \N {} +766 244 2025-02-24 11:12:28.205288-08 2025-02-24 10:55:23.216173-08 test t A100 \N t \N \N \N {} +767 244 2025-02-24 10:50:20.125343-08 2025-02-24 10:53:26.469742-08 benchmark t A100 \N t \N \N \N {} +2691 925 2025-02-26 15:03:38.341726-08 2025-02-26 14:39:04.512785-08 test f H100 \N t \N \N \N {} +770 244 2025-02-24 09:48:07.049405-08 2025-02-24 11:16:17.375008-08 benchmark f A100 \N t \N \N \N {} +771 244 2025-02-24 10:16:24.565263-08 2025-02-24 10:42:42.951293-08 leaderboard f A100 0.003206431 t \N \N \N {} +774 247 2025-02-24 11:24:50.833311-08 2025-02-24 11:11:00.480374-08 test f A100 \N t \N \N \N {} +775 248 2025-02-24 11:18:01.396675-08 2025-02-24 09:46:03.356509-08 benchmark f A100 \N t \N \N \N {} +1357 454 2025-02-25 03:22:20.667093-08 2025-02-25 02:18:17.785956-08 benchmark f H100 \N f \N \N \N {} +2692 925 2025-02-26 15:31:22.064733-08 2025-02-26 15:24:18.486092-08 benchmark f H100 \N t \N \N \N {} +2693 925 2025-02-26 15:40:09.437246-08 2025-02-26 14:33:37.515202-08 leaderboard f H100 0.0014418213333333332 t \N \N \N {} +2694 925 2025-02-26 14:21:30.999182-08 2025-02-26 14:59:14.162816-08 test f A100 \N t \N \N \N {} +2695 925 2025-02-26 14:49:05.851761-08 2025-02-26 16:02:19.46454-08 benchmark f A100 \N t \N \N \N {} +3187 1121 2025-02-28 04:13:45.106756-08 2025-02-28 05:15:10.243175-08 test t A100 \N t \N \N \N {} +2697 925 2025-02-26 15:49:51.58385-08 2025-02-26 15:15:43.400087-08 test t H100 \N t \N \N \N {} +2698 925 2025-02-26 15:20:13.529064-08 2025-02-26 14:54:28.716289-08 benchmark t H100 \N t \N \N \N {} +2699 925 2025-02-26 15:12:28.653251-08 2025-02-26 15:26:50.456366-08 leaderboard t H100 0.0014572976666666667 t \N \N \N {} +2700 925 2025-02-26 14:23:46.440832-08 2025-02-26 15:33:30.662407-08 test f T4 \N t \N \N \N {} +2706 925 2025-02-26 15:56:56.719925-08 2025-02-26 15:31:05.149568-08 test f L4 \N t \N \N \N {} +2701 925 2025-02-26 14:27:02.624231-08 2025-02-26 14:29:33.448522-08 benchmark f T4 \N t \N \N \N {} +2702 925 2025-02-26 15:37:17.423193-08 2025-02-26 14:15:41.379785-08 leaderboard f T4 0.01748257666666667 t \N \N \N {} +2703 925 2025-02-26 15:02:07.78409-08 2025-02-26 14:30:59.074601-08 test t T4 \N t \N \N \N {} +2704 925 2025-02-26 15:25:00.161914-08 2025-02-26 14:41:31.01698-08 benchmark t T4 \N t \N \N \N {} +2705 925 2025-02-26 15:45:54.49678-08 2025-02-26 15:14:52.211382-08 leaderboard t T4 0.01740308366666667 t \N \N \N {} +2712 926 2025-02-26 14:49:00.905432-08 2025-02-26 15:15:12.066984-08 test f H100 \N f \N \N \N {} +2707 925 2025-02-26 15:14:33.474068-08 2025-02-26 16:13:05.73025-08 benchmark f L4 \N t \N \N \N {} +2708 925 2025-02-26 14:20:21.316161-08 2025-02-26 14:20:47.541084-08 leaderboard f L4 0.017136907333333333 t \N \N \N {} +2709 925 2025-02-26 14:35:12.137581-08 2025-02-26 15:27:12.663189-08 test t L4 \N t \N \N \N {} +2710 925 2025-02-26 14:22:20.587234-08 2025-02-26 15:41:26.009938-08 benchmark t L4 \N t \N \N \N {} +2711 925 2025-02-26 14:28:34.108955-08 2025-02-26 15:21:09.620226-08 leaderboard t L4 0.017147484 t \N \N \N {} +2757 955 2025-02-26 19:44:57.978305-08 2025-02-26 20:19:00.046496-08 test f A100 \N f \N \N \N {} +3488 1197 2025-02-28 12:13:22.706712-08 2025-02-28 11:38:53.381675-08 test t A100 \N t \N \N \N {} +2715 927 2025-02-26 15:38:50.899973-08 2025-02-26 15:07:34.985733-08 leaderboard f H100 0.0008018275555555555 t \N \N \N {} +787 251 2025-02-24 11:51:27.707677-08 2025-02-24 11:37:57.611418-08 benchmark t T4 \N t \N \N \N {} +798 251 2025-02-24 10:21:21.926398-08 2025-02-24 11:59:58.050957-08 test f T4 \N t \N \N \N {} +799 251 2025-02-24 10:39:23.728982-08 2025-02-24 12:03:26.185356-08 benchmark f T4 \N t \N \N \N {} +803 251 2025-02-24 11:17:20.227202-08 2025-02-24 12:07:21.562687-08 leaderboard f L4 0.017377531 t \N \N \N {} +804 251 2025-02-24 10:36:19.381723-08 2025-02-24 11:38:04.632489-08 test t L4 \N t \N \N \N {} +805 251 2025-02-24 11:30:07.481337-08 2025-02-24 11:39:59.955081-08 benchmark t L4 \N t \N \N \N {} +812 253 2025-02-24 12:20:58.985139-08 2025-02-24 11:40:09.015087-08 benchmark t A100 \N f \N \N \N {} +814 255 2025-02-24 12:02:11.606734-08 2025-02-24 13:15:58.848433-08 test f H100 \N t \N \N \N {} +815 255 2025-02-24 12:02:01.586263-08 2025-02-24 12:14:04.067053-08 benchmark f H100 \N t \N \N \N {} +816 255 2025-02-24 12:48:59.000761-08 2025-02-24 12:42:09.155319-08 leaderboard f H100 0.006132886 t \N \N \N {} +817 255 2025-02-24 12:15:55.811254-08 2025-02-24 12:36:09.13928-08 test t H100 \N t \N \N \N {} +844 262 2025-02-24 13:35:47.457777-08 2025-02-24 13:57:51.505602-08 test t A100 \N t \N \N \N {} +2716 927 2025-02-26 15:52:42.380063-08 2025-02-26 14:28:51.260977-08 test t H100 \N t \N \N \N {} +2717 927 2025-02-26 16:02:37.652274-08 2025-02-26 16:05:08.637406-08 benchmark t H100 \N t \N \N \N {} +2780 968 2025-02-26 20:29:07.654188-08 2025-02-26 19:21:21.673508-08 test f A100 \N t \N \N \N {} +3491 1198 2025-02-28 12:21:58.688283-08 2025-02-28 12:05:53.605746-08 test f A100 \N t \N \N \N {} +2719 928 2025-02-26 16:32:16.602958-08 2025-02-26 16:19:31.629908-08 test f H100 \N f \N \N \N {} +2720 930 2025-02-26 15:23:05.898632-08 2025-02-26 15:07:22.998261-08 test f H100 \N f \N \N \N {} +2721 934 2025-02-26 17:22:57.87553-08 2025-02-26 15:53:13.995243-08 test f T4 \N t \N \N \N {} +2722 935 2025-02-26 16:04:13.430604-08 2025-02-26 15:30:59.454902-08 benchmark f H100 \N t \N \N \N {} +2723 936 2025-02-26 15:35:49.505504-08 2025-02-26 17:20:48.238503-08 benchmark f H100 \N t \N \N \N {} +2782 970 2025-02-26 21:07:51.669113-08 2025-02-26 20:49:24.139845-08 test f A100 \N f \N \N \N {} +2729 937 2025-02-26 17:32:52.663266-08 2025-02-26 16:47:18.507852-08 leaderboard t H100 0.0009355417142857144 t \N \N \N {} +2730 938 2025-02-26 16:48:14.702847-08 2025-02-26 16:57:10.702808-08 benchmark f H100 \N t \N \N \N {} +2731 939 2025-02-26 17:31:10.651035-08 2025-02-26 17:03:20.742136-08 test f H100 \N t \N \N \N {} +2732 939 2025-02-26 16:04:46.223011-08 2025-02-26 17:44:01.018405-08 benchmark f H100 \N t \N \N \N {} +2733 939 2025-02-26 16:09:00.549881-08 2025-02-26 16:26:03.875919-08 leaderboard f H100 0.00047589718181818185 t \N \N \N {} +2751 952 2025-02-26 18:36:20.359926-08 2025-02-26 18:56:33.112066-08 leaderboard t H100 0.00004619103 t \N \N \N {} +2752 952 2025-02-26 17:58:47.604278-08 2025-02-26 18:46:38.011565-08 test f H100 \N t \N \N \N {} +2759 957 2025-02-26 19:37:58.190081-08 2025-02-26 19:32:35.710825-08 test f A100 \N f \N \N \N {} +2760 958 2025-02-26 19:20:20.101499-08 2025-02-26 18:54:34.381861-08 test f A100 \N t \N \N \N {} +818 255 2025-02-24 12:36:47.623295-08 2025-02-24 11:44:27.318735-08 benchmark t H100 \N t \N \N \N {} +819 255 2025-02-24 12:45:56.514079-08 2025-02-24 13:03:45.699083-08 leaderboard t H100 0.006123367 t \N \N \N {} +820 256 2025-02-24 12:00:59.412291-08 2025-02-24 11:48:22.822008-08 test t A100 \N f \N \N \N {} +827 258 2025-02-24 12:14:03.313645-08 2025-02-24 13:12:29.40344-08 benchmark f A100 \N t \N \N \N {} +829 259 2025-02-24 13:27:44.987486-08 2025-02-24 12:15:45.854513-08 test f H100 \N t \N \N \N {} +835 260 2025-02-24 12:44:46.406235-08 2025-02-24 12:32:35.981113-08 test f H100 \N f \N \N \N {} +836 260 2025-02-24 11:57:43.314454-08 2025-02-24 13:37:10.081022-08 test t H100 \N f \N \N \N {} +839 261 2025-02-24 13:24:41.686947-08 2025-02-24 14:04:01.45224-08 benchmark f L4 \N t \N \N \N {} +840 261 2025-02-24 12:44:55.198063-08 2025-02-24 13:15:16.477866-08 benchmark f T4 \N t \N \N \N {} +841 262 2025-02-24 14:40:33.295376-08 2025-02-24 14:37:39.287063-08 test f H100 \N t \N \N \N {} +842 262 2025-02-24 13:32:24.520488-08 2025-02-24 12:51:42.430233-08 benchmark f H100 \N t \N \N \N {} +845 262 2025-02-24 13:16:57.772484-08 2025-02-24 13:43:01.738354-08 benchmark t A100 \N t \N \N \N {} +846 262 2025-02-24 14:40:12.521043-08 2025-02-24 12:41:50.695193-08 leaderboard t A100 0.0035759156666666666 t \N \N \N {} +847 262 2025-02-24 13:06:53.677846-08 2025-02-24 13:04:43.106693-08 test t T4 \N t \N \N \N {} +848 262 2025-02-24 13:21:20.33413-08 2025-02-24 14:07:43.524626-08 benchmark t T4 \N t \N \N \N {} +849 262 2025-02-24 13:08:55.082034-08 2025-02-24 14:11:39.783402-08 leaderboard t T4 0.017268438333333334 t \N \N \N {} +855 262 2025-02-24 13:00:14.369727-08 2025-02-24 12:58:55.821216-08 leaderboard f A100 0.0035754176666666663 t \N \N \N {} +856 263 2025-02-24 14:12:04.211732-08 2025-02-24 14:32:29.922479-08 benchmark f A100 \N f \N \N \N {} +857 262 2025-02-24 13:01:52.259649-08 2025-02-24 13:08:26.610715-08 test f T4 \N t \N \N \N {} +858 262 2025-02-24 13:09:26.79495-08 2025-02-24 14:17:18.323779-08 benchmark f T4 \N t \N \N \N {} +859 262 2025-02-24 14:15:52.323889-08 2025-02-24 14:32:11.723833-08 leaderboard f T4 0.017249578 t \N \N \N {} +1459 486 2025-02-25 04:17:57.329398-08 2025-02-25 04:19:00.137084-08 test t A100 \N t \N \N \N {} +861 262 2025-02-24 13:55:33.663644-08 2025-02-24 14:37:09.657969-08 benchmark t H100 \N t \N \N \N {} +862 262 2025-02-24 14:11:40.394727-08 2025-02-24 14:04:47.293746-08 leaderboard t H100 0.00157941125 t \N \N \N {} +863 262 2025-02-24 14:14:35.256564-08 2025-02-24 13:32:29.562338-08 test t L4 \N t \N \N \N {} +864 262 2025-02-24 12:48:02.755552-08 2025-02-24 14:13:00.945425-08 benchmark t L4 \N t \N \N \N {} +865 262 2025-02-24 12:46:48.53663-08 2025-02-24 14:29:57.105212-08 leaderboard t L4 0.017202579333333332 t \N \N \N {} +870 266 2025-02-24 14:10:29.80768-08 2025-02-24 13:31:05.267924-08 test t H100 \N f \N \N \N {} +882 268 2025-02-24 13:48:16.246206-08 2025-02-24 13:16:22.804481-08 benchmark t H100 \N t \N \N \N {} +883 268 2025-02-24 13:47:51.158916-08 2025-02-24 14:12:31.13551-08 leaderboard t H100 0.001499999 t \N \N \N {} +884 269 2025-02-24 13:36:44.335857-08 2025-02-24 13:23:48.204042-08 test f L4 \N t \N \N \N {} +885 269 2025-02-24 13:40:59.689967-08 2025-02-24 14:59:46.238789-08 test f A100 \N t \N \N \N {} +886 269 2025-02-24 14:18:48.147333-08 2025-02-24 14:54:09.931319-08 test f T4 \N t \N \N \N {} +887 269 2025-02-24 13:58:15.398454-08 2025-02-24 14:08:43.206835-08 test f H100 \N t \N \N \N {} +2761 959 2025-02-26 20:04:15.692556-08 2025-02-26 20:10:38.466984-08 test f A100 \N t \N \N \N {} +3506 1200 2025-02-28 12:29:44.776123-08 2025-02-28 11:41:55.023296-08 test t A100 \N t \N \N \N {} +2762 960 2025-02-26 19:50:11.129181-08 2025-02-26 19:57:28.353335-08 test f A100 \N t \N \N \N {} +888 270 2025-02-24 14:36:02.624025-08 2025-02-24 15:09:25.11226-08 test f H100 \N t \N \N \N {} +893 270 2025-02-24 13:25:28.447878-08 2025-02-24 14:26:32.820494-08 leaderboard t H100 0.006313673333333333 t \N \N \N {} +978 288 2025-02-24 16:16:39.684041-08 2025-02-24 16:53:33.866555-08 test f H100 \N t \N \N \N {} +894 271 2025-02-24 13:37:51.574913-08 2025-02-24 13:30:44.90995-08 benchmark f A100 \N t \N \N \N {} +895 271 2025-02-24 14:42:33.925165-08 2025-02-24 13:50:25.827947-08 benchmark f L4 \N t \N \N \N {} +896 271 2025-02-24 15:14:26.679127-08 2025-02-24 13:24:56.860101-08 benchmark f H100 \N t \N \N \N {} +2763 961 2025-02-26 20:33:39.926563-08 2025-02-26 18:53:59.629244-08 test f A100 \N f \N \N \N {} +3591 1229 2025-02-28 14:00:43.377485-08 2025-02-28 13:02:18.799382-08 benchmark t H100 \N t \N \N \N {} +922 272 2025-02-24 14:21:16.338815-08 2025-02-24 13:57:53.832712-08 test f L4 \N t \N \N \N {} +930 275 2025-02-24 14:06:04.090352-08 2025-02-24 15:32:28.313994-08 test t H100 \N f \N \N \N {} +931 275 2025-02-24 15:08:25.725574-08 2025-02-24 14:48:09.110921-08 test f H100 \N f \N \N \N {} +932 276 2025-02-24 15:08:07.755574-08 2025-02-24 14:00:19.773242-08 benchmark f H100 \N t \N \N \N {} +933 277 2025-02-24 15:11:54.355362-08 2025-02-24 14:30:44.412152-08 benchmark f H100 \N t \N \N \N {} +934 278 2025-02-24 15:11:08.217229-08 2025-02-24 13:56:46.532795-08 test t A100 \N t \N \N \N {} +2765 963 2025-02-26 21:05:46.567488-08 2025-02-26 21:01:24.814437-08 test f A100 \N t \N \N \N {} +949 278 2025-02-24 14:01:41.92456-08 2025-02-24 13:43:58.586813-08 test t L4 \N t \N \N \N {} +953 278 2025-02-24 15:30:55.085434-08 2025-02-24 15:03:27.528219-08 benchmark t T4 \N t \N \N \N {} +954 278 2025-02-24 14:13:18.813125-08 2025-02-24 14:25:32.061848-08 leaderboard t T4 0.01679078633333333 t \N \N \N {} +955 278 2025-02-24 13:55:20.752079-08 2025-02-24 14:29:46.038209-08 test f T4 \N t \N \N \N {} +956 278 2025-02-24 14:26:05.784131-08 2025-02-24 14:04:02.728948-08 benchmark f T4 \N t \N \N \N {} +957 278 2025-02-24 14:40:08.470773-08 2025-02-24 14:15:08.834032-08 leaderboard f T4 0.01680730033333333 t \N \N \N {} +958 279 2025-02-24 13:58:02.12755-08 2025-02-24 14:09:39.155243-08 test f A100 \N t \N \N \N {} +962 283 2025-02-24 15:58:39.196492-08 2025-02-24 14:11:06.156964-08 benchmark f A100 \N t \N \N \N {} +963 284 2025-02-24 14:28:10.002662-08 2025-02-24 14:33:20.990272-08 benchmark f A100 \N t \N \N \N {} +964 285 2025-02-24 14:50:05.118746-08 2025-02-24 16:06:00.21924-08 benchmark f A100 \N t \N \N \N {} +966 286 2025-02-24 15:11:06.589828-08 2025-02-24 15:54:57.512172-08 benchmark f A100 \N t \N \N \N {} +2766 964 2025-02-26 19:08:30.791267-08 2025-02-26 20:25:23.844568-08 test f A100 \N t \N \N \N {} +967 286 2025-02-24 15:50:38.06952-08 2025-02-24 14:35:41.812052-08 leaderboard f A100 0.003564924125 t \N \N \N {} +968 286 2025-02-24 15:34:26.225087-08 2025-02-24 14:31:55.790602-08 test t A100 \N t \N \N \N {} +975 288 2025-02-24 16:06:09.241772-08 2025-02-24 15:20:44.834631-08 test t H100 \N t \N \N \N {} +1003 289 2025-02-24 16:59:20.310533-08 2025-02-24 16:33:52.660533-08 test f H100 \N t \N \N \N {} +1007 289 2025-02-24 15:48:45.290987-08 2025-02-24 16:12:36.369625-08 benchmark f T4 \N t \N \N \N {} +1008 289 2025-02-24 16:43:13.03202-08 2025-02-24 16:09:32.189032-08 leaderboard f T4 0.017463506 t \N \N \N {} +1009 289 2025-02-24 15:26:10.010531-08 2025-02-24 15:31:30.134666-08 test f L4 \N t \N \N \N {} +1010 289 2025-02-24 15:34:17.620631-08 2025-02-24 15:20:56.610577-08 benchmark f L4 \N t \N \N \N {} +1011 289 2025-02-24 15:17:08.27862-08 2025-02-24 16:53:24.408023-08 leaderboard f L4 0.017437469 t \N \N \N {} +1013 289 2025-02-24 17:10:06.760302-08 2025-02-24 16:01:34.694517-08 benchmark t L4 \N t \N \N \N {} +1014 289 2025-02-24 17:02:49.759681-08 2025-02-24 16:46:54.792055-08 leaderboard t L4 0.017468976333333334 t \N \N \N {} +1015 289 2025-02-24 16:23:10.660698-08 2025-02-24 16:34:48.55816-08 test t H100 \N t \N \N \N {} +1016 289 2025-02-24 17:08:58.086549-08 2025-02-24 17:12:19.202409-08 benchmark t H100 \N t \N \N \N {} +1017 289 2025-02-24 15:29:25.320543-08 2025-02-24 16:41:10.791533-08 leaderboard t H100 0.001555808 t \N \N \N {} +1021 290 2025-02-24 17:27:27.359016-08 2025-02-24 17:10:34.608977-08 test f T4 \N f \N \N \N {} +1022 290 2025-02-24 16:04:38.081723-08 2025-02-24 16:01:10.893306-08 test t T4 \N f \N \N \N {} +1023 290 2025-02-24 17:30:53.275597-08 2025-02-24 16:15:31.909539-08 test f H100 \N f \N \N \N {} +1024 290 2025-02-24 16:19:28.643489-08 2025-02-24 17:41:16.119971-08 test t L4 \N f \N \N \N {} +2767 965 2025-02-26 20:43:06.138197-08 2025-02-26 20:38:46.941894-08 test f A100 \N t \N \N \N {} +1025 290 2025-02-24 15:50:33.00311-08 2025-02-24 17:22:52.823354-08 test f L4 \N f \N \N \N {} +1026 291 2025-02-24 16:00:27.990747-08 2025-02-24 17:05:43.520942-08 test t A100 \N t \N \N \N {} +1027 291 2025-02-24 17:29:02.565531-08 2025-02-24 17:17:14.052814-08 benchmark t A100 \N t \N \N \N {} +1028 291 2025-02-24 16:02:23.141852-08 2025-02-24 16:33:31.463874-08 leaderboard t A100 0.003320683 t \N \N \N {} +1029 291 2025-02-24 16:21:24.320655-08 2025-02-24 16:33:35.189391-08 test f A100 \N t \N \N \N {} +1030 291 2025-02-24 16:54:28.950428-08 2025-02-24 16:34:49.566872-08 benchmark f A100 \N t \N \N \N {} +1041 291 2025-02-24 16:51:50.87004-08 2025-02-24 16:52:44.22599-08 test t T4 \N t \N \N \N {} +1031 291 2025-02-24 16:57:17.908772-08 2025-02-24 17:21:40.225798-08 leaderboard f A100 0.0033077086666666667 t \N \N \N {} +1061 292 2025-02-24 16:14:47.786921-08 2025-02-24 16:02:43.643719-08 leaderboard f H100 0.0014229046666666668 t \N \N \N {} +1062 292 2025-02-24 16:05:24.343352-08 2025-02-24 16:26:39.742121-08 test t T4 \N t \N \N \N {} +1068 292 2025-02-24 16:14:27.218676-08 2025-02-24 16:12:18.471021-08 test f L4 \N t \N \N \N {} +1071 292 2025-02-24 16:21:46.18687-08 2025-02-24 17:29:33.25851-08 test t H100 \N t \N \N \N {} +1077 295 2025-02-24 17:38:05.121185-08 2025-02-24 17:38:57.063894-08 benchmark f H100 \N t \N \N \N {} +1078 295 2025-02-24 17:58:44.68097-08 2025-02-24 17:14:34.343302-08 leaderboard f H100 0.0010347983333333333 t \N \N \N {} +1079 295 2025-02-24 17:04:05.158398-08 2025-02-24 17:25:58.879946-08 test t H100 \N t \N \N \N {} +1080 295 2025-02-24 16:58:08.102388-08 2025-02-24 17:56:34.485958-08 benchmark t H100 \N t \N \N \N {} +1081 295 2025-02-24 16:55:29.797081-08 2025-02-24 17:47:07.608762-08 leaderboard t H100 0.001039122 t \N \N \N {} +1096 304 2025-02-24 18:56:04.543979-08 2025-02-24 17:04:43.557942-08 benchmark f A100 \N t \N \N \N {} +1082 296 2025-02-24 17:31:42.820566-08 2025-02-24 16:33:59.876329-08 benchmark f H100 \N f \N \N \N {} +1084 299 2025-02-24 17:39:48.424492-08 2025-02-24 17:07:52.030306-08 benchmark f A100 \N t \N \N \N {} +1085 298 2025-02-24 16:35:28.403851-08 2025-02-24 17:02:30.160259-08 benchmark f H100 \N f \N \N \N {} +1086 300 2025-02-24 17:14:06.318787-08 2025-02-24 16:50:30.934776-08 test t A100 \N t \N \N \N {} +1091 300 2025-02-24 17:29:22.731228-08 2025-02-24 17:12:13.670748-08 leaderboard f A100 0.014157763333333333 t \N \N \N {} +1092 301 2025-02-24 17:59:41.646434-08 2025-02-24 16:56:21.661999-08 benchmark f H100 \N f \N \N \N {} +1093 302 2025-02-24 18:21:23.246489-08 2025-02-24 17:07:12.364112-08 benchmark f A100 \N f \N \N \N {} +1094 303 2025-02-24 17:50:55.598954-08 2025-02-24 17:13:45.078131-08 benchmark f H100 \N t \N \N \N {} +1095 304 2025-02-24 17:38:46.646025-08 2025-02-24 17:11:44.810342-08 test f A100 \N t \N \N \N {} +1486 495 2025-02-25 05:26:36.575717-08 2025-02-25 05:35:53.585466-08 test f A100 \N f \N \N \N {} +1097 304 2025-02-24 17:03:14.22191-08 2025-02-24 17:48:13.256134-08 leaderboard f A100 0.006075353 t \N \N \N {} +3576 1223 2025-02-28 12:52:29.871321-08 2025-02-28 13:12:16.401319-08 test f H100 \N t \N \N \N {} +1098 304 2025-02-24 18:52:41.173136-08 2025-02-24 18:10:45.04049-08 test t A100 \N t \N \N \N {} +1099 304 2025-02-24 17:19:09.825043-08 2025-02-24 17:28:38.91665-08 benchmark t A100 \N t \N \N \N {} +1100 304 2025-02-24 18:23:50.783076-08 2025-02-24 18:13:43.170325-08 leaderboard t A100 0.006081086333333333 t \N \N \N {} +1105 304 2025-02-24 17:34:55.399719-08 2025-02-24 17:27:11.226139-08 benchmark f T4 \N t \N \N \N {} +1106 304 2025-02-24 18:28:11.076985-08 2025-02-24 18:13:11.8461-08 leaderboard f T4 0.028245293666666667 t \N \N \N {} +1107 304 2025-02-24 18:33:57.535639-08 2025-02-24 17:46:08.937512-08 test t T4 \N t \N \N \N {} +1113 304 2025-02-24 18:34:00.598847-08 2025-02-24 18:51:13.208664-08 test t L4 \N t \N \N \N {} +1114 304 2025-02-24 18:06:21.101459-08 2025-02-24 17:31:57.238174-08 benchmark t L4 \N t \N \N \N {} +1119 305 2025-02-24 17:42:57.925612-08 2025-02-24 17:15:14.683808-08 benchmark f H100 \N f \N \N \N {} +1120 306 2025-02-24 17:21:15.587258-08 2025-02-24 18:44:01.440783-08 benchmark f A100 \N t \N \N \N {} +1121 306 2025-02-24 18:36:34.865426-08 2025-02-24 17:49:17.471548-08 benchmark f H100 \N t \N \N \N {} +1122 306 2025-02-24 17:26:55.287166-08 2025-02-24 18:35:50.569086-08 benchmark f T4 \N t \N \N \N {} +1125 308 2025-02-24 18:34:58.15099-08 2025-02-24 17:56:55.545892-08 benchmark f H100 \N t \N \N \N {} +1126 309 2025-02-24 18:10:41.370592-08 2025-02-24 19:01:32.847335-08 test f T4 \N t \N \N \N {} +1127 310 2025-02-24 18:21:38.286944-08 2025-02-24 19:30:50.197993-08 test f T4 \N f \N \N \N {} +1128 311 2025-02-24 19:40:10.137721-08 2025-02-24 19:12:06.15593-08 test f T4 \N f \N \N \N {} +1144 321 2025-02-24 19:09:00.011949-08 2025-02-24 19:27:42.347167-08 benchmark f A100 \N t \N \N \N {} +1145 321 2025-02-24 18:28:50.942544-08 2025-02-24 20:07:48.608885-08 leaderboard f A100 0.008048783333333333 t \N \N \N {} +1146 321 2025-02-24 19:54:14.030406-08 2025-02-24 19:26:42.983807-08 test t A100 \N t \N \N \N {} +1147 321 2025-02-24 19:37:58.992398-08 2025-02-24 18:38:17.162254-08 benchmark t A100 \N t \N \N \N {} +1155 328 2025-02-24 18:59:30.528684-08 2025-02-24 18:51:38.131222-08 benchmark f A100 \N t \N \N \N {} +1160 329 2025-02-24 19:38:57.967787-08 2025-02-24 18:57:02.949354-08 benchmark t H100 \N t \N \N \N {} +1161 329 2025-02-24 19:14:33.290908-08 2025-02-24 20:08:23.310109-08 leaderboard t H100 0.0015865033333333333 t \N \N \N {} +1166 334 2025-02-24 19:50:26.32731-08 2025-02-24 20:31:43.298423-08 test f A100 \N f \N \N \N {} +1167 335 2025-02-24 19:52:47.832137-08 2025-02-24 18:40:52.747222-08 test f A100 \N f \N \N \N {} +1168 336 2025-02-24 18:58:04.317224-08 2025-02-24 20:40:01.428171-08 test f A100 \N t \N \N \N {} +1169 337 2025-02-24 19:33:06.587039-08 2025-02-24 20:20:18.024082-08 benchmark f A100 \N t \N \N \N {} +1171 339 2025-02-24 20:25:27.128083-08 2025-02-24 18:45:26.166042-08 benchmark f A100 \N t \N \N \N {} +1175 343 2025-02-24 20:01:36.447161-08 2025-02-24 20:17:57.794145-08 benchmark f A100 \N t \N \N \N {} +1176 344 2025-02-24 18:57:03.668266-08 2025-02-24 20:32:08.204872-08 benchmark f A100 \N t \N \N \N {} +1177 345 2025-02-24 19:19:48.575647-08 2025-02-24 20:35:58.402184-08 benchmark f A100 \N f \N \N \N {} +1180 348 2025-02-24 20:38:35.466753-08 2025-02-24 20:27:20.582819-08 benchmark f A100 \N t \N \N \N {} +1181 349 2025-02-24 19:50:45.13357-08 2025-02-24 19:35:45.516463-08 test f A100 \N f \N \N \N {} +1186 356 2025-02-24 20:44:38.220701-08 2025-02-24 20:35:27.553404-08 benchmark f A100 \N f \N \N \N {} +1187 357 2025-02-24 20:31:41.284738-08 2025-02-24 20:51:31.622096-08 benchmark f A100 \N t \N \N \N {} +1191 364 2025-02-24 19:38:43.168308-08 2025-02-24 20:03:40.713134-08 benchmark f A100 \N t \N \N \N {} +1192 365 2025-02-24 20:20:30.975678-08 2025-02-24 20:47:45.806082-08 test f A100 \N t \N \N \N {} +1198 366 2025-02-24 20:50:37.04848-08 2025-02-24 20:03:24.288635-08 benchmark f A100 \N f \N \N \N {} +1199 367 2025-02-24 20:01:53.464636-08 2025-02-24 20:50:55.06292-08 benchmark f A100 \N f \N \N \N {} +1200 368 2025-02-24 20:48:53.017372-08 2025-02-24 21:07:09.746977-08 benchmark f A100 \N f \N \N \N {} +1203 371 2025-02-24 21:03:40.316811-08 2025-02-24 20:06:59.770551-08 benchmark f A100 \N f \N \N \N {} +1204 372 2025-02-24 21:16:31.917525-08 2025-02-24 20:19:23.12487-08 benchmark f A100 \N f \N \N \N {} +1212 380 2025-02-24 21:30:39.643844-08 2025-02-24 21:22:48.195881-08 benchmark f A100 \N f \N \N \N {} +1213 381 2025-02-24 21:27:50.804873-08 2025-02-24 20:30:39.877648-08 benchmark f A100 \N f \N \N \N {} +1214 382 2025-02-24 21:26:22.567911-08 2025-02-24 20:42:27.609326-08 benchmark f A100 \N t \N \N \N {} +1215 383 2025-02-24 21:43:35.675505-08 2025-02-24 21:41:12.864918-08 benchmark f A100 \N f \N \N \N {} +1217 385 2025-02-24 20:39:07.414152-08 2025-02-24 21:16:56.728524-08 benchmark f A100 \N t \N \N \N {} +1218 386 2025-02-24 21:24:25.618173-08 2025-02-24 20:59:42.5791-08 benchmark f A100 \N f \N \N \N {} +1219 387 2025-02-24 20:37:38.559826-08 2025-02-24 20:50:09.030352-08 benchmark f A100 \N t \N \N \N {} +1220 388 2025-02-24 21:12:35.63089-08 2025-02-24 21:07:52.286646-08 test f H100 \N f \N \N \N {} +1221 390 2025-02-24 20:41:29.07709-08 2025-02-24 21:50:07.217387-08 test f A100 \N f \N \N \N {} +1222 391 2025-02-24 20:36:54.532658-08 2025-02-24 20:27:33.575008-08 test f A100 \N f \N \N \N {} +1223 391 2025-02-24 22:09:52.859803-08 2025-02-24 20:25:04.338196-08 test f L4 \N f \N \N \N {} +1224 391 2025-02-24 20:57:57.971901-08 2025-02-24 22:07:50.668858-08 test f T4 \N f \N \N \N {} +1225 391 2025-02-24 21:26:12.689911-08 2025-02-24 20:21:51.434441-08 test f H100 \N f \N \N \N {} +1226 392 2025-02-24 21:22:04.179078-08 2025-02-24 21:02:36.047543-08 test f A100 \N f \N \N \N {} +1227 393 2025-02-24 21:27:17.29266-08 2025-02-24 21:05:23.934691-08 test t A100 \N t \N \N \N {} +1228 393 2025-02-24 21:57:42.36025-08 2025-02-24 20:56:27.385816-08 benchmark t A100 \N t \N \N \N {} +1232 393 2025-02-24 21:19:55.551644-08 2025-02-24 20:43:59.005894-08 leaderboard f A100 0.0033388506666666667 t \N \N \N {} +1265 416 2025-02-24 21:37:21.368251-08 2025-02-24 21:44:53.806727-08 leaderboard t H100 0.0021056229500000002 t \N \N \N {} +1233 394 2025-02-24 20:48:53.683982-08 2025-02-24 21:10:47.005539-08 test f A100 \N f \N \N \N {} +1234 395 2025-02-24 21:42:25.587766-08 2025-02-24 21:13:54.206602-08 test f A100 \N f \N \N \N {} +1235 396 2025-02-24 21:31:47.507986-08 2025-02-24 21:41:14.621874-08 test f A100 \N f \N \N \N {} +1236 397 2025-02-24 20:53:51.866306-08 2025-02-24 21:41:52.555281-08 test f A100 \N f \N \N \N {} +1237 398 2025-02-24 22:13:49.271446-08 2025-02-24 21:54:00.876085-08 test f A100 \N f \N \N \N {} +1273 424 2025-02-24 23:15:56.010969-08 2025-02-24 22:22:18.360257-08 test f H100 \N f \N \N \N {} +1238 399 2025-02-24 21:20:05.240709-08 2025-02-24 22:10:17.303543-08 test f A100 \N f \N \N \N {} +1239 400 2025-02-24 21:02:33.153711-08 2025-02-24 22:14:52.737511-08 test f A100 \N f \N \N \N {} +1242 403 2025-02-24 21:44:20.600901-08 2025-02-24 22:19:00.048671-08 test f A100 \N t \N \N \N {} +1274 425 2025-02-24 22:32:28.342997-08 2025-02-24 22:46:18.153042-08 test f H100 \N f \N \N \N {} +1244 405 2025-02-24 21:15:42.934708-08 2025-02-24 22:00:36.897134-08 benchmark f A100 \N t \N \N \N {} +1245 406 2025-02-24 22:41:21.855098-08 2025-02-24 21:22:41.861309-08 test f A100 \N f \N \N \N {} +1246 407 2025-02-24 22:14:38.935217-08 2025-02-24 21:17:28.999889-08 test f A100 \N f \N \N \N {} +1247 408 2025-02-24 21:15:17.037056-08 2025-02-24 22:47:31.85541-08 test f A100 \N f \N \N \N {} +1255 413 2025-02-24 22:41:32.544432-08 2025-02-24 21:50:59.410312-08 test t A100 \N t \N \N \N {} +1275 426 2025-02-24 22:50:00.313014-08 2025-02-24 22:01:51.946596-08 test f H100 \N f \N \N \N {} +1256 413 2025-02-24 21:28:53.296618-08 2025-02-24 21:56:09.683656-08 benchmark t A100 \N t \N \N \N {} +1257 413 2025-02-24 22:52:41.725644-08 2025-02-24 21:02:44.177771-08 leaderboard t A100 0.00082829875 t \N \N \N {} +1258 414 2025-02-24 22:43:01.622934-08 2025-02-24 21:44:54.450946-08 test f H100 \N t \N \N \N {} +1259 415 2025-02-24 21:05:33.640351-08 2025-02-24 22:22:12.322858-08 benchmark f H100 \N t \N \N \N {} +1260 416 2025-02-24 22:50:18.115204-08 2025-02-24 22:10:37.88296-08 test f H100 \N t \N \N \N {} +1263 416 2025-02-24 22:41:58.320403-08 2025-02-24 22:46:15.121319-08 test t H100 \N t \N \N \N {} +1264 416 2025-02-24 21:33:11.644917-08 2025-02-24 22:43:20.018249-08 benchmark t H100 \N t \N \N \N {} +1266 417 2025-02-24 23:17:42.124344-08 2025-02-24 22:14:36.706679-08 test f L4 \N f \N \N \N {} +1271 422 2025-02-24 23:24:55.93824-08 2025-02-24 23:09:05.952447-08 test f H100 \N f \N \N \N {} +1272 423 2025-02-24 23:33:25.559896-08 2025-02-24 23:04:20.171899-08 test f H100 \N f \N \N \N {} +1277 427 2025-02-25 00:21:05.704041-08 2025-02-24 23:17:15.254253-08 benchmark t H100 \N t \N \N \N {} +1278 427 2025-02-24 23:05:44.473555-08 2025-02-24 22:36:18.623353-08 leaderboard t H100 0.00151173175 t \N \N \N {} +1279 427 2025-02-24 23:22:06.892269-08 2025-02-25 00:08:22.994675-08 test f H100 \N t \N \N \N {} +1280 427 2025-02-25 00:06:49.982745-08 2025-02-25 00:02:16.454215-08 benchmark f H100 \N t \N \N \N {} +1281 427 2025-02-24 23:30:10.509612-08 2025-02-24 22:24:37.733967-08 leaderboard f H100 0.0014917988095238094 t \N \N \N {} +1282 428 2025-02-25 00:29:11.621733-08 2025-02-25 00:25:31.895544-08 test f L4 \N t \N \N \N {} +1283 429 2025-02-25 00:23:40.437427-08 2025-02-25 00:28:38.668036-08 benchmark f L4 \N t \N \N \N {} +1420 472 2025-02-25 05:09:52.78126-08 2025-02-25 03:33:17.606456-08 test f A100 \N t \N \N \N {} +1284 430 2025-02-24 22:56:08.41611-08 2025-02-24 23:53:11.011713-08 benchmark f L4 \N t \N \N \N {} +1291 434 2025-02-25 00:38:27.016325-08 2025-02-25 01:33:09.270327-08 test t A100 \N f \N \N \N {} +1292 435 2025-02-25 01:13:37.578908-08 2025-02-25 02:04:12.790967-08 test t A100 \N f \N \N \N {} +1293 435 2025-02-25 00:34:17.674028-08 2025-02-25 01:20:00.978456-08 test f A100 \N f \N \N \N {} +1294 436 2025-02-25 00:52:16.868772-08 2025-02-25 01:46:34.105184-08 test t A100 \N t \N \N \N {} +1295 436 2025-02-25 00:34:18.664042-08 2025-02-25 01:29:38.828363-08 benchmark t A100 \N t \N \N \N {} +2768 965 2025-02-26 19:49:02.478379-08 2025-02-26 19:27:59.359226-08 benchmark f A100 \N t \N \N \N {} +2769 965 2025-02-26 20:58:22.764397-08 2025-02-26 20:38:26.615803-08 leaderboard f A100 0.014282038666666667 t \N \N \N {} +2770 966 2025-02-26 20:24:44.026742-08 2025-02-26 19:35:29.731923-08 test t A100 \N t \N \N \N {} +2771 966 2025-02-26 19:37:41.565742-08 2025-02-26 20:03:41.368376-08 benchmark t A100 \N t \N \N \N {} +2790 974 2025-02-26 20:10:51.878483-08 2025-02-26 21:28:32.546366-08 benchmark t A100 \N t \N \N \N {} +1296 436 2025-02-25 00:18:28.614962-08 2025-02-25 00:32:08.809058-08 leaderboard t A100 0.01866328503614458 t \N \N \N {} +1297 436 2025-02-25 00:51:06.440802-08 2025-02-25 00:45:31.265356-08 test f A100 \N t \N \N \N {} +1423 472 2025-02-25 04:26:56.092902-08 2025-02-25 04:03:45.687235-08 test t A100 \N t \N \N \N {} +1298 436 2025-02-25 00:31:34.02733-08 2025-02-25 01:09:08.022812-08 benchmark f A100 \N t \N \N \N {} +1299 436 2025-02-25 01:34:03.732205-08 2025-02-25 01:19:15.994949-08 leaderboard f A100 0.01933390226 t \N \N \N {} +1302 438 2025-02-25 02:13:44.700479-08 2025-02-25 01:53:56.844408-08 benchmark f A100 \N t \N \N \N {} +1303 438 2025-02-25 01:52:21.421101-08 2025-02-25 01:13:41.748765-08 leaderboard f A100 0.00318387075 t \N \N \N {} +1304 438 2025-02-25 00:58:34.99604-08 2025-02-25 00:26:07.558315-08 test t A100 \N t \N \N \N {} +1426 473 2025-02-25 03:33:23.323189-08 2025-02-25 04:45:59.064294-08 test t A100 \N t \N \N \N {} +1522 506 2025-02-25 06:29:30.73263-08 2025-02-25 06:29:47.306703-08 test f H100 \N t \N \N \N {} +1305 438 2025-02-25 01:45:16.792963-08 2025-02-25 00:41:59.854224-08 benchmark t A100 \N t \N \N \N {} +1308 439 2025-02-25 01:05:46.577398-08 2025-02-25 01:11:50.101033-08 benchmark t A100 \N t \N \N \N {} +1309 439 2025-02-25 00:31:25.37309-08 2025-02-25 01:33:59.391129-08 leaderboard t A100 0.01829094864864865 t \N \N \N {} +1429 473 2025-02-25 04:12:04.123883-08 2025-02-25 04:05:49.033486-08 test f A100 \N t \N \N \N {} +1310 439 2025-02-25 00:48:25.781713-08 2025-02-25 00:53:44.022446-08 test f A100 \N t \N \N \N {} +1311 439 2025-02-25 00:44:11.592056-08 2025-02-25 01:42:04.821849-08 benchmark f A100 \N t \N \N \N {} +3577 1224 2025-02-28 13:39:20.462449-08 2025-02-28 14:41:42.556499-08 benchmark f H100 \N t \N \N \N {} +2781 969 2025-02-26 19:55:03.2042-08 2025-02-26 20:10:38.143515-08 test f A100 \N f \N \N \N {} +2792 975 2025-02-26 20:50:18.58077-08 2025-02-26 21:14:29.36909-08 test t A100 \N t \N \N \N {} +2785 973 2025-02-26 19:58:45.213429-08 2025-02-26 20:46:29.95438-08 test f A100 \N t \N \N \N {} +2786 974 2025-02-26 21:50:24.917282-08 2025-02-26 21:26:01.992988-08 test f A100 \N t \N \N \N {} +1317 441 2025-02-25 01:27:39.741757-08 2025-02-25 01:07:56.270804-08 test f A100 \N t \N \N \N {} +1318 441 2025-02-25 00:45:28.035248-08 2025-02-25 01:01:14.020655-08 benchmark f A100 \N f \N \N \N {} +1319 442 2025-02-25 01:17:35.027985-08 2025-02-25 01:08:09.855491-08 test f T4 \N t \N \N \N {} +1320 442 2025-02-25 02:23:45.784619-08 2025-02-25 01:07:58.424072-08 benchmark f T4 \N t \N \N \N {} +1321 442 2025-02-25 02:07:32.64996-08 2025-02-25 01:07:33.995652-08 leaderboard f T4 0.017366781666666668 t \N \N \N {} +1323 442 2025-02-25 02:26:58.697335-08 2025-02-25 02:40:12.565833-08 benchmark t T4 \N t \N \N \N {} +1324 442 2025-02-25 02:12:07.116909-08 2025-02-25 00:49:02.771059-08 leaderboard t T4 0.017343184 t \N \N \N {} +1325 443 2025-02-25 02:37:32.638106-08 2025-02-25 02:42:15.65973-08 test t A100 \N t \N \N \N {} +1331 444 2025-02-25 01:34:34.727263-08 2025-02-25 01:39:09.005069-08 test f A100 \N f \N \N \N {} +1332 444 2025-02-25 02:59:13.676931-08 2025-02-25 02:28:54.180908-08 test t A100 \N f \N \N \N {} +1333 445 2025-02-25 01:51:47.936405-08 2025-02-25 03:05:33.196327-08 test f A100 \N t \N \N \N {} +1334 445 2025-02-25 02:55:01.288465-08 2025-02-25 02:42:58.024491-08 benchmark f A100 \N t \N \N \N {} +1337 445 2025-02-25 02:37:39.036581-08 2025-02-25 03:06:46.109288-08 benchmark t A100 \N t \N \N \N {} +1338 445 2025-02-25 02:27:46.062726-08 2025-02-25 02:38:14.514333-08 leaderboard t A100 0.0030933733333333335 t \N \N \N {} +2902 1013 2025-02-27 02:13:19.283137-08 2025-02-27 00:49:55.016565-08 benchmark f L4 \N t \N \N \N {} +2972 1054 2025-02-27 11:12:54.54238-08 2025-02-27 09:51:41.285788-08 benchmark f A100 \N t \N \N \N {} +3032 1085 2025-02-27 16:19:10.737007-08 2025-02-27 15:35:26.72855-08 benchmark t H100 \N t \N \N \N {} +3033 1085 2025-02-27 15:30:06.20078-08 2025-02-27 14:54:00.220725-08 leaderboard t H100 0.0025986981400000003 t \N \N \N {} +3190 1122 2025-02-28 05:22:46.19105-08 2025-02-28 05:19:12.334732-08 test f A100 \N t \N \N \N {} +2787 974 2025-02-26 21:02:26.857575-08 2025-02-26 20:41:35.440073-08 benchmark f A100 \N t \N \N \N {} +2788 974 2025-02-26 21:28:18.31903-08 2025-02-26 20:11:08.511332-08 leaderboard f A100 0.003155315 t \N \N \N {} +3512 1201 2025-02-28 12:09:29.633702-08 2025-02-28 11:30:22.549861-08 test t A100 \N t \N \N \N {} +2794 975 2025-02-26 21:18:52.089772-08 2025-02-26 20:24:31.866857-08 leaderboard t A100 0.003347336 t \N \N \N {} +2796 975 2025-02-26 21:50:59.541074-08 2025-02-26 21:19:54.451136-08 benchmark f A100 \N t \N \N \N {} +2797 975 2025-02-26 21:39:11.70514-08 2025-02-26 20:18:16.784568-08 leaderboard f A100 0.0033508456666666666 t \N \N \N {} +2798 976 2025-02-26 20:38:37.194925-08 2025-02-26 20:58:59.28349-08 test f A100 \N t \N \N \N {} +2799 976 2025-02-26 21:00:51.351138-08 2025-02-26 21:11:09.227246-08 benchmark f A100 \N t \N \N \N {} +2814 982 2025-02-26 23:11:35.068266-08 2025-02-26 22:05:12.788499-08 test t A100 \N t \N \N \N {} +2815 982 2025-02-26 22:41:38.849192-08 2025-02-26 22:55:52.272396-08 benchmark t A100 \N t \N \N \N {} +2816 982 2025-02-26 22:55:13.597102-08 2025-02-26 22:18:52.107493-08 leaderboard t A100 0.0031956527999999996 t \N \N \N {} +2817 982 2025-02-26 22:04:16.387504-08 2025-02-26 22:18:31.80599-08 test f A100 \N t \N \N \N {} +2818 982 2025-02-26 23:45:04.257267-08 2025-02-26 22:14:37.906328-08 benchmark f A100 \N t \N \N \N {} +2819 982 2025-02-26 22:55:44.283823-08 2025-02-26 23:06:19.863356-08 leaderboard f A100 0.00318407825 t \N \N \N {} +1362 459 2025-02-25 02:55:11.371716-08 2025-02-25 03:57:41.197189-08 benchmark t A100 \N t \N \N \N {} +1363 459 2025-02-25 04:23:27.373732-08 2025-02-25 03:20:58.157958-08 leaderboard t A100 0.0030870156666666666 t \N \N \N {} +2903 1014 2025-02-27 01:15:06.478798-08 2025-02-27 01:26:08.615896-08 benchmark f L4 \N t \N \N \N {} +3034 1086 2025-02-27 16:07:05.961453-08 2025-02-27 15:24:55.001093-08 test t A100 \N t \N \N \N {} +3035 1086 2025-02-27 14:56:09.755895-08 2025-02-27 14:44:03.736702-08 benchmark t A100 \N t \N \N \N {} +3036 1086 2025-02-27 15:11:18.717508-08 2025-02-27 16:25:37.497879-08 leaderboard t A100 0.00219666254 t \N \N \N {} +2822 983 2025-02-26 23:58:17.638886-08 2025-02-26 23:08:43.554386-08 leaderboard f T4 0.017258812666666668 t \N \N \N {} +2823 983 2025-02-26 23:08:02.482508-08 2025-02-26 22:46:14.565363-08 test t T4 \N t \N \N \N {} +2824 983 2025-02-26 22:41:21.612063-08 2025-02-26 23:37:03.076526-08 benchmark t T4 \N t \N \N \N {} +2825 983 2025-02-26 22:11:02.562982-08 2025-02-26 23:08:38.480756-08 leaderboard t T4 0.017232594333333334 t \N \N \N {} +2826 984 2025-02-26 23:55:23.356201-08 2025-02-26 22:34:06.41761-08 test t L4 \N t \N \N \N {} +2827 984 2025-02-26 23:39:16.573547-08 2025-02-26 23:45:06.294967-08 benchmark t L4 \N t \N \N \N {} +1365 459 2025-02-25 04:10:18.717828-08 2025-02-25 03:53:14.773432-08 benchmark f A100 \N t \N \N \N {} +1366 459 2025-02-25 02:36:43.695926-08 2025-02-25 03:37:20.520163-08 leaderboard f A100 0.003083136 t \N \N \N {} +2904 1015 2025-02-27 01:39:23.749307-08 2025-02-27 00:47:34.63079-08 benchmark f L4 \N t \N \N \N {} +3037 1086 2025-02-27 15:04:43.578785-08 2025-02-27 16:05:13.802056-08 test f A100 \N t \N \N \N {} +3038 1086 2025-02-27 15:52:40.863821-08 2025-02-27 15:55:36.239122-08 benchmark f A100 \N t \N \N \N {} +3039 1086 2025-02-27 14:57:57.258869-08 2025-02-27 16:22:35.716253-08 leaderboard f A100 0.006317708599999999 t \N \N \N {} +2828 984 2025-02-26 22:43:53.565232-08 2025-02-26 22:20:13.280092-08 leaderboard t L4 0.01789583566666667 t \N \N \N {} +2835 986 2025-02-26 23:41:34.087432-08 2025-02-26 22:22:07.716492-08 test f H100 \N f \N \N \N {} +2839 988 2025-02-26 23:14:11.180538-08 2025-02-26 22:55:38.097536-08 benchmark t H100 \N t \N \N \N {} +2840 988 2025-02-26 23:51:51.947336-08 2025-02-27 00:11:57.409637-08 leaderboard t H100 0.0014754095555555556 t \N \N \N {} +2841 988 2025-02-26 23:43:56.311146-08 2025-02-26 22:51:36.110909-08 test f H100 \N t \N \N \N {} +2842 988 2025-02-27 00:31:41.8135-08 2025-02-26 23:01:52.346478-08 benchmark f H100 \N t \N \N \N {} +1368 460 2025-02-25 03:56:45.320682-08 2025-02-25 04:12:23.36629-08 benchmark f H100 \N t \N \N \N {} +1369 460 2025-02-25 03:15:26.015945-08 2025-02-25 03:02:05.905441-08 leaderboard f H100 0.0014066026666666668 t \N \N \N {} +2905 1016 2025-02-27 01:18:12.382929-08 2025-02-27 01:17:06.286041-08 test t L4 \N t \N \N \N {} +2906 1016 2025-02-27 01:26:20.055734-08 2025-02-27 00:56:16.777841-08 benchmark t L4 \N t \N \N \N {} +2907 1016 2025-02-27 02:18:18.942716-08 2025-02-27 02:21:22.058687-08 leaderboard t L4 0.017129494 t \N \N \N {} +3040 1087 2025-02-27 16:44:40.535196-08 2025-02-27 15:03:57.635039-08 test f A100 \N t \N \N \N {} +3193 1122 2025-02-28 03:38:44.016859-08 2025-02-28 03:42:55.449712-08 test t A100 \N t \N \N \N {} +2847 989 2025-02-26 23:11:09.581049-08 2025-02-26 23:57:11.977062-08 test f H100 \N t \N \N \N {} +2848 989 2025-02-26 23:28:17.877389-08 2025-02-26 22:53:38.145262-08 benchmark f H100 \N t \N \N \N {} +1371 460 2025-02-25 03:58:04.611454-08 2025-02-25 02:31:49.105023-08 benchmark t H100 \N t \N \N \N {} +1372 460 2025-02-25 04:03:19.955243-08 2025-02-25 04:08:28.45914-08 leaderboard t H100 0.001423033 t \N \N \N {} +2908 1016 2025-02-27 01:21:48.281072-08 2025-02-27 00:41:29.117391-08 test f L4 \N t \N \N \N {} +1374 461 2025-02-25 04:09:34.446371-08 2025-02-25 02:35:46.24839-08 benchmark t H100 \N t \N \N \N {} +1375 461 2025-02-25 03:23:18.745022-08 2025-02-25 04:16:13.049744-08 leaderboard t H100 0.0014008253333333332 t \N \N \N {} +2911 1017 2025-02-27 03:00:45.796745-08 2025-02-27 01:43:35.233574-08 test f H100 \N f \N \N \N {} +3042 1089 2025-02-27 16:38:00.448626-08 2025-02-27 15:58:00.42158-08 benchmark f A100 \N t \N \N \N {} +3074 1102 2025-02-28 02:56:01.659272-08 2025-02-28 01:40:10.731732-08 benchmark t A100 \N t \N \N \N {} +3075 1102 2025-02-28 02:21:16.939778-08 2025-02-28 02:01:28.710259-08 leaderboard t A100 0.0030923946666666664 t \N \N \N {} +2849 989 2025-02-27 00:12:17.835592-08 2025-02-26 22:40:43.431694-08 leaderboard f H100 0.001638096 t \N \N \N {} +2850 990 2025-02-26 22:57:30.808633-08 2025-02-27 00:18:26.83744-08 test t H100 \N t \N \N \N {} +2851 990 2025-02-26 23:06:02.704806-08 2025-02-26 23:22:40.730823-08 benchmark t H100 \N t \N \N \N {} +2852 990 2025-02-26 23:16:39.034056-08 2025-02-27 00:31:18.681964-08 leaderboard t H100 0.001507587888888889 t \N \N \N {} +2855 990 2025-02-26 22:47:36.052869-08 2025-02-27 00:05:47.184279-08 leaderboard f H100 0.0015178355 t \N \N \N {} +2856 991 2025-02-26 23:22:25.154438-08 2025-02-27 00:25:57.062615-08 test f H100 \N f \N \N \N {} +1377 461 2025-02-25 03:11:23.466982-08 2025-02-25 03:14:58.304434-08 benchmark f H100 \N t \N \N \N {} +1378 461 2025-02-25 03:19:43.776884-08 2025-02-25 03:47:15.782517-08 leaderboard f H100 0.0014126543333333333 t \N \N \N {} +2912 1018 2025-02-27 02:15:28.857362-08 2025-02-27 03:05:57.617691-08 test f H100 \N t \N \N \N {} +3043 1090 2025-02-27 16:23:02.016071-08 2025-02-27 15:37:11.121549-08 benchmark f A100 \N t \N \N \N {} +3076 1103 2025-02-28 01:45:24.142839-08 2025-02-28 01:57:12.178482-08 test f A100 \N t \N \N \N {} +3119 1110 2025-02-28 03:01:10.825974-08 2025-02-28 02:12:58.251641-08 benchmark t A100 \N t \N \N \N {} +2857 991 2025-02-27 00:12:21.904324-08 2025-02-26 23:35:25.811072-08 test t H100 \N f \N \N \N {} +2858 992 2025-02-27 00:04:38.346043-08 2025-02-26 23:29:33.532762-08 test t H100 \N t \N \N \N {} +2859 992 2025-02-27 00:22:03.205134-08 2025-02-26 23:23:33.781116-08 benchmark t H100 \N t \N \N \N {} +2864 993 2025-02-26 23:55:03.486115-08 2025-02-26 23:18:13.205693-08 test f H100 \N t \N \N \N {} +2865 993 2025-02-27 00:45:02.104993-08 2025-02-26 23:57:45.704039-08 benchmark f H100 \N t \N \N \N {} +2866 993 2025-02-26 23:20:23.784092-08 2025-02-26 23:18:10.376928-08 leaderboard f H100 0.0015042338999999999 t \N \N \N {} +1382 464 2025-02-25 04:24:56.79314-08 2025-02-25 04:46:29.308114-08 benchmark f H100 \N t \N \N \N {} +1383 464 2025-02-25 03:22:15.683757-08 2025-02-25 03:47:50.3734-08 leaderboard f H100 0.0014120043333333333 t \N \N \N {} +2913 1019 2025-02-27 02:15:25.259011-08 2025-02-27 02:01:14.038287-08 test f H100 \N f \N \N \N {} +3044 1091 2025-02-27 16:34:45.373039-08 2025-02-27 17:04:56.053201-08 benchmark f A100 \N t \N \N \N {} +3077 1103 2025-02-28 02:26:24.054371-08 2025-02-28 02:57:27.049866-08 benchmark f A100 \N t \N \N \N {} +2867 993 2025-02-27 00:20:03.256964-08 2025-02-26 23:38:08.774778-08 test t H100 \N t \N \N \N {} +2868 993 2025-02-27 00:17:21.143055-08 2025-02-26 23:43:44.61358-08 benchmark t H100 \N t \N \N \N {} +2869 993 2025-02-27 00:41:17.765937-08 2025-02-27 00:38:24.740261-08 leaderboard t H100 0.0014829258999999998 t \N \N \N {} +2873 995 2025-02-26 23:14:08.542885-08 2025-02-27 00:24:07.258227-08 benchmark t H100 \N t \N \N \N {} +2874 995 2025-02-26 22:57:32.493875-08 2025-02-26 23:39:16.1763-08 leaderboard t H100 0.0014810648 t \N \N \N {} +2875 995 2025-02-26 23:55:02.996586-08 2025-02-27 00:01:45.681815-08 test f H100 \N t \N \N \N {} +2889 1000 2025-02-27 00:47:29.323693-08 2025-02-27 01:19:30.043071-08 benchmark f L4 \N t \N \N \N {} +2876 995 2025-02-27 00:48:27.410452-08 2025-02-26 23:02:52.737338-08 benchmark f H100 \N t \N \N \N {} +2877 995 2025-02-27 00:46:15.879218-08 2025-02-26 22:53:09.26022-08 leaderboard f H100 0.00151809525 t \N \N \N {} +2878 996 2025-02-26 23:32:33.325133-08 2025-02-27 00:20:52.668636-08 test t H100 \N f \N \N \N {} +2879 996 2025-02-26 23:32:09.509312-08 2025-02-27 00:16:13.600353-08 test f H100 \N f \N \N \N {} +2880 997 2025-02-26 23:10:26.947465-08 2025-02-27 00:59:43.704157-08 test f H100 \N f \N \N \N {} +2895 1006 2025-02-27 00:30:50.677826-08 2025-02-27 02:02:45.191772-08 benchmark f T4 \N t \N \N \N {} +2881 997 2025-02-26 23:16:25.148851-08 2025-02-26 23:15:22.787437-08 test t H100 \N f \N \N \N {} +1385 464 2025-02-25 03:54:35.416434-08 2025-02-25 03:14:13.277902-08 benchmark t H100 \N t \N \N \N {} +1386 464 2025-02-25 03:44:28.181743-08 2025-02-25 04:52:22.685863-08 leaderboard t H100 0.0013942456666666667 t \N \N \N {} +2914 1021 2025-02-27 04:12:21.245081-08 2025-02-27 02:35:09.897785-08 test f H100 \N t \N \N \N {} +3045 1092 2025-02-27 15:48:36.342402-08 2025-02-27 16:50:15.479403-08 benchmark f A100 \N t \N \N \N {} +2882 998 2025-02-26 23:31:45.732141-08 2025-02-26 23:28:03.798982-08 test f H100 \N t \N \N \N {} +2883 998 2025-02-27 00:46:38.657542-08 2025-02-27 00:13:36.156442-08 benchmark f H100 \N t \N \N \N {} +2884 998 2025-02-26 23:36:22.797455-08 2025-02-27 00:38:35.235535-08 leaderboard f H100 0.0014596549 t \N \N \N {} +2885 998 2025-02-27 00:25:48.975564-08 2025-02-27 00:17:37.217535-08 test t H100 \N t \N \N \N {} +2886 998 2025-02-27 01:13:36.817864-08 2025-02-27 00:29:21.912198-08 benchmark t H100 \N t \N \N \N {} +2887 998 2025-02-27 00:25:46.035637-08 2025-02-27 00:48:51.353021-08 leaderboard t H100 0.00146596 t \N \N \N {} +2888 999 2025-02-26 23:44:36.851934-08 2025-02-27 00:13:44.746988-08 test f H100 \N f \N \N \N {} +2891 1002 2025-02-27 00:08:35.698636-08 2025-02-27 00:37:38.529546-08 benchmark f L4 \N t \N \N \N {} +2892 1003 2025-02-27 01:04:08.050505-08 2025-02-27 01:56:08.100836-08 benchmark f T4 \N f \N \N \N {} +2893 1004 2025-02-27 01:20:48.626974-08 2025-02-27 02:00:18.32054-08 benchmark f T4 \N t \N \N \N {} +2894 1005 2025-02-27 00:48:26.399604-08 2025-02-27 01:32:13.188985-08 benchmark f T4 \N t \N \N \N {} +3515 1202 2025-02-28 10:44:20.111423-08 2025-02-28 10:41:52.613484-08 test f A100 \N t \N \N \N {} +3883 1296 2025-03-01 03:31:39.319577-08 2025-03-01 04:35:51.070787-08 test t A100 \N t \N \N \N {} +2896 1007 2025-02-27 00:30:01.989297-08 2025-02-27 01:26:11.483563-08 benchmark f L4 \N t \N \N \N {} +2897 1008 2025-02-27 00:19:27.820406-08 2025-02-27 01:13:08.554592-08 benchmark f L4 \N t \N \N \N {} +2898 1009 2025-02-27 00:14:37.058625-08 2025-02-27 00:33:25.198225-08 benchmark f T4 \N t \N \N \N {} +2899 1010 2025-02-27 01:32:50.152071-08 2025-02-27 01:26:35.416449-08 benchmark f L4 \N f \N \N \N {} +2900 1011 2025-02-27 00:40:17.675693-08 2025-02-27 00:34:54.012193-08 benchmark f L4 \N t \N \N \N {} +2901 1012 2025-02-27 00:57:52.617774-08 2025-02-27 01:00:23.764973-08 benchmark f L4 \N t \N \N \N {} +3031 1085 2025-02-27 15:24:29.601416-08 2025-02-27 14:51:34.912701-08 test t H100 \N t \N \N \N {} +6462 2006 2025-03-14 07:51:45.870673-07 2025-03-14 07:40:39.999265-07 benchmark f A100 \N t \N \N \N {} +3102 1107 2025-02-28 01:54:23.349256-08 2025-02-28 01:56:27.939879-08 leaderboard f A100 0.003092532 t \N \N \N {} +6142 1883 2025-03-11 11:28:02.967532-07 2025-03-11 11:35:29.392865-07 test t T4 \N f \N \N \N {} +6314 1954 2025-03-12 20:17:28.206705-07 2025-03-12 20:22:28.033335-07 test f A100 \N t \N \N \N {} +6315 1954 2025-03-12 19:44:04.442778-07 2025-03-12 20:05:34.313377-07 benchmark f A100 \N t \N \N \N {} +6894 2143 2025-03-16 12:07:38.644838-07 2025-03-16 12:03:19.501535-07 test f L4 \N f \N \N \N {} +1388 465 2025-02-25 03:44:13.489138-08 2025-02-25 04:58:52.178206-08 benchmark t H100 \N t \N \N \N {} +1389 465 2025-02-25 04:48:22.345502-08 2025-02-25 03:34:29.544054-08 leaderboard t H100 0.000999849 t \N \N \N {} +2915 1022 2025-02-27 03:08:40.368035-08 2025-02-27 03:55:02.355278-08 benchmark f A100 \N t \N \N \N {} +3046 1093 2025-02-27 16:57:16.382054-08 2025-02-27 16:07:28.917105-08 test t A100 \N t \N \N \N {} +6895 2144 2025-03-16 12:40:59.707177-07 2025-03-16 12:04:30.038571-07 benchmark f T4 \N f \N \N \N {} +3108 1108 2025-02-28 02:25:29.231129-08 2025-02-28 01:30:03.455293-08 leaderboard t A100 0.0030845013333333335 t \N \N \N {} +6144 1885 2025-03-11 11:27:23.29413-07 2025-03-11 12:06:00.249613-07 test f T4 \N f \N \N \N {} +6317 1954 2025-03-12 20:05:57.526603-07 2025-03-12 19:36:21.76166-07 test t A100 \N t \N \N \N {} +6318 1954 2025-03-12 20:14:29.660956-07 2025-03-12 20:07:14.51853-07 benchmark t A100 \N t \N \N \N {} +6319 1954 2025-03-12 20:17:32.676671-07 2025-03-12 19:52:30.024723-07 leaderboard t A100 0.0013705575900000001 t \N \N \N {} +6465 2008 2025-03-14 06:25:10.510564-07 2025-03-14 06:10:34.856104-07 benchmark f A100 \N t \N \N \N {} +6467 2008 2025-03-14 07:05:50.283776-07 2025-03-14 06:54:47.878347-07 test t A100 \N t \N \N \N {} +6468 2008 2025-03-14 07:57:23.655081-07 2025-03-14 07:57:46.860585-07 benchmark t A100 \N t \N \N \N {} +6321 1956 2025-03-13 09:39:59.949032-07 2025-03-13 10:36:07.030735-07 test t T4 \N f \N \N \N {} +6470 2009 2025-03-14 07:54:17.367584-07 2025-03-14 07:52:14.667761-07 test t A100 \N t \N \N \N {} +6471 2009 2025-03-14 08:25:39.938379-07 2025-03-14 06:36:09.564554-07 benchmark t A100 \N t \N \N \N {} +3122 1110 2025-02-28 03:18:32.981187-08 2025-02-28 02:42:29.642779-08 benchmark f A100 \N t \N \N \N {} +3123 1110 2025-02-28 02:50:51.08401-08 2025-02-28 01:46:09.197929-08 leaderboard f A100 0.003097124 t \N \N \N {} +6322 1956 2025-03-13 08:55:36.22372-07 2025-03-13 10:04:13.347907-07 test f T4 \N f \N \N \N {} +6473 2009 2025-03-14 07:46:08.399013-07 2025-03-14 08:25:12.694431-07 test f A100 \N t \N \N \N {} +3578 1225 2025-02-28 14:25:25.372721-08 2025-02-28 13:59:09.123935-08 benchmark f A100 \N t \N \N \N {} +3128 1111 2025-02-28 02:02:43.50836-08 2025-02-28 01:29:59.933956-08 benchmark t A100 \N t \N \N \N {} +3129 1111 2025-02-28 02:09:35.864061-08 2025-02-28 02:11:31.900287-08 leaderboard t A100 0.00320221 t \N \N \N {} +6150 1888 2025-03-11 12:00:16.100477-07 2025-03-11 12:59:35.590119-07 test f T4 \N f \N \N \N {} +6324 1957 2025-03-13 10:47:36.919965-07 2025-03-13 09:33:50.353862-07 test f T4 \N f \N \N \N {} +3132 1112 2025-02-28 02:00:15.175704-08 2025-02-28 01:42:45.822482-08 leaderboard f A100 0.0030739046666666664 t \N \N \N {} +6151 1888 2025-03-11 11:24:19.34183-07 2025-03-11 11:15:09.363246-07 test t T4 \N f \N \N \N {} +6325 1958 2025-03-13 10:54:39.858929-07 2025-03-13 10:57:53.21954-07 test t T4 \N f \N \N \N {} +6481 2010 2025-03-14 07:16:40.068566-07 2025-03-14 08:11:25.193743-07 leaderboard f A100 0.0026579623333333334 t \N \N \N {} +3134 1112 2025-02-28 02:21:04.795319-08 2025-02-28 03:19:20.667414-08 benchmark t A100 \N t \N \N \N {} +3135 1112 2025-02-28 02:49:56.527812-08 2025-02-28 02:10:59.95073-08 leaderboard t A100 0.0030922646666666667 t \N \N \N {} +6153 1889 2025-03-11 11:42:50.98288-07 2025-03-11 13:36:59.142943-07 benchmark t T4 \N f \N \N \N {} +1391 465 2025-02-25 04:55:41.868356-08 2025-02-25 03:36:33.891601-08 benchmark f H100 \N t \N \N \N {} +1392 465 2025-02-25 04:37:36.417233-08 2025-02-25 03:28:58.50135-08 leaderboard f H100 0.00140509 t \N \N \N {} +1393 466 2025-02-25 03:10:36.309883-08 2025-02-25 03:21:53.515918-08 test f H100 \N f \N \N \N {} +1394 466 2025-02-25 03:11:29.000771-08 2025-02-25 03:41:58.917775-08 test t H100 \N f \N \N \N {} +2916 1023 2025-02-27 02:46:36.620437-08 2025-02-27 02:58:33.660277-08 test f A100 \N t \N \N \N {} +3078 1103 2025-02-28 01:22:48.676241-08 2025-02-28 02:52:22.813395-08 leaderboard f A100 0.0031237426666666664 t \N \N \N {} +6155 1889 2025-03-11 12:10:23.846696-07 2025-03-11 12:13:59.236186-07 benchmark f T4 \N f \N \N \N {} +1397 468 2025-02-25 03:38:51.530876-08 2025-02-25 04:52:35.406014-08 benchmark f A100 \N t \N \N \N {} +1398 468 2025-02-25 04:38:38.385416-08 2025-02-25 03:58:06.257632-08 leaderboard f A100 0.0033990103333333336 t \N \N \N {} +2917 1023 2025-02-27 03:14:37.23347-08 2025-02-27 03:11:45.911539-08 benchmark f A100 \N t \N \N \N {} +2918 1023 2025-02-27 03:03:16.65023-08 2025-02-27 04:18:18.937857-08 leaderboard f A100 0.0001010261724137931 t \N \N \N {} +3079 1103 2025-02-28 01:31:17.940598-08 2025-02-28 02:41:55.766318-08 test t A100 \N t \N \N \N {} +6897 2146 2025-03-16 12:39:46.739273-07 2025-03-16 13:06:56.642031-07 benchmark f T4 \N f \N \N \N {} +3141 1113 2025-02-28 01:34:25.294505-08 2025-02-28 02:09:38.976633-08 leaderboard t A100 0.0030885226666666665 t \N \N \N {} +3579 1226 2025-02-28 13:23:19.822733-08 2025-02-28 13:38:01.202424-08 benchmark f H100 \N t \N \N \N {} +3144 1114 2025-02-28 03:32:16.197256-08 2025-02-28 02:47:43.563886-08 leaderboard t A100 0.003070266 t \N \N \N {} +6158 1890 2025-03-11 13:28:57.157083-07 2025-03-11 12:19:09.927667-07 test f T4 \N t \N \N \N {} +3147 1114 2025-02-28 03:28:26.003711-08 2025-02-28 01:44:07.837604-08 leaderboard f A100 0.0030685503333333333 t \N \N \N {} +6160 1891 2025-03-11 13:43:23.540375-07 2025-03-11 13:07:39.214045-07 test f T4 \N t \N \N \N {} +6484 2011 2025-03-14 07:02:10.29025-07 2025-03-14 08:11:39.923824-07 leaderboard f A100 0.0025160156666666667 t \N \N \N {} +3149 1115 2025-02-28 02:35:43.630546-08 2025-02-28 02:15:39.443589-08 benchmark f A100 \N t \N \N \N {} +3150 1115 2025-02-28 02:15:43.119849-08 2025-02-28 01:43:19.893985-08 leaderboard f A100 0.0030932896666666667 t \N \N \N {} +6161 1892 2025-03-11 11:57:49.455042-07 2025-03-11 12:21:21.948804-07 benchmark f T4 \N f \N \N \N {} +3580 1227 2025-02-28 13:22:39.155063-08 2025-02-28 13:51:24.355953-08 benchmark f H100 \N t \N \N \N {} +3152 1115 2025-02-28 01:56:27.157682-08 2025-02-28 01:41:45.62036-08 benchmark t A100 \N t \N \N \N {} +3153 1115 2025-02-28 03:24:29.539728-08 2025-02-28 03:03:11.575433-08 leaderboard t A100 0.0030942826666666666 t \N \N \N {} +6162 1893 2025-03-11 12:05:53.460089-07 2025-03-11 13:52:23.016257-07 benchmark f T4 \N f \N \N \N {} +3155 1116 2025-02-28 01:56:03.204295-08 2025-02-28 02:29:07.453242-08 benchmark f A100 \N t \N \N \N {} +3156 1116 2025-02-28 03:41:05.90631-08 2025-02-28 03:37:39.667047-08 leaderboard f A100 0.0030983496666666666 t \N \N \N {} +3581 1228 2025-02-28 14:07:30.971092-08 2025-02-28 14:44:25.55772-08 test f A100 \N t \N \N \N {} +6487 2011 2025-03-14 07:33:24.422282-07 2025-03-14 07:03:24.285193-07 leaderboard t A100 0.0024820293333333333 t \N \N \N {} +6674 2065 2025-03-14 15:48:12.867984-07 2025-03-14 15:10:31.440491-07 test t T4 \N f \N \N \N {} +1406 469 2025-02-25 04:38:40.125755-08 2025-02-25 04:37:21.18501-08 benchmark t A100 \N t \N \N \N {} +1407 469 2025-02-25 04:41:04.15737-08 2025-02-25 04:10:44.089578-08 leaderboard t A100 0.0030951176666666664 t \N \N \N {} +2925 1024 2025-02-27 04:23:16.250435-08 2025-02-27 03:24:58.288515-08 test t T4 \N t \N \N \N {} +1412 470 2025-02-25 03:40:12.003397-08 2025-02-25 04:12:17.525955-08 benchmark f A100 \N t \N \N \N {} +1413 470 2025-02-25 04:37:09.951863-08 2025-02-25 03:30:09.91909-08 leaderboard f A100 0.003099017 t \N \N \N {} +2931 1025 2025-02-27 03:01:05.861477-08 2025-02-27 04:00:58.846217-08 test t L4 \N t \N \N \N {} +2932 1025 2025-02-27 04:33:16.006317-08 2025-02-27 04:33:30.552743-08 benchmark t L4 \N t \N \N \N {} +2933 1025 2025-02-27 03:00:47.162532-08 2025-02-27 03:18:26.934968-08 leaderboard t L4 0.00011731689361702128 t \N \N \N {} +3127 1111 2025-02-28 03:03:45.476308-08 2025-02-28 01:34:30.667207-08 test t A100 \N t \N \N \N {} +1415 471 2025-02-25 05:07:03.031352-08 2025-02-25 04:44:07.11511-08 benchmark f A100 \N t \N \N \N {} +1416 471 2025-02-25 03:49:37.382368-08 2025-02-25 03:16:26.151128-08 leaderboard f A100 0.003083305 t \N \N \N {} +2934 1026 2025-02-27 03:29:31.212038-08 2025-02-27 03:36:48.365495-08 benchmark f A100 \N t \N \N \N {} +3049 1093 2025-02-27 15:47:45.331679-08 2025-02-27 16:54:04.404866-08 test f A100 \N t \N \N \N {} +3130 1112 2025-02-28 02:53:32.669829-08 2025-02-28 01:59:58.104291-08 test f A100 \N t \N \N \N {} +3158 1116 2025-02-28 03:00:27.935274-08 2025-02-28 03:06:58.848252-08 benchmark t A100 \N t \N \N \N {} +3582 1228 2025-02-28 14:47:56.576495-08 2025-02-28 13:38:26.115181-08 benchmark f A100 \N t \N \N \N {} +6334 1962 2025-03-13 12:17:44.907621-07 2025-03-13 10:47:39.798292-07 test f T4 \N f \N \N \N {} +6488 2012 2025-03-14 07:37:48.319936-07 2025-03-14 06:40:10.451158-07 test t A100 \N t \N \N \N {} +6905 2149 2025-03-16 13:54:55.728222-07 2025-03-16 12:29:31.998021-07 test f A100 \N f \N \N \N {} +3161 1117 2025-02-28 03:20:59.962421-08 2025-02-28 03:07:10.955256-08 benchmark t A100 \N t \N \N \N {} +6489 2012 2025-03-14 07:07:28.772602-07 2025-03-14 06:43:54.465366-07 benchmark t A100 \N t \N \N \N {} +6490 2012 2025-03-14 07:43:28.617109-07 2025-03-14 07:02:38.240011-07 leaderboard t A100 0.002517398 t \N \N \N {} +6912 2154 2025-03-16 16:29:02.680063-07 2025-03-16 16:07:12.630657-07 test t T4 \N f \N \N \N {} +3164 1117 2025-02-28 01:55:34.743977-08 2025-02-28 03:46:05.60909-08 benchmark f A100 \N t \N \N \N {} +3165 1117 2025-02-28 03:22:15.291884-08 2025-02-28 03:01:38.071188-08 leaderboard f A100 0.0031003043333333335 t \N \N \N {} +3166 1118 2025-02-28 02:36:20.544219-08 2025-02-28 02:03:38.914711-08 test t A100 \N t \N \N \N {} +3167 1118 2025-02-28 02:27:47.915164-08 2025-02-28 02:31:18.69465-08 benchmark t A100 \N t \N \N \N {} +3168 1118 2025-02-28 03:38:38.742518-08 2025-02-28 02:42:05.29698-08 leaderboard t A100 0.0031557713333333335 t \N \N \N {} +3467 1192 2025-02-28 11:34:31.331723-08 2025-02-28 10:29:39.69554-08 test f A100 \N f \N \N \N {} +1418 471 2025-02-25 04:36:59.718452-08 2025-02-25 03:23:10.279404-08 benchmark t A100 \N t \N \N \N {} +1419 471 2025-02-25 03:23:16.170077-08 2025-02-25 04:14:16.278504-08 leaderboard t A100 0.0030839833333333334 t \N \N \N {} +2935 1027 2025-02-27 04:13:09.20703-08 2025-02-27 03:53:36.446829-08 benchmark f A100 \N t \N \N \N {} +3133 1112 2025-02-28 01:44:54.216121-08 2025-02-28 02:38:03.906338-08 test t A100 \N t \N \N \N {} +3518 1202 2025-02-28 11:33:17.396025-08 2025-02-28 12:16:13.271104-08 test t A100 \N t \N \N \N {} +6913 2154 2025-03-16 16:38:39.962668-07 2025-03-16 14:53:33.732977-07 test f T4 \N f \N \N \N {} +3568 1211 2025-02-28 11:12:30.656088-08 2025-02-28 12:38:32.554868-08 test t H100 \N f \N \N \N {} +3171 1118 2025-02-28 03:57:16.999936-08 2025-02-28 02:52:16.718421-08 leaderboard f A100 0.003146471 t \N \N \N {} +3172 1119 2025-02-28 03:47:11.316659-08 2025-02-28 03:09:45.561459-08 test f A100 \N t \N \N \N {} +3173 1119 2025-02-28 02:39:13.039039-08 2025-02-28 02:10:32.463474-08 benchmark f A100 \N t \N \N \N {} +6914 2155 2025-03-16 15:06:19.793513-07 2025-03-16 16:22:51.137738-07 test t T4 \N f \N \N \N {} +3177 1119 2025-02-28 03:56:27.839518-08 2025-02-28 03:53:00.543375-08 leaderboard t A100 0.003148472 t \N \N \N {} +6166 1897 2025-03-11 12:32:48.919729-07 2025-03-11 13:04:35.751745-07 benchmark f T4 \N t \N \N \N {} +6336 1963 2025-03-13 11:44:12.666263-07 2025-03-13 11:35:54.4531-07 test t T4 \N f \N \N \N {} +6491 2012 2025-03-14 06:56:50.114221-07 2025-03-14 07:03:33.962589-07 test f A100 \N t \N \N \N {} +6915 2155 2025-03-16 16:05:45.981468-07 2025-03-16 16:53:11.056967-07 test f T4 \N f \N \N \N {} +3886 1296 2025-03-01 04:03:42.524707-08 2025-03-01 04:26:34.298074-08 test f A100 \N t \N \N \N {} +6167 1898 2025-03-11 14:02:34.088566-07 2025-03-11 13:21:01.318443-07 test f T4 \N t \N \N \N {} +6168 1898 2025-03-11 13:56:37.141151-07 2025-03-11 12:54:02.739765-07 benchmark f T4 \N t \N \N \N {} +6169 1898 2025-03-11 13:26:27.542536-07 2025-03-11 12:35:42.421543-07 leaderboard f T4 3.1035724833333336 t \N \N \N {} +6337 1964 2025-03-13 12:31:57.776048-07 2025-03-13 12:44:18.662819-07 test t T4 \N f \N \N \N {} +6675 2065 2025-03-14 15:10:20.43581-07 2025-03-14 15:25:35.47986-07 test f T4 \N f \N \N \N {} +6170 1898 2025-03-11 14:05:54.41326-07 2025-03-11 13:31:34.094196-07 test t T4 \N t \N \N \N {} +6171 1898 2025-03-11 13:56:20.919926-07 2025-03-11 12:56:21.007587-07 benchmark t T4 \N t \N \N \N {} +6172 1898 2025-03-11 13:32:59.303232-07 2025-03-11 14:04:58.378204-07 leaderboard t T4 3.1852416556666667 t \N \N \N {} +6338 1964 2025-03-13 11:48:09.106344-07 2025-03-13 11:48:18.838973-07 test f T4 \N f \N \N \N {} +6676 2066 2025-03-14 15:18:53.997082-07 2025-03-14 14:33:10.917221-07 test f T4 \N f \N \N \N {} +3185 1121 2025-02-28 04:01:47.89985-08 2025-02-28 04:25:50.220487-08 benchmark f A100 \N t \N \N \N {} +3186 1121 2025-02-28 04:48:14.978807-08 2025-02-28 04:05:51.546019-08 leaderboard f A100 0.0031212863333333336 t \N \N \N {} +6173 1899 2025-03-11 13:25:54.795056-07 2025-03-11 13:19:39.098623-07 test t T4 \N f \N \N \N {} +6339 1965 2025-03-13 11:39:17.316162-07 2025-03-13 11:28:19.690857-07 test t T4 \N f \N \N \N {} +3468 1193 2025-02-28 12:15:00.613441-08 2025-02-28 10:38:48.030816-08 test f A100 \N t \N \N \N {} +1421 472 2025-02-25 04:29:57.622938-08 2025-02-25 04:49:21.634274-08 benchmark f A100 \N t \N \N \N {} +1422 472 2025-02-25 03:37:41.571503-08 2025-02-25 03:49:33.02462-08 leaderboard f A100 0.0030871423333333333 t \N \N \N {} +2936 1028 2025-02-27 03:16:50.844905-08 2025-02-27 03:44:10.314673-08 benchmark f A100 \N t \N \N \N {} +3136 1113 2025-02-28 02:58:56.415099-08 2025-02-28 02:44:36.404391-08 test f A100 \N t \N \N \N {} +6492 2012 2025-03-14 08:00:25.963285-07 2025-03-14 06:46:07.163228-07 benchmark f A100 \N t \N \N \N {} +6493 2012 2025-03-14 07:09:08.72814-07 2025-03-14 07:18:27.447251-07 leaderboard f A100 0.002505465 t \N \N \N {} +6599 2045 2025-03-14 12:56:47.350056-07 2025-03-14 13:11:04.653905-07 benchmark f A100 \N t \N \N \N {} +6600 2045 2025-03-14 13:04:51.216706-07 2025-03-14 13:43:13.503645-07 leaderboard f A100 0.0025198196666666667 t \N \N \N {} +6645 2053 2025-03-14 13:47:46.801513-07 2025-03-14 12:15:48.666184-07 benchmark f A100 \N t \N \N \N {} +3192 1122 2025-02-28 03:46:09.723947-08 2025-02-28 04:28:57.570091-08 leaderboard f A100 0.0031000573333333334 t \N \N \N {} +6341 1966 2025-03-13 12:45:43.296749-07 2025-03-13 12:17:33.727157-07 test f T4 \N t \N \N \N {} +6342 1966 2025-03-13 12:13:23.993863-07 2025-03-13 12:35:05.634615-07 benchmark f T4 \N t \N \N \N {} +6343 1966 2025-03-13 13:00:24.511463-07 2025-03-13 12:19:23.080746-07 leaderboard f T4 3.5582660766666665 t \N \N \N {} +6495 2013 2025-03-14 12:06:59.03304-07 2025-03-14 11:21:26.358885-07 test f H100 \N f \N \N \N {} +3198 1123 2025-02-28 04:42:25.083061-08 2025-02-28 04:52:17.118702-08 leaderboard f A100 0.0031039456666666666 t \N \N \N {} +3521 1203 2025-02-28 12:13:18.73249-08 2025-02-28 11:42:59.789557-08 test t A100 \N f \N \N \N {} +3200 1123 2025-02-28 04:09:22.67407-08 2025-02-28 05:42:51.81569-08 benchmark t A100 \N t \N \N \N {} +3201 1123 2025-02-28 04:49:03.536471-08 2025-02-28 05:30:26.750128-08 leaderboard t A100 0.003107339 t \N \N \N {} +6176 1900 2025-03-11 14:27:12.915155-07 2025-03-11 13:27:30.861214-07 test f T4 \N f \N \N \N {} +6344 1966 2025-03-13 12:39:39.896955-07 2025-03-13 11:42:10.53502-07 test t T4 \N t \N \N \N {} +6345 1966 2025-03-13 11:46:33.159161-07 2025-03-13 11:42:35.832436-07 benchmark t T4 \N t \N \N \N {} +6346 1966 2025-03-13 12:49:02.202475-07 2025-03-13 11:43:42.365767-07 leaderboard t T4 3.6324577356666663 t \N \N \N {} +3202 1124 2025-02-28 05:47:04.982464-08 2025-02-28 04:47:56.713443-08 test f A100 \N t \N \N \N {} +3203 1124 2025-02-28 04:08:24.397878-08 2025-02-28 04:24:14.653298-08 benchmark f A100 \N t \N \N \N {} +3204 1124 2025-02-28 05:40:56.722412-08 2025-02-28 05:44:46.320859-08 leaderboard f A100 0.0030972656666666664 t \N \N \N {} +3205 1124 2025-02-28 04:40:01.037489-08 2025-02-28 03:58:41.704134-08 test t A100 \N t \N \N \N {} +3206 1124 2025-02-28 05:42:55.343085-08 2025-02-28 04:23:54.198083-08 benchmark t A100 \N t \N \N \N {} +3207 1124 2025-02-28 05:15:33.617644-08 2025-02-28 05:36:54.360407-08 leaderboard t A100 0.0031034153333333336 t \N \N \N {} +3212 1125 2025-02-28 05:41:25.119279-08 2025-02-28 06:47:46.688795-08 benchmark f A100 \N t \N \N \N {} +3218 1126 2025-02-28 06:15:42.624802-08 2025-02-28 05:18:36.139802-08 benchmark f A100 \N t \N \N \N {} +3224 1127 2025-02-28 05:23:45.306771-08 2025-02-28 06:39:04.576528-08 benchmark f A100 \N t \N \N \N {} +3230 1130 2025-02-28 06:11:34.161879-08 2025-02-28 07:19:05.529092-08 benchmark t A100 \N t \N \N \N {} +3522 1203 2025-02-28 11:17:41.164832-08 2025-02-28 10:45:35.314206-08 test f A100 \N f \N \N \N {} +3238 1133 2025-02-28 05:58:19.469647-08 2025-02-28 05:27:59.168429-08 leaderboard t A100 0.0030885113333333335 t \N \N \N {} +3471 1193 2025-02-28 12:08:38.072989-08 2025-02-28 11:36:29.661671-08 test t A100 \N t \N \N \N {} +1424 472 2025-02-25 05:07:18.96443-08 2025-02-25 04:27:31.619653-08 benchmark t A100 \N t \N \N \N {} +1425 472 2025-02-25 04:26:35.663772-08 2025-02-25 03:52:24.217898-08 leaderboard t A100 0.0031005363333333337 t \N \N \N {} +2937 1029 2025-02-27 04:45:23.397168-08 2025-02-27 04:26:08.472553-08 benchmark f A100 \N t \N \N \N {} +3050 1093 2025-02-27 15:36:27.175994-08 2025-02-27 15:34:55.376904-08 benchmark f A100 \N t \N \N \N {} +3239 1134 2025-02-28 06:50:37.72243-08 2025-02-28 06:25:11.379874-08 benchmark f A100 \N t \N \N \N {} +3240 1133 2025-02-28 05:32:42.89057-08 2025-02-28 06:35:04.012708-08 test f A100 \N t \N \N \N {} +3241 1133 2025-02-28 05:55:48.275123-08 2025-02-28 06:28:43.235753-08 benchmark f A100 \N t \N \N \N {} +3242 1133 2025-02-28 06:28:41.176944-08 2025-02-28 06:11:12.832836-08 leaderboard f A100 0.003086809 t \N \N \N {} +3243 1136 2025-02-28 05:47:39.958836-08 2025-02-28 05:44:16.957515-08 benchmark f A100 \N t \N \N \N {} +3244 1135 2025-02-28 06:38:46.857624-08 2025-02-28 07:16:05.450915-08 test f A100 \N t \N \N \N {} +3245 1135 2025-02-28 06:04:38.048901-08 2025-02-28 07:07:00.823655-08 benchmark f A100 \N t \N \N \N {} +3246 1135 2025-02-28 05:37:48.064623-08 2025-02-28 05:38:45.603311-08 leaderboard f A100 0.0030916216666666664 t \N \N \N {} +3247 1135 2025-02-28 05:52:38.194905-08 2025-02-28 05:43:19.652095-08 test t A100 \N t \N \N \N {} +3523 1204 2025-02-28 11:24:15.172488-08 2025-02-28 11:35:08.725248-08 test f A100 \N t \N \N \N {} +3252 1137 2025-02-28 07:21:26.0063-08 2025-02-28 07:16:56.022377-08 leaderboard f A100 0.0030857416666666667 t \N \N \N {} +3253 1137 2025-02-28 06:00:28.572485-08 2025-02-28 05:34:19.879496-08 test t A100 \N t \N \N \N {} +3254 1137 2025-02-28 06:10:53.662809-08 2025-02-28 06:32:30.162306-08 benchmark t A100 \N t \N \N \N {} +3255 1137 2025-02-28 07:12:35.431057-08 2025-02-28 06:05:27.261706-08 leaderboard t A100 0.0030894023333333334 t \N \N \N {} +3256 1138 2025-02-28 05:44:23.296315-08 2025-02-28 05:54:30.649398-08 benchmark f A100 \N t \N \N \N {} +3258 1139 2025-02-28 06:04:00.85979-08 2025-02-28 06:27:21.740985-08 benchmark f A100 \N f \N \N \N {} +3139 1113 2025-02-28 02:22:31.319401-08 2025-02-28 02:55:45.560083-08 test t A100 \N t \N \N \N {} +1427 473 2025-02-25 04:47:05.317584-08 2025-02-25 03:32:09.61539-08 benchmark t A100 \N t \N \N \N {} +1428 473 2025-02-25 04:44:32.069859-08 2025-02-25 03:55:20.86169-08 leaderboard t A100 0.003175767 t \N \N \N {} +2938 1030 2025-02-27 04:38:20.668328-08 2025-02-27 04:24:06.448431-08 test f A100 \N t \N \N \N {} +2939 1030 2025-02-27 03:05:03.419869-08 2025-02-27 04:22:37.87157-08 benchmark f A100 \N t \N \N \N {} +2940 1030 2025-02-27 04:07:07.030129-08 2025-02-27 04:03:57.310379-08 leaderboard f A100 0.00007918067999999999 t \N \N \N {} +3142 1114 2025-02-28 01:49:44.822785-08 2025-02-28 02:25:24.680714-08 test t A100 \N t \N \N \N {} +3259 1139 2025-02-28 06:24:17.521761-08 2025-02-28 07:13:37.812929-08 test t A100 \N t \N \N \N {} +3562 1210 2025-02-28 11:57:02.979824-08 2025-02-28 10:51:28.47295-08 test t A100 \N t \N \N \N {} +1430 473 2025-02-25 04:40:01.662393-08 2025-02-25 04:30:55.139642-08 benchmark f A100 \N t \N \N \N {} +1431 473 2025-02-25 03:31:35.707171-08 2025-02-25 03:31:53.832049-08 leaderboard f A100 0.00317240225 t \N \N \N {} +1432 474 2025-02-25 04:06:52.039054-08 2025-02-25 04:58:28.878029-08 test f A100 \N f \N \N \N {} +1438 479 2025-02-25 05:28:40.021595-08 2025-02-25 04:21:12.668713-08 test f A100 \N f \N \N \N {} +1445 484 2025-02-25 04:56:56.648978-08 2025-02-25 04:48:16.147785-08 benchmark t A100 \N t \N \N \N {} +1446 484 2025-02-25 05:51:53.636258-08 2025-02-25 04:20:30.28663-08 leaderboard t A100 0.0030884286666666666 t \N \N \N {} +2942 1030 2025-02-27 04:11:40.863984-08 2025-02-27 03:12:21.326746-08 benchmark t A100 \N t \N \N \N {} +2943 1030 2025-02-27 04:47:50.715647-08 2025-02-27 04:01:18.005466-08 leaderboard t A100 0.0000793426 t \N \N \N {} +3260 1139 2025-02-28 07:19:43.310162-08 2025-02-28 06:04:41.287437-08 benchmark t A100 \N f \N \N \N {} +3526 1204 2025-02-28 12:03:21.457321-08 2025-02-28 12:30:39.578159-08 test t A100 \N t \N \N \N {} +6647 2054 2025-03-14 13:43:53.190099-07 2025-03-14 12:39:40.431314-07 test f A100 \N t \N \N \N {} +6500 2015 2025-03-14 12:17:09.225085-07 2025-03-14 10:53:14.727282-07 leaderboard f H100 0.006131182333333333 t \N \N \N {} +6607 2046 2025-03-14 13:24:07.723525-07 2025-03-14 12:31:15.468896-07 test f A100 \N t \N \N \N {} +6504 2016 2025-03-14 12:12:39.778966-07 2025-03-14 12:30:56.454439-07 benchmark f A100 \N t \N \N \N {} +3274 1143 2025-02-28 07:21:32.682632-08 2025-02-28 07:30:37.256411-08 leaderboard f A100 0.003102605 t \N \N \N {} +6184 1903 2025-03-11 17:13:03.62363-07 2025-03-11 16:22:00.434413-07 test f T4 \N t \N \N \N {} +6185 1903 2025-03-11 16:06:37.941692-07 2025-03-11 16:41:23.331167-07 benchmark f T4 \N t \N \N \N {} +6186 1903 2025-03-11 16:13:32.336442-07 2025-03-11 16:38:58.569653-07 leaderboard f T4 3.2504637173333335 t \N \N \N {} +6352 1969 2025-03-13 12:36:05.935244-07 2025-03-13 13:06:47.416038-07 test f T4 \N f \N \N \N {} +6505 2017 2025-03-14 12:15:29.300377-07 2025-03-14 12:34:01.640423-07 test t A100 \N t \N \N \N {} +6506 2017 2025-03-14 11:18:20.450795-07 2025-03-14 12:30:54.661761-07 benchmark t A100 \N t \N \N \N {} +6608 2046 2025-03-14 13:48:29.521438-07 2025-03-14 13:48:51.889234-07 benchmark f A100 \N t \N \N \N {} +6609 2046 2025-03-14 12:28:36.914817-07 2025-03-14 12:50:31.439118-07 leaderboard f A100 0.002515373 t \N \N \N {} +3279 1144 2025-02-28 06:01:10.470211-08 2025-02-28 07:19:57.064988-08 benchmark t A100 \N t \N \N \N {} +3280 1144 2025-02-28 05:49:45.171368-08 2025-02-28 05:55:10.670943-08 leaderboard t A100 0.0031060543333333336 t \N \N \N {} +3624 1239 2025-02-28 16:58:22.066529-08 2025-02-28 16:33:30.877062-08 benchmark t A100 \N t \N \N \N {} +3281 1145 2025-02-28 05:37:52.569256-08 2025-02-28 07:15:46.585683-08 benchmark f A100 \N t \N \N \N {} +3282 1146 2025-02-28 05:57:19.501056-08 2025-02-28 05:49:46.099644-08 benchmark f A100 \N t \N \N \N {} +3287 1148 2025-02-28 07:03:20.664723-08 2025-02-28 07:36:21.224833-08 test f A100 \N t \N \N \N {} +3288 1148 2025-02-28 06:40:03.960027-08 2025-02-28 07:14:31.066145-08 benchmark f A100 \N t \N \N \N {} +3289 1148 2025-02-28 06:38:52.008227-08 2025-02-28 06:21:28.630464-08 leaderboard f A100 0.0037143906666666665 t \N \N \N {} +3290 1148 2025-02-28 07:14:12.948729-08 2025-02-28 06:08:46.103883-08 test t A100 \N t \N \N \N {} +3291 1148 2025-02-28 07:54:28.192996-08 2025-02-28 06:24:00.652714-08 benchmark t A100 \N t \N \N \N {} +3292 1148 2025-02-28 06:29:06.723096-08 2025-02-28 06:39:10.572344-08 leaderboard t A100 0.0036469966666666663 t \N \N \N {} +3293 1148 2025-02-28 06:40:23.470841-08 2025-02-28 07:24:25.091246-08 test f H100 \N t \N \N \N {} +3294 1148 2025-02-28 07:40:06.006249-08 2025-02-28 07:18:53.588443-08 benchmark f H100 \N t \N \N \N {} +3148 1115 2025-02-28 02:02:27.848298-08 2025-02-28 02:01:06.813683-08 test f A100 \N t \N \N \N {} +1448 484 2025-02-25 05:29:00.477152-08 2025-02-25 05:41:06.371795-08 benchmark f A100 \N t \N \N \N {} +1449 484 2025-02-25 04:52:55.95894-08 2025-02-25 04:18:57.804532-08 leaderboard f A100 0.003093351 t \N \N \N {} +2944 1031 2025-02-27 07:43:30.027296-08 2025-02-27 09:06:35.166274-08 benchmark f A100 \N t \N \N \N {} +3295 1148 2025-02-28 07:32:20.445776-08 2025-02-28 07:54:04.00939-08 leaderboard f H100 0.0018497506666666668 t \N \N \N {} +3296 1148 2025-02-28 07:09:18.873569-08 2025-02-28 07:00:06.853282-08 test t T4 \N t \N \N \N {} +3298 1148 2025-02-28 06:42:57.127187-08 2025-02-28 07:48:36.011787-08 leaderboard t T4 0.010518033333333334 t \N \N \N {} +3299 1148 2025-02-28 07:34:33.292144-08 2025-02-28 07:45:13.263944-08 test t H100 \N t \N \N \N {} +3305 1148 2025-02-28 06:56:08.877192-08 2025-02-28 07:30:09.470963-08 test f L4 \N t \N \N \N {} +3306 1148 2025-02-28 06:49:29.091858-08 2025-02-28 06:59:56.481035-08 benchmark f L4 \N t \N \N \N {} +3316 1151 2025-02-28 07:32:43.276426-08 2025-02-28 06:52:43.01807-08 test t H100 \N f \N \N \N {} +3317 1151 2025-02-28 06:04:40.63296-08 2025-02-28 06:57:06.096217-08 test t T4 \N f \N \N \N {} +3318 1151 2025-02-28 06:13:37.060218-08 2025-02-28 06:40:07.898882-08 test f L4 \N f \N \N \N {} +3529 1205 2025-02-28 11:00:20.479694-08 2025-02-28 11:27:21.065676-08 test f A100 \N t \N \N \N {} +3319 1151 2025-02-28 07:44:25.505195-08 2025-02-28 06:17:10.426155-08 test t L4 \N f \N \N \N {} +3569 1212 2025-02-28 12:31:15.223201-08 2025-02-28 12:02:50.307398-08 benchmark f H100 \N t \N \N \N {} +3327 1153 2025-02-28 07:48:24.592846-08 2025-02-28 06:08:43.919391-08 test t A100 \N f \N \N \N {} +3328 1153 2025-02-28 06:13:02.122552-08 2025-02-28 07:59:20.324753-08 test f A100 \N f \N \N \N {} +3535 1206 2025-02-28 11:07:39.749164-08 2025-02-28 11:05:51.393804-08 test f A100 \N t \N \N \N {} +3330 1153 2025-02-28 06:48:38.54272-08 2025-02-28 06:12:06.160611-08 test t H100 \N f \N \N \N {} +3331 1153 2025-02-28 06:44:57.334042-08 2025-02-28 06:27:45.550938-08 test f H100 \N f \N \N \N {} +3332 1153 2025-02-28 06:51:06.071549-08 2025-02-28 07:43:30.61761-08 test f T4 \N f \N \N \N {} +3333 1153 2025-02-28 07:22:29.016321-08 2025-02-28 07:25:37.384757-08 test t T4 \N f \N \N \N {} +3538 1206 2025-02-28 11:19:04.318559-08 2025-02-28 11:59:22.984501-08 test t A100 \N t \N \N \N {} +3337 1154 2025-02-28 06:12:20.751759-08 2025-02-28 07:24:13.732841-08 leaderboard t A100 0.003106905 t \N \N \N {} +3338 1154 2025-02-28 06:28:19.088439-08 2025-02-28 07:42:01.461767-08 test f A100 \N t \N \N \N {} +3339 1154 2025-02-28 07:24:35.655311-08 2025-02-28 07:21:32.784212-08 benchmark f A100 \N t \N \N \N {} +3340 1154 2025-02-28 06:30:11.131902-08 2025-02-28 07:23:31.696175-08 leaderboard f A100 0.0030856966666666665 t \N \N \N {} +3341 1155 2025-02-28 06:29:19.511228-08 2025-02-28 07:29:02.42522-08 test t A100 \N t \N \N \N {} +3342 1155 2025-02-28 07:51:44.284677-08 2025-02-28 07:48:29.018028-08 benchmark t A100 \N t \N \N \N {} +3343 1155 2025-02-28 06:47:28.573079-08 2025-02-28 06:44:05.949627-08 leaderboard t A100 0.0030834483333333335 t \N \N \N {} +3363 1161 2025-02-28 09:17:07.230642-08 2025-02-28 08:40:19.665408-08 test t T4 \N f \N \N \N {} +3348 1156 2025-02-28 07:16:58.973512-08 2025-02-28 07:33:23.078046-08 benchmark f A100 \N t \N \N \N {} +6188 1904 2025-03-11 18:10:38.388791-07 2025-03-11 18:14:58.210124-07 test t T4 \N f \N \N \N {} +6354 1970 2025-03-13 12:17:14.735768-07 2025-03-13 13:49:45.117322-07 test f T4 \N f \N \N \N {} +6508 2017 2025-03-14 11:42:27.682678-07 2025-03-14 12:36:52.887607-07 test f A100 \N t \N \N \N {} +6610 2047 2025-03-14 13:00:31.603918-07 2025-03-14 13:30:16.003681-07 test f A100 \N t \N \N \N {} +3570 1213 2025-02-28 11:12:09.580297-08 2025-02-28 11:15:38.840673-08 benchmark f H100 \N t \N \N \N {} +3541 1207 2025-02-28 11:14:12.554775-08 2025-02-28 11:14:24.949948-08 test t A100 \N t \N \N \N {} +3364 1161 2025-02-28 08:18:06.851362-08 2025-02-28 09:42:01.27446-08 test f T4 \N f \N \N \N {} +3365 1162 2025-02-28 09:22:47.122104-08 2025-02-28 08:23:26.975718-08 test f L4 \N f \N \N \N {} +3051 1093 2025-02-27 15:52:12.940276-08 2025-02-27 17:09:05.153707-08 leaderboard f A100 0.0031512003333333334 t \N \N \N {} +1451 485 2025-02-25 04:52:22.63147-08 2025-02-25 05:18:27.658437-08 benchmark t A100 \N t \N \N \N {} +1452 485 2025-02-25 05:30:56.8532-08 2025-02-25 05:31:55.77683-08 leaderboard t A100 0.0030837706666666663 t \N \N \N {} +2945 1032 2025-02-27 08:03:40.62355-08 2025-02-27 07:23:04.933128-08 test f A100 \N f \N \N \N {} +3151 1115 2025-02-28 03:28:35.730379-08 2025-02-28 02:37:28.944792-08 test t A100 \N t \N \N \N {} +3178 1120 2025-02-28 04:33:49.102329-08 2025-02-28 04:08:21.310974-08 test f A100 \N t \N \N \N {} +1454 485 2025-02-25 04:56:31.907725-08 2025-02-25 05:13:12.842041-08 benchmark f A100 \N t \N \N \N {} +1455 485 2025-02-25 04:05:06.520603-08 2025-02-25 04:56:19.51449-08 leaderboard f A100 0.003085375 t \N \N \N {} +2946 1033 2025-02-27 08:49:30.211741-08 2025-02-27 09:05:35.900691-08 test f T4 \N f \N \N \N {} +3003 1070 2025-02-27 13:00:06.212495-08 2025-02-27 11:57:37.768801-08 test f A100 \N f \N \N \N {} +3053 1094 2025-02-27 16:12:39.715401-08 2025-02-27 16:16:10.311482-08 benchmark f A100 \N t \N \N \N {} +3366 1162 2025-02-28 09:33:20.151769-08 2025-02-28 08:19:11.959347-08 test t L4 \N f \N \N \N {} +3571 1214 2025-02-28 12:01:55.895534-08 2025-02-28 12:13:51.232283-08 benchmark f H100 \N f \N \N \N {} +3373 1164 2025-02-28 09:54:50.944709-08 2025-02-28 09:48:36.826131-08 leaderboard f L4 0.01711390133333333 t \N \N \N {} +3374 1164 2025-02-28 10:03:17.913215-08 2025-02-28 08:48:38.575349-08 test t L4 \N t \N \N \N {} +3375 1164 2025-02-28 10:12:12.736365-08 2025-02-28 09:31:26.847036-08 benchmark t L4 \N t \N \N \N {} +3376 1164 2025-02-28 10:13:36.426125-08 2025-02-28 10:11:53.721613-08 leaderboard t L4 0.01711174266666667 t \N \N \N {} +3054 1094 2025-02-27 16:06:27.122167-08 2025-02-27 16:37:13.76371-08 leaderboard f A100 0.003111287 t \N \N \N {} +3154 1116 2025-02-28 02:29:06.631341-08 2025-02-28 01:58:59.085604-08 test f A100 \N t \N \N \N {} +1457 486 2025-02-25 04:36:15.216458-08 2025-02-25 05:15:34.163173-08 benchmark f A100 \N t \N \N \N {} +1458 486 2025-02-25 04:14:50.938805-08 2025-02-25 05:01:24.288432-08 leaderboard f A100 0.0030878703333333335 t \N \N \N {} +2947 1034 2025-02-27 09:13:44.098036-08 2025-02-27 09:11:18.488648-08 test f A100 \N f \N \N \N {} +3056 1094 2025-02-27 16:41:39.934588-08 2025-02-27 16:25:19.394991-08 benchmark t A100 \N t \N \N \N {} +3213 1125 2025-02-28 06:48:12.252064-08 2025-02-28 06:03:15.742155-08 leaderboard f A100 0.0030832093333333335 t \N \N \N {} +1460 486 2025-02-25 04:41:59.887933-08 2025-02-25 04:23:57.454866-08 benchmark t A100 \N t \N \N \N {} +1461 486 2025-02-25 05:49:45.663044-08 2025-02-25 04:12:57.109398-08 leaderboard t A100 0.003092806 t \N \N \N {} +2948 1035 2025-02-27 08:17:18.304303-08 2025-02-27 07:43:31.506965-08 test f T4 \N f \N \N \N {} +3005 1072 2025-02-27 13:09:50.152666-08 2025-02-27 12:57:15.8831-08 test f A100 \N t \N \N \N {} +3057 1094 2025-02-27 17:34:54.982672-08 2025-02-27 16:26:07.35543-08 leaderboard t A100 0.0031275906666666667 t \N \N \N {} +3080 1103 2025-02-28 02:02:56.286012-08 2025-02-28 02:38:01.79013-08 benchmark t A100 \N t \N \N \N {} +3081 1103 2025-02-28 01:56:35.783923-08 2025-02-28 01:56:41.958069-08 leaderboard t A100 0.0031360743333333336 t \N \N \N {} +3219 1126 2025-02-28 05:38:06.586102-08 2025-02-28 05:05:43.074581-08 leaderboard f A100 0.003108399 t \N \N \N {} +1463 487 2025-02-25 04:52:33.166187-08 2025-02-25 04:41:41.65243-08 benchmark t H100 \N t \N \N \N {} +1464 487 2025-02-25 05:44:02.421962-08 2025-02-25 04:30:44.563192-08 leaderboard t H100 0.0013963186666666667 t \N \N \N {} +2949 1036 2025-02-27 09:05:30.430291-08 2025-02-27 08:16:44.717534-08 test f A100 \N f \N \N \N {} +3006 1073 2025-02-27 14:03:17.549065-08 2025-02-27 12:33:09.482347-08 benchmark f A100 \N t \N \N \N {} +3583 1228 2025-02-28 13:36:44.96965-08 2025-02-28 13:58:50.097299-08 leaderboard f A100 0.0021488616666666665 t \N \N \N {} +3082 1104 2025-02-28 01:20:46.117646-08 2025-02-28 01:48:56.099424-08 test t A100 \N t \N \N \N {} +3083 1104 2025-02-28 02:40:27.969759-08 2025-02-28 03:00:37.086929-08 benchmark t A100 \N t \N \N \N {} +1466 487 2025-02-25 06:00:31.318157-08 2025-02-25 05:21:10.22327-08 benchmark f H100 \N t \N \N \N {} +1467 487 2025-02-25 05:08:04.83803-08 2025-02-25 05:35:28.456713-08 leaderboard f H100 0.0013990763333333333 t \N \N \N {} +1468 488 2025-02-25 04:11:00.167833-08 2025-02-25 05:53:43.66447-08 test f A100 \N f \N \N \N {} +3386 1166 2025-02-28 08:58:24.708767-08 2025-02-28 08:59:35.546096-08 test f L4 \N t \N \N \N {} +3572 1216 2025-02-28 13:25:32.928609-08 2025-02-28 13:06:59.918087-08 test f H100 \N f \N \N \N {} +3402 1173 2025-02-28 08:50:27.553378-08 2025-02-28 09:25:30.916359-08 leaderboard f L4 0.017073246333333333 t \N \N \N {} +3403 1173 2025-02-28 09:24:43.249263-08 2025-02-28 10:19:27.328955-08 test t L4 \N f \N \N \N {} +3404 1174 2025-02-28 09:36:18.604104-08 2025-02-28 10:01:25.201171-08 test t L4 \N t \N \N \N {} +3407 1174 2025-02-28 10:04:35.458188-08 2025-02-28 09:06:27.208576-08 test f L4 \N f \N \N \N {} +3409 1175 2025-02-28 10:42:07.749021-08 2025-02-28 10:23:25.763853-08 test t L4 \N f \N \N \N {} +3410 1176 2025-02-28 09:35:38.594297-08 2025-02-28 10:42:52.909956-08 test f L4 \N t \N \N \N {} +3411 1176 2025-02-28 10:40:17.639715-08 2025-02-28 09:51:35.220213-08 benchmark f L4 \N t \N \N \N {} +3412 1176 2025-02-28 10:11:43.370341-08 2025-02-28 10:27:44.06876-08 leaderboard f L4 0.017120271333333333 t \N \N \N {} +3547 1208 2025-02-28 12:27:27.702985-08 2025-02-28 11:17:47.788966-08 test f A100 \N t \N \N \N {} +3413 1176 2025-02-28 11:01:01.943548-08 2025-02-28 09:36:24.833158-08 test t L4 \N t \N \N \N {} +3414 1176 2025-02-28 09:58:29.179458-08 2025-02-28 09:26:37.706314-08 benchmark t L4 \N t \N \N \N {} +3415 1176 2025-02-28 10:55:29.876048-08 2025-02-28 09:14:25.427957-08 leaderboard t L4 0.017113951333333332 t \N \N \N {} +3416 1177 2025-02-28 10:07:18.386046-08 2025-02-28 10:14:01.64194-08 test f L4 \N f \N \N \N {} +3417 1177 2025-02-28 09:37:22.22405-08 2025-02-28 10:32:18.953094-08 test t L4 \N f \N \N \N {} +3550 1208 2025-02-28 11:50:19.101588-08 2025-02-28 12:45:50.950117-08 test t A100 \N t \N \N \N {} +3422 1180 2025-02-28 10:56:47.404757-08 2025-02-28 10:56:22.71621-08 test f L4 \N f \N \N \N {} +1470 490 2025-02-25 04:41:11.741201-08 2025-02-25 06:11:53.932837-08 benchmark f A100 \N t \N \N \N {} +2950 1037 2025-02-27 07:35:16.658999-08 2025-02-27 09:21:21.845387-08 test f A100 \N t \N \N \N {} +3007 1074 2025-02-27 13:18:44.138568-08 2025-02-27 13:54:19.684997-08 test t A100 \N t \N \N \N {} +3008 1074 2025-02-27 12:10:25.097092-08 2025-02-27 12:22:43.495573-08 benchmark t A100 \N t \N \N \N {} +1472 491 2025-02-25 04:16:51.40767-08 2025-02-25 04:44:20.032912-08 benchmark t A100 \N t \N \N \N {} +1473 491 2025-02-25 04:39:56.631178-08 2025-02-25 04:50:34.949043-08 leaderboard t A100 0.02900779833333333 t \N \N \N {} +3084 1104 2025-02-28 01:25:02.828823-08 2025-02-28 01:40:54.029102-08 leaderboard t A100 0.003145336 t \N \N \N {} +3092 1105 2025-02-28 03:07:23.548326-08 2025-02-28 02:21:49.2888-08 benchmark f A100 \N t \N \N \N {} +3093 1105 2025-02-28 03:14:38.771483-08 2025-02-28 02:41:25.038891-08 leaderboard f A100 0.003120996 t \N \N \N {} +3792 1279 2025-03-01 02:43:50.248573-08 2025-03-01 02:16:59.092747-08 test t A100 \N t \N \N \N {} +1475 491 2025-02-25 05:21:21.516015-08 2025-02-25 05:04:36.503808-08 benchmark f A100 \N t \N \N \N {} +1476 491 2025-02-25 04:41:54.691799-08 2025-02-25 05:44:48.514036-08 leaderboard f A100 0.02915152266666667 t \N \N \N {} +3423 1180 2025-02-28 09:29:33.8094-08 2025-02-28 10:27:43.704792-08 test t L4 \N f \N \N \N {} +3424 1181 2025-02-28 09:43:15.513902-08 2025-02-28 09:32:49.900783-08 test f L4 \N f \N \N \N {} +3425 1181 2025-02-28 09:43:45.066653-08 2025-02-28 10:25:58.378242-08 test t L4 \N f \N \N \N {} +3426 1182 2025-02-28 10:47:18.289906-08 2025-02-28 10:12:47.391263-08 test f L4 \N f \N \N \N {} +3427 1182 2025-02-28 09:47:39.824926-08 2025-02-28 11:31:24.156151-08 test t L4 \N f \N \N \N {} +3430 1184 2025-02-28 10:56:20.592407-08 2025-02-28 09:53:03.031846-08 test t L4 \N t \N \N \N {} +3431 1184 2025-02-28 11:16:58.29005-08 2025-02-28 10:48:40.540813-08 benchmark t L4 \N t \N \N \N {} +3432 1184 2025-02-28 09:40:03.831648-08 2025-02-28 11:23:08.875773-08 leaderboard t L4 0.017144596666666668 t \N \N \N {} +3433 1184 2025-02-28 10:51:16.362382-08 2025-02-28 11:01:47.958995-08 test f L4 \N f \N \N \N {} +3553 1209 2025-02-28 12:15:04.393062-08 2025-02-28 12:20:51.635003-08 test t A100 \N t \N \N \N {} +3436 1185 2025-02-28 10:58:38.735282-08 2025-02-28 09:55:56.205615-08 leaderboard t L4 0.017078157666666666 t \N \N \N {} +3437 1185 2025-02-28 09:43:25.915772-08 2025-02-28 11:05:27.656365-08 test f L4 \N f \N \N \N {} +3438 1186 2025-02-28 10:35:22.023921-08 2025-02-28 10:01:02.327904-08 test f L4 \N t \N \N \N {} +3443 1186 2025-02-28 10:41:33.074483-08 2025-02-28 10:48:09.890989-08 leaderboard t L4 0.017077551 t \N \N \N {} +3455 1190 2025-02-28 11:31:02.465158-08 2025-02-28 11:33:20.889525-08 benchmark f A100 \N t \N \N \N {} +3444 1187 2025-02-28 11:17:11.801344-08 2025-02-28 10:45:20.9261-08 test f L4 \N f \N \N \N {} +3445 1187 2025-02-28 11:46:40.120384-08 2025-02-28 11:16:52.048543-08 test t L4 \N f \N \N \N {} +3446 1188 2025-02-28 12:11:52.882164-08 2025-02-28 11:38:47.364032-08 test t A100 \N t \N \N \N {} +3447 1188 2025-02-28 11:53:16.182886-08 2025-02-28 11:57:38.888939-08 benchmark t A100 \N t \N \N \N {} +3448 1188 2025-02-28 11:31:39.142552-08 2025-02-28 10:34:32.539419-08 leaderboard t A100 0.003089445 t \N \N \N {} +3460 1191 2025-02-28 10:50:29.600852-08 2025-02-28 10:55:28.066601-08 test f A100 \N t \N \N \N {} +3451 1188 2025-02-28 11:25:05.48421-08 2025-02-28 11:26:09.847138-08 leaderboard f A100 0.003099992 t \N \N \N {} +3452 1189 2025-02-28 11:27:07.929922-08 2025-02-28 10:27:25.882031-08 test f A100 \N f \N \N \N {} +2956 1040 2025-02-27 07:53:09.243149-08 2025-02-27 08:42:54.201109-08 test t A100 \N t \N \N \N {} +3014 1076 2025-02-27 13:56:41.106043-08 2025-02-27 13:46:16.794281-08 benchmark f A100 \N t \N \N \N {} +1487 496 2025-02-25 06:02:20.071956-08 2025-02-25 06:20:28.361733-08 test t A100 \N f \N \N \N {} +1488 496 2025-02-25 06:37:53.255009-08 2025-02-25 07:06:16.467792-08 test f A100 \N f \N \N \N {} +3453 1189 2025-02-28 11:50:14.581562-08 2025-02-28 10:31:03.166396-08 test t A100 \N f \N \N \N {} +3463 1191 2025-02-28 10:39:12.659548-08 2025-02-28 11:48:27.810322-08 test t A100 \N t \N \N \N {} +3556 1209 2025-02-28 12:30:43.675467-08 2025-02-28 12:41:17.817806-08 test f A100 \N t \N \N \N {} +6514 2018 2025-03-14 12:18:15.299016-07 2025-03-14 11:04:40.892319-07 test f A100 \N t \N \N \N {} +6515 2018 2025-03-14 11:49:32.366043-07 2025-03-14 11:56:59.199313-07 benchmark f A100 \N t \N \N \N {} +6360 1972 2025-03-13 12:35:05.799956-07 2025-03-13 13:55:16.538902-07 benchmark f T4 \N f \N \N \N {} +6517 2019 2025-03-14 11:49:17.362182-07 2025-03-14 10:59:40.482475-07 benchmark f A100 \N t \N \N \N {} +6363 1973 2025-03-13 15:12:13.132853-07 2025-03-13 13:39:11.582645-07 test t T4 \N t \N \N \N {} +6364 1973 2025-03-13 13:54:16.988636-07 2025-03-13 14:15:30.672524-07 benchmark t T4 \N f \N \N \N {} +6521 2020 2025-03-14 11:11:59.364683-07 2025-03-14 12:39:27.185178-07 test t A100 \N t \N \N \N {} +3479 1196 2025-02-28 10:53:45.214965-08 2025-02-28 11:40:18.287407-08 benchmark f A100 \N t \N \N \N {} +3480 1196 2025-02-28 12:21:44.871556-08 2025-02-28 12:18:32.615765-08 leaderboard f A100 0.0030916253333333333 t \N \N \N {} +3481 1195 2025-02-28 10:38:20.149926-08 2025-02-28 11:37:11.075833-08 test f A100 \N t \N \N \N {} +3482 1195 2025-02-28 11:40:32.30581-08 2025-02-28 11:07:23.741189-08 benchmark f A100 \N f \N \N \N {} +3483 1195 2025-02-28 11:06:17.163408-08 2025-02-28 11:09:41.125501-08 test t A100 \N t \N \N \N {} +6196 1908 2025-03-11 17:42:13.201789-07 2025-03-11 16:36:53.748973-07 test f T4 \N f \N \N \N {} +6394 1987 2025-03-13 16:17:38.428534-07 2025-03-13 17:49:29.688605-07 test t T4 \N f \N \N \N {} +6367 1974 2025-03-13 15:18:15.442709-07 2025-03-13 14:52:02.574384-07 test t T4 \N t \N \N \N {} +6611 2047 2025-03-14 12:25:13.352133-07 2025-03-14 12:54:20.14153-07 benchmark f A100 \N t \N \N \N {} +3492 1198 2025-02-28 10:31:42.778359-08 2025-02-28 12:29:56.888072-08 benchmark f A100 \N t \N \N \N {} +3493 1198 2025-02-28 10:35:47.467941-08 2025-02-28 11:50:52.415689-08 leaderboard f A100 0.0030801673333333336 t \N \N \N {} +6198 1909 2025-03-11 17:57:18.149458-07 2025-03-11 17:27:06.275858-07 test f T4 \N f \N \N \N {} +6369 1975 2025-03-13 16:40:00.881683-07 2025-03-13 16:06:45.585849-07 test f T4 \N f \N \N \N {} +6916 2156 2025-03-16 15:11:39.340389-07 2025-03-16 16:54:21.426913-07 test f T4 \N t \N \N \N {} +3495 1198 2025-02-28 12:22:48.855653-08 2025-02-28 11:22:36.934387-08 benchmark t A100 \N t \N \N \N {} +3496 1198 2025-02-28 11:02:01.041551-08 2025-02-28 10:55:18.662378-08 leaderboard t A100 0.0030712036666666665 t \N \N \N {} +6199 1910 2025-03-11 17:51:06.602448-07 2025-03-11 17:45:19.999575-07 test t T4 \N f \N \N \N {} +6370 1975 2025-03-13 17:09:02.372261-07 2025-03-13 15:49:54.86776-07 test t T4 \N f \N \N \N {} +6528 2021 2025-03-14 11:20:09.013884-07 2025-03-14 11:51:31.5343-07 benchmark f A100 \N t \N \N \N {} +6529 2021 2025-03-14 12:17:59.33897-07 2025-03-14 11:57:07.878145-07 leaderboard f A100 0.0024379966666666663 t \N \N \N {} +6613 2047 2025-03-14 13:28:02.649127-07 2025-03-14 13:08:42.984819-07 test t A100 \N t \N \N \N {} +6677 2066 2025-03-14 14:28:21.53377-07 2025-03-14 15:18:12.092115-07 test t T4 \N f \N \N \N {} +3498 1199 2025-02-28 10:55:47.187252-08 2025-02-28 11:02:06.984812-08 benchmark f A100 \N t \N \N \N {} +3499 1199 2025-02-28 11:57:15.471579-08 2025-02-28 11:00:10.750543-08 leaderboard f A100 0.0030867046666666663 t \N \N \N {} +6200 1910 2025-03-11 18:13:57.459745-07 2025-03-11 17:39:14.073278-07 test f T4 \N f \N \N \N {} +6371 1976 2025-03-13 16:10:15.976652-07 2025-03-13 16:59:38.423812-07 test t T4 \N f \N \N \N {} +6530 2022 2025-03-14 12:42:51.830687-07 2025-03-14 12:44:20.985583-07 test f A100 \N t \N \N \N {} +6531 2022 2025-03-14 12:43:29.503929-07 2025-03-14 11:48:37.299314-07 benchmark f A100 \N t \N \N \N {} +6532 2022 2025-03-14 12:33:03.009856-07 2025-03-14 11:10:10.079119-07 leaderboard f A100 0.0031564046666666666 t \N \N \N {} +6614 2047 2025-03-14 12:41:54.04883-07 2025-03-14 12:42:12.07854-07 benchmark t A100 \N t \N \N \N {} +6615 2047 2025-03-14 12:28:03.957753-07 2025-03-14 12:16:08.077601-07 leaderboard t A100 0.0025148893333333333 t \N \N \N {} +3501 1199 2025-02-28 10:44:00.815458-08 2025-02-28 12:25:29.005606-08 benchmark t A100 \N t \N \N \N {} +1489 497 2025-02-25 06:09:45.421735-08 2025-02-25 06:06:56.895044-08 test t A100 \N f \N \N \N {} +1490 497 2025-02-25 07:30:16.061013-08 2025-02-25 05:58:05.353035-08 test f A100 \N f \N \N \N {} +1493 499 2025-02-25 07:29:41.937766-08 2025-02-25 07:35:35.73218-08 test t A100 \N f \N \N \N {} +1494 499 2025-02-25 06:55:16.544585-08 2025-02-25 06:42:04.831851-08 test f A100 \N f \N \N \N {} +1495 500 2025-02-25 06:11:38.525649-08 2025-02-25 06:06:09.247788-08 test f A100 \N f \N \N \N {} +1496 500 2025-02-25 06:28:17.374206-08 2025-02-25 07:36:57.6453-08 test t A100 \N f \N \N \N {} +1497 501 2025-02-25 06:22:45.354033-08 2025-02-25 06:04:40.972205-08 test t A100 \N f \N \N \N {} +1498 501 2025-02-25 07:26:32.437486-08 2025-02-25 06:26:05.660958-08 test f A100 \N f \N \N \N {} +2957 1040 2025-02-27 08:15:29.867269-08 2025-02-27 08:02:02.503639-08 benchmark t A100 \N t \N \N \N {} +2958 1040 2025-02-27 08:46:34.959091-08 2025-02-27 08:49:51.207345-08 leaderboard t A100 0.0031735606666666665 t \N \N \N {} +3106 1108 2025-02-28 02:29:24.19387-08 2025-02-28 02:25:44.234748-08 test t A100 \N t \N \N \N {} +2772 966 2025-02-26 19:49:15.757238-08 2025-02-26 20:10:22.740071-08 leaderboard t A100 0.014294890199999999 t \N \N \N {} +3502 1199 2025-02-28 11:15:50.983592-08 2025-02-28 11:56:22.474618-08 leaderboard t A100 0.00308841 t \N \N \N {} +1500 502 2025-02-25 05:50:41.376697-08 2025-02-25 06:42:15.925754-08 benchmark f T4 \N t \N \N \N {} +1501 502 2025-02-25 06:35:11.719711-08 2025-02-25 06:16:59.633058-08 leaderboard f T4 0.016468595 t \N \N \N {} +3015 1077 2025-02-27 14:04:51.482078-08 2025-02-27 14:44:42.488782-08 benchmark f H100 \N t \N \N \N {} +3109 1108 2025-02-28 02:01:50.848448-08 2025-02-28 01:49:08.698992-08 test f A100 \N t \N \N \N {} +3157 1116 2025-02-28 02:17:56.372163-08 2025-02-28 02:21:38.342465-08 test t A100 \N t \N \N \N {} +6201 1911 2025-03-11 18:01:48.518324-07 2025-03-11 18:32:00.643951-07 test t T4 \N f \N \N \N {} +6372 1976 2025-03-13 15:35:23.586028-07 2025-03-13 16:38:44.74512-07 test f T4 \N f \N \N \N {} +6533 2022 2025-03-14 12:55:57.39393-07 2025-03-14 12:29:44.638225-07 test t A100 \N t \N \N \N {} +6534 2022 2025-03-14 11:24:57.714064-07 2025-03-14 12:27:54.539497-07 benchmark t A100 \N t \N \N \N {} +6535 2022 2025-03-14 11:34:47.019969-07 2025-03-14 11:00:15.310359-07 leaderboard t A100 0.0039016193333333334 t \N \N \N {} +6616 2048 2025-03-14 12:53:00.225497-07 2025-03-14 13:45:31.447067-07 test t A100 \N t \N \N \N {} +6617 2048 2025-03-14 12:44:51.583069-07 2025-03-14 13:05:24.372941-07 benchmark t A100 \N t \N \N \N {} +3504 1200 2025-02-28 10:37:28.152845-08 2025-02-28 12:03:13.176444-08 benchmark f A100 \N t \N \N \N {} +3505 1200 2025-02-28 11:14:32.252149-08 2025-02-28 11:47:51.650604-08 leaderboard f A100 0.003092401 t \N \N \N {} +6202 1911 2025-03-11 17:19:23.061602-07 2025-03-11 18:30:37.372737-07 test f T4 \N f \N \N \N {} +6373 1977 2025-03-13 16:45:46.79345-07 2025-03-13 17:04:17.571101-07 test t T4 \N t \N \N \N {} +6375 1977 2025-03-13 15:59:28.148453-07 2025-03-13 15:45:43.825301-07 test f T4 \N t \N \N \N {} +6376 1977 2025-03-13 15:47:35.023166-07 2025-03-13 15:51:13.628825-07 benchmark f T4 \N f \N \N \N {} +6539 2023 2025-03-14 12:34:47.676474-07 2025-03-14 11:26:38.083854-07 test f A100 \N t \N \N \N {} +6540 2023 2025-03-14 12:11:02.732324-07 2025-03-14 12:35:39.482955-07 benchmark f A100 \N t \N \N \N {} +3510 1201 2025-02-28 12:09:30.867012-08 2025-02-28 11:14:15.654068-08 benchmark f A100 \N t \N \N \N {} +3511 1201 2025-02-28 12:22:28.128389-08 2025-02-28 10:55:21.592984-08 leaderboard f A100 0.0030901086666666666 t \N \N \N {} +6204 1912 2025-03-11 18:30:54.347773-07 2025-03-11 18:09:47.823091-07 test t T4 \N f \N \N \N {} +6205 1913 2025-03-11 17:40:25.001398-07 2025-03-11 16:51:30.586544-07 test f T4 \N f \N \N \N {} +6542 2024 2025-03-14 11:14:42.747731-07 2025-03-14 12:11:19.545486-07 benchmark f A100 \N t \N \N \N {} +6619 2048 2025-03-14 12:04:08.070953-07 2025-03-14 12:48:51.357583-07 test f A100 \N t \N \N \N {} +6650 2054 2025-03-14 13:53:44.337385-07 2025-03-14 13:48:46.599335-07 test t A100 \N t \N \N \N {} +6656 2058 2025-03-14 13:54:10.740579-07 2025-03-14 12:34:46.676855-07 test t A100 \N t \N \N \N {} +3516 1202 2025-02-28 12:33:09.097002-08 2025-02-28 10:58:45.829133-08 benchmark f A100 \N t \N \N \N {} +3517 1202 2025-02-28 11:58:25.410318-08 2025-02-28 11:30:47.992124-08 leaderboard f A100 0.003084266 t \N \N \N {} +6206 1913 2025-03-11 18:05:22.9157-07 2025-03-11 17:36:14.408717-07 test t T4 \N f \N \N \N {} +6379 1980 2025-03-13 16:36:27.760868-07 2025-03-13 16:20:53.675757-07 test t T4 \N f \N \N \N {} +6548 2025 2025-03-14 11:20:57.236222-07 2025-03-14 12:24:25.970334-07 leaderboard f A100 0.002452236 t \N \N \N {} +3524 1204 2025-02-28 12:20:39.907459-08 2025-02-28 11:12:35.546414-08 benchmark f A100 \N t \N \N \N {} +1503 502 2025-02-25 06:45:45.886953-08 2025-02-25 06:50:12.165567-08 benchmark t T4 \N t \N \N \N {} +1504 502 2025-02-25 07:36:18.732222-08 2025-02-25 06:56:09.60856-08 leaderboard t T4 0.016514245333333333 t \N \N \N {} +1505 503 2025-02-25 07:24:08.994752-08 2025-02-25 07:42:21.014151-08 test t A100 \N f \N \N \N {} +1506 503 2025-02-25 07:27:58.277977-08 2025-02-25 06:58:20.391073-08 test f A100 \N f \N \N \N {} +3112 1109 2025-02-28 02:49:13.950386-08 2025-02-28 03:20:33.833093-08 test t A100 \N t \N \N \N {} +3525 1204 2025-02-28 10:57:17.218778-08 2025-02-28 12:11:28.522376-08 leaderboard f A100 0.0030873746666666667 t \N \N \N {} +6549 2026 2025-03-14 11:43:25.469312-07 2025-03-14 11:20:32.899243-07 test t A100 \N t \N \N \N {} +6550 2026 2025-03-14 12:20:36.010926-07 2025-03-14 12:14:30.57356-07 benchmark t A100 \N t \N \N \N {} +3527 1204 2025-02-28 11:04:54.217825-08 2025-02-28 11:23:20.827974-08 benchmark t A100 \N t \N \N \N {} +3528 1204 2025-02-28 12:01:09.198033-08 2025-02-28 12:15:21.496381-08 leaderboard t A100 0.003084299 t \N \N \N {} +6382 1981 2025-03-13 16:02:38.157698-07 2025-03-13 16:43:08.39172-07 test f T4 \N f \N \N \N {} +6551 2026 2025-03-14 11:33:10.201136-07 2025-03-14 12:55:26.704593-07 leaderboard t A100 0.0025385736666666664 t \N \N \N {} +6623 2049 2025-03-14 12:51:48.541281-07 2025-03-14 13:37:41.702339-07 benchmark f A100 \N t \N \N \N {} +3530 1205 2025-02-28 11:05:21.637922-08 2025-02-28 10:59:24.063245-08 benchmark f A100 \N t \N \N \N {} +3531 1205 2025-02-28 11:51:46.708741-08 2025-02-28 10:54:12.608529-08 leaderboard f A100 0.003087388 t \N \N \N {} +6383 1982 2025-03-13 16:30:23.243012-07 2025-03-13 17:19:59.354647-07 test f T4 \N f \N \N \N {} +6552 2026 2025-03-14 12:22:01.49041-07 2025-03-14 13:00:33.669546-07 test f A100 \N t \N \N \N {} +6553 2026 2025-03-14 11:20:49.862991-07 2025-03-14 11:11:51.945168-07 benchmark f A100 \N t \N \N \N {} +3533 1205 2025-02-28 10:59:21.335705-08 2025-02-28 11:32:43.758531-08 benchmark t A100 \N t \N \N \N {} +3534 1205 2025-02-28 11:42:18.774429-08 2025-02-28 11:30:52.036125-08 leaderboard t A100 0.0030747123333333334 t \N \N \N {} +6211 1916 2025-03-11 18:51:26.328567-07 2025-03-11 17:34:02.97454-07 test f T4 \N f \N \N \N {} +6384 1982 2025-03-13 16:29:22.478385-07 2025-03-13 16:29:50.49526-07 test t T4 \N f \N \N \N {} +6554 2026 2025-03-14 11:32:39.712087-07 2025-03-14 13:01:51.543072-07 leaderboard f A100 0.0024447563333333333 t \N \N \N {} +6624 2049 2025-03-14 13:31:46.298149-07 2025-03-14 13:02:46.422741-07 leaderboard f A100 0.003148123 t \N \N \N {} +6659 2058 2025-03-14 12:36:09.483207-07 2025-03-14 12:28:00.634473-07 test f A100 \N t \N \N \N {} +6971 2169 2025-03-16 19:08:50.226241-07 2025-03-16 19:52:33.539167-07 leaderboard t T4 0.00026675565000000004 t \N \N \N {} +3536 1206 2025-02-28 12:01:21.310531-08 2025-02-28 10:59:24.032043-08 benchmark f A100 \N t \N \N \N {} +1508 504 2025-02-25 07:36:12.320858-08 2025-02-25 06:45:09.929248-08 benchmark f A100 \N t \N \N \N {} +1509 504 2025-02-25 06:24:05.118745-08 2025-02-25 07:05:31.47031-08 leaderboard f A100 0.003201205285714286 t \N \N \N {} +3016 1078 2025-02-27 15:09:58.383782-08 2025-02-27 15:09:34.428187-08 benchmark f A100 \N t \N \N \N {} +3086 1104 2025-02-28 02:37:38.797917-08 2025-02-28 02:36:42.886532-08 benchmark f A100 \N t \N \N \N {} +3537 1206 2025-02-28 11:44:02.859148-08 2025-02-28 12:17:13.765459-08 leaderboard f A100 0.0030771996666666664 t \N \N \N {} +6555 2027 2025-03-14 11:55:38.194672-07 2025-03-14 13:12:55.354266-07 benchmark f A100 \N t \N \N \N {} +6625 2049 2025-03-14 12:03:46.633551-07 2025-03-14 13:14:01.521077-07 test t A100 \N t \N \N \N {} +3539 1206 2025-02-28 12:05:54.440089-08 2025-02-28 11:48:11.736334-08 benchmark t A100 \N t \N \N \N {} +3540 1206 2025-02-28 11:50:30.166906-08 2025-02-28 11:34:39.086558-08 leaderboard t A100 0.003082872 t \N \N \N {} +6626 2049 2025-03-14 12:41:30.656497-07 2025-03-14 12:04:51.089204-07 benchmark t A100 \N t \N \N \N {} +3542 1207 2025-02-28 10:49:11.612247-08 2025-02-28 12:30:07.673052-08 benchmark t A100 \N t \N \N \N {} +3543 1207 2025-02-28 11:35:24.228637-08 2025-02-28 11:37:10.231465-08 leaderboard t A100 0.0031054823333333333 t \N \N \N {} +3546 1207 2025-02-28 11:42:05.635418-08 2025-02-28 12:05:11.256023-08 leaderboard f A100 0.0030748303333333333 t \N \N \N {} +3593 1230 2025-02-28 13:36:11.392976-08 2025-02-28 15:07:41.559929-08 benchmark f A100 \N t \N \N \N {} +3594 1231 2025-02-28 13:56:55.748686-08 2025-02-28 14:50:55.966775-08 test f H100 \N t \N \N \N {} +3595 1231 2025-02-28 14:21:28.207811-08 2025-02-28 13:22:24.302597-08 benchmark f H100 \N t \N \N \N {} +3596 1231 2025-02-28 13:34:32.178088-08 2025-02-28 14:18:02.106124-08 leaderboard f H100 0.0012008532 t \N \N \N {} +3597 1231 2025-02-28 14:33:14.730553-08 2025-02-28 13:51:57.351344-08 test t H100 \N t \N \N \N {} +3598 1231 2025-02-28 13:27:10.531166-08 2025-02-28 14:13:56.712585-08 benchmark t H100 \N t \N \N \N {} +3650 1248 2025-03-01 01:03:42.861509-08 2025-03-01 01:06:00.433924-08 leaderboard t A100 0.0031414876666666664 t \N \N \N {} +3652 1248 2025-03-01 02:29:49.148831-08 2025-03-01 01:52:31.634785-08 benchmark f A100 \N t \N \N \N {} +3658 1249 2025-03-01 01:37:20.868883-08 2025-03-01 01:41:30.386305-08 benchmark t A100 \N t \N \N \N {} +3659 1249 2025-03-01 02:08:57.983273-08 2025-03-01 02:33:34.854721-08 leaderboard t A100 0.0031066326666666665 t \N \N \N {} +3660 1250 2025-03-01 00:47:36.371435-08 2025-03-01 02:29:41.827776-08 test t A100 \N t \N \N \N {} +1511 504 2025-02-25 07:09:54.962145-08 2025-02-25 06:23:56.896069-08 benchmark t A100 \N t \N \N \N {} +1512 504 2025-02-25 06:32:05.54038-08 2025-02-25 07:46:23.871842-08 leaderboard t A100 0.0032573723333333333 t \N \N \N {} +3017 1079 2025-02-27 15:33:40.79789-08 2025-02-27 15:03:19.237768-08 benchmark f H100 \N t \N \N \N {} +3087 1104 2025-02-28 01:35:18.867193-08 2025-02-28 03:11:29.495526-08 leaderboard f A100 0.0031234573333333336 t \N \N \N {} +3095 1106 2025-02-28 02:29:26.843816-08 2025-02-28 03:12:54.70341-08 benchmark t A100 \N t \N \N \N {} +3661 1250 2025-03-01 02:05:16.755613-08 2025-03-01 01:23:08.151871-08 benchmark t A100 \N t \N \N \N {} +3662 1250 2025-03-01 01:38:13.707281-08 2025-03-01 01:47:51.283498-08 leaderboard t A100 0.0031076263333333336 t \N \N \N {} +3663 1250 2025-03-01 01:59:32.222492-08 2025-03-01 01:21:35.81262-08 test f A100 \N t \N \N \N {} +3664 1250 2025-03-01 00:54:01.041112-08 2025-03-01 01:19:58.345142-08 benchmark f A100 \N t \N \N \N {} +3892 1297 2025-03-01 03:24:01.588752-08 2025-03-01 03:18:14.954764-08 test f A100 \N t \N \N \N {} +3665 1250 2025-03-01 01:14:04.759133-08 2025-03-01 02:40:17.88009-08 leaderboard f A100 0.0031095133333333335 t \N \N \N {} +3666 1251 2025-03-01 00:49:00.146116-08 2025-03-01 01:10:29.020161-08 test t A100 \N t \N \N \N {} +3667 1251 2025-03-01 01:22:57.30705-08 2025-03-01 01:48:27.82583-08 benchmark t A100 \N t \N \N \N {} +3668 1251 2025-03-01 00:53:18.345319-08 2025-03-01 01:17:18.281427-08 leaderboard t A100 0.0031184623333333334 t \N \N \N {} +3675 1252 2025-03-01 00:51:13.234543-08 2025-03-01 01:19:47.389964-08 test t A100 \N t \N \N \N {} +3684 1254 2025-03-01 01:05:05.887393-08 2025-03-01 02:05:56.520759-08 benchmark f A100 \N t \N \N \N {} +3685 1255 2025-03-01 01:33:40.444936-08 2025-03-01 02:40:57.193542-08 benchmark f A100 \N t \N \N \N {} +3686 1256 2025-03-01 02:04:57.939143-08 2025-03-01 01:28:58.395269-08 benchmark f A100 \N t \N \N \N {} +3687 1257 2025-03-01 02:11:44.828858-08 2025-03-01 02:14:45.504175-08 benchmark f A100 \N t \N \N \N {} +3703 1263 2025-03-01 01:53:07.320683-08 2025-03-01 01:53:17.511001-08 test f A100 \N t \N \N \N {} +3688 1259 2025-03-01 02:02:24.550405-08 2025-03-01 01:42:30.841662-08 benchmark f H100 \N t \N \N \N {} +3689 1259 2025-03-01 02:51:13.657136-08 2025-03-01 01:36:15.328544-08 benchmark f A100 \N t \N \N \N {} +3695 1261 2025-03-01 02:37:45.341146-08 2025-03-01 02:45:50.374217-08 benchmark f A100 \N t \N \N \N {} +3696 1261 2025-03-01 02:43:06.297215-08 2025-03-01 02:38:52.020845-08 leaderboard f A100 0.0030843926666666664 t \N \N \N {} +1514 505 2025-02-25 06:11:31.116371-08 2025-02-25 07:53:55.571435-08 benchmark f A100 \N t \N \N \N {} +1515 505 2025-02-25 06:32:19.052576-08 2025-02-25 07:10:25.937469-08 leaderboard f A100 0.0032101916 t \N \N \N {} +3018 1080 2025-02-27 14:22:23.426466-08 2025-02-27 14:44:08.206039-08 benchmark f H100 \N f \N \N \N {} +3088 1105 2025-02-28 02:12:41.458282-08 2025-02-28 02:42:49.673656-08 test t A100 \N t \N \N \N {} +3096 1106 2025-02-28 03:02:13.970567-08 2025-02-28 02:27:13.461777-08 leaderboard t A100 0.0030811183333333335 t \N \N \N {} +3115 1109 2025-02-28 02:52:03.948052-08 2025-02-28 02:24:21.911819-08 test f A100 \N t \N \N \N {} +3705 1263 2025-03-01 02:09:53.923033-08 2025-03-01 02:59:38.816636-08 leaderboard f A100 0.0031043356666666665 t \N \N \N {} +3706 1263 2025-03-01 02:45:35.826522-08 2025-03-01 01:31:39.66555-08 test t A100 \N t \N \N \N {} +3707 1263 2025-03-01 02:23:56.866303-08 2025-03-01 02:48:25.991039-08 benchmark t A100 \N t \N \N \N {} +3708 1263 2025-03-01 02:48:53.22977-08 2025-03-01 01:38:57.844846-08 leaderboard t A100 0.0030862586666666665 t \N \N \N {} +3709 1264 2025-03-01 02:07:56.190346-08 2025-03-01 01:40:21.086921-08 test f A100 \N t \N \N \N {} +3710 1264 2025-03-01 03:21:38.843116-08 2025-03-01 01:42:57.620283-08 benchmark f A100 \N t \N \N \N {} +3711 1264 2025-03-01 01:50:57.95035-08 2025-03-01 02:14:10.580881-08 leaderboard f A100 0.003111637 t \N \N \N {} +3922 1304 2025-03-01 04:37:37.399916-08 2025-03-01 05:09:08.597247-08 test t A100 \N t \N \N \N {} +3713 1264 2025-03-01 02:34:54.769991-08 2025-03-01 02:40:30.444044-08 benchmark t A100 \N t \N \N \N {} +3714 1264 2025-03-01 03:26:13.41665-08 2025-03-01 01:31:26.999441-08 leaderboard t A100 0.003092787 t \N \N \N {} +3715 1265 2025-03-01 02:52:42.719228-08 2025-03-01 03:08:21.753062-08 test f A100 \N t \N \N \N {} +3716 1265 2025-03-01 01:34:00.130632-08 2025-03-01 01:35:33.719501-08 benchmark f A100 \N t \N \N \N {} +3717 1265 2025-03-01 03:18:58.998043-08 2025-03-01 01:56:17.857259-08 leaderboard f A100 0.00310427 t \N \N \N {} +3718 1265 2025-03-01 02:49:09.783091-08 2025-03-01 01:54:04.181859-08 test t A100 \N t \N \N \N {} +3719 1265 2025-03-01 01:35:45.872919-08 2025-03-01 01:33:30.016652-08 benchmark t A100 \N t \N \N \N {} +3720 1265 2025-03-01 03:24:17.497602-08 2025-03-01 01:42:24.245018-08 leaderboard t A100 0.0030956306666666666 t \N \N \N {} +3925 1305 2025-03-01 04:56:09.680619-08 2025-03-01 04:07:20.707758-08 test t A100 \N t \N \N \N {} +5248 1613 2025-03-06 08:34:35.577688-08 2025-03-06 08:01:22.528601-08 benchmark f H100 \N t \N \N \N {} +3726 1267 2025-03-01 02:36:57.461321-08 2025-03-01 02:35:02.941022-08 benchmark f A100 \N t \N \N \N {} +3727 1267 2025-03-01 01:38:43.401017-08 2025-03-01 02:32:47.651112-08 leaderboard f A100 0.0030772653333333336 t \N \N \N {} +3728 1268 2025-03-01 02:49:19.195623-08 2025-03-01 02:53:55.254272-08 test t A100 \N t \N \N \N {} +1517 505 2025-02-25 07:05:29.686612-08 2025-02-25 06:37:54.120803-08 benchmark t A100 \N t \N \N \N {} +1518 505 2025-02-25 07:41:30.63433-08 2025-02-25 06:35:14.486014-08 leaderboard t A100 0.0031655055 t \N \N \N {} +3019 1081 2025-02-27 14:48:37.142158-08 2025-02-27 15:06:31.119054-08 benchmark f H100 \N t \N \N \N {} +3097 1106 2025-02-28 03:19:00.154781-08 2025-02-28 02:16:02.356601-08 test f A100 \N t \N \N \N {} +3640 1244 2025-03-01 00:18:40.054434-08 2025-02-28 23:54:18.664151-08 test t A100 \N t \N \N \N {} +3740 1270 2025-03-01 02:37:04.861998-08 2025-03-01 03:28:09.585563-08 benchmark f A100 \N t \N \N \N {} +3741 1271 2025-03-01 02:44:07.033471-08 2025-03-01 03:41:23.057192-08 test f A100 \N t \N \N \N {} +3742 1271 2025-03-01 02:44:35.686645-08 2025-03-01 02:00:04.66024-08 benchmark f A100 \N t \N \N \N {} +3743 1271 2025-03-01 02:38:32.681534-08 2025-03-01 02:37:50.930025-08 leaderboard f A100 0.0030790513333333333 t \N \N \N {} +3744 1271 2025-03-01 03:21:20.979999-08 2025-03-01 02:59:00.012827-08 test t A100 \N t \N \N \N {} +3961 1321 2025-03-01 05:32:54.083384-08 2025-03-01 05:23:18.742132-08 test f A100 \N t \N \N \N {} +3745 1271 2025-03-01 03:15:49.381338-08 2025-03-01 02:53:14.040077-08 benchmark t A100 \N t \N \N \N {} +3746 1271 2025-03-01 02:06:31.769676-08 2025-03-01 01:46:51.112379-08 leaderboard t A100 0.0031014993333333333 t \N \N \N {} +3747 1272 2025-03-01 02:53:41.383476-08 2025-03-01 01:51:32.778962-08 test f A100 \N t \N \N \N {} +3748 1272 2025-03-01 02:45:32.603439-08 2025-03-01 03:10:17.870749-08 benchmark f A100 \N t \N \N \N {} +3749 1272 2025-03-01 02:08:50.626266-08 2025-03-01 02:33:18.807788-08 leaderboard f A100 0.0030842223333333333 t \N \N \N {} +3750 1272 2025-03-01 02:05:35.450725-08 2025-03-01 01:53:09.147022-08 test t A100 \N t \N \N \N {} +3751 1272 2025-03-01 02:35:41.645448-08 2025-03-01 02:31:48.954685-08 benchmark t A100 \N t \N \N \N {} +3756 1273 2025-03-01 03:40:36.388523-08 2025-03-01 02:50:54.185135-08 test t A100 \N t \N \N \N {} +3757 1273 2025-03-01 03:17:02.112839-08 2025-03-01 03:24:26.296631-08 benchmark t A100 \N t \N \N \N {} +3758 1273 2025-03-01 02:23:04.796296-08 2025-03-01 03:14:47.333595-08 leaderboard t A100 0.0031108526666666666 t \N \N \N {} +3759 1274 2025-03-01 03:30:42.935942-08 2025-03-01 02:39:37.720415-08 test f A100 \N t \N \N \N {} +3760 1274 2025-03-01 01:53:05.004026-08 2025-03-01 03:03:30.821872-08 benchmark f A100 \N t \N \N \N {} +3967 1322 2025-03-01 06:17:47.572392-08 2025-03-01 05:09:34.491694-08 test t A100 \N t \N \N \N {} +3761 1274 2025-03-01 02:33:50.346797-08 2025-03-01 02:26:36.964124-08 leaderboard f A100 0.0030782356666666667 t \N \N \N {} +3762 1274 2025-03-01 01:58:07.815646-08 2025-03-01 01:59:41.282385-08 test t A100 \N t \N \N \N {} +3763 1274 2025-03-01 03:17:32.966317-08 2025-03-01 01:57:52.417038-08 benchmark t A100 \N t \N \N \N {} +3764 1274 2025-03-01 02:01:00.831839-08 2025-03-01 02:45:44.696937-08 leaderboard t A100 0.0030974313333333335 t \N \N \N {} +3765 1275 2025-03-01 01:54:45.759236-08 2025-03-01 02:30:33.498327-08 test t A100 \N t \N \N \N {} +3766 1275 2025-03-01 01:51:25.781893-08 2025-03-01 01:50:19.627497-08 benchmark t A100 \N t \N \N \N {} +3767 1275 2025-03-01 02:55:04.68876-08 2025-03-01 03:34:18.634501-08 leaderboard t A100 0.003100908 t \N \N \N {} +3769 1275 2025-03-01 02:04:44.900517-08 2025-03-01 01:53:44.454925-08 benchmark f A100 \N t \N \N \N {} +3770 1275 2025-03-01 03:44:33.933845-08 2025-03-01 03:07:56.729501-08 leaderboard f A100 0.003100629 t \N \N \N {} +3771 1276 2025-03-01 03:45:41.230682-08 2025-03-01 03:07:19.802554-08 test f A100 \N t \N \N \N {} +3772 1276 2025-03-01 02:49:49.866518-08 2025-03-01 02:09:15.199733-08 benchmark f A100 \N t \N \N \N {} +3773 1276 2025-03-01 01:49:21.076445-08 2025-03-01 03:19:58.2239-08 leaderboard f A100 0.0030967063333333334 t \N \N \N {} +1520 506 2025-02-25 07:09:15.225758-08 2025-02-25 07:45:41.962123-08 benchmark t H100 \N t \N \N \N {} +1521 506 2025-02-25 07:34:19.048083-08 2025-02-25 07:59:41.638877-08 leaderboard t H100 0.00147709 t \N \N \N {} +3020 1082 2025-02-27 16:12:37.20378-08 2025-02-27 16:11:58.805622-08 benchmark f H100 \N t \N \N \N {} +3118 1110 2025-02-28 01:59:42.263954-08 2025-02-28 02:02:39.491672-08 test t A100 \N t \N \N \N {} +3774 1276 2025-03-01 02:29:59.337188-08 2025-03-01 03:02:44.666583-08 test t A100 \N t \N \N \N {} +5249 1613 2025-03-06 08:14:23.750809-08 2025-03-06 08:08:33.670096-08 leaderboard f H100 0.0014276973333333333 t \N \N \N {} +3781 1277 2025-03-01 03:22:11.445902-08 2025-03-01 02:55:41.41361-08 benchmark f A100 \N t \N \N \N {} +3782 1277 2025-03-01 03:39:09.245513-08 2025-03-01 03:08:32.889614-08 leaderboard f A100 0.0030874316666666666 t \N \N \N {} +3783 1278 2025-03-01 03:31:43.822781-08 2025-03-01 02:37:43.947702-08 test t A100 \N t \N \N \N {} +6787 2100 2025-03-15 05:14:40.803878-07 2025-03-15 04:12:35.487565-07 test t A100 \N t \N \N \N {} +6239 1924 2025-03-12 01:52:17.600422-07 2025-03-12 01:58:47.314377-07 test f T4 \N f \N \N \N {} +6241 1925 2025-03-12 01:51:25.617795-07 2025-03-12 00:25:45.175368-07 benchmark f T4 \N t \N \N \N {} +6242 1925 2025-03-12 00:18:20.920266-07 2025-03-12 00:37:09.552561-07 leaderboard f T4 3.623034557 t \N \N \N {} +6400 1990 2025-03-13 16:37:41.150108-07 2025-03-13 17:03:46.340373-07 test f T4 \N f \N \N \N {} +6684 2070 2025-03-14 16:12:52.115243-07 2025-03-14 15:40:16.793308-07 test t T4 \N f \N \N \N {} +3796 1280 2025-03-01 03:59:00.997836-08 2025-03-01 02:29:44.953873-08 benchmark f A100 \N t \N \N \N {} +3797 1280 2025-03-01 03:51:51.435647-08 2025-03-01 02:43:07.654041-08 leaderboard f A100 0.0030720253333333335 t \N \N \N {} +6243 1925 2025-03-12 01:00:01.616489-07 2025-03-12 00:47:35.44717-07 test t T4 \N t \N \N \N {} +6402 1991 2025-03-13 17:32:53.433867-07 2025-03-13 18:39:52.759282-07 benchmark f T4 \N t \N \N \N {} +3799 1280 2025-03-01 02:05:15.848809-08 2025-03-01 03:19:03.034027-08 benchmark t A100 \N t \N \N \N {} +3800 1280 2025-03-01 03:51:27.480737-08 2025-03-01 03:58:54.299511-08 leaderboard t A100 0.0031044756666666665 t \N \N \N {} +6246 1926 2025-03-12 01:05:44.944448-07 2025-03-12 01:36:04.272554-07 test t T4 \N t \N \N \N {} +6247 1926 2025-03-12 02:16:02.972538-07 2025-03-12 00:53:47.157218-07 benchmark t T4 \N t \N \N \N {} +6248 1926 2025-03-12 02:04:35.195202-07 2025-03-12 00:39:34.3763-07 leaderboard t T4 3.6946682543333336 t \N \N \N {} +6403 1991 2025-03-13 17:33:19.394407-07 2025-03-13 18:00:50.499601-07 leaderboard f T4 0.012992361650000001 t \N \N \N {} +6564 2035 2025-03-14 13:11:15.918389-07 2025-03-14 13:28:42.918799-07 benchmark f A100 \N t \N \N \N {} +3802 1281 2025-03-01 02:49:11.571703-08 2025-03-01 03:03:34.155129-08 benchmark f A100 \N t \N \N \N {} +3803 1281 2025-03-01 03:11:47.348485-08 2025-03-01 02:42:24.967997-08 leaderboard f A100 0.0030754246666666665 t \N \N \N {} +6249 1926 2025-03-12 02:18:23.699919-07 2025-03-12 01:38:21.807805-07 test f T4 \N t \N \N \N {} +6641 2053 2025-03-14 13:13:01.717161-07 2025-03-14 12:56:42.018712-07 test t A100 \N t \N \N \N {} +3827 1286 2025-03-01 03:21:43.151398-08 2025-03-01 04:03:44.372245-08 benchmark f A100 \N t \N \N \N {} +3828 1286 2025-03-01 02:16:32.267354-08 2025-03-01 02:27:13.261291-08 leaderboard f A100 0.0032163636666666666 t \N \N \N {} +6258 1934 2025-03-12 07:02:26.8957-07 2025-03-12 06:48:21.923788-07 benchmark f A100 \N t \N \N \N {} +6412 1994 2025-03-13 17:40:48.083573-07 2025-03-13 17:33:28.539709-07 test f T4 \N f \N \N \N {} +6573 2039 2025-03-14 11:47:52.741123-07 2025-03-14 13:13:09.01997-07 test t A100 \N t \N \N \N {} +6574 2039 2025-03-14 13:30:31.134825-07 2025-03-14 12:56:58.461643-07 benchmark t A100 \N t \N \N \N {} +6575 2039 2025-03-14 13:14:01.573829-07 2025-03-14 13:06:52.578339-07 leaderboard t A100 0.0025201923333333337 t \N \N \N {} +1523 506 2025-02-25 07:26:02.548226-08 2025-02-25 07:25:46.716931-08 benchmark f H100 \N t \N \N \N {} +1529 510 2025-02-25 06:46:53.396184-08 2025-02-25 07:55:23.310917-08 benchmark f A100 \N t \N \N \N {} +1530 511 2025-02-25 08:01:36.384337-08 2025-02-25 07:34:22.650715-08 benchmark f A100 \N t \N \N \N {} +3021 1083 2025-02-27 15:20:59.403393-08 2025-02-27 15:45:28.074308-08 test t H100 \N t \N \N \N {} +3022 1083 2025-02-27 15:38:29.43689-08 2025-02-27 15:22:38.825121-08 benchmark t H100 \N t \N \N \N {} +3023 1083 2025-02-27 16:15:56.367001-08 2025-02-27 16:18:18.215051-08 leaderboard t H100 0.0025371098599999997 t \N \N \N {} +3070 1102 2025-02-28 02:33:54.637798-08 2025-02-28 01:22:56.978086-08 test f A100 \N t \N \N \N {} +1547 512 2025-02-25 06:52:50.899054-08 2025-02-25 06:49:27.130424-08 benchmark t T4 \N t \N \N \N {} +1548 512 2025-02-25 06:32:04.498659-08 2025-02-25 07:33:16.893589-08 leaderboard t T4 0.021058454833333334 t \N \N \N {} +1549 512 2025-02-25 07:27:14.309213-08 2025-02-25 07:47:52.099855-08 test f L4 \N t \N \N \N {} +1550 512 2025-02-25 06:57:32.191599-08 2025-02-25 07:03:02.042374-08 benchmark f L4 \N t \N \N \N {} +1552 512 2025-02-25 06:58:24.333124-08 2025-02-25 06:33:18.568674-08 test t L4 \N t \N \N \N {} +1556 513 2025-02-25 06:14:45.491004-08 2025-02-25 07:16:46.376307-08 benchmark t A100 \N t \N \N \N {} +1557 513 2025-02-25 07:34:02.46493-08 2025-02-25 08:06:10.020194-08 leaderboard t A100 0.007567747333333333 t \N \N \N {} +3024 1083 2025-02-27 14:39:16.285794-08 2025-02-27 16:33:31.980742-08 test f H100 \N t \N \N \N {} +3025 1083 2025-02-27 16:31:07.646609-08 2025-02-27 14:51:20.50896-08 benchmark f H100 \N t \N \N \N {} +3026 1083 2025-02-27 14:40:27.566259-08 2025-02-27 16:02:44.891034-08 leaderboard f H100 0.00193133143 t \N \N \N {} +3071 1102 2025-02-28 01:56:21.879786-08 2025-02-28 02:17:53.253173-08 benchmark f A100 \N t \N \N \N {} +1559 513 2025-02-25 07:00:51.226524-08 2025-02-25 06:53:52.941813-08 benchmark f A100 \N t \N \N \N {} +1560 513 2025-02-25 07:40:54.286852-08 2025-02-25 07:20:37.31234-08 leaderboard f A100 0.007643499333333333 t \N \N \N {} +3027 1084 2025-02-27 14:58:06.567407-08 2025-02-27 14:38:42.214639-08 benchmark f A100 \N t \N \N \N {} +3072 1102 2025-02-28 02:51:58.714497-08 2025-02-28 02:17:05.194701-08 leaderboard f A100 0.0031002953333333335 t \N \N \N {} +3089 1105 2025-02-28 03:09:52.010667-08 2025-02-28 01:20:13.790445-08 benchmark t A100 \N t \N \N \N {} +3090 1105 2025-02-28 02:51:45.323434-08 2025-02-28 02:33:52.770692-08 leaderboard t A100 0.0030956803333333336 t \N \N \N {} +1562 514 2025-02-25 07:35:28.616691-08 2025-02-25 06:29:07.554479-08 benchmark f A100 \N t \N \N \N {} +1563 514 2025-02-25 07:01:33.808022-08 2025-02-25 06:30:57.182348-08 leaderboard f A100 0.00319349475 t \N \N \N {} +3028 1085 2025-02-27 15:14:39.199509-08 2025-02-27 15:48:14.221616-08 test f H100 \N t \N \N \N {} +3029 1085 2025-02-27 15:48:18.376186-08 2025-02-27 15:01:22.031548-08 benchmark f H100 \N t \N \N \N {} +3030 1085 2025-02-27 15:34:36.352873-08 2025-02-27 15:03:55.584707-08 leaderboard f H100 0.0018411454 t \N \N \N {} +3073 1102 2025-02-28 02:05:44.512427-08 2025-02-28 02:46:15.64099-08 test t A100 \N t \N \N \N {} +1565 514 2025-02-25 06:29:07.165341-08 2025-02-25 07:51:19.989787-08 benchmark t A100 \N t \N \N \N {} +1566 514 2025-02-25 06:29:37.966737-08 2025-02-25 06:28:42.906056-08 leaderboard t A100 0.0032033456 t \N \N \N {} +1573 516 2025-02-25 06:33:48.857537-08 2025-02-25 07:43:18.501261-08 test t A100 \N t \N \N \N {} +1574 516 2025-02-25 08:15:17.851507-08 2025-02-25 08:32:05.853279-08 benchmark t A100 \N t \N \N \N {} +1579 517 2025-02-25 06:55:30.527319-08 2025-02-25 07:43:47.516317-08 test f T4 \N t \N \N \N {} +1581 519 2025-02-25 07:22:27.620876-08 2025-02-25 07:28:11.702548-08 test t H100 \N t \N \N \N {} +1589 522 2025-02-25 08:42:40.422194-08 2025-02-25 08:02:12.607584-08 benchmark f A100 \N t \N \N \N {} +1593 526 2025-02-25 07:56:16.683261-08 2025-02-25 08:23:55.488514-08 test f A100 \N t \N \N \N {} +1594 527 2025-02-25 07:07:18.475511-08 2025-02-25 08:55:21.597339-08 benchmark f A100 \N t \N \N \N {} +1609 533 2025-02-25 08:14:37.090411-08 2025-02-25 07:43:04.159111-08 test f H100 \N t \N \N \N {} +1625 539 2025-02-25 08:06:46.284758-08 2025-02-25 09:01:35.105248-08 test t H100 \N t \N \N \N {} +6919 2156 2025-03-16 16:25:14.914949-07 2025-03-16 16:52:24.968404-07 test t T4 \N t \N \N \N {} +3830 1287 2025-03-01 02:52:44.123034-08 2025-03-01 03:04:16.432487-08 benchmark f A100 \N t \N \N \N {} +3831 1287 2025-03-01 03:18:09.205414-08 2025-03-01 02:51:41.151421-08 leaderboard f A100 0.0030734603333333337 t \N \N \N {} +6414 1995 2025-03-13 18:08:53.756809-07 2025-03-13 17:29:29.189385-07 test f T4 \N f \N \N \N {} +6579 2040 2025-03-14 12:48:20.53274-07 2025-03-14 13:09:22.939558-07 test t A100 \N t \N \N \N {} +6580 2040 2025-03-14 12:55:54.163861-07 2025-03-14 12:30:14.281856-07 benchmark t A100 \N t \N \N \N {} +6581 2040 2025-03-14 12:55:49.972531-07 2025-03-14 13:31:53.117136-07 leaderboard t A100 0.0025155676666666666 t \N \N \N {} +6664 2060 2025-03-14 13:51:59.840678-07 2025-03-14 12:26:11.365457-07 test f T4 \N f \N \N \N {} +3836 1288 2025-03-01 04:15:45.238418-08 2025-03-01 03:47:16.55003-08 benchmark f A100 \N t \N \N \N {} +3837 1288 2025-03-01 03:57:06.451424-08 2025-03-01 02:21:13.360645-08 leaderboard f A100 0.0030780183333333337 t \N \N \N {} +3840 1288 2025-03-01 02:23:11.113845-08 2025-03-01 03:57:50.503081-08 leaderboard t A100 0.0030832926666666664 t \N \N \N {} +6263 1939 2025-03-12 07:51:31.81151-07 2025-03-12 07:26:02.800143-07 benchmark f A100 \N t \N \N \N {} +6264 1939 2025-03-12 08:16:12.289193-07 2025-03-12 08:05:41.403712-07 leaderboard f A100 0.00006743858 t \N \N \N {} +6416 1996 2025-03-13 18:45:10.175348-07 2025-03-13 18:56:40.818899-07 test t T4 \N f \N \N \N {} +3842 1289 2025-03-01 04:13:05.572388-08 2025-03-01 02:54:57.38981-08 benchmark f A100 \N t \N \N \N {} +3843 1289 2025-03-01 02:45:06.634242-08 2025-03-01 03:39:15.781501-08 leaderboard f A100 0.0030902456666666665 t \N \N \N {} +6265 1939 2025-03-12 08:51:35.314623-07 2025-03-12 08:24:58.103706-07 test t A100 \N t \N \N \N {} +6266 1939 2025-03-12 07:29:33.603048-07 2025-03-12 08:44:42.162501-07 benchmark t A100 \N t \N \N \N {} +6267 1939 2025-03-12 07:09:09.345344-07 2025-03-12 07:39:08.752326-07 leaderboard t A100 0.00006758480769230768 t \N \N \N {} +3845 1289 2025-03-01 04:09:16.526703-08 2025-03-01 02:35:11.693428-08 benchmark t A100 \N t \N \N \N {} +3846 1289 2025-03-01 03:33:27.94603-08 2025-03-01 02:24:48.469479-08 leaderboard t A100 0.0030774643333333334 t \N \N \N {} +6269 1940 2025-03-12 08:08:47.902846-07 2025-03-12 07:11:33.996302-07 benchmark f H100 \N t \N \N \N {} +6270 1940 2025-03-12 07:11:39.417039-07 2025-03-12 08:15:41.878762-07 leaderboard f H100 0.00004438749 t \N \N \N {} +6418 1997 2025-03-13 18:59:25.941543-07 2025-03-13 19:24:02.914954-07 test f T4 \N f \N \N \N {} +3848 1290 2025-03-01 03:36:59.507399-08 2025-03-01 03:50:52.826622-08 benchmark f A100 \N t \N \N \N {} +3849 1290 2025-03-01 03:20:17.52254-08 2025-03-01 04:10:31.201707-08 leaderboard f A100 0.0030958916666666667 t \N \N \N {} +6587 2041 2025-03-14 12:56:09.267865-07 2025-03-14 12:56:01.714341-07 leaderboard t A100 0.0025133303333333334 t \N \N \N {} +3857 1291 2025-03-01 03:54:20.136882-08 2025-03-01 02:57:32.144781-08 benchmark f A100 \N t \N \N \N {} +1595 528 2025-02-25 07:57:34.081957-08 2025-02-25 08:46:00.770848-08 benchmark f A100 \N t \N \N \N {} +1596 529 2025-02-25 08:42:32.027812-08 2025-02-25 08:28:47.379598-08 benchmark f H100 \N t \N \N \N {} +1597 530 2025-02-25 08:13:24.437551-08 2025-02-25 08:25:42.046848-08 benchmark f H100 \N t \N \N \N {} +1598 531 2025-02-25 07:18:37.587178-08 2025-02-25 07:26:28.050318-08 test t A100 \N f \N \N \N {} +1599 531 2025-02-25 07:10:05.242952-08 2025-02-25 07:51:39.460156-08 test f A100 \N f \N \N \N {} +1600 532 2025-02-25 07:30:03.467759-08 2025-02-25 07:48:58.024839-08 test f A100 \N t \N \N \N {} +1676 550 2025-02-25 08:20:23.376176-08 2025-02-25 09:14:51.808165-08 leaderboard f H100 0.0015442018125 t \N \N \N {} +1601 532 2025-02-25 09:03:02.38871-08 2025-02-25 09:05:51.634571-08 benchmark f A100 \N t \N \N \N {} +1602 532 2025-02-25 08:03:27.143172-08 2025-02-25 08:12:43.058027-08 leaderboard f A100 0.003081489 t \N \N \N {} +1610 533 2025-02-25 08:22:53.82146-08 2025-02-25 08:19:04.111413-08 benchmark f H100 \N t \N \N \N {} +1611 533 2025-02-25 08:59:03.973218-08 2025-02-25 08:01:47.560678-08 leaderboard f H100 0.0010464732307692308 t \N \N \N {} +1612 534 2025-02-25 08:13:49.798132-08 2025-02-25 07:38:47.976643-08 benchmark f A100 \N t \N \N \N {} +1613 535 2025-02-25 07:35:30.962867-08 2025-02-25 08:49:27.767886-08 benchmark f H100 \N f \N \N \N {} +1623 539 2025-02-25 09:08:19.427448-08 2025-02-25 07:54:22.18938-08 benchmark f H100 \N t \N \N \N {} +1624 539 2025-02-25 09:12:30.335139-08 2025-02-25 09:19:28.18725-08 leaderboard f H100 0.001406501 t \N \N \N {} +1632 540 2025-02-25 08:41:53.357227-08 2025-02-25 07:46:39.377359-08 benchmark t H100 \N t \N \N \N {} +1633 540 2025-02-25 07:58:37.599753-08 2025-02-25 08:48:17.13888-08 leaderboard t H100 0.0014054696666666667 t \N \N \N {} +1635 542 2025-02-25 07:55:32.669761-08 2025-02-25 08:25:28.836254-08 test f A100 \N t \N \N \N {} +1636 542 2025-02-25 07:43:32.658836-08 2025-02-25 08:54:50.899175-08 benchmark f A100 \N t \N \N \N {} +1637 542 2025-02-25 09:16:00.130562-08 2025-02-25 08:20:39.142553-08 leaderboard f A100 0.003090287 t \N \N \N {} +1638 542 2025-02-25 07:35:49.713063-08 2025-02-25 08:24:45.44148-08 test t A100 \N t \N \N \N {} +2773 966 2025-02-26 20:06:46.265649-08 2025-02-26 20:37:56.859709-08 test f A100 \N t \N \N \N {} +6281 1943 2025-03-12 08:27:24.754837-07 2025-03-12 09:00:16.045266-07 test t L4 \N t \N \N \N {} +6282 1943 2025-03-12 09:15:13.475839-07 2025-03-12 08:12:28.990502-07 benchmark t L4 \N t \N \N \N {} +6283 1943 2025-03-12 07:28:57.790932-07 2025-03-12 07:49:47.805831-07 leaderboard t L4 0.00009934762 t \N \N \N {} +6665 2060 2025-03-14 14:00:15.697848-07 2025-03-14 13:40:02.788256-07 test t T4 \N f \N \N \N {} +3863 1292 2025-03-01 04:08:38.155179-08 2025-03-01 03:06:47.8337-08 benchmark t A100 \N t \N \N \N {} +6287 1944 2025-03-12 10:01:22.337882-07 2025-03-12 10:02:16.243525-07 test f T4 \N t \N \N \N {} +6288 1944 2025-03-12 10:35:51.44506-07 2025-03-12 10:53:31.762253-07 benchmark f T4 \N t \N \N \N {} +6289 1944 2025-03-12 11:15:49.75933-07 2025-03-12 10:30:20.890743-07 leaderboard f T4 3.5460847016666666 t \N \N \N {} +6421 1999 2025-03-14 05:54:56.594885-07 2025-03-14 05:07:15.151352-07 benchmark f A100 \N t \N \N \N {} +6422 1999 2025-03-14 04:48:17.257143-07 2025-03-14 04:38:30.49237-07 leaderboard f A100 0.002516429 t \N \N \N {} +6666 2061 2025-03-14 15:01:59.053891-07 2025-03-14 14:18:38.868311-07 test t T4 \N f \N \N \N {} +6291 1944 2025-03-12 09:57:38.536736-07 2025-03-12 10:33:16.849388-07 benchmark t T4 \N t \N \N \N {} +1639 542 2025-02-25 09:00:22.32772-08 2025-02-25 07:59:32.226862-08 benchmark t A100 \N t \N \N \N {} +1640 542 2025-02-25 09:11:58.980108-08 2025-02-25 08:19:49.179574-08 leaderboard t A100 0.0030916486666666665 t \N \N \N {} +1641 543 2025-02-25 07:54:29.239831-08 2025-02-25 09:35:38.991324-08 test t A100 \N t \N \N \N {} +1646 543 2025-02-25 08:14:16.552698-08 2025-02-25 08:35:18.8746-08 leaderboard f A100 0.0030903186666666666 t \N \N \N {} +1656 545 2025-02-25 08:18:12.922307-08 2025-02-25 09:29:21.123215-08 leaderboard t A100 0.0031894605 t \N \N \N {} +1657 546 2025-02-25 08:26:28.000804-08 2025-02-25 09:50:00.709982-08 test t A100 \N t \N \N \N {} +1658 546 2025-02-25 09:48:18.886362-08 2025-02-25 09:10:41.999421-08 benchmark t A100 \N t \N \N \N {} +1659 546 2025-02-25 07:55:49.954375-08 2025-02-25 09:05:11.341596-08 leaderboard t A100 0.00063344525 t \N \N \N {} +1660 546 2025-02-25 08:01:12.970128-08 2025-02-25 08:24:15.501382-08 test f A100 \N t \N \N \N {} +1661 546 2025-02-25 09:18:23.952092-08 2025-02-25 08:15:13.029454-08 benchmark f A100 \N t \N \N \N {} +6292 1944 2025-03-12 10:58:18.817212-07 2025-03-12 10:46:59.456595-07 leaderboard t T4 3.53122528 t \N \N \N {} +1662 546 2025-02-25 08:35:51.067428-08 2025-02-25 08:31:39.935762-08 leaderboard f A100 0.0005320072 t \N \N \N {} +1663 547 2025-02-25 09:32:55.917437-08 2025-02-25 08:44:26.311971-08 benchmark f H100 \N f \N \N \N {} +1684 557 2025-02-25 09:44:40.003266-08 2025-02-25 08:13:24.412498-08 benchmark f A100 \N f \N \N \N {} +1664 548 2025-02-25 08:06:02.985208-08 2025-02-25 08:56:36.303961-08 benchmark f H100 \N t \N \N \N {} +1666 549 2025-02-25 09:38:40.901485-08 2025-02-25 09:32:01.788835-08 benchmark t H100 \N t \N \N \N {} +1667 549 2025-02-25 08:26:34.238735-08 2025-02-25 08:25:38.843118-08 leaderboard t H100 0.000413247 t \N \N \N {} +1668 549 2025-02-25 09:52:24.604979-08 2025-02-25 08:02:29.9005-08 test f H100 \N t \N \N \N {} +1685 557 2025-02-25 09:19:53.854754-08 2025-02-25 08:46:52.219004-08 test t A100 \N t \N \N \N {} +1669 549 2025-02-25 08:53:05.362645-08 2025-02-25 08:47:47.898667-08 benchmark f H100 \N t \N \N \N {} +1670 549 2025-02-25 08:59:06.217181-08 2025-02-25 09:09:07.897493-08 leaderboard f H100 0.0004161602 t \N \N \N {} +6423 1999 2025-03-14 04:42:51.926225-07 2025-03-14 04:23:11.348669-07 test t A100 \N t \N \N \N {} +1678 553 2025-02-25 10:02:18.136454-08 2025-02-25 08:09:06.800521-08 test f H100 \N f \N \N \N {} +1682 556 2025-02-25 08:53:34.265116-08 2025-02-25 08:39:01.371239-08 benchmark f A100 \N t \N \N \N {} +1686 557 2025-02-25 08:35:22.195064-08 2025-02-25 09:08:11.056354-08 benchmark t A100 \N f \N \N \N {} +6424 1999 2025-03-14 05:09:18.158024-07 2025-03-14 04:42:27.024761-07 benchmark t A100 \N t \N \N \N {} +6667 2061 2025-03-14 15:25:37.443228-07 2025-03-14 15:06:32.582085-07 test f T4 \N f \N \N \N {} +6643 2053 2025-03-14 13:12:40.43597-07 2025-03-14 13:44:06.267317-07 leaderboard t A100 0.0024720393333333337 t \N \N \N {} +6668 2062 2025-03-14 14:17:49.224385-07 2025-03-14 14:26:32.460019-07 test t T4 \N f \N \N \N {} +3876 1294 2025-03-01 04:14:10.442655-08 2025-03-01 02:49:35.269118-08 leaderboard t A100 0.0030766496666666666 t \N \N \N {} +6294 1945 2025-03-12 10:57:16.296692-07 2025-03-12 11:33:24.443736-07 test f T4 \N f \N \N \N {} +6426 2000 2025-03-14 06:30:53.7156-07 2025-03-14 06:12:43.209048-07 test f H100 \N t \N \N \N {} +6427 2000 2025-03-14 06:57:59.87542-07 2025-03-14 06:15:38.776251-07 benchmark f H100 \N t \N \N \N {} +6428 2000 2025-03-14 07:35:27.590998-07 2025-03-14 06:32:04.615938-07 leaderboard f H100 0.0014073136666666668 t \N \N \N {} +6591 2042 2025-03-14 11:50:38.099796-07 2025-03-14 12:40:25.372521-07 test f A100 \N t \N \N \N {} +6644 2053 2025-03-14 13:49:44.146284-07 2025-03-14 12:26:28.495965-07 test f A100 \N t \N \N \N {} +6669 2062 2025-03-14 16:02:16.728023-07 2025-03-14 14:51:42.161638-07 test f T4 \N f \N \N \N {} +6436 2001 2025-03-14 06:50:57.915641-07 2025-03-14 07:00:20.008226-07 benchmark t A100 \N t \N \N \N {} +6437 2001 2025-03-14 07:44:58.201024-07 2025-03-14 07:24:40.594633-07 leaderboard t A100 0.002543368 t \N \N \N {} +3887 1296 2025-03-01 04:21:55.785115-08 2025-03-01 04:09:44.553825-08 benchmark f A100 \N t \N \N \N {} +3888 1296 2025-03-01 03:39:58.6244-08 2025-03-01 03:09:40.552669-08 leaderboard f A100 0.00309473 t \N \N \N {} +6299 1948 2025-03-12 10:32:56.767467-07 2025-03-12 11:22:11.851694-07 test t T4 \N t \N \N \N {} +6300 1948 2025-03-12 10:31:11.093009-07 2025-03-12 11:06:33.740742-07 benchmark t T4 \N f \N \N \N {} +6438 2002 2025-03-14 07:39:39.918952-07 2025-03-14 07:42:19.454653-07 test t A100 \N t \N \N \N {} +3891 1297 2025-03-01 04:29:07.565859-08 2025-03-01 03:39:00.355245-08 leaderboard t A100 0.0030872086666666665 t \N \N \N {} +6301 1949 2025-03-12 11:11:45.381428-07 2025-03-12 11:41:44.803347-07 test f T4 \N t \N \N \N {} +6302 1949 2025-03-12 11:19:11.369574-07 2025-03-12 11:47:00.702215-07 benchmark f T4 \N t \N \N \N {} +6303 1949 2025-03-12 11:08:31.94301-07 2025-03-12 11:22:31.160474-07 leaderboard f T4 5.264521755 t \N \N \N {} +3894 1297 2025-03-01 03:08:30.539157-08 2025-03-01 04:20:39.317282-08 leaderboard f A100 0.003077314 t \N \N \N {} +3895 1298 2025-03-01 03:26:56.396066-08 2025-03-01 04:02:10.736561-08 benchmark f A100 \N t \N \N \N {} +3896 1298 2025-03-01 04:30:06.792705-08 2025-03-01 05:08:52.916106-08 benchmark f H100 \N t \N \N \N {} +3897 1299 2025-03-01 03:32:41.876039-08 2025-03-01 03:28:04.603739-08 benchmark f H100 \N t \N \N \N {} +3902 1301 2025-03-01 03:45:08.15341-08 2025-03-01 03:30:46.472923-08 benchmark t A100 \N t \N \N \N {} +3903 1301 2025-03-01 05:18:11.482755-08 2025-03-01 04:15:04.738792-08 leaderboard t A100 0.0017082903333333333 t \N \N \N {} +3904 1301 2025-03-01 05:20:05.351706-08 2025-03-01 04:57:15.691666-08 test f H100 \N t \N \N \N {} +3905 1301 2025-03-01 04:11:22.209627-08 2025-03-01 03:29:30.979359-08 benchmark f H100 \N t \N \N \N {} +3909 1301 2025-03-01 05:05:05.368527-08 2025-03-01 05:21:07.368377-08 leaderboard t H100 0.0009410016 t \N \N \N {} +3911 1302 2025-03-01 04:15:21.468517-08 2025-03-01 04:16:28.01241-08 benchmark f A100 \N t \N \N \N {} +3912 1302 2025-03-01 05:15:06.388507-08 2025-03-01 05:04:17.52923-08 benchmark f H100 \N f \N \N \N {} +3914 1303 2025-03-01 03:58:49.506515-08 2025-03-01 05:03:02.215649-08 benchmark t A100 \N t \N \N \N {} +3915 1303 2025-03-01 05:02:46.480424-08 2025-03-01 05:10:24.739066-08 leaderboard t A100 0.0017033548333333334 t \N \N \N {} +3916 1303 2025-03-01 03:49:31.466637-08 2025-03-01 04:14:50.882192-08 test f A100 \N t \N \N \N {} +3917 1303 2025-03-01 04:37:55.798922-08 2025-03-01 05:09:32.972724-08 benchmark f A100 \N t \N \N \N {} +3918 1303 2025-03-01 04:24:18.36739-08 2025-03-01 04:58:17.307429-08 leaderboard f A100 0.0017008582 t \N \N \N {} +6304 1949 2025-03-12 10:47:37.368806-07 2025-03-12 12:09:25.453957-07 test t T4 \N t \N \N \N {} +1687 558 2025-02-25 09:27:10.084906-08 2025-02-25 09:48:43.848525-08 test f A100 \N f \N \N \N {} +1688 558 2025-02-25 08:29:44.125579-08 2025-02-25 08:24:09.285219-08 test t A100 \N f \N \N \N {} +1689 559 2025-02-25 09:17:25.101881-08 2025-02-25 09:24:03.785066-08 test f A100 \N t \N \N \N {} +1690 559 2025-02-25 08:36:17.748573-08 2025-02-25 09:33:30.838585-08 benchmark f A100 \N f \N \N \N {} +1691 559 2025-02-25 08:55:41.256091-08 2025-02-25 08:48:26.806972-08 test t A100 \N t \N \N \N {} +1692 559 2025-02-25 10:04:14.443146-08 2025-02-25 08:43:41.839368-08 benchmark t A100 \N f \N \N \N {} +1694 561 2025-02-25 10:05:36.595463-08 2025-02-25 10:09:40.331096-08 test f A100 \N t \N \N \N {} +3567 1211 2025-02-28 11:38:42.437274-08 2025-02-28 11:48:25.289361-08 leaderboard f H100 0.0011942561428571429 t \N \N \N {} +1696 562 2025-02-25 09:44:07.680498-08 2025-02-25 09:28:05.067515-08 test f A100 \N t \N \N \N {} +1697 562 2025-02-25 09:28:26.771379-08 2025-02-25 09:08:24.955188-08 benchmark f A100 \N t \N \N \N {} +1698 562 2025-02-25 08:47:34.033962-08 2025-02-25 08:35:38.541602-08 leaderboard f A100 0.023603107 t \N \N \N {} +1699 562 2025-02-25 09:50:30.387686-08 2025-02-25 10:01:59.310649-08 test t A100 \N t \N \N \N {} +1700 562 2025-02-25 08:34:58.738598-08 2025-02-25 08:38:55.902288-08 benchmark t A100 \N t \N \N \N {} +1701 562 2025-02-25 09:37:56.413765-08 2025-02-25 10:02:25.234169-08 leaderboard t A100 0.02387513042857143 t \N \N \N {} +6305 1949 2025-03-12 11:11:14.460487-07 2025-03-12 11:31:45.219851-07 benchmark t T4 \N t \N \N \N {} +6306 1949 2025-03-12 10:28:32.79482-07 2025-03-12 10:15:38.766761-07 leaderboard t T4 5.288479138666667 t \N \N \N {} +6443 2002 2025-03-14 07:34:37.078718-07 2025-03-14 07:14:50.86738-07 leaderboard f A100 0.0025309973333333337 t \N \N \N {} +6595 2043 2025-03-14 11:56:33.434461-07 2025-03-14 12:42:20.017678-07 test t A100 \N f \N \N \N {} +6685 2070 2025-03-14 15:59:15.307918-07 2025-03-14 15:02:13.119994-07 test f T4 \N f \N \N \N {} +3962 1321 2025-03-01 05:24:43.049186-08 2025-03-01 05:35:56.69-08 benchmark f A100 \N t \N \N \N {} +3963 1321 2025-03-01 06:08:58.229359-08 2025-03-01 05:22:31.386839-08 leaderboard f A100 0.0030768063333333337 t \N \N \N {} +6310 1951 2025-03-12 12:07:14.331628-07 2025-03-12 12:08:49.086672-07 test f T4 \N f \N \N \N {} +6453 2004 2025-03-14 06:42:37.075587-07 2025-03-14 07:33:35.882343-07 test t A100 \N t \N \N \N {} +6454 2004 2025-03-14 06:29:13.454127-07 2025-03-14 07:27:34.849727-07 benchmark t A100 \N t \N \N \N {} +6455 2004 2025-03-14 07:29:30.832561-07 2025-03-14 06:24:40.619546-07 leaderboard t A100 0.0025143146666666664 t \N \N \N {} +3965 1321 2025-03-01 05:11:28.825751-08 2025-03-01 04:35:18.89017-08 benchmark t A100 \N t \N \N \N {} +3966 1321 2025-03-01 06:16:12.443097-08 2025-03-01 04:37:00.745753-08 leaderboard t A100 0.0030776773333333336 t \N \N \N {} +6456 2005 2025-03-14 07:23:16.64199-07 2025-03-14 07:01:50.750143-07 test f A100 \N t \N \N \N {} +6457 2005 2025-03-14 06:22:29.76973-07 2025-03-14 06:09:06.773695-07 benchmark f A100 \N t \N \N \N {} +1703 565 2025-02-25 08:37:05.35978-08 2025-02-25 08:21:52.880091-08 test t A100 \N f \N \N \N {} +1713 572 2025-02-25 10:08:35.53301-08 2025-02-25 09:47:59.416619-08 benchmark f A100 \N t \N \N \N {} +1714 574 2025-02-25 08:57:21.205944-08 2025-02-25 08:58:46.708203-08 test f T4 \N f \N \N \N {} +1715 574 2025-02-25 08:22:30.782939-08 2025-02-25 09:22:33.716306-08 test t T4 \N f \N \N \N {} +1716 577 2025-02-25 09:25:17.463083-08 2025-02-25 09:32:17.15906-08 test t T4 \N f \N \N \N {} +1717 577 2025-02-25 08:30:00.171838-08 2025-02-25 09:40:29.944237-08 test f T4 \N f \N \N \N {} +1719 576 2025-02-25 10:16:42.240136-08 2025-02-25 09:11:50.029271-08 test f A100 \N t \N \N \N {} +3983 1326 2025-03-01 05:25:23.898128-08 2025-03-01 04:57:04.509129-08 test t A100 \N t \N \N \N {} +3992 1327 2025-03-01 05:54:28.752918-08 2025-03-01 05:57:31.677252-08 test t A100 \N t \N \N \N {} +3984 1326 2025-03-01 06:08:48.324057-08 2025-03-01 05:49:30.098232-08 benchmark t A100 \N t \N \N \N {} +3985 1326 2025-03-01 05:42:14.249969-08 2025-03-01 04:34:37.024307-08 leaderboard t A100 0.0030766156666666663 t \N \N \N {} +3986 1326 2025-03-01 04:55:11.733421-08 2025-03-01 05:15:01.56334-08 test f A100 \N t \N \N \N {} +3987 1326 2025-03-01 04:56:41.549319-08 2025-03-01 06:23:31.903277-08 benchmark f A100 \N t \N \N \N {} +3988 1326 2025-03-01 06:05:23.530527-08 2025-03-01 05:11:56.630856-08 leaderboard f A100 0.003088671 t \N \N \N {} +3991 1327 2025-03-01 05:48:54.261383-08 2025-03-01 06:04:01.141671-08 leaderboard f A100 0.003079475 t \N \N \N {} +3993 1327 2025-03-01 06:15:07.619494-08 2025-03-01 05:26:34.114027-08 benchmark t A100 \N t \N \N \N {} +3994 1327 2025-03-01 05:16:38.809307-08 2025-03-01 05:49:08.846786-08 leaderboard t A100 0.0031005003333333335 t \N \N \N {} +3995 1328 2025-03-01 04:33:03.029344-08 2025-03-01 04:45:00.030881-08 test f A100 \N t \N \N \N {} +3996 1328 2025-03-01 05:09:28.825437-08 2025-03-01 06:19:26.3327-08 benchmark f A100 \N t \N \N \N {} +1720 576 2025-02-25 08:55:00.326542-08 2025-02-25 10:00:29.575335-08 benchmark f A100 \N t \N \N \N {} +1721 576 2025-02-25 08:21:46.705866-08 2025-02-25 10:00:43.93478-08 leaderboard f A100 0.00010041933333333332 t \N \N \N {} +1722 576 2025-02-25 10:17:49.927789-08 2025-02-25 08:37:55.11806-08 test t A100 \N t \N \N \N {} +1723 576 2025-02-25 09:33:22.088092-08 2025-02-25 09:24:57.641368-08 benchmark t A100 \N t \N \N \N {} +1724 576 2025-02-25 08:39:15.730368-08 2025-02-25 08:39:06.879053-08 leaderboard t A100 0.00009493443478260869 t \N \N \N {} +3997 1328 2025-03-01 05:05:03.87852-08 2025-03-01 05:17:53.273059-08 leaderboard f A100 0.0030810756666666667 t \N \N \N {} +4001 1329 2025-03-01 05:01:28.939311-08 2025-03-01 05:15:07.131981-08 test t A100 \N t \N \N \N {} +4002 1329 2025-03-01 06:12:19.11185-08 2025-03-01 06:29:57.519732-08 benchmark t A100 \N t \N \N \N {} +4003 1329 2025-03-01 05:41:46.640503-08 2025-03-01 05:30:00.149015-08 leaderboard t A100 0.003084419 t \N \N \N {} +4004 1329 2025-03-01 04:37:14.969684-08 2025-03-01 05:59:19.610966-08 test f A100 \N t \N \N \N {} +4005 1329 2025-03-01 05:09:39.146484-08 2025-03-01 05:15:15.340274-08 benchmark f A100 \N t \N \N \N {} +4022 1332 2025-03-01 06:29:41.552466-08 2025-03-01 06:00:54.805207-08 test t A100 \N t \N \N \N {} +2084 702 2025-02-25 14:51:18.373776-08 2025-02-25 14:07:09.597234-08 test t T4 \N t \N \N \N {} +1728 582 2025-02-25 09:02:10.715863-08 2025-02-25 08:32:41.855511-08 test f T4 \N t \N \N \N {} +1729 582 2025-02-25 10:08:04.120077-08 2025-02-25 09:31:28.613967-08 benchmark f T4 \N f \N \N \N {} +1730 582 2025-02-25 09:12:24.120001-08 2025-02-25 08:35:23.070755-08 test t T4 \N t \N \N \N {} +1731 582 2025-02-25 08:45:58.419751-08 2025-02-25 09:50:28.503841-08 benchmark t T4 \N f \N \N \N {} +1732 581 2025-02-25 08:57:10.342617-08 2025-02-25 09:35:43.172418-08 test f T4 \N f \N \N \N {} +4023 1332 2025-03-01 05:59:07.048515-08 2025-03-01 05:19:06.578904-08 benchmark t A100 \N t \N \N \N {} +4024 1332 2025-03-01 06:30:59.985589-08 2025-03-01 05:19:38.789542-08 leaderboard t A100 0.0032329573333333334 t \N \N \N {} +4026 1333 2025-03-01 04:39:47.738838-08 2025-03-01 06:18:45.96386-08 benchmark f A100 \N t \N \N \N {} +4027 1333 2025-03-01 06:14:49.837768-08 2025-03-01 04:40:08.352975-08 leaderboard f A100 0.003070329 t \N \N \N {} +4028 1333 2025-03-01 05:10:32.553371-08 2025-03-01 04:42:50.943725-08 test t A100 \N t \N \N \N {} +4038 1335 2025-03-01 04:42:30.486974-08 2025-03-01 06:10:26.032842-08 test f A100 \N f \N \N \N {} +4039 1336 2025-03-01 06:21:06.572554-08 2025-03-01 06:39:44.644728-08 test t A100 \N t \N \N \N {} +4040 1336 2025-03-01 05:35:17.533391-08 2025-03-01 05:00:53.904121-08 benchmark t A100 \N t \N \N \N {} +4041 1336 2025-03-01 04:42:43.164597-08 2025-03-01 05:32:09.887961-08 leaderboard t A100 0.003087946 t \N \N \N {} +4043 1336 2025-03-01 05:22:32.085041-08 2025-03-01 05:29:36.766261-08 benchmark f A100 \N t \N \N \N {} +4044 1336 2025-03-01 04:46:43.10424-08 2025-03-01 05:19:36.970936-08 leaderboard f A100 0.0030770363333333336 t \N \N \N {} +4047 1337 2025-03-01 06:26:58.612027-08 2025-03-01 05:02:55.827739-08 leaderboard t A100 0.0030799923333333333 t \N \N \N {} +4048 1337 2025-03-01 04:51:49.552059-08 2025-03-01 05:32:32.010537-08 test f A100 \N t \N \N \N {} +4049 1337 2025-03-01 06:28:22.821883-08 2025-03-01 05:36:52.041982-08 benchmark f A100 \N t \N \N \N {} +4050 1337 2025-03-01 06:44:28.006933-08 2025-03-01 06:34:44.60449-08 leaderboard f A100 0.0030728746666666665 t \N \N \N {} +1733 581 2025-02-25 09:41:55.102032-08 2025-02-25 09:45:47.156909-08 test t T4 \N f \N \N \N {} +1739 583 2025-02-25 08:29:33.716534-08 2025-02-25 09:20:58.544378-08 benchmark f H100 \N t \N \N \N {} +1740 583 2025-02-25 09:22:37.035257-08 2025-02-25 08:36:17.972523-08 leaderboard f H100 0.00007956952 t \N \N \N {} +1747 587 2025-02-25 10:20:06.796411-08 2025-02-25 09:35:43.20184-08 test f T4 \N f \N \N \N {} +1750 589 2025-02-25 09:17:20.905239-08 2025-02-25 08:39:09.677788-08 test f A100 \N f \N \N \N {} +1754 593 2025-02-25 10:35:01.237155-08 2025-02-25 09:42:17.876251-08 benchmark f H100 \N f \N \N \N {} +2232 731 2025-02-25 20:55:56.356497-08 2025-02-25 21:54:01.459326-08 test f H100 \N f \N \N \N {} +1752 590 2025-02-25 09:26:44.50262-08 2025-02-25 09:02:57.304272-08 test f T4 \N t \N \N \N {} +1753 592 2025-02-25 10:16:57.848423-08 2025-02-25 09:02:46.758153-08 benchmark f H100 \N f \N \N \N {} +1755 594 2025-02-25 09:52:50.948573-08 2025-02-25 09:17:35.552434-08 test t T4 \N t \N \N \N {} +4051 1338 2025-03-01 04:53:46.779577-08 2025-03-01 05:51:05.617144-08 test t A100 \N t \N \N \N {} +4052 1338 2025-03-01 05:14:44.787303-08 2025-03-01 05:10:58.544283-08 benchmark t A100 \N t \N \N \N {} +4056 1338 2025-03-01 06:06:59.390985-08 2025-03-01 05:45:01.431744-08 leaderboard f A100 0.003124134 t \N \N \N {} +4057 1339 2025-03-01 05:45:53.840411-08 2025-03-01 04:57:01.712759-08 test f A100 \N t \N \N \N {} +4058 1339 2025-03-01 05:12:08.845443-08 2025-03-01 06:00:32.90691-08 benchmark f A100 \N t \N \N \N {} +4059 1339 2025-03-01 06:17:04.287969-08 2025-03-01 06:40:13.98261-08 leaderboard f A100 0.0030740783333333336 t \N \N \N {} +4060 1339 2025-03-01 05:30:41.282216-08 2025-03-01 06:19:05.26954-08 test t A100 \N t \N \N \N {} +4826 1490 2025-03-02 13:31:40.085228-08 2025-03-02 14:56:41.325187-08 test t A100 \N t \N \N \N {} +4069 1341 2025-03-01 06:46:40.043845-08 2025-03-01 05:08:54.482442-08 test t A100 \N t \N \N \N {} +4070 1341 2025-03-01 06:53:39.351776-08 2025-03-01 05:56:37.814647-08 benchmark t A100 \N t \N \N \N {} +4071 1341 2025-03-01 05:41:03.872073-08 2025-03-01 04:57:16.263647-08 leaderboard t A100 0.003135153 t \N \N \N {} +4072 1341 2025-03-01 06:36:12.592251-08 2025-03-01 05:33:09.049244-08 test f A100 \N t \N \N \N {} +4073 1341 2025-03-01 04:57:36.121576-08 2025-03-01 06:04:24.508424-08 benchmark f A100 \N t \N \N \N {} +4074 1341 2025-03-01 06:20:16.915193-08 2025-03-01 05:10:41.943093-08 leaderboard f A100 0.0031289636666666665 t \N \N \N {} +4079 1342 2025-03-01 06:28:17.155594-08 2025-03-01 05:18:07.512479-08 benchmark t A100 \N t \N \N \N {} +4080 1342 2025-03-01 05:17:17.167143-08 2025-03-01 06:19:18.451357-08 leaderboard t A100 0.003087757 t \N \N \N {} +1762 596 2025-02-25 09:48:18.872842-08 2025-02-25 09:08:03.874583-08 benchmark f H100 \N f \N \N \N {} +1763 596 2025-02-25 09:26:41.784533-08 2025-02-25 08:49:26.718913-08 benchmark f A100 \N f \N \N \N {} +1765 598 2025-02-25 10:31:00.584025-08 2025-02-25 09:31:07.814689-08 test f T4 \N f \N \N \N {} +4081 1343 2025-03-01 05:02:29.799238-08 2025-03-01 05:20:01.319572-08 test t A100 \N t \N \N \N {} +4082 1343 2025-03-01 06:56:56.021121-08 2025-03-01 05:37:48.321713-08 benchmark t A100 \N t \N \N \N {} +4083 1343 2025-03-01 05:51:20.985976-08 2025-03-01 06:10:38.11579-08 leaderboard t A100 0.003137047 t \N \N \N {} +4087 1344 2025-03-01 06:50:56.204942-08 2025-03-01 06:57:42.428539-08 test f A100 \N t \N \N \N {} +4088 1344 2025-03-01 05:59:31.101205-08 2025-03-01 05:10:00.245441-08 benchmark f A100 \N t \N \N \N {} +4089 1344 2025-03-01 06:31:19.255093-08 2025-03-01 05:37:19.624461-08 leaderboard f A100 0.0031286543333333334 t \N \N \N {} +4090 1344 2025-03-01 05:39:26.723837-08 2025-03-01 05:18:13.835391-08 test t A100 \N t \N \N \N {} +4091 1344 2025-03-01 05:16:22.102322-08 2025-03-01 05:57:13.821456-08 benchmark t A100 \N t \N \N \N {} +4092 1344 2025-03-01 06:48:00.846042-08 2025-03-01 06:36:07.876646-08 leaderboard t A100 0.0031320843333333334 t \N \N \N {} +4095 1346 2025-03-01 06:54:50.741501-08 2025-03-01 06:37:05.660285-08 test f A100 \N t \N \N \N {} +4096 1346 2025-03-01 06:43:56.667076-08 2025-03-01 05:03:01.487169-08 benchmark f A100 \N t \N \N \N {} +4097 1346 2025-03-01 06:20:12.926762-08 2025-03-01 05:15:59.317062-08 leaderboard f A100 0.0030682336666666664 t \N \N \N {} +4098 1346 2025-03-01 06:31:50.959915-08 2025-03-01 06:24:17.817323-08 test t A100 \N t \N \N \N {} +4099 1346 2025-03-01 05:34:30.160891-08 2025-03-01 05:36:09.807594-08 benchmark t A100 \N t \N \N \N {} +4100 1346 2025-03-01 06:40:10.997004-08 2025-03-01 06:45:08.049407-08 leaderboard t A100 0.003080214 t \N \N \N {} +4103 1348 2025-03-01 06:49:47.231825-08 2025-03-01 05:57:18.775954-08 test f A100 \N t \N \N \N {} +4104 1348 2025-03-01 06:30:42.302161-08 2025-03-01 06:25:51.821249-08 benchmark f A100 \N t \N \N \N {} +4105 1348 2025-03-01 05:19:35.876581-08 2025-03-01 05:20:21.682221-08 leaderboard f A100 0.0030814403333333336 t \N \N \N {} +4106 1348 2025-03-01 06:43:59.230765-08 2025-03-01 06:48:14.067513-08 test t A100 \N t \N \N \N {} +4107 1348 2025-03-01 06:33:05.642007-08 2025-03-01 05:34:35.401784-08 benchmark t A100 \N t \N \N \N {} +4108 1348 2025-03-01 06:33:53.834077-08 2025-03-01 06:05:52.452967-08 leaderboard t A100 0.0030692503333333335 t \N \N \N {} +4112 1349 2025-03-01 05:20:23.470116-08 2025-03-01 06:42:43.607444-08 test f A100 \N t \N \N \N {} +4113 1349 2025-03-01 06:18:41.622091-08 2025-03-01 06:59:47.322159-08 benchmark f A100 \N t \N \N \N {} +4114 1349 2025-03-01 06:55:36.386367-08 2025-03-01 06:13:36.564538-08 leaderboard f A100 0.0030793546666666666 t \N \N \N {} +4115 1350 2025-03-01 06:47:22.861306-08 2025-03-01 06:36:04.525113-08 test f A100 \N t \N \N \N {} +4116 1350 2025-03-01 05:51:42.091206-08 2025-03-01 06:44:09.234411-08 benchmark f A100 \N t \N \N \N {} +4117 1350 2025-03-01 07:00:25.183656-08 2025-03-01 06:56:10.192153-08 leaderboard f A100 0.003075028 t \N \N \N {} +4121 1351 2025-03-01 07:03:16.315538-08 2025-03-01 05:35:39.265352-08 test f A100 \N t \N \N \N {} +4122 1351 2025-03-01 06:26:56.34005-08 2025-03-01 06:15:23.856925-08 benchmark f A100 \N t \N \N \N {} +4123 1351 2025-03-01 06:27:06.070522-08 2025-03-01 05:20:19.58787-08 leaderboard f A100 0.003077125 t \N \N \N {} +4124 1351 2025-03-01 06:56:18.609617-08 2025-03-01 06:45:35.932387-08 test t A100 \N t \N \N \N {} +4125 1351 2025-03-01 05:59:31.787912-08 2025-03-01 05:12:15.522556-08 benchmark t A100 \N t \N \N \N {} +4127 1352 2025-03-01 06:15:58.415656-08 2025-03-01 05:15:59.770051-08 test t A100 \N t \N \N \N {} +4128 1352 2025-03-01 05:38:07.3835-08 2025-03-01 05:42:40.878001-08 benchmark t A100 \N t \N \N \N {} +4129 1352 2025-03-01 06:36:57.941443-08 2025-03-01 06:23:59.790542-08 leaderboard t A100 0.0030807613333333336 t \N \N \N {} +4130 1352 2025-03-01 06:43:58.284545-08 2025-03-01 05:17:08.992478-08 test f A100 \N t \N \N \N {} +4131 1352 2025-03-01 05:18:15.942457-08 2025-03-01 06:36:47.992588-08 benchmark f A100 \N t \N \N \N {} +4132 1352 2025-03-01 05:45:57.090043-08 2025-03-01 05:13:09.294944-08 leaderboard f A100 0.003084765 t \N \N \N {} +4133 1353 2025-03-01 05:45:21.57909-08 2025-03-01 05:50:08.24199-08 test t A100 \N t \N \N \N {} +4142 1354 2025-03-01 05:19:50.397498-08 2025-03-01 06:24:38.013703-08 test t A100 \N t \N \N \N {} +1767 600 2025-02-25 09:57:39.280293-08 2025-02-25 09:01:18.92218-08 benchmark f H100 \N t \N \N \N {} +1769 602 2025-02-25 09:26:39.998077-08 2025-02-25 10:45:34.040607-08 benchmark f H100 \N t \N \N \N {} +1770 603 2025-02-25 09:14:10.5262-08 2025-02-25 10:34:36.545027-08 benchmark f H100 \N t \N \N \N {} +1771 604 2025-02-25 09:18:20.318954-08 2025-02-25 09:47:55.570928-08 test t H100 \N t \N \N \N {} +4141 1354 2025-03-01 05:54:11.96859-08 2025-03-01 06:24:29.905592-08 leaderboard f A100 0.0030812046666666665 t \N \N \N {} +4145 1355 2025-03-01 05:52:29.99551-08 2025-03-01 05:50:58.136374-08 test f A100 \N t \N \N \N {} +4146 1355 2025-03-01 06:50:16.294841-08 2025-03-01 06:10:42.413759-08 benchmark f A100 \N t \N \N \N {} +4147 1355 2025-03-01 06:54:23.821235-08 2025-03-01 07:02:38.84073-08 leaderboard f A100 0.00309179 t \N \N \N {} +4148 1355 2025-03-01 05:36:48.581155-08 2025-03-01 06:55:13.189694-08 test t A100 \N t \N \N \N {} +4149 1355 2025-03-01 06:20:18.889242-08 2025-03-01 06:29:20.71451-08 benchmark t A100 \N t \N \N \N {} +4150 1355 2025-03-01 05:46:07.870361-08 2025-03-01 06:38:31.9395-08 leaderboard t A100 0.0030817543333333335 t \N \N \N {} +4151 1356 2025-03-01 07:33:56.778034-08 2025-03-01 06:51:39.921874-08 test f A100 \N f \N \N \N {} +4154 1359 2025-03-01 09:55:27.574174-08 2025-03-01 08:42:50.314131-08 benchmark f A100 \N t \N \N \N {} +4155 1359 2025-03-01 09:47:12.768835-08 2025-03-01 09:52:39.842141-08 benchmark f H100 \N t \N \N \N {} +4156 1360 2025-03-01 09:34:23.502047-08 2025-03-01 09:57:57.454449-08 benchmark f A100 \N t \N \N \N {} +4157 1360 2025-03-01 08:24:34.434752-08 2025-03-01 08:43:59.423015-08 benchmark f H100 \N t \N \N \N {} +4158 1361 2025-03-01 09:17:52.106156-08 2025-03-01 10:06:57.89908-08 test t H100 \N t \N \N \N {} +4159 1361 2025-03-01 09:16:48.1668-08 2025-03-01 08:15:25.825071-08 benchmark t H100 \N t \N \N \N {} +4160 1361 2025-03-01 09:01:03.843546-08 2025-03-01 09:36:32.754952-08 leaderboard t H100 0.0014479743333333333 t \N \N \N {} +4161 1361 2025-03-01 08:25:39.569213-08 2025-03-01 08:40:52.057697-08 test f H100 \N t \N \N \N {} +4162 1361 2025-03-01 08:47:33.253096-08 2025-03-01 09:19:00.121841-08 benchmark f H100 \N t \N \N \N {} +4163 1361 2025-03-01 09:21:19.831956-08 2025-03-01 09:37:55.855839-08 leaderboard f H100 0.001452041 t \N \N \N {} +4164 1363 2025-03-01 08:46:09.799691-08 2025-03-01 10:24:44.599745-08 benchmark f H100 \N t \N \N \N {} +4211 1390 2025-03-01 15:10:09.501721-08 2025-03-01 15:41:51.166433-08 benchmark f A100 \N t \N \N \N {} +4168 1364 2025-03-01 09:03:53.682811-08 2025-03-01 10:14:06.09417-08 leaderboard t A100 0.0031122263333333336 t \N \N \N {} +4169 1364 2025-03-01 09:49:16.863307-08 2025-03-01 10:08:01.584412-08 test f A100 \N t \N \N \N {} +1772 604 2025-02-25 10:42:51.901096-08 2025-02-25 10:19:33.430206-08 benchmark t H100 \N t \N \N \N {} +1773 604 2025-02-25 09:32:09.301008-08 2025-02-25 10:21:06.499152-08 leaderboard t H100 0.006142186 t \N \N \N {} +1774 604 2025-02-25 10:43:27.145802-08 2025-02-25 09:56:58.387594-08 test f H100 \N t \N \N \N {} +1775 604 2025-02-25 10:39:50.506911-08 2025-02-25 09:47:48.72776-08 benchmark f H100 \N t \N \N \N {} +1776 604 2025-02-25 09:10:42.599596-08 2025-02-25 09:30:41.654648-08 leaderboard f H100 0.006143060666666667 t \N \N \N {} +2414 816 2025-02-26 12:08:46.674357-08 2025-02-26 11:49:40.827855-08 benchmark f H100 \N f \N \N \N {} +1777 605 2025-02-25 10:52:46.64778-08 2025-02-25 09:29:41.278017-08 benchmark f H100 \N t \N \N \N {} +4213 1391 2025-03-01 15:19:09.143259-08 2025-03-01 14:01:35.420716-08 benchmark f H100 \N f \N \N \N {} +1778 605 2025-02-25 10:28:01.040475-08 2025-02-25 09:43:43.248513-08 benchmark f A100 \N t \N \N \N {} +1779 608 2025-02-25 10:43:12.211893-08 2025-02-25 10:07:05.017448-08 benchmark f H100 \N t \N \N \N {} +1780 609 2025-02-25 10:20:07.015553-08 2025-02-25 09:42:49.0735-08 benchmark f H100 \N t \N \N \N {} +2233 732 2025-02-25 22:08:54.161836-08 2025-02-25 22:20:50.391923-08 test f H100 \N t \N \N \N {} +1781 610 2025-02-25 10:34:24.601084-08 2025-02-25 10:19:19.681462-08 benchmark f H100 \N f \N \N \N {} +4677 1467 2025-03-02 05:46:48.283452-08 2025-03-02 06:33:48.786791-08 test f A100 \N t \N \N \N {} +1782 611 2025-02-25 10:20:28.964054-08 2025-02-25 09:52:02.934599-08 benchmark f H100 \N t \N \N \N {} +1791 616 2025-02-25 10:57:56.194437-08 2025-02-25 11:14:23.934399-08 benchmark t A100 \N t \N \N \N {} +1797 616 2025-02-25 10:18:29.893893-08 2025-02-25 10:05:32.012644-08 benchmark t T4 \N t \N \N \N {} +1802 616 2025-02-25 10:35:10.93135-08 2025-02-25 10:50:54.582852-08 test t H100 \N t \N \N \N {} +1807 616 2025-02-25 10:38:00.594029-08 2025-02-25 09:44:40.215164-08 leaderboard f L4 0.0002189721 t \N \N \N {} +1808 616 2025-02-25 10:30:31.534914-08 2025-02-25 10:05:10.928409-08 test t L4 \N t \N \N \N {} +1809 616 2025-02-25 10:42:40.135466-08 2025-02-25 10:28:37.438567-08 benchmark t L4 \N t \N \N \N {} +1813 619 2025-02-25 10:40:45.957535-08 2025-02-25 10:54:37.204319-08 benchmark f H100 \N t \N \N \N {} +1814 620 2025-02-25 10:47:50.841428-08 2025-02-25 09:37:03.436577-08 test t A100 \N t \N \N \N {} +1815 620 2025-02-25 11:04:50.249753-08 2025-02-25 09:57:25.703303-08 benchmark t A100 \N t \N \N \N {} +1820 621 2025-02-25 10:25:46.173072-08 2025-02-25 11:00:06.872432-08 test f A100 \N t \N \N \N {} +1825 621 2025-02-25 10:28:18.242991-08 2025-02-25 10:32:06.378285-08 leaderboard f H100 0.00006850659090909092 t \N \N \N {} +1826 621 2025-02-25 09:41:56.614729-08 2025-02-25 10:07:25.694167-08 test t A100 \N t \N \N \N {} +1827 621 2025-02-25 09:37:04.460998-08 2025-02-25 09:48:35.57183-08 benchmark t A100 \N t \N \N \N {} +1828 621 2025-02-25 10:08:41.300361-08 2025-02-25 11:30:23.969988-08 leaderboard t A100 0.00012062903571428571 t \N \N \N {} +1830 621 2025-02-25 09:45:11.698381-08 2025-02-25 10:09:44.153963-08 benchmark t H100 \N t \N \N \N {} +1838 621 2025-02-25 10:17:56.497108-08 2025-02-25 10:28:25.597743-08 test f L4 \N t \N \N \N {} +1841 621 2025-02-25 10:04:54.287311-08 2025-02-25 10:49:27.389838-08 test t L4 \N t \N \N \N {} +1842 621 2025-02-25 10:15:18.832896-08 2025-02-25 09:43:06.885167-08 benchmark t L4 \N t \N \N \N {} +1843 621 2025-02-25 11:14:37.289396-08 2025-02-25 09:37:11.924231-08 leaderboard t L4 0.00015086871428571428 t \N \N \N {} +1846 624 2025-02-25 10:33:21.190201-08 2025-02-25 11:11:47.766913-08 benchmark f A100 \N t \N \N \N {} +1847 625 2025-02-25 11:32:53.839235-08 2025-02-25 11:35:34.878827-08 benchmark f H100 \N t \N \N \N {} +1848 626 2025-02-25 10:29:31.639414-08 2025-02-25 09:50:52.046112-08 test f H100 \N t \N \N \N {} +1849 626 2025-02-25 10:56:50.659567-08 2025-02-25 11:09:07.456975-08 benchmark f H100 \N t \N \N \N {} +1850 626 2025-02-25 11:01:56.450365-08 2025-02-25 11:13:04.781975-08 leaderboard f H100 0.0014884195 t \N \N \N {} +3801 1281 2025-03-01 02:21:25.649213-08 2025-03-01 03:35:34.124629-08 test f A100 \N t \N \N \N {} +1852 626 2025-02-25 09:52:11.744323-08 2025-02-25 10:17:21.865184-08 benchmark t H100 \N t \N \N \N {} +1853 626 2025-02-25 11:11:09.345105-08 2025-02-25 09:56:58.318051-08 leaderboard t H100 0.0015167958125 t \N \N \N {} +1864 629 2025-02-25 10:03:17.408394-08 2025-02-25 10:31:15.377248-08 test t T4 \N f \N \N \N {} +1865 630 2025-02-25 10:33:02.54222-08 2025-02-25 09:57:27.268701-08 benchmark f A100 \N t \N \N \N {} +1866 631 2025-02-25 11:40:59.404486-08 2025-02-25 11:06:13.593002-08 benchmark f A100 \N t \N \N \N {} +1867 632 2025-02-25 10:30:21.975844-08 2025-02-25 10:10:22.159184-08 benchmark f A100 \N t \N \N \N {} +4171 1364 2025-03-01 09:02:25.076208-08 2025-03-01 09:47:26.827613-08 leaderboard f A100 0.003101713 t \N \N \N {} +4172 1365 2025-03-01 10:16:31.355759-08 2025-03-01 09:11:20.987034-08 benchmark f A100 \N t \N \N \N {} +4173 1366 2025-03-01 10:41:44.391735-08 2025-03-01 10:08:59.397316-08 test t A100 \N t \N \N \N {} +4174 1366 2025-03-01 10:41:36.980935-08 2025-03-01 10:58:56.581662-08 benchmark t A100 \N t \N \N \N {} +4686 1469 2025-03-02 07:07:48.25556-08 2025-03-02 07:20:29.156987-08 test f A100 \N t \N \N \N {} +4177 1366 2025-03-01 10:12:47.989298-08 2025-03-01 09:39:27.09578-08 benchmark f A100 \N t \N \N \N {} +1868 633 2025-02-25 11:43:07.576973-08 2025-02-25 10:22:50.962065-08 benchmark f A100 \N t \N \N \N {} +2454 860 2025-02-26 14:41:46.094612-08 2025-02-26 14:44:15.460594-08 test f A100 \N t \N \N \N {} +1869 634 2025-02-25 10:28:18.751593-08 2025-02-25 11:15:24.986518-08 benchmark f A100 \N t \N \N \N {} +1870 635 2025-02-25 11:53:39.07753-08 2025-02-25 11:27:22.45986-08 test f A100 \N t \N \N \N {} +1871 635 2025-02-25 11:53:25.443777-08 2025-02-25 10:35:30.145788-08 benchmark f A100 \N t \N \N \N {} +1874 635 2025-02-25 11:16:09.682168-08 2025-02-25 11:49:38.387512-08 benchmark t A100 \N t \N \N \N {} +1876 636 2025-02-25 11:49:31.647654-08 2025-02-25 11:20:21.285756-08 test f H100 \N t \N \N \N {} +1882 637 2025-02-25 11:02:50.678205-08 2025-02-25 11:53:07.5339-08 test f A100 \N t \N \N \N {} +1883 637 2025-02-25 12:15:08.69237-08 2025-02-25 12:05:12.143404-08 benchmark f A100 \N t \N \N \N {} +1884 637 2025-02-25 12:15:07.966893-08 2025-02-25 12:16:54.89578-08 leaderboard f A100 0.003200573 t \N \N \N {} +2419 822 2025-02-26 13:19:13.535475-08 2025-02-26 13:18:00.548275-08 test f T4 \N f \N \N \N {} +1889 639 2025-02-25 13:15:11.381768-08 2025-02-25 11:52:40.943412-08 benchmark f A100 \N t \N \N \N {} +1890 640 2025-02-25 13:03:11.780508-08 2025-02-25 11:39:48.194084-08 benchmark f A100 \N t \N \N \N {} +1892 641 2025-02-25 12:41:04.706804-08 2025-02-25 12:31:14.054076-08 benchmark t A100 \N t \N \N \N {} +1893 641 2025-02-25 12:48:58.704728-08 2025-02-25 12:33:09.050832-08 leaderboard t A100 0.003076926 t \N \N \N {} +1894 641 2025-02-25 13:10:49.145744-08 2025-02-25 11:53:53.607428-08 test f A100 \N t \N \N \N {} +1899 644 2025-02-25 12:19:53.732245-08 2025-02-25 13:40:01.838553-08 benchmark f A100 \N t \N \N \N {} +1900 646 2025-02-25 12:52:16.315942-08 2025-02-25 12:55:52.154965-08 test f T4 \N f \N \N \N {} +1901 647 2025-02-25 12:46:38.219456-08 2025-02-25 13:22:45.626265-08 benchmark f A100 \N t \N \N \N {} +1913 658 2025-02-25 13:14:34.483591-08 2025-02-25 12:48:18.641973-08 leaderboard f L4 0.017395948 t \N \N \N {} +1902 648 2025-02-25 13:22:19.470394-08 2025-02-25 13:20:10.149185-08 benchmark f A100 \N t \N \N \N {} +1903 649 2025-02-25 13:39:40.813885-08 2025-02-25 13:36:01.166239-08 benchmark f A100 \N t \N \N \N {} +1917 659 2025-02-25 12:18:43.146652-08 2025-02-25 13:44:37.824554-08 test t H100 \N t \N \N \N {} +1918 659 2025-02-25 12:38:33.511656-08 2025-02-25 12:09:10.447116-08 benchmark t H100 \N t \N \N \N {} +1919 659 2025-02-25 12:30:56.836878-08 2025-02-25 12:57:56.451178-08 leaderboard t H100 0.0014114212 t \N \N \N {} +1920 659 2025-02-25 13:27:03.426084-08 2025-02-25 14:05:30.708212-08 test f H100 \N t \N \N \N {} +1921 659 2025-02-25 13:32:15.353997-08 2025-02-25 13:54:42.055867-08 benchmark f H100 \N t \N \N \N {} +1922 659 2025-02-25 13:10:35.530509-08 2025-02-25 13:20:46.182913-08 leaderboard f H100 0.00142643575 t \N \N \N {} +3807 1283 2025-03-01 03:58:57.932103-08 2025-03-01 02:04:21.585952-08 test t A100 \N t \N \N \N {} +4178 1366 2025-03-01 09:15:06.396332-08 2025-03-01 09:50:59.628509-08 leaderboard f A100 0.0031052183333333335 t \N \N \N {} +4179 1367 2025-03-01 10:00:51.737363-08 2025-03-01 10:00:20.686025-08 benchmark f A100 \N t \N \N \N {} +4245 1401 2025-03-01 17:40:55.269982-08 2025-03-01 17:02:11.828026-08 benchmark f H100 \N t \N \N \N {} +4180 1368 2025-03-01 09:53:14.622803-08 2025-03-01 10:45:13.461477-08 benchmark f A100 \N t \N \N \N {} +4181 1369 2025-03-01 10:19:49.557836-08 2025-03-01 10:31:18.303541-08 benchmark f A100 \N t \N \N \N {} +4182 1370 2025-03-01 10:05:46.891703-08 2025-03-01 10:47:36.638039-08 benchmark f A100 \N t \N \N \N {} +4183 1371 2025-03-01 10:09:50.741844-08 2025-03-01 09:32:36.999656-08 benchmark f A100 \N t \N \N \N {} +4246 1401 2025-03-01 16:27:23.159511-08 2025-03-01 16:38:26.273265-08 benchmark f A100 \N t \N \N \N {} +4186 1374 2025-03-01 10:29:07.179112-08 2025-03-01 10:42:10.711955-08 benchmark f A100 \N f \N \N \N {} +4187 1375 2025-03-01 10:45:16.587627-08 2025-03-01 10:28:33.902324-08 benchmark f A100 \N f \N \N \N {} +4188 1376 2025-03-01 12:01:30.707689-08 2025-03-01 11:24:17.478908-08 test f A100 \N t \N \N \N {} +4189 1377 2025-03-01 12:12:57.585435-08 2025-03-01 11:53:33.240089-08 benchmark f A100 \N t \N \N \N {} +4190 1379 2025-03-01 12:16:36.769477-08 2025-03-01 13:18:08.829651-08 test f A100 \N t \N \N \N {} +4199 1388 2025-03-01 12:52:38.473441-08 2025-03-01 13:32:16.90025-08 test f A100 \N t \N \N \N {} +4200 1388 2025-03-01 13:20:31.349585-08 2025-03-01 12:39:30.194675-08 benchmark f A100 \N t \N \N \N {} +4201 1388 2025-03-01 13:30:38.204623-08 2025-03-01 12:55:51.306591-08 leaderboard f A100 0.4955258506666667 t \N \N \N {} +4805 1487 2025-03-02 12:59:51.989286-08 2025-03-02 12:35:17.947042-08 benchmark t T4 \N f \N \N \N {} +4202 1388 2025-03-01 12:42:31.113413-08 2025-03-01 13:17:21.163086-08 test t A100 \N t \N \N \N {} +4207 1389 2025-03-01 13:01:25.628609-08 2025-03-01 14:02:58.147439-08 leaderboard f H100 0.3205130943333333 t \N \N \N {} +4208 1389 2025-03-01 12:35:43.992201-08 2025-03-01 13:54:37.702884-08 test t H100 \N t \N \N \N {} +4209 1389 2025-03-01 13:46:31.426364-08 2025-03-01 14:02:32.562267-08 benchmark t H100 \N t \N \N \N {} +4210 1389 2025-03-01 12:35:26.710521-08 2025-03-01 13:30:46.516965-08 leaderboard t H100 0.31866500166666667 t \N \N \N {} +4212 1390 2025-03-01 14:35:10.227405-08 2025-03-01 15:03:07.466266-08 benchmark f H100 \N f \N \N \N {} +4214 1392 2025-03-01 15:54:46.023585-08 2025-03-01 15:50:44.87514-08 benchmark f A100 \N t \N \N \N {} +4215 1393 2025-03-01 15:05:15.318883-08 2025-03-01 15:06:05.433626-08 benchmark f A100 \N t \N \N \N {} +4216 1394 2025-03-01 16:24:31.141388-08 2025-03-01 16:30:42.940699-08 test f A100 \N t \N \N \N {} +4217 1394 2025-03-01 15:20:57.053534-08 2025-03-01 15:01:30.702462-08 benchmark f A100 \N t \N \N \N {} +2651 906 2025-02-26 14:32:29.620169-08 2025-02-26 14:03:45.689019-08 benchmark f A100 \N f \N \N \N {} +4773 1485 2025-03-02 12:07:24.955555-08 2025-03-02 12:12:01.099174-08 test t T4 \N t \N \N \N {} +4774 1485 2025-03-02 12:20:07.259834-08 2025-03-02 12:27:22.223302-08 benchmark t T4 \N t \N \N \N {} +4775 1485 2025-03-02 13:25:29.463588-08 2025-03-02 12:38:52.204635-08 leaderboard t T4 0.013066945 t \N \N \N {} +4776 1486 2025-03-02 13:33:31.030105-08 2025-03-02 12:15:35.189097-08 test t A100 \N t \N \N \N {} +4777 1486 2025-03-02 12:13:19.346928-08 2025-03-02 12:46:44.113151-08 benchmark t A100 \N f \N \N \N {} +4778 1486 2025-03-02 13:36:11.751592-08 2025-03-02 12:27:50.077759-08 test f A100 \N t \N \N \N {} +4779 1486 2025-03-02 13:14:12.733535-08 2025-03-02 13:18:37.639912-08 benchmark f A100 \N f \N \N \N {} +6738 2086 2025-03-15 03:23:36.712806-07 2025-03-15 04:23:06.297428-07 test f A100 \N t \N \N \N {} +4781 1486 2025-03-02 12:38:20.279542-08 2025-03-02 13:10:45.650515-08 benchmark t L4 \N t \N \N \N {} +1947 661 2025-02-25 13:28:58.74373-08 2025-02-25 13:47:27.943218-08 benchmark f H100 \N t \N \N \N {} +1949 663 2025-02-25 14:01:25.359523-08 2025-02-25 14:10:06.975147-08 benchmark f H100 \N t \N \N \N {} +1951 665 2025-02-25 12:52:25.628404-08 2025-02-25 13:09:46.048653-08 test f T4 \N t \N \N \N {} +1952 666 2025-02-25 13:17:01.172423-08 2025-02-25 13:04:42.018743-08 test f T4 \N t \N \N \N {} +5081 1553 2025-03-04 16:37:31.290979-08 2025-03-04 17:11:58.415963-08 leaderboard f H100 0.0014990966666666666 t \N \N \N {} +5082 1554 2025-03-04 17:14:21.826358-08 2025-03-04 18:23:09.521817-08 test f A100 \N f \N \N \N {} +5106 1565 2025-03-04 17:28:02.642565-08 2025-03-04 17:19:33.683442-08 leaderboard t A100 0.006331331333333333 t \N \N \N {} +5083 1555 2025-03-04 16:57:56.662828-08 2025-03-04 17:02:59.352083-08 test f A100 \N f \N \N \N {} +5258 1613 2025-03-06 07:52:29.561261-08 2025-03-06 07:25:55.418568-08 leaderboard t T4 0.016500428666666667 t \N \N \N {} +5120 1569 2025-03-04 23:12:31.354705-08 2025-03-04 21:49:58.854631-08 test t A100 \N t \N \N \N {} +5121 1569 2025-03-04 23:09:51.663868-08 2025-03-04 21:37:24.887769-08 benchmark t A100 \N t \N \N \N {} +5122 1569 2025-03-04 22:02:16.559653-08 2025-03-04 22:32:25.782751-08 leaderboard t A100 0.001608786 t \N \N \N {} +5123 1569 2025-03-04 22:05:47.583362-08 2025-03-04 21:33:49.87689-08 test t L4 \N t \N \N \N {} +5124 1569 2025-03-04 21:35:17.104808-08 2025-03-04 22:09:20.389921-08 benchmark t L4 \N t \N \N \N {} +5125 1569 2025-03-04 22:38:25.160967-08 2025-03-04 21:26:50.027334-08 leaderboard t L4 0.009029581666666666 t \N \N \N {} +5126 1569 2025-03-04 22:29:13.641854-08 2025-03-04 23:15:17.197089-08 test f H100 \N t \N \N \N {} +5127 1569 2025-03-04 22:01:12.379517-08 2025-03-04 23:10:54.429836-08 benchmark f H100 \N t \N \N \N {} +5128 1569 2025-03-04 23:11:31.285632-08 2025-03-04 21:42:40.185619-08 leaderboard f H100 0.0010705946666666668 t \N \N \N {} +5129 1569 2025-03-04 21:57:38.963122-08 2025-03-04 21:31:36.054672-08 test t H100 \N t \N \N \N {} +5130 1569 2025-03-04 21:27:09.873699-08 2025-03-04 22:20:40.268062-08 benchmark t H100 \N t \N \N \N {} +6778 2099 2025-03-15 03:30:57.66264-07 2025-03-15 04:43:08.964646-07 test f A100 \N t \N \N \N {} +5134 1569 2025-03-04 22:04:14.888682-08 2025-03-04 22:46:38.905995-08 leaderboard f T4 0.014848202 t \N \N \N {} +5135 1569 2025-03-04 22:03:01.49793-08 2025-03-04 22:36:42.090475-08 test t T4 \N t \N \N \N {} +5136 1569 2025-03-04 22:08:23.328887-08 2025-03-04 22:16:18.514342-08 benchmark t T4 \N t \N \N \N {} +5137 1569 2025-03-04 23:14:20.184391-08 2025-03-04 22:39:50.12814-08 leaderboard t T4 0.014794430666666665 t \N \N \N {} +5141 1570 2025-03-04 23:13:42.914895-08 2025-03-04 21:59:06.843057-08 test f A100 \N t \N \N \N {} +5142 1570 2025-03-04 21:36:21.746898-08 2025-03-04 22:16:20.82538-08 benchmark f A100 \N t \N \N \N {} +5143 1570 2025-03-04 22:33:56.277934-08 2025-03-04 22:10:31.797238-08 leaderboard f A100 0.0017140163333333332 t \N \N \N {} +5144 1570 2025-03-04 23:21:19.677221-08 2025-03-04 22:48:43.348393-08 test t A100 \N t \N \N \N {} +5145 1570 2025-03-04 23:16:49.330286-08 2025-03-04 22:14:55.550056-08 benchmark t A100 \N t \N \N \N {} +5146 1570 2025-03-04 22:23:09.667416-08 2025-03-04 21:43:24.622016-08 leaderboard t A100 0.0017186463333333333 t \N \N \N {} +5147 1570 2025-03-04 23:10:37.127588-08 2025-03-04 22:50:15.83577-08 test t H100 \N t \N \N \N {} +5148 1570 2025-03-04 22:15:05.089919-08 2025-03-04 21:59:44.284611-08 benchmark t H100 \N t \N \N \N {} +5149 1570 2025-03-04 22:40:39.695868-08 2025-03-04 22:51:12.192064-08 leaderboard t H100 0.0010933593333333333 t \N \N \N {} +5150 1570 2025-03-04 22:06:34.697175-08 2025-03-04 22:22:03.050409-08 test f H100 \N t \N \N \N {} +5151 1570 2025-03-04 23:14:32.375663-08 2025-03-04 23:16:06.022274-08 benchmark f H100 \N t \N \N \N {} +6781 2099 2025-03-15 04:17:18.910592-07 2025-03-15 04:27:25.440486-07 test t A100 \N t \N \N \N {} +5154 1571 2025-03-04 21:29:49.913428-08 2025-03-04 22:56:49.726281-08 benchmark t A100 \N t \N \N \N {} +5155 1571 2025-03-04 22:09:17.380828-08 2025-03-04 23:17:01.528387-08 leaderboard t A100 0.00149756 t \N \N \N {} +5156 1571 2025-03-04 22:42:35.691391-08 2025-03-04 22:30:28.371632-08 test f A100 \N t \N \N \N {} +1953 666 2025-02-25 13:11:27.663789-08 2025-02-25 12:56:49.679792-08 benchmark f T4 \N t \N \N \N {} +1954 666 2025-02-25 14:01:28.591074-08 2025-02-25 12:36:59.206687-08 leaderboard f T4 0.03533333425 t \N \N \N {} +1955 666 2025-02-25 13:24:34.96066-08 2025-02-25 12:23:48.496925-08 test t T4 \N t \N \N \N {} +1956 666 2025-02-25 13:11:21.334723-08 2025-02-25 13:18:01.451228-08 benchmark t T4 \N t \N \N \N {} +1957 666 2025-02-25 14:11:36.256788-08 2025-02-25 12:59:40.696196-08 leaderboard t T4 0.035418843285714284 t \N \N \N {} +1958 667 2025-02-25 13:33:38.816072-08 2025-02-25 13:27:18.187004-08 benchmark f A100 \N t \N \N \N {} +1975 679 2025-02-25 13:39:40.730805-08 2025-02-25 12:47:36.780104-08 leaderboard f T4 0.053832773 t \N \N \N {} +5170 1574 2025-03-04 22:47:12.679139-08 2025-03-04 22:51:50.789451-08 test t H100 \N f \N \N \N {} +5177 1575 2025-03-04 23:31:30.176049-08 2025-03-04 23:39:24.402655-08 benchmark t H100 \N t \N \N \N {} +5175 1575 2025-03-04 23:09:05.999145-08 2025-03-04 23:03:45.299121-08 leaderboard f H100 0.0010994093333333334 t \N \N \N {} +5176 1575 2025-03-04 21:58:21.866372-08 2025-03-04 23:01:50.540448-08 test t H100 \N t \N \N \N {} +5197 1589 2025-03-05 04:48:37.908985-08 2025-03-05 05:42:21.123456-08 test f H100 \N f \N \N \N {} +5178 1575 2025-03-04 23:49:34.473234-08 2025-03-04 22:47:13.576304-08 leaderboard t H100 0.001105298 t \N \N \N {} +5179 1576 2025-03-05 05:05:54.983075-08 2025-03-05 04:15:53.893439-08 benchmark f H100 \N t \N \N \N {} +1969 674 2025-02-25 14:18:09.782068-08 2025-02-25 12:29:36.512538-08 benchmark f A100 \N t \N \N \N {} +1971 675 2025-02-25 13:57:33.707833-08 2025-02-25 14:11:11.249992-08 benchmark f A100 \N t \N \N \N {} +1973 679 2025-02-25 13:04:53.521829-08 2025-02-25 14:18:59.793772-08 test f T4 \N t \N \N \N {} +1974 679 2025-02-25 14:13:13.965846-08 2025-02-25 13:57:26.833503-08 benchmark f T4 \N t \N \N \N {} +5180 1577 2025-03-05 03:52:35.002755-08 2025-03-05 04:21:45.713683-08 benchmark f H100 \N t \N \N \N {} +5181 1578 2025-03-05 05:17:31.803633-08 2025-03-05 05:50:42.297338-08 test t H100 \N t \N \N \N {} +5182 1578 2025-03-05 04:56:30.788431-08 2025-03-05 05:45:31.103522-08 benchmark t H100 \N t \N \N \N {} +5183 1578 2025-03-05 05:01:35.150294-08 2025-03-05 05:14:55.594706-08 leaderboard t H100 0.0014070166666666668 t \N \N \N {} +5184 1578 2025-03-05 05:28:07.773821-08 2025-03-05 05:16:37.68584-08 test f H100 \N t \N \N \N {} +5233 1611 2025-03-06 07:25:53.030783-08 2025-03-06 07:55:02.790842-08 test f T4 \N f \N \N \N {} +5190 1582 2025-03-05 04:44:16.876495-08 2025-03-05 04:31:50.278622-08 test f H100 \N f \N \N \N {} +5199 1591 2025-03-05 05:58:19.154295-08 2025-03-05 05:51:19.562708-08 test f H100 \N f \N \N \N {} +5200 1592 2025-03-05 06:51:32.869767-08 2025-03-05 07:03:41.586486-08 test f A100 \N f \N \N \N {} +5201 1593 2025-03-05 11:53:54.672796-08 2025-03-05 10:22:24.681764-08 test f T4 \N f \N \N \N {} +5202 1593 2025-03-05 11:34:29.199094-08 2025-03-05 11:32:56.415861-08 test f H100 \N f \N \N \N {} +5203 1594 2025-03-05 10:15:31.539292-08 2025-03-05 11:41:36.518701-08 test f T4 \N f \N \N \N {} +5204 1595 2025-03-05 11:50:17.097474-08 2025-03-05 10:50:25.088498-08 test f T4 \N f \N \N \N {} +5205 1596 2025-03-05 10:17:15.840178-08 2025-03-05 11:21:12.907749-08 test f T4 \N t \N \N \N {} +5206 1597 2025-03-05 10:38:59.781202-08 2025-03-05 11:53:31.710828-08 benchmark f T4 \N t \N \N \N {} +5207 1598 2025-03-05 15:43:10.18498-08 2025-03-05 15:44:26.256063-08 test f T4 \N f \N \N \N {} +5208 1599 2025-03-05 16:08:47.877222-08 2025-03-05 16:17:45.165244-08 test f T4 \N f \N \N \N {} +5215 1605 2025-03-05 22:28:57.064698-08 2025-03-05 22:38:29.672952-08 test t A100 \N t \N \N \N {} +5216 1605 2025-03-06 00:10:40.145439-08 2025-03-05 23:04:41.009576-08 benchmark t A100 \N t \N \N \N {} +5217 1605 2025-03-06 00:13:35.847747-08 2025-03-05 23:40:14.544266-08 leaderboard t A100 0.0022575486666666666 t \N \N \N {} +5244 1613 2025-03-06 07:19:13.628319-08 2025-03-06 06:35:37.599294-08 test f A100 \N t \N \N \N {} +5245 1613 2025-03-06 06:54:50.780479-08 2025-03-06 06:45:07.226442-08 benchmark f A100 \N t \N \N \N {} +5246 1613 2025-03-06 07:28:04.913981-08 2025-03-06 08:10:28.487649-08 leaderboard f A100 0.0025635133333333335 t \N \N \N {} +5276 1614 2025-03-06 07:19:59.675744-08 2025-03-06 07:43:31.134657-08 leaderboard f H100 0.00140798875 t \N \N \N {} +5277 1614 2025-03-06 08:41:38.090423-08 2025-03-06 08:37:03.818704-08 test t H100 \N t \N \N \N {} +5278 1614 2025-03-06 08:18:49.218286-08 2025-03-06 08:16:42.816157-08 benchmark t H100 \N t \N \N \N {} +5279 1614 2025-03-06 07:03:16.465007-08 2025-03-06 06:46:29.690376-08 leaderboard t H100 0.0014296095 t \N \N \N {} +5280 1615 2025-03-06 08:22:03.432718-08 2025-03-06 08:24:28.297651-08 test f A100 \N f \N \N \N {} +5281 1617 2025-03-06 12:18:02.879373-08 2025-03-06 11:00:55.592702-08 test f L4 \N f \N \N \N {} +5286 1616 2025-03-06 12:36:10.700755-08 2025-03-06 11:50:29.347443-08 test t T4 \N t \N \N \N {} +5287 1616 2025-03-06 12:25:41.61897-08 2025-03-06 11:16:54.998394-08 benchmark t T4 \N t \N \N \N {} +5312 1620 2025-03-06 12:33:59.587669-08 2025-03-06 11:40:59.729965-08 test f L4 \N t \N \N \N {} +5313 1620 2025-03-06 10:43:35.438642-08 2025-03-06 12:38:29.763377-08 benchmark f L4 \N t \N \N \N {} +5314 1620 2025-03-06 12:06:29.829934-08 2025-03-06 12:34:56.969836-08 leaderboard f L4 0.017135117666666668 t \N \N \N {} +1977 679 2025-02-25 12:51:05.905455-08 2025-02-25 12:53:16.176109-08 benchmark t T4 \N t \N \N \N {} +1978 679 2025-02-25 13:37:32.257654-08 2025-02-25 13:03:09.630302-08 leaderboard t T4 0.05386631333333334 t \N \N \N {} +3812 1283 2025-03-01 02:43:07.132486-08 2025-03-01 03:36:19.063234-08 test f A100 \N t \N \N \N {} +5584 1707 2025-03-09 09:22:46.945115-07 2025-03-09 08:37:58.159049-07 test t T4 \N t \N \N \N {} +5550 1693 2025-03-08 16:00:57.23753-08 2025-03-08 17:10:45.887607-08 test f H100 \N f \N \N \N {} +5551 1694 2025-03-08 17:58:29.601266-08 2025-03-08 17:28:23.434527-08 test t H100 \N f \N \N \N {} +5552 1694 2025-03-08 16:17:00.585648-08 2025-03-08 17:26:10.548997-08 test f H100 \N f \N \N \N {} +5553 1695 2025-03-08 17:31:35.366001-08 2025-03-08 16:43:40.087454-08 test t H100 \N f \N \N \N {} +5568 1702 2025-03-08 17:12:40.242062-08 2025-03-08 18:24:17.952735-08 test f H100 \N f \N \N \N {} +5582 1707 2025-03-09 08:41:44.387525-07 2025-03-09 09:18:33.589319-07 benchmark f T4 \N t \N \N \N {} +5557 1697 2025-03-08 18:09:07.446602-08 2025-03-08 16:49:50.71993-08 test f H100 \N f \N \N \N {} +5558 1697 2025-03-08 16:45:59.890505-08 2025-03-08 17:49:20.795913-08 test t H100 \N f \N \N \N {} +5559 1698 2025-03-08 16:44:33.058529-08 2025-03-08 16:55:51.404262-08 test t H100 \N f \N \N \N {} +5560 1698 2025-03-08 17:45:21.683334-08 2025-03-08 17:40:25.662216-08 test f H100 \N f \N \N \N {} +5561 1699 2025-03-08 17:23:07.204592-08 2025-03-08 16:29:40.8521-08 test f H100 \N f \N \N \N {} +5562 1699 2025-03-08 17:14:04.744552-08 2025-03-08 16:45:33.633138-08 test t H100 \N f \N \N \N {} +5565 1701 2025-03-08 17:19:00.706175-08 2025-03-08 16:50:25.370782-08 test t H100 \N f \N \N \N {} +5566 1701 2025-03-08 18:00:39.905743-08 2025-03-08 18:04:31.681329-08 test f H100 \N f \N \N \N {} +5583 1707 2025-03-09 10:07:02.952329-07 2025-03-09 08:33:06.895971-07 leaderboard f T4 0.016582775333333334 t \N \N \N {} +5569 1703 2025-03-08 17:21:28.286711-08 2025-03-08 17:59:25.372338-08 test f H100 \N f \N \N \N {} +5570 1703 2025-03-08 18:05:15.430458-08 2025-03-08 17:19:42.190919-08 test t H100 \N f \N \N \N {} +5571 1704 2025-03-09 09:07:05.945234-07 2025-03-09 09:40:55.738939-07 test f T4 \N f \N \N \N {} +5572 1704 2025-03-09 08:38:56.049572-07 2025-03-09 09:19:08.106745-07 test t T4 \N f \N \N \N {} +5573 1705 2025-03-09 09:08:47.834128-07 2025-03-09 08:44:55.26167-07 test t T4 \N f \N \N \N {} +6852 2125 2025-03-15 10:54:23.765567-07 2025-03-15 10:56:50.760245-07 test t A100 \N t \N \N \N {} +5574 1705 2025-03-09 08:19:09.822926-07 2025-03-09 08:17:05.627722-07 test f T4 \N f \N \N \N {} +5575 1706 2025-03-09 08:15:37.716285-07 2025-03-09 09:07:48.253727-07 test t T4 \N t \N \N \N {} +5576 1706 2025-03-09 09:03:21.820355-07 2025-03-09 09:23:13.212892-07 benchmark t T4 \N t \N \N \N {} +5577 1706 2025-03-09 09:24:37.224862-07 2025-03-09 09:06:00.619832-07 leaderboard t T4 0.017249751 t \N \N \N {} +5578 1706 2025-03-09 09:20:25.180918-07 2025-03-09 08:18:24.424228-07 test f T4 \N t \N \N \N {} +5579 1706 2025-03-09 08:21:53.936645-07 2025-03-09 08:29:28.573712-07 benchmark f T4 \N t \N \N \N {} +5580 1706 2025-03-09 08:30:06.61725-07 2025-03-09 09:22:13.972549-07 leaderboard f T4 0.017213831 t \N \N \N {} +6855 2126 2025-03-15 10:01:43.438198-07 2025-03-15 09:26:54.425537-07 test f A100 \N t \N \N \N {} +5586 1707 2025-03-09 08:33:05.304746-07 2025-03-09 08:45:18.547459-07 leaderboard t T4 0.016620549 t \N \N \N {} +2800 976 2025-02-26 21:36:15.417887-08 2025-02-26 21:39:28.329229-08 leaderboard f A100 0.0031626593333333335 t \N \N \N {} +5634 1718 2025-03-09 09:13:24.672764-07 2025-03-09 10:18:48.792588-07 test f T4 \N t \N \N \N {} +5635 1718 2025-03-09 10:16:44.041935-07 2025-03-09 09:08:48.252549-07 benchmark f T4 \N t \N \N \N {} +5641 1719 2025-03-09 10:29:41.062663-07 2025-03-09 10:42:23.185635-07 benchmark t T4 \N t \N \N \N {} +5642 1719 2025-03-09 10:03:38.725028-07 2025-03-09 10:55:41.903081-07 leaderboard t T4 0.01683973533333333 t \N \N \N {} +5643 1720 2025-03-09 09:57:20.289686-07 2025-03-09 10:34:13.152652-07 test f T4 \N t \N \N \N {} +5644 1720 2025-03-09 09:16:46.353693-07 2025-03-09 10:33:41.020996-07 benchmark f T4 \N t \N \N \N {} +5650 1721 2025-03-09 10:43:52.72403-07 2025-03-09 09:09:04.6489-07 benchmark t T4 \N t \N \N \N {} +5651 1721 2025-03-09 10:26:40.021059-07 2025-03-09 10:38:45.997727-07 leaderboard t T4 0.016620553333333333 t \N \N \N {} +5653 1722 2025-03-09 09:41:53.232041-07 2025-03-09 10:03:19.288174-07 benchmark t T4 \N t \N \N \N {} +5659 1723 2025-03-09 10:23:53.251832-07 2025-03-09 10:13:49.143494-07 benchmark f T4 \N t \N \N \N {} +2801 976 2025-02-26 21:49:50.078075-08 2025-02-26 21:02:25.183977-08 test t A100 \N t \N \N \N {} +6096 1844 2025-03-10 22:34:55.225767-07 2025-03-10 23:16:53.927941-07 leaderboard t A100 0.02345483125 t \N \N \N {} +6097 1845 2025-03-10 22:27:27.260452-07 2025-03-10 23:10:29.127667-07 test f H100 \N t \N \N \N {} +6098 1845 2025-03-10 22:33:31.470385-07 2025-03-10 23:33:08.346801-07 benchmark f H100 \N t \N \N \N {} +6099 1845 2025-03-10 23:19:50.390365-07 2025-03-10 23:55:52.423957-07 leaderboard f H100 0.007574126333333333 t \N \N \N {} +6100 1845 2025-03-10 23:23:59.756697-07 2025-03-10 22:36:01.810723-07 test t H100 \N t \N \N \N {} +6103 1846 2025-03-10 23:34:33.401447-07 2025-03-10 22:38:46.785595-07 test t L4 \N t \N \N \N {} +6104 1846 2025-03-10 22:40:13.180982-07 2025-03-10 22:03:25.587196-07 benchmark t L4 \N t \N \N \N {} +6105 1846 2025-03-10 22:07:31.471617-07 2025-03-10 22:36:23.340517-07 leaderboard t L4 0.289734965 t \N \N \N {} +6106 1846 2025-03-10 22:19:05.931425-07 2025-03-10 22:31:11.627074-07 test f L4 \N t \N \N \N {} +225 37 2025-02-23 10:59:25.499888-08 2025-02-23 11:56:22.371923-08 test f T4 \N t \N \N \N {} +332 80 2025-02-23 15:08:05.190515-08 2025-02-23 14:44:23.958644-08 test f H100 \N t \N \N \N {} +203 36 2025-02-23 11:46:05.335042-08 2025-02-23 12:11:22.163693-08 benchmark f H100 \N t \N \N \N {} +358 94 2025-02-23 18:49:28.760988-08 2025-02-23 19:12:31.385313-08 leaderboard f H100 0.012098538666666665 t \N \N \N {} +359 95 2025-02-23 17:48:36.820226-08 2025-02-23 19:17:34.067307-08 test f H100 \N t \N \N \N {} +1206 374 2025-02-24 20:28:11.986011-08 2025-02-24 19:36:30.92237-08 benchmark f A100 \N f \N \N \N {} +1207 375 2025-02-24 20:12:22.417421-08 2025-02-24 20:46:01.292845-08 benchmark f A100 \N f \N \N \N {} +1414 471 2025-02-25 03:42:52.544828-08 2025-02-25 04:20:26.74565-08 test f A100 \N t \N \N \N {} +1208 376 2025-02-24 19:48:12.808077-08 2025-02-24 20:45:57.879274-08 benchmark f A100 \N t \N \N \N {} +1209 377 2025-02-24 21:24:44.702336-08 2025-02-24 20:39:40.644905-08 benchmark f A100 \N f \N \N \N {} +1210 378 2025-02-24 21:34:59.47251-08 2025-02-24 21:22:02.439541-08 benchmark f A100 \N t \N \N \N {} +1211 379 2025-02-24 21:22:12.927474-08 2025-02-24 19:58:49.367481-08 benchmark f A100 \N t \N \N \N {} +1243 404 2025-02-24 22:06:51.766358-08 2025-02-24 22:05:43.599472-08 benchmark f A100 \N t \N \N \N {} +1216 384 2025-02-24 21:02:40.893555-08 2025-02-24 20:57:25.702865-08 benchmark f A100 \N f \N \N \N {} +1229 393 2025-02-24 20:52:22.785486-08 2025-02-24 21:45:46.13494-08 leaderboard t A100 0.0033359293333333337 t \N \N \N {} +1230 393 2025-02-24 21:16:21.658637-08 2025-02-24 20:25:01.054235-08 test f A100 \N t \N \N \N {} +1231 393 2025-02-24 22:03:36.537687-08 2025-02-24 21:08:30.465614-08 benchmark f A100 \N t \N \N \N {} +1240 401 2025-02-24 21:15:55.733324-08 2025-02-24 21:57:58.972958-08 test f A100 \N f \N \N \N {} +1241 402 2025-02-24 20:44:20.914092-08 2025-02-24 21:56:00.068563-08 test f A100 \N f \N \N \N {} +1248 409 2025-02-24 22:06:04.453015-08 2025-02-24 22:17:03.583941-08 test f A100 \N f \N \N \N {} +1417 471 2025-02-25 03:51:19.78766-08 2025-02-25 03:47:58.900665-08 test t A100 \N t \N \N \N {} +1249 410 2025-02-24 21:39:05.838922-08 2025-02-24 20:58:53.013953-08 test f A100 \N f \N \N \N {} +1250 411 2025-02-24 22:40:19.562086-08 2025-02-24 21:27:25.950857-08 test f A100 \N t \N \N \N {} +1404 469 2025-02-25 03:53:45.5055-08 2025-02-25 04:07:42.563768-08 leaderboard f A100 0.003393497 t \N \N \N {} +2922 1024 2025-02-27 04:31:40.23618-08 2025-02-27 02:56:46.161479-08 test f T4 \N t \N \N \N {} +2923 1024 2025-02-27 02:49:24.009049-08 2025-02-27 03:11:16.56453-08 benchmark f T4 \N t \N \N \N {} +2924 1024 2025-02-27 04:11:37.18876-08 2025-02-27 04:34:12.87404-08 leaderboard f T4 0.00039354559999999996 t \N \N \N {} +3121 1110 2025-02-28 03:11:43.352425-08 2025-02-28 01:42:01.408142-08 test f A100 \N t \N \N \N {} +3466 1192 2025-02-28 10:34:17.327676-08 2025-02-28 12:02:48.511506-08 test t A100 \N f \N \N \N {} +2926 1024 2025-02-27 03:07:44.576399-08 2025-02-27 04:23:59.379675-08 benchmark t T4 \N t \N \N \N {} +2927 1024 2025-02-27 03:35:18.806103-08 2025-02-27 04:01:32.334755-08 leaderboard t T4 0.000564142375 t \N \N \N {} +3124 1111 2025-02-28 03:22:39.920088-08 2025-02-28 03:21:57.983095-08 test f A100 \N t \N \N \N {} +3174 1119 2025-02-28 03:45:25.024721-08 2025-02-28 03:11:06.974641-08 leaderboard f A100 0.0031492663333333335 t \N \N \N {} +1409 470 2025-02-25 03:16:55.220474-08 2025-02-25 04:31:56.202607-08 benchmark t A100 \N t \N \N \N {} +1410 470 2025-02-25 04:11:58.013793-08 2025-02-25 03:21:32.360753-08 leaderboard t A100 0.0030943103333333334 t \N \N \N {} +2928 1025 2025-02-27 04:12:51.460546-08 2025-02-27 02:49:12.29781-08 test f L4 \N t \N \N \N {} +2929 1025 2025-02-27 03:26:34.775483-08 2025-02-27 04:12:58.357761-08 benchmark f L4 \N t \N \N \N {} +2930 1025 2025-02-27 03:19:52.147083-08 2025-02-27 03:27:21.649998-08 leaderboard f L4 0.00011601083783783784 t \N \N \N {} +3047 1093 2025-02-27 15:53:15.961217-08 2025-02-27 15:41:20.80553-08 benchmark t A100 \N t \N \N \N {} +3048 1093 2025-02-27 15:45:02.22711-08 2025-02-27 15:31:35.36741-08 leaderboard t A100 0.0031404533333333337 t \N \N \N {} +1433 475 2025-02-25 04:29:39.664763-08 2025-02-25 05:08:55.732423-08 test f A100 \N f \N \N \N {} +1436 477 2025-02-25 05:11:23.154044-08 2025-02-25 03:41:03.100879-08 test f A100 \N f \N \N \N {} +1437 478 2025-02-25 05:37:15.695274-08 2025-02-25 03:55:07.870743-08 test f A100 \N f \N \N \N {} +1441 481 2025-02-25 04:15:28.477993-08 2025-02-25 05:06:41.311478-08 test f A100 \N f \N \N \N {} +1442 481 2025-02-25 05:43:19.547838-08 2025-02-25 05:02:59.725963-08 test t A100 \N f \N \N \N {} +2941 1030 2025-02-27 04:33:34.388412-08 2025-02-27 03:16:04.017304-08 test t A100 \N t \N \N \N {} +3145 1114 2025-02-28 02:20:48.546337-08 2025-02-28 03:25:08.187009-08 test f A100 \N t \N \N \N {} +3002 1069 2025-02-27 11:57:26.315579-08 2025-02-27 12:26:49.008997-08 test f A100 \N f \N \N \N {} +3052 1094 2025-02-27 17:23:36.313978-08 2025-02-27 17:02:03.79401-08 test f A100 \N t \N \N \N {} +3004 1071 2025-02-27 12:13:32.872228-08 2025-02-27 13:48:22.621332-08 test f A100 \N f \N \N \N {} +3055 1094 2025-02-27 15:46:34.78066-08 2025-02-27 17:32:46.992293-08 test t A100 \N t \N \N \N {} +2951 1038 2025-02-27 08:08:02.949594-08 2025-02-27 07:45:52.672741-08 test f A100 \N t \N \N \N {} +1539 512 2025-02-25 07:42:24.70614-08 2025-02-25 07:55:31.991399-08 leaderboard t H100 0.0014827313333333333 t \N \N \N {} +1540 512 2025-02-25 06:33:27.810792-08 2025-02-25 07:40:47.978218-08 test f H100 \N t \N \N \N {} +1541 512 2025-02-25 07:57:36.75449-08 2025-02-25 07:43:37.029383-08 benchmark f H100 \N t \N \N \N {} +1542 512 2025-02-25 06:29:41.231954-08 2025-02-25 06:14:39.960451-08 leaderboard f H100 0.0014742606666666668 t \N \N \N {} +1543 512 2025-02-25 07:38:34.722488-08 2025-02-25 07:23:26.785114-08 test f T4 \N t \N \N \N {} +1544 512 2025-02-25 06:25:49.430545-08 2025-02-25 07:43:59.473865-08 benchmark f T4 \N t \N \N \N {} +1545 512 2025-02-25 07:44:49.156876-08 2025-02-25 07:09:53.612336-08 leaderboard f T4 0.021663530142857143 t \N \N \N {} +1546 512 2025-02-25 06:35:54.036675-08 2025-02-25 07:10:22.696453-08 test t T4 \N t \N \N \N {} +1561 514 2025-02-25 08:21:20.423985-08 2025-02-25 07:53:25.914837-08 test f A100 \N t \N \N \N {} +1564 514 2025-02-25 06:26:23.245478-08 2025-02-25 07:52:14.438492-08 test t A100 \N t \N \N \N {} +1551 512 2025-02-25 07:08:46.613751-08 2025-02-25 06:42:56.111896-08 leaderboard f L4 0.017496731666666668 t \N \N \N {} +1553 512 2025-02-25 06:58:42.387657-08 2025-02-25 08:00:31.070279-08 benchmark t L4 \N t \N \N \N {} +1554 512 2025-02-25 07:24:12.688624-08 2025-02-25 06:31:02.770404-08 leaderboard t L4 0.017451008666666667 t \N \N \N {} +3231 1130 2025-02-28 05:20:47.157439-08 2025-02-28 06:11:32.587664-08 leaderboard t A100 0.0031105466666666664 t \N \N \N {} +3091 1105 2025-02-28 02:11:54.38507-08 2025-02-28 02:14:17.819311-08 test f A100 \N t \N \N \N {} +3250 1137 2025-02-28 07:15:32.403653-08 2025-02-28 05:44:54.894859-08 test f A100 \N t \N \N \N {} +1567 515 2025-02-25 06:40:37.056239-08 2025-02-25 08:18:23.071234-08 test f A100 \N t \N \N \N {} +1568 515 2025-02-25 07:51:44.723769-08 2025-02-25 06:52:35.487372-08 benchmark f A100 \N t \N \N \N {} +1569 515 2025-02-25 08:12:57.053326-08 2025-02-25 07:23:24.825804-08 leaderboard f A100 0.00322647325 t \N \N \N {} +1570 515 2025-02-25 07:59:41.537278-08 2025-02-25 08:18:25.884866-08 test t A100 \N t \N \N \N {} +1571 515 2025-02-25 07:33:12.913858-08 2025-02-25 07:10:10.306586-08 benchmark t A100 \N t \N \N \N {} +1572 515 2025-02-25 07:41:19.245397-08 2025-02-25 07:29:16.962881-08 leaderboard t A100 0.003220757 t \N \N \N {} +1575 516 2025-02-25 07:41:18.473174-08 2025-02-25 07:29:29.868748-08 leaderboard t A100 0.0032304103333333336 t \N \N \N {} +1738 583 2025-02-25 09:31:47.936241-08 2025-02-25 09:08:32.636598-08 test f H100 \N t \N \N \N {} +1748 587 2025-02-25 10:24:59.666061-08 2025-02-25 09:27:46.691352-08 test t T4 \N f \N \N \N {} +1749 588 2025-02-25 08:59:07.160327-08 2025-02-25 08:38:22.92218-08 benchmark f H100 \N f \N \N \N {} +4803 1487 2025-03-02 12:55:09.203143-08 2025-03-02 13:27:27.333426-08 benchmark t A100 \N f \N \N \N {} +1756 594 2025-02-25 09:11:38.300189-08 2025-02-25 10:28:52.150779-08 benchmark t T4 \N t \N \N \N {} +1757 594 2025-02-25 08:46:53.338218-08 2025-02-25 09:57:23.906953-08 leaderboard t T4 0.017254103 t \N \N \N {} +1758 594 2025-02-25 08:57:48.338091-08 2025-02-25 09:10:25.874419-08 test f T4 \N t \N \N \N {} +1759 594 2025-02-25 09:38:33.456671-08 2025-02-25 09:43:17.759144-08 benchmark f T4 \N t \N \N \N {} +1760 594 2025-02-25 09:40:53.716064-08 2025-02-25 10:08:25.184778-08 leaderboard f T4 0.017332067125 t \N \N \N {} +4804 1487 2025-03-02 12:53:07.905973-08 2025-03-02 13:20:31.048316-08 test t T4 \N t \N \N \N {} +1783 612 2025-02-25 10:30:16.608473-08 2025-02-25 10:08:59.631399-08 benchmark f A100 \N t \N \N \N {} +2280 767 2025-02-26 03:31:52.024635-08 2025-02-26 03:22:12.199287-08 leaderboard f A100 0.0008225438000000001 t \N \N \N {} +2281 767 2025-02-26 03:40:30.007645-08 2025-02-26 04:29:52.334337-08 test t A100 \N t \N \N \N {} +2300 774 2025-02-26 04:14:40.857031-08 2025-02-26 04:33:29.483727-08 test f A100 \N t \N \N \N {} +3588 1229 2025-02-28 14:32:08.489693-08 2025-02-28 14:05:23.563906-08 benchmark f H100 \N t \N \N \N {} +2282 767 2025-02-26 03:24:59.166944-08 2025-02-26 04:45:17.998433-08 benchmark t A100 \N t \N \N \N {} +2284 768 2025-02-26 04:18:53.859554-08 2025-02-26 03:24:39.46805-08 benchmark f A100 \N t \N \N \N {} +3283 1147 2025-02-28 06:11:40.284397-08 2025-02-28 07:19:05.74238-08 benchmark f H100 \N t \N \N \N {} +2483 870 2025-02-26 13:16:44.041088-08 2025-02-26 13:47:23.050449-08 benchmark f A100 \N t \N \N \N {} +2484 870 2025-02-26 13:10:26.731301-08 2025-02-26 14:42:14.746386-08 leaderboard f A100 0.0035295413333333333 t \N \N \N {} +2485 870 2025-02-26 13:33:50.524318-08 2025-02-26 14:10:40.249698-08 test f T4 \N t \N \N \N {} +2486 870 2025-02-26 13:05:54.760424-08 2025-02-26 13:38:28.475426-08 benchmark f T4 \N t \N \N \N {} +2499 869 2025-02-26 13:54:53.311947-08 2025-02-26 13:58:02.187272-08 leaderboard t H100 0.002322158 t \N \N \N {} +3392 1168 2025-02-28 09:12:51.277351-08 2025-02-28 09:23:36.312793-08 test f L4 \N f \N \N \N {} +2510 872 2025-02-26 13:10:41.998428-08 2025-02-26 13:52:20.643803-08 test f H100 \N t \N \N \N {} +2505 872 2025-02-26 14:18:21.923745-08 2025-02-26 14:06:00.875303-08 benchmark t H100 \N t \N \N \N {} +2506 872 2025-02-26 13:16:25.959445-08 2025-02-26 14:06:50.761927-08 leaderboard t H100 0.0014855651428571429 t \N \N \N {} +2507 872 2025-02-26 14:54:25.186275-08 2025-02-26 13:13:47.344332-08 test f A100 \N t \N \N \N {} +2593 884 2025-02-26 14:14:00.181524-08 2025-02-26 13:46:27.570181-08 benchmark f L4 \N t \N \N \N {} +2594 884 2025-02-26 13:32:06.001069-08 2025-02-26 15:05:08.151616-08 leaderboard f L4 0.017391052333333334 t \N \N \N {} +2595 884 2025-02-26 15:04:28.988097-08 2025-02-26 15:18:03.931934-08 test t L4 \N t \N \N \N {} +3420 1179 2025-02-28 10:40:09.83029-08 2025-02-28 10:08:07.480948-08 test f L4 \N f \N \N \N {} +2596 884 2025-02-26 14:56:24.039776-08 2025-02-26 14:30:30.751724-08 benchmark t L4 \N t \N \N \N {} +2597 884 2025-02-26 14:35:42.960818-08 2025-02-26 15:14:14.400532-08 leaderboard t L4 0.017414738333333332 t \N \N \N {} +4203 1388 2025-03-01 13:04:56.163242-08 2025-03-01 14:12:53.003168-08 benchmark t A100 \N t \N \N \N {} +4136 1353 2025-03-01 05:57:54.388305-08 2025-03-01 05:50:17.87171-08 test f A100 \N t \N \N \N {} +4137 1353 2025-03-01 05:16:52.584167-08 2025-03-01 06:12:22.385227-08 benchmark f A100 \N t \N \N \N {} +4138 1353 2025-03-01 05:33:09.219491-08 2025-03-01 06:16:54.189813-08 leaderboard f A100 0.003070895 t \N \N \N {} +4139 1354 2025-03-01 06:20:09.60044-08 2025-03-01 05:18:22.96192-08 test f A100 \N t \N \N \N {} +4140 1354 2025-03-01 05:58:29.731003-08 2025-03-01 05:41:56.193045-08 benchmark f A100 \N t \N \N \N {} +4218 1394 2025-03-01 15:09:41.391316-08 2025-03-01 15:57:05.307002-08 leaderboard f A100 0.003101874 t \N \N \N {} +4695 1470 2025-03-02 07:38:44.249494-08 2025-03-02 07:11:45.382141-08 test f A100 \N t \N \N \N {} +4219 1394 2025-03-01 14:45:22.969713-08 2025-03-01 14:48:00.593278-08 test f H100 \N t \N \N \N {} +4220 1394 2025-03-01 15:38:36.009293-08 2025-03-01 16:26:03.890826-08 benchmark f H100 \N t \N \N \N {} +4221 1394 2025-03-01 15:14:09.768318-08 2025-03-01 14:35:45.346475-08 leaderboard f H100 0.00139569175 t \N \N \N {} +4222 1394 2025-03-01 14:47:10.848925-08 2025-03-01 16:25:02.786375-08 test t A100 \N t \N \N \N {} +4430 1443 2025-03-01 23:16:02.466485-08 2025-03-01 23:58:09.823321-08 benchmark f T4 \N t \N \N \N {} +4431 1443 2025-03-01 23:17:08.006949-08 2025-03-02 00:41:05.844634-08 leaderboard f T4 0.009837714666666665 t \N \N \N {} +4432 1443 2025-03-02 00:29:59.441844-08 2025-03-02 00:59:33.051229-08 test t T4 \N t \N \N \N {} +4433 1443 2025-03-02 00:28:05.820758-08 2025-03-01 23:33:08.733358-08 benchmark t T4 \N t \N \N \N {} +4434 1443 2025-03-01 23:22:06.634593-08 2025-03-01 23:36:43.202641-08 leaderboard t T4 0.009814015333333334 t \N \N \N {} +4435 1444 2025-03-02 00:23:07.374461-08 2025-03-02 00:27:41.123548-08 test f A100 \N t \N \N \N {} +4436 1444 2025-03-02 00:26:01.958483-08 2025-03-01 23:49:21.361713-08 benchmark f A100 \N t \N \N \N {} +6459 2005 2025-03-14 06:48:41.779094-07 2025-03-14 06:41:15.666539-07 test t A100 \N t \N \N \N {} +4439 1444 2025-03-01 23:56:43.720976-08 2025-03-02 00:29:41.591684-08 benchmark f T4 \N t \N \N \N {} +4440 1444 2025-03-02 01:00:37.296922-08 2025-03-01 23:59:24.879229-08 leaderboard f T4 0.009901969333333333 t \N \N \N {} +4441 1444 2025-03-02 00:32:35.468671-08 2025-03-02 00:07:20.667198-08 test f H100 \N t \N \N \N {} +4442 1444 2025-03-02 00:23:25.716537-08 2025-03-01 23:07:19.512954-08 benchmark f H100 \N t \N \N \N {} +4443 1444 2025-03-01 23:26:32.233267-08 2025-03-02 00:51:40.562071-08 leaderboard f H100 0.0011824463333333333 t \N \N \N {} +4447 1444 2025-03-01 23:33:37.582909-08 2025-03-02 00:50:04.426217-08 test t A100 \N t \N \N \N {} +4448 1444 2025-03-01 23:21:44.542236-08 2025-03-02 00:08:03.502567-08 benchmark t A100 \N t \N \N \N {} +4449 1444 2025-03-01 23:37:38.569687-08 2025-03-02 00:55:50.035923-08 leaderboard t A100 0.002102921 t \N \N \N {} +4450 1444 2025-03-02 00:52:28.079251-08 2025-03-01 23:41:04.354527-08 test t H100 \N t \N \N \N {} +4451 1444 2025-03-01 23:53:41.734266-08 2025-03-02 00:14:25.295609-08 benchmark t H100 \N t \N \N \N {} +4452 1444 2025-03-01 23:41:52.983649-08 2025-03-02 00:12:57.932353-08 leaderboard t H100 0.001193107 t \N \N \N {} +4453 1444 2025-03-02 00:27:47.992493-08 2025-03-01 23:08:52.737482-08 test f L4 \N t \N \N \N {} +4454 1444 2025-03-01 23:32:46.550951-08 2025-03-01 23:59:44.887779-08 benchmark f L4 \N t \N \N \N {} +4455 1444 2025-03-02 01:06:16.006685-08 2025-03-02 01:03:15.272313-08 leaderboard f L4 0.009334652333333334 t \N \N \N {} +4456 1444 2025-03-02 00:34:05.739298-08 2025-03-01 23:57:09.516919-08 test t L4 \N t \N \N \N {} +4457 1444 2025-03-01 23:54:57.096397-08 2025-03-02 00:38:20.034987-08 benchmark t L4 \N t \N \N \N {} +6704 2080 2025-03-15 03:44:28.237409-07 2025-03-15 02:59:07.329339-07 test t A100 \N t \N \N \N {} +4460 1445 2025-03-02 00:36:15.053645-08 2025-03-02 00:04:32.394667-08 benchmark f A100 \N t \N \N \N {} +4461 1445 2025-03-02 00:39:57.735714-08 2025-03-01 23:20:00.105801-08 leaderboard f A100 0.0018449429166666666 t \N \N \N {} +4462 1445 2025-03-02 00:55:37.500529-08 2025-03-02 00:06:09.985896-08 test t H100 \N t \N \N \N {} +4463 1445 2025-03-02 01:06:57.541572-08 2025-03-02 00:21:30.815554-08 benchmark t H100 \N t \N \N \N {} +4464 1445 2025-03-02 00:13:41.720834-08 2025-03-01 23:43:07.361692-08 leaderboard t H100 0.0010767913333333333 t \N \N \N {} +4468 1445 2025-03-02 01:09:32.900149-08 2025-03-01 23:58:50.176924-08 test f T4 \N t \N \N \N {} +4469 1445 2025-03-02 00:07:37.426171-08 2025-03-02 01:02:05.35442-08 benchmark f T4 \N t \N \N \N {} +4470 1445 2025-03-02 00:31:13.200358-08 2025-03-02 00:29:49.702127-08 leaderboard f T4 0.009909986333333334 t \N \N \N {} +4471 1445 2025-03-02 00:16:25.62727-08 2025-03-02 00:16:09.130264-08 test t T4 \N t \N \N \N {} +4472 1445 2025-03-02 00:04:53.640853-08 2025-03-01 23:24:24.363564-08 benchmark t T4 \N t \N \N \N {} +4473 1445 2025-03-02 01:02:57.604673-08 2025-03-01 23:55:02.547237-08 leaderboard t T4 0.009850056666666666 t \N \N \N {} +4474 1445 2025-03-01 23:14:41.583535-08 2025-03-02 01:03:01.853819-08 test t A100 \N t \N \N \N {} +4475 1445 2025-03-01 23:54:31.749676-08 2025-03-02 00:19:06.090943-08 benchmark t A100 \N t \N \N \N {} +4476 1445 2025-03-01 23:28:11.845822-08 2025-03-02 00:35:45.681919-08 leaderboard t A100 0.001977769 t \N \N \N {} +4477 1445 2025-03-01 23:10:57.421619-08 2025-03-01 23:46:37.516111-08 test f L4 \N t \N \N \N {} +4478 1445 2025-03-02 00:00:13.580017-08 2025-03-01 23:29:52.639891-08 benchmark f L4 \N t \N \N \N {} +6707 2080 2025-03-15 03:00:01.742093-07 2025-03-15 02:58:02.613361-07 test f A100 \N t \N \N \N {} +4488 1447 2025-03-02 00:17:43.55991-08 2025-03-02 01:03:56.914497-08 test t H100 \N t \N \N \N {} +4489 1447 2025-03-02 01:08:54.903269-08 2025-03-01 23:34:35.298743-08 benchmark t H100 \N f \N \N \N {} +4490 1447 2025-03-01 23:20:16.331584-08 2025-03-02 01:02:40.427181-08 test f A100 \N t \N \N \N {} +4491 1447 2025-03-01 23:58:12.688525-08 2025-03-02 00:00:00.160252-08 benchmark f A100 \N f \N \N \N {} +4492 1447 2025-03-02 00:06:10.369337-08 2025-03-02 00:53:30.708303-08 test f T4 \N t \N \N \N {} +6710 2081 2025-03-15 03:34:31.806339-07 2025-03-15 04:12:10.063053-07 test f A100 \N t \N \N \N {} +4493 1447 2025-03-02 01:03:35.98745-08 2025-03-02 00:54:11.188019-08 benchmark f T4 \N f \N \N \N {} +4494 1447 2025-03-02 00:30:24.438417-08 2025-03-01 23:49:43.972048-08 test t T4 \N t \N \N \N {} +4495 1447 2025-03-02 00:06:52.894921-08 2025-03-02 00:53:07.74224-08 benchmark t T4 \N f \N \N \N {} +4496 1447 2025-03-01 23:56:15.387541-08 2025-03-02 01:11:05.860239-08 test f L4 \N t \N \N \N {} +4497 1447 2025-03-01 23:59:26.042618-08 2025-03-02 00:05:19.797422-08 benchmark f L4 \N f \N \N \N {} +4676 1467 2025-03-02 06:38:57.431816-08 2025-03-02 05:44:56.24281-08 leaderboard t A100 0.0030988743333333333 t \N \N \N {} +4679 1467 2025-03-02 06:07:05.077247-08 2025-03-02 06:27:34.618874-08 leaderboard f A100 0.0030814753333333333 t \N \N \N {} +4681 1468 2025-03-02 05:51:53.90541-08 2025-03-02 05:57:04.320667-08 benchmark f A100 \N t \N \N \N {} +4682 1468 2025-03-02 06:59:21.935782-08 2025-03-02 07:08:58.21826-08 leaderboard f A100 0.0030820743333333333 t \N \N \N {} +4683 1468 2025-03-02 07:33:48.301938-08 2025-03-02 05:51:18.662826-08 test t A100 \N t \N \N \N {} +4684 1468 2025-03-02 06:04:29.029074-08 2025-03-02 06:40:01.015883-08 benchmark t A100 \N t \N \N \N {} +4685 1468 2025-03-02 05:46:36.618897-08 2025-03-02 07:31:14.275548-08 leaderboard t A100 0.0030780586666666666 t \N \N \N {} +4689 1469 2025-03-02 05:56:50.321497-08 2025-03-02 05:54:49.230629-08 test t A100 \N t \N \N \N {} +4690 1469 2025-03-02 06:10:42.998637-08 2025-03-02 06:36:41.015153-08 benchmark t A100 \N t \N \N \N {} +4691 1469 2025-03-02 06:43:44.220952-08 2025-03-02 07:42:47.37538-08 leaderboard t A100 0.00307954 t \N \N \N {} +4692 1470 2025-03-02 07:27:34.955858-08 2025-03-02 07:19:49.777082-08 test t A100 \N t \N \N \N {} +4797 1487 2025-03-02 12:47:39.303587-08 2025-03-02 14:24:58.396542-08 benchmark t H100 \N f \N \N \N {} +4798 1487 2025-03-02 14:03:11.635121-08 2025-03-02 12:41:11.568145-08 test f A100 \N t \N \N \N {} +4799 1487 2025-03-02 14:16:43.466988-08 2025-03-02 13:43:02.452058-08 benchmark f A100 \N f \N \N \N {} +4800 1487 2025-03-02 12:52:53.590829-08 2025-03-02 13:24:26.405768-08 test f H100 \N t \N \N \N {} +4801 1487 2025-03-02 12:48:49.45642-08 2025-03-02 14:27:42.149075-08 benchmark f H100 \N f \N \N \N {} +6752 2093 2025-03-15 03:33:18.596707-07 2025-03-15 05:00:28.91036-07 test f A100 \N t \N \N \N {} +4846 1493 2025-03-02 14:07:28.816942-08 2025-03-02 13:42:53.598883-08 leaderboard f A100 0.001957988 t \N \N \N {} +4847 1493 2025-03-02 13:42:25.008987-08 2025-03-02 15:06:27.494787-08 test t A100 \N t \N \N \N {} +4848 1493 2025-03-02 15:03:52.371645-08 2025-03-02 15:22:56.10921-08 benchmark t A100 \N t \N \N \N {} +4849 1493 2025-03-02 13:36:12.4802-08 2025-03-02 13:52:29.977751-08 leaderboard t A100 0.0019754496666666665 t \N \N \N {} +4850 1494 2025-03-02 14:37:31.400329-08 2025-03-02 14:01:28.604796-08 test f L4 \N t \N \N \N {} +4851 1494 2025-03-02 14:43:42.095203-08 2025-03-02 13:36:47.15097-08 benchmark f L4 \N t \N \N \N {} +4974 1533 2025-03-03 22:57:29.38809-08 2025-03-03 21:55:26.67234-08 test f H100 \N t \N \N \N {} +4865 1495 2025-03-02 14:13:05.528764-08 2025-03-02 14:22:55.062967-08 benchmark t A100 \N t \N \N \N {} +4866 1495 2025-03-02 15:19:34.20592-08 2025-03-02 15:11:10.458129-08 leaderboard t A100 0.0017338363333333333 t \N \N \N {} +4867 1495 2025-03-02 13:51:41.741994-08 2025-03-02 14:01:52.182359-08 test f A100 \N t \N \N \N {} +4880 1501 2025-03-03 08:43:05.642759-08 2025-03-03 09:27:05.185793-08 benchmark f A100 \N t \N \N \N {} +4881 1502 2025-03-03 09:35:30.629754-08 2025-03-03 09:05:15.805475-08 benchmark f A100 \N t \N \N \N {} +4894 1507 2025-03-03 13:08:55.752365-08 2025-03-03 13:41:41.116144-08 test f A100 \N t \N \N \N {} +4895 1507 2025-03-03 14:18:00.216385-08 2025-03-03 14:01:18.093034-08 benchmark f A100 \N t \N \N \N {} +4896 1507 2025-03-03 12:36:28.706864-08 2025-03-03 13:53:27.430536-08 leaderboard f A100 0.023420895 t \N \N \N {} +4902 1508 2025-03-03 13:21:12.73487-08 2025-03-03 13:02:32.458846-08 leaderboard f H100 0.007582660333333333 t \N \N \N {} +4904 1510 2025-03-03 14:37:41.941114-08 2025-03-03 13:46:22.666922-08 test f T4 \N f \N \N \N {} +5251 1613 2025-03-06 07:16:47.168832-08 2025-03-06 08:00:37.45677-08 benchmark t L4 \N t \N \N \N {} +4942 1522 2025-03-03 14:30:23.866503-08 2025-03-03 15:41:40.516523-08 benchmark f H100 \N t \N \N \N {} +4943 1522 2025-03-03 14:48:33.754294-08 2025-03-03 15:31:43.342939-08 leaderboard f H100 0.0009028792280701754 t \N \N \N {} +5163 1571 2025-03-04 22:04:28.946672-08 2025-03-04 22:40:14.074477-08 benchmark t H100 \N t \N \N \N {} +5164 1571 2025-03-04 22:14:23.494998-08 2025-03-04 21:56:46.443828-08 leaderboard t H100 0.0010517243333333333 t \N \N \N {} +5165 1572 2025-03-04 22:44:09.245212-08 2025-03-04 22:55:47.144621-08 test f H100 \N f \N \N \N {} +5166 1572 2025-03-04 22:51:29.817905-08 2025-03-04 22:29:55.447229-08 test t H100 \N f \N \N \N {} +5167 1573 2025-03-04 23:41:43.36022-08 2025-03-04 22:29:50.136285-08 test f H100 \N f \N \N \N {} +5168 1573 2025-03-04 23:23:11.4983-08 2025-03-04 22:11:11.957865-08 test t H100 \N f \N \N \N {} +5169 1574 2025-03-04 22:13:23.349969-08 2025-03-04 22:18:11.101194-08 test f H100 \N f \N \N \N {} +5588 1708 2025-03-09 10:19:34.720879-07 2025-03-09 08:50:34.606837-07 test t T4 \N f \N \N \N {} +5589 1709 2025-03-09 08:35:57.715637-07 2025-03-09 08:57:16.363193-07 test f T4 \N t \N \N \N {} +5590 1709 2025-03-09 08:48:05.388801-07 2025-03-09 08:51:52.822296-07 benchmark f T4 \N t \N \N \N {} +5598 1710 2025-03-09 09:29:22.059881-07 2025-03-09 09:55:48.650399-07 test f T4 \N t \N \N \N {} +5599 1710 2025-03-09 08:55:39.716901-07 2025-03-09 09:35:45.035771-07 benchmark f T4 \N t \N \N \N {} +5600 1710 2025-03-09 08:57:39.621985-07 2025-03-09 09:06:05.154876-07 leaderboard f T4 0.016820244 t \N \N \N {} +5603 1712 2025-03-09 08:59:13.701641-07 2025-03-09 10:05:42.666778-07 test f T4 \N t \N \N \N {} +5604 1712 2025-03-09 08:36:53.940611-07 2025-03-09 09:15:28.314509-07 benchmark f T4 \N t \N \N \N {} +5605 1712 2025-03-09 09:42:44.562696-07 2025-03-09 08:51:10.667131-07 leaderboard f T4 0.017130335 t \N \N \N {} +5606 1712 2025-03-09 09:25:40.097472-07 2025-03-09 09:24:28.034725-07 test t T4 \N t \N \N \N {} +5609 1713 2025-03-09 09:05:10.861954-07 2025-03-09 09:44:03.851528-07 test f T4 \N f \N \N \N {} +5610 1713 2025-03-09 09:44:19.160845-07 2025-03-09 09:34:04.669254-07 test t T4 \N f \N \N \N {} +5611 1714 2025-03-09 08:42:31.949236-07 2025-03-09 08:41:53.417604-07 test f T4 \N t \N \N \N {} +5612 1714 2025-03-09 09:57:39.262941-07 2025-03-09 09:05:14.002676-07 benchmark f T4 \N f \N \N \N {} +5615 1715 2025-03-09 09:17:39.516615-07 2025-03-09 09:12:41.592177-07 test t T4 \N t \N \N \N {} +5616 1715 2025-03-09 10:38:09.131269-07 2025-03-09 10:32:40.256801-07 benchmark t T4 \N t \N \N \N {} +5617 1715 2025-03-09 10:30:21.604304-07 2025-03-09 09:39:45.046006-07 leaderboard t T4 0.020089393 t \N \N \N {} +5618 1715 2025-03-09 09:22:37.846053-07 2025-03-09 09:51:45.425382-07 test f T4 \N t \N \N \N {} +5619 1715 2025-03-09 10:06:35.483321-07 2025-03-09 08:49:05.978541-07 benchmark f T4 \N t \N \N \N {} +5620 1715 2025-03-09 09:57:04.884393-07 2025-03-09 09:09:40.630581-07 leaderboard f T4 0.020089969 t \N \N \N {} +5621 1716 2025-03-09 09:09:36.619819-07 2025-03-09 09:58:56.525307-07 test t T4 \N t \N \N \N {} +5922 1799 2025-03-10 12:08:29.726347-07 2025-03-10 12:16:05.005078-07 test t T4 \N f \N \N \N {} +5927 1802 2025-03-10 12:32:14.926187-07 2025-03-10 12:54:34.768223-07 test t T4 \N f \N \N \N {} +5928 1802 2025-03-10 12:50:16.888351-07 2025-03-10 13:21:42.853839-07 test f T4 \N f \N \N \N {} +5929 1803 2025-03-10 13:37:22.444553-07 2025-03-10 13:05:55.49148-07 test t T4 \N f \N \N \N {} +5930 1803 2025-03-10 12:23:55.172369-07 2025-03-10 12:26:21.880798-07 test f T4 \N f \N \N \N {} +5935 1806 2025-03-10 13:48:38.054827-07 2025-03-10 13:23:13.229669-07 test t T4 \N f \N \N \N {} +5978 1820 2025-03-10 13:29:06.016869-07 2025-03-10 14:22:13.274644-07 leaderboard f H100 0.0002589177142857143 t \N \N \N {} +5959 1814 2025-03-10 12:44:05.167824-07 2025-03-10 14:16:11.26654-07 test f T4 \N f \N \N \N {} +5979 1820 2025-03-10 14:17:15.853549-07 2025-03-10 13:56:33.556629-07 test t H100 \N t \N \N \N {} +5964 1816 2025-03-10 13:24:28.295909-07 2025-03-10 12:59:46.71286-07 test f T4 \N f \N \N \N {} +5987 1819 2025-03-10 12:59:46.080421-07 2025-03-10 13:46:59.872067-07 leaderboard t A100 0.0007810198461538461 t \N \N \N {} +5968 1818 2025-03-10 12:49:46.935079-07 2025-03-10 14:29:30.320975-07 benchmark f T4 \N t \N \N \N {} +5970 1818 2025-03-10 12:50:38.37402-07 2025-03-10 12:59:21.234188-07 test t T4 \N t \N \N \N {} +2078 702 2025-02-25 14:41:18.894451-08 2025-02-25 14:59:05.107708-08 test t H100 \N t \N \N \N {} +2079 702 2025-02-25 13:46:28.574004-08 2025-02-25 14:33:01.405183-08 benchmark t H100 \N t \N \N \N {} +2080 702 2025-02-25 14:30:42.768365-08 2025-02-25 13:11:14.979116-08 leaderboard t H100 0.0019116912857142856 t \N \N \N {} +2081 702 2025-02-25 14:40:48.443884-08 2025-02-25 13:54:57.498846-08 test f T4 \N t \N \N \N {} +2082 702 2025-02-25 14:30:54.980313-08 2025-02-25 14:44:49.165667-08 benchmark f T4 \N t \N \N \N {} +2085 702 2025-02-25 14:18:01.193831-08 2025-02-25 14:59:07.45264-08 benchmark t T4 \N t \N \N \N {} +2086 702 2025-02-25 14:20:00.930365-08 2025-02-25 13:58:38.993991-08 leaderboard t T4 0.02001272633333333 t \N \N \N {} +6950 2162 2025-03-16 16:48:51.39716-07 2025-03-16 15:12:01.465889-07 leaderboard f T4 0.00032559321276595747 t \N \N \N {} +6974 2169 2025-03-16 19:35:21.550002-07 2025-03-16 20:31:39.018156-07 leaderboard f T4 0.00024278 t \N \N \N {} +6976 2170 2025-03-16 20:19:56.143081-07 2025-03-16 19:14:22.00651-07 benchmark f T4 \N t \N \N \N {} +6977 2170 2025-03-16 20:16:18.708649-07 2025-03-16 20:36:29.899891-07 leaderboard f T4 0.00021283630303030305 t \N \N \N {} +6978 2170 2025-03-16 20:24:22.625394-07 2025-03-16 20:12:10.318919-07 test t T4 \N t \N \N \N {} +6979 2170 2025-03-16 20:24:19.649393-07 2025-03-16 19:07:58.941721-07 benchmark t T4 \N t \N \N \N {} +6980 2170 2025-03-16 19:29:57.914998-07 2025-03-16 20:10:27.993196-07 leaderboard t T4 0.00021353688461538462 t \N \N \N {} +6981 2171 2025-03-16 20:30:12.137849-07 2025-03-16 20:46:29.948675-07 test t T4 \N t \N \N \N {} +6983 2171 2025-03-16 20:50:17.763964-07 2025-03-16 19:14:31.186365-07 leaderboard t T4 0.0002254876842105263 t \N \N \N {} +6984 2171 2025-03-16 21:04:10.03094-07 2025-03-16 19:42:37.771415-07 test f T4 \N t \N \N \N {} +7060 2185 2025-03-16 21:00:49.129817-07 2025-03-16 20:45:01.000696-07 test t L4 \N t \N \N \N {} +6985 2171 2025-03-16 20:30:05.903023-07 2025-03-16 20:53:27.393446-07 benchmark f T4 \N t \N \N \N {} +6986 2171 2025-03-16 20:39:51.238598-07 2025-03-16 21:10:32.960644-07 leaderboard f T4 0.00024411110344827586 t \N \N \N {} +6987 2172 2025-03-16 20:16:03.916165-07 2025-03-16 20:44:26.852206-07 test t T4 \N t \N \N \N {} +6995 2173 2025-03-16 19:35:01.975689-07 2025-03-16 20:14:16.885975-07 leaderboard t T4 0.00023485917647058822 t \N \N \N {} +6996 2173 2025-03-16 20:21:16.631389-07 2025-03-16 20:29:29.668201-07 test f T4 \N t \N \N \N {} +6997 2173 2025-03-16 20:27:58.230216-07 2025-03-16 20:09:23.910606-07 benchmark f T4 \N t \N \N \N {} +6998 2173 2025-03-16 20:58:20.871815-07 2025-03-16 20:25:33.751661-07 leaderboard f T4 0.000249013375 t \N \N \N {} +6999 2174 2025-03-16 19:59:32.772964-07 2025-03-16 20:00:12.299702-07 test t T4 \N f \N \N \N {} +7000 2174 2025-03-16 20:57:34.509848-07 2025-03-16 20:51:30.601681-07 test f T4 \N f \N \N \N {} +7001 2175 2025-03-16 21:17:45.195814-07 2025-03-16 20:18:47.813213-07 test t T4 \N t \N \N \N {} +7002 2175 2025-03-16 21:20:26.692848-07 2025-03-16 19:32:57.604151-07 benchmark t T4 \N t \N \N \N {} +7003 2175 2025-03-16 20:44:57.359938-07 2025-03-16 19:58:30.404808-07 leaderboard t T4 0.0002332306 t \N \N \N {} +7004 2175 2025-03-16 21:10:00.970602-07 2025-03-16 21:02:06.246562-07 test f T4 \N t \N \N \N {} +7005 2175 2025-03-16 20:07:09.492932-07 2025-03-16 20:42:42.346737-07 benchmark f T4 \N t \N \N \N {} +7008 2176 2025-03-16 20:09:25.465826-07 2025-03-16 20:12:12.087915-07 benchmark t T4 \N t \N \N \N {} +7009 2176 2025-03-16 20:39:02.786771-07 2025-03-16 20:32:18.347951-07 leaderboard t T4 0.0003024322631578947 t \N \N \N {} +7010 2176 2025-03-16 20:09:27.479373-07 2025-03-16 19:53:27.585268-07 test f T4 \N t \N \N \N {} +7011 2176 2025-03-16 20:40:20.373215-07 2025-03-16 20:35:48.949342-07 benchmark f T4 \N t \N \N \N {} +7012 2176 2025-03-16 20:27:53.725911-07 2025-03-16 21:32:11.632172-07 leaderboard f T4 0.0003064759545454545 t \N \N \N {} +7014 2177 2025-03-16 20:47:48.960713-07 2025-03-16 21:32:50.346065-07 benchmark t T4 \N t \N \N \N {} +7015 2177 2025-03-16 20:22:27.227663-07 2025-03-16 21:27:05.019116-07 leaderboard t T4 0.00023873836363636365 t \N \N \N {} +7016 2177 2025-03-16 20:52:07.570894-07 2025-03-16 21:28:14.032602-07 test f T4 \N t \N \N \N {} +7017 2177 2025-03-16 21:04:02.088955-07 2025-03-16 20:13:59.587543-07 benchmark f T4 \N t \N \N \N {} +7018 2177 2025-03-16 20:52:17.778254-07 2025-03-16 21:07:11.801446-07 leaderboard f T4 0.00021685985 t \N \N \N {} +7019 2178 2025-03-16 20:51:58.999601-07 2025-03-16 20:03:37.893564-07 test f T4 \N t \N \N \N {} +7043 2182 2025-03-16 21:03:58.995548-07 2025-03-16 20:05:41.20667-07 test f H100 \N t \N \N \N {} +7049 2183 2025-03-16 21:44:08.747705-07 2025-03-16 20:09:06.554136-07 test f L4 \N t \N \N \N {} +7050 2183 2025-03-16 21:20:04.412484-07 2025-03-16 21:04:19.112094-07 benchmark f L4 \N t \N \N \N {} +7051 2183 2025-03-16 20:20:24.080976-07 2025-03-16 20:12:16.039498-07 leaderboard f L4 0.00013962746913580247 t \N \N \N {} +7052 2183 2025-03-16 21:06:27.300343-07 2025-03-16 21:35:48.036723-07 test t L4 \N t \N \N \N {} +7053 2183 2025-03-16 21:08:28.281165-07 2025-03-16 21:05:19.303361-07 benchmark t L4 \N t \N \N \N {} +7054 2183 2025-03-16 21:03:17.39211-07 2025-03-16 21:12:38.71214-07 leaderboard t L4 0.00013929977922077922 t \N \N \N {} +7057 2185 2025-03-16 20:57:18.459356-07 2025-03-16 20:58:40.616284-07 test f L4 \N t \N \N \N {} +7058 2185 2025-03-16 21:09:41.620917-07 2025-03-16 20:26:34.270924-07 benchmark f L4 \N t \N \N \N {} +7072 2187 2025-03-16 21:18:46.105748-07 2025-03-16 21:30:58.658604-07 test t A100 \N t \N \N \N {} +3823 1286 2025-03-01 02:51:18.738565-08 2025-03-01 02:35:56.135696-08 test t A100 \N t \N \N \N {} +2423 828 2025-02-26 14:04:34.908137-08 2025-02-26 12:48:44.157236-08 benchmark f H100 \N t \N \N \N {} +2844 989 2025-02-26 23:05:37.389856-08 2025-02-26 23:57:49.949237-08 test t H100 \N t \N \N \N {} +3826 1286 2025-03-01 03:10:29.776175-08 2025-03-01 02:15:26.657539-08 test f A100 \N t \N \N \N {} +2853 990 2025-02-26 23:48:13.530611-08 2025-02-27 00:27:37.952577-08 test f H100 \N t \N \N \N {} +3829 1287 2025-03-01 03:52:39.094858-08 2025-03-01 03:40:13.588698-08 test f A100 \N t \N \N \N {} +2860 992 2025-02-26 23:57:24.598857-08 2025-02-26 22:46:51.241065-08 leaderboard t H100 0.001483009888888889 t \N \N \N {} +3832 1287 2025-03-01 02:40:18.855128-08 2025-03-01 02:20:51.580001-08 test t A100 \N t \N \N \N {} +3575 1222 2025-02-28 14:25:08.626031-08 2025-02-28 13:57:58.851842-08 test f H100 \N t \N \N \N {} +2861 992 2025-02-27 00:10:48.414508-08 2025-02-27 00:15:26.826737-08 test f H100 \N t \N \N \N {} +3835 1288 2025-03-01 03:56:27.936941-08 2025-03-01 03:22:45.323479-08 test f A100 \N t \N \N \N {} +3838 1288 2025-03-01 02:49:58.666605-08 2025-03-01 03:28:40.936194-08 test t A100 \N t \N \N \N {} +3586 1228 2025-02-28 13:04:01.825429-08 2025-02-28 13:19:18.114759-08 leaderboard t A100 0.0018734915714285713 t \N \N \N {} +2208 723 2025-02-25 15:12:24.768432-08 2025-02-25 14:15:55.618693-08 benchmark f A100 \N t \N \N \N {} +3841 1289 2025-03-01 02:47:05.119415-08 2025-03-01 04:04:07.592057-08 test f A100 \N t \N \N \N {} +4808 1487 2025-03-02 13:15:32.005735-08 2025-03-02 14:08:45.412831-08 test t L4 \N t \N \N \N {} +2209 723 2025-02-25 15:18:48.649607-08 2025-02-25 15:21:36.817173-08 benchmark f H100 \N t \N \N \N {} +6755 2093 2025-03-15 04:31:09.91887-07 2025-03-15 04:32:31.426367-07 test t A100 \N t \N \N \N {} +2217 724 2025-02-25 14:40:36.314858-08 2025-02-25 15:59:23.436752-08 benchmark t H100 \N t \N \N \N {} +2227 726 2025-02-25 21:11:10.262768-08 2025-02-25 21:16:28.616268-08 test f H100 \N f \N \N \N {} +2971 1053 2025-02-27 10:22:46.566956-08 2025-02-27 11:14:00.450534-08 test f H100 \N f \N \N \N {} +2229 728 2025-02-25 21:49:59.544732-08 2025-02-25 21:13:13.071273-08 test f H100 \N f \N \N \N {} +2249 748 2025-02-26 01:20:54.630857-08 2025-02-26 01:48:02.906843-08 test f H100 \N f \N \N \N {} +2251 750 2025-02-26 01:02:46.596404-08 2025-02-26 01:40:29.396697-08 benchmark f H100 \N f \N \N \N {} +2203 721 2025-02-25 14:21:58.314111-08 2025-02-25 15:40:17.725093-08 test f A100 \N f \N \N \N {} +2204 721 2025-02-25 15:30:04.482461-08 2025-02-25 15:34:55.709429-08 test f H100 \N f \N \N \N {} +2205 721 2025-02-25 15:37:24.227133-08 2025-02-25 14:52:16.301155-08 test f T4 \N f \N \N \N {} +2206 721 2025-02-25 14:14:52.248428-08 2025-02-25 14:35:57.115198-08 test f L4 \N f \N \N \N {} +2207 722 2025-02-25 14:23:22.656547-08 2025-02-25 14:28:52.286564-08 test f A100 \N t \N \N \N {} +2231 730 2025-02-25 22:25:36.727385-08 2025-02-25 22:07:18.246829-08 test f H100 \N f \N \N \N {} +2252 751 2025-02-26 01:22:15.758845-08 2025-02-26 00:47:07.586743-08 benchmark f H100 \N f \N \N \N {} +2210 724 2025-02-25 15:01:55.831184-08 2025-02-25 15:27:25.721588-08 test f H100 \N t \N \N \N {} +2211 724 2025-02-25 16:00:18.379653-08 2025-02-25 15:14:19.774224-08 benchmark f H100 \N t \N \N \N {} +2212 724 2025-02-25 15:21:00.820815-08 2025-02-25 14:21:32.48071-08 leaderboard f H100 0.00347018762 t \N \N \N {} +2213 724 2025-02-25 15:55:13.720814-08 2025-02-25 14:39:28.319218-08 test t A100 \N t \N \N \N {} +2214 724 2025-02-25 15:15:10.350587-08 2025-02-25 15:20:05.920535-08 benchmark t A100 \N t \N \N \N {} +2222 724 2025-02-25 15:51:08.090008-08 2025-02-25 14:52:07.078563-08 test t L4 \N f \N \N \N {} +2223 724 2025-02-25 15:59:35.150135-08 2025-02-25 15:41:40.617138-08 test t T4 \N f \N \N \N {} +2224 724 2025-02-25 15:59:58.549919-08 2025-02-25 16:16:59.086077-08 test f T4 \N f \N \N \N {} +2225 724 2025-02-25 15:31:29.288566-08 2025-02-25 14:20:08.177759-08 test f L4 \N f \N \N \N {} +2236 735 2025-02-25 22:08:20.946921-08 2025-02-25 22:28:19.364454-08 test f H100 \N f \N \N \N {} +2241 740 2025-02-25 23:14:33.485835-08 2025-02-25 23:51:46.740346-08 benchmark f A100 \N t \N \N \N {} +2242 741 2025-02-25 23:35:50.055644-08 2025-02-25 22:49:24.455708-08 benchmark f A100 \N t \N \N \N {} +2243 742 2025-02-26 00:25:26.264646-08 2025-02-25 23:17:09.093175-08 benchmark f A100 \N f \N \N \N {} +2245 744 2025-02-26 00:42:02.958881-08 2025-02-26 00:45:16.278457-08 test f H100 \N f \N \N \N {} +2254 753 2025-02-26 02:08:05.477951-08 2025-02-26 01:13:20.051352-08 benchmark f H100 \N f \N \N \N {} +2255 754 2025-02-26 00:59:44.093208-08 2025-02-26 02:58:08.265275-08 benchmark f H100 \N f \N \N \N {} +2259 758 2025-02-26 01:17:58.081473-08 2025-02-26 01:34:49.549302-08 benchmark f H100 \N t \N \N \N {} +2260 759 2025-02-26 01:37:29.394797-08 2025-02-26 02:47:14.747208-08 benchmark f H100 \N t \N \N \N {} +2261 760 2025-02-26 01:39:24.386253-08 2025-02-26 02:18:01.358833-08 benchmark f H100 \N t \N \N \N {} +2262 761 2025-02-26 03:15:59.591174-08 2025-02-26 01:36:18.508508-08 benchmark f H100 \N t \N \N \N {} +2265 762 2025-02-26 02:01:34.231469-08 2025-02-26 03:54:41.609703-08 leaderboard t H100 0.0014416166666666667 t \N \N \N {} +2274 766 2025-02-26 04:05:07.529508-08 2025-02-26 04:58:07.255773-08 leaderboard f A100 0.0008179115 t \N \N \N {} +2275 766 2025-02-26 03:48:00.408098-08 2025-02-26 04:08:59.825239-08 test t A100 \N t \N \N \N {} +2301 774 2025-02-26 04:32:05.77669-08 2025-02-26 04:38:11.895003-08 benchmark f A100 \N t \N \N \N {} +2302 774 2025-02-26 04:13:31.801702-08 2025-02-26 04:01:09.656019-08 leaderboard f A100 0.0031047866666666663 t \N \N \N {} +2303 774 2025-02-26 04:24:37.740211-08 2025-02-26 03:39:00.774209-08 test t A100 \N t \N \N \N {} +2308 777 2025-02-26 04:25:35.388029-08 2025-02-26 03:30:27.492815-08 benchmark f A100 \N t \N \N \N {} +2309 778 2025-02-26 03:49:41.847565-08 2025-02-26 05:07:38.629246-08 test t A100 \N t \N \N \N {} +2310 778 2025-02-26 04:40:13.137449-08 2025-02-26 03:29:03.613607-08 benchmark t A100 \N t \N \N \N {} +2316 781 2025-02-26 08:04:59.695461-08 2025-02-26 08:20:17.559059-08 benchmark t A100 \N t \N \N \N {} +2317 781 2025-02-26 07:31:58.361931-08 2025-02-26 07:59:50.437989-08 leaderboard t A100 0.0031859682 t \N \N \N {} +2324 783 2025-02-26 08:10:22.678801-08 2025-02-26 07:07:39.819759-08 benchmark t A100 \N t \N \N \N {} +2325 783 2025-02-26 08:24:49.179778-08 2025-02-26 08:36:36.413259-08 leaderboard t A100 0.00325122275 t \N \N \N {} +2335 785 2025-02-26 08:25:39.641455-08 2025-02-26 06:58:31.005281-08 benchmark t A100 \N t \N \N \N {} +2336 785 2025-02-26 08:09:56.776695-08 2025-02-26 08:13:07.269228-08 leaderboard t A100 0.004335441666666667 t \N \N \N {} +2340 787 2025-02-26 07:39:15.682026-08 2025-02-26 08:18:25.113435-08 test f A100 \N f \N \N \N {} +2341 788 2025-02-26 07:31:19.708086-08 2025-02-26 08:57:53.961399-08 test t A100 \N f \N \N \N {} +2342 788 2025-02-26 08:25:02.656473-08 2025-02-26 08:25:08.005045-08 test f A100 \N f \N \N \N {} +2343 789 2025-02-26 08:43:37.93693-08 2025-02-26 08:07:41.07638-08 test t A100 \N t \N \N \N {} +2347 789 2025-02-26 09:06:31.119554-08 2025-02-26 07:11:28.595374-08 benchmark f A100 \N t \N \N \N {} +2348 789 2025-02-26 07:29:52.547362-08 2025-02-26 08:50:51.272086-08 leaderboard f A100 0.003266832777777778 t \N \N \N {} +2359 793 2025-02-26 09:11:48.758042-08 2025-02-26 08:04:27.206933-08 test t H100 \N f \N \N \N {} +2360 793 2025-02-26 09:01:14.507106-08 2025-02-26 08:36:00.01203-08 test f H100 \N f \N \N \N {} +2361 794 2025-02-26 10:31:01.311335-08 2025-02-26 10:24:32.132628-08 test f T4 \N t \N \N \N {} +2365 795 2025-02-26 10:42:54.506552-08 2025-02-26 10:33:48.974343-08 benchmark f T4 \N f \N \N \N {} +2366 796 2025-02-26 10:39:57.933478-08 2025-02-26 09:13:35.87411-08 test f T4 \N t \N \N \N {} +2367 796 2025-02-26 08:55:18.203941-08 2025-02-26 10:26:58.100193-08 test f A100 \N f \N \N \N {} +2368 796 2025-02-26 09:46:15.118953-08 2025-02-26 10:28:06.814641-08 test f L4 \N f \N \N \N {} +2373 800 2025-02-26 11:48:00.696003-08 2025-02-26 11:05:20.167368-08 test f L4 \N t \N \N \N {} +2374 800 2025-02-26 11:07:59.074012-08 2025-02-26 12:43:47.36544-08 benchmark f L4 \N t \N \N \N {} +2375 800 2025-02-26 12:41:54.306115-08 2025-02-26 11:06:41.948227-08 leaderboard f L4 0.019241309 t \N \N \N {} +2376 800 2025-02-26 11:45:20.290145-08 2025-02-26 12:31:25.711521-08 test t L4 \N t \N \N \N {} +2377 800 2025-02-26 12:39:01.06552-08 2025-02-26 12:57:56.856584-08 benchmark t L4 \N t \N \N \N {} +2378 800 2025-02-26 11:19:43.410096-08 2025-02-26 12:49:08.158954-08 leaderboard t L4 0.01928871 t \N \N \N {} +2379 801 2025-02-26 11:38:56.285241-08 2025-02-26 12:19:54.264843-08 test f A100 \N t \N \N \N {} +2382 804 2025-02-26 12:57:43.215115-08 2025-02-26 13:30:55.355298-08 benchmark f H100 \N t \N \N \N {} +2383 805 2025-02-26 13:12:00.965515-08 2025-02-26 12:47:43.718436-08 test t H100 \N t \N \N \N {} +2384 805 2025-02-26 12:02:09.112152-08 2025-02-26 13:33:00.538551-08 benchmark t H100 \N t \N \N \N {} +2385 805 2025-02-26 12:07:29.57397-08 2025-02-26 11:40:32.117689-08 leaderboard t H100 0.006281822 t \N \N \N {} +2386 805 2025-02-26 13:23:11.459374-08 2025-02-26 12:01:38.625408-08 test f H100 \N t \N \N \N {} +2387 805 2025-02-26 12:40:46.998681-08 2025-02-26 13:19:43.340082-08 benchmark f H100 \N t \N \N \N {} +2388 805 2025-02-26 12:28:02.085984-08 2025-02-26 12:00:56.151234-08 leaderboard f H100 0.006283131666666667 t \N \N \N {} +2400 812 2025-02-26 12:35:43.081119-08 2025-02-26 11:46:25.709461-08 leaderboard f A100 0.0073987305599999996 t \N \N \N {} +2399 812 2025-02-26 13:00:44.684656-08 2025-02-26 12:40:03.546738-08 benchmark f A100 \N t \N \N \N {} +2412 814 2025-02-26 11:55:21.072277-08 2025-02-26 13:21:18.388476-08 benchmark f H100 \N f \N \N \N {} +2406 812 2025-02-26 11:58:36.640565-08 2025-02-26 12:25:12.325022-08 benchmark t H100 \N t \N \N \N {} +2413 815 2025-02-26 12:34:40.3804-08 2025-02-26 12:01:23.839657-08 benchmark f H100 \N f \N \N \N {} +2407 812 2025-02-26 13:09:11.965272-08 2025-02-26 13:07:20.626098-08 leaderboard t H100 0.0034966516099999997 t \N \N \N {} +2408 813 2025-02-26 12:32:37.101507-08 2025-02-26 13:16:23.500546-08 benchmark f H100 \N t \N \N \N {} +2409 812 2025-02-26 13:29:54.187186-08 2025-02-26 13:23:28.632317-08 test t L4 \N f \N \N \N {} +2424 829 2025-02-26 13:07:19.553-08 2025-02-26 14:01:46.739578-08 benchmark f H100 \N f \N \N \N {} +2425 830 2025-02-26 14:02:43.362147-08 2025-02-26 12:33:07.345304-08 benchmark f H100 \N t \N \N \N {} +2426 831 2025-02-26 12:31:38.224085-08 2025-02-26 13:19:43.236285-08 benchmark f H100 \N t \N \N \N {} +3850 1290 2025-03-01 04:03:10.861329-08 2025-03-01 03:39:38.665791-08 test t A100 \N t \N \N \N {} +2428 834 2025-02-26 13:15:44.571141-08 2025-02-26 13:44:55.710615-08 benchmark f H100 \N t \N \N \N {} +2429 835 2025-02-26 13:42:00.820827-08 2025-02-26 13:22:37.497485-08 benchmark f H100 \N t \N \N \N {} +2430 836 2025-02-26 13:48:13.271344-08 2025-02-26 13:35:52.895605-08 benchmark f H100 \N t \N \N \N {} +2431 839 2025-02-26 12:27:35.852032-08 2025-02-26 13:54:55.127064-08 test f H100 \N t \N \N \N {} +2432 839 2025-02-26 13:13:16.24415-08 2025-02-26 12:31:35.696649-08 benchmark f H100 \N t \N \N \N {} +2433 839 2025-02-26 13:25:16.844207-08 2025-02-26 12:41:49.998945-08 leaderboard f H100 0.0014103766666666668 t \N \N \N {} +2434 839 2025-02-26 13:02:21.500262-08 2025-02-26 13:19:32.907466-08 test t H100 \N t \N \N \N {} +3275 1144 2025-02-28 05:52:25.859901-08 2025-02-28 06:09:38.762195-08 test f A100 \N t \N \N \N {} +2437 843 2025-02-26 14:26:52.109887-08 2025-02-26 14:21:54.916482-08 benchmark f A100 \N t \N \N \N {} +2438 845 2025-02-26 12:52:37.260995-08 2025-02-26 12:43:22.265476-08 test f L4 \N t \N \N \N {} +2439 845 2025-02-26 13:15:51.000933-08 2025-02-26 13:07:58.39425-08 benchmark f L4 \N t \N \N \N {} +2440 845 2025-02-26 13:28:47.225016-08 2025-02-26 13:45:32.97832-08 leaderboard f L4 0.043612971333333334 t \N \N \N {} +2443 845 2025-02-26 14:12:08.997237-08 2025-02-26 12:35:40.416269-08 leaderboard t L4 0.04366572966666667 t \N \N \N {} +2444 850 2025-02-26 14:13:58.990169-08 2025-02-26 12:46:54.480342-08 benchmark f A100 \N t \N \N \N {} +2445 851 2025-02-26 14:31:38.156136-08 2025-02-26 14:03:21.128088-08 test f A100 \N f \N \N \N {} +2446 851 2025-02-26 14:23:33.344316-08 2025-02-26 12:39:36.843848-08 test t A100 \N f \N \N \N {} +3853 1291 2025-03-01 03:13:24.343849-08 2025-03-01 02:57:41.913482-08 test t A100 \N t \N \N \N {} +2448 854 2025-02-26 14:40:16.012015-08 2025-02-26 13:30:53.984566-08 benchmark f A100 \N t \N \N \N {} +2449 855 2025-02-26 13:12:11.591988-08 2025-02-26 14:39:17.48303-08 benchmark f A100 \N t \N \N \N {} +2450 857 2025-02-26 13:02:50.277788-08 2025-02-26 13:15:55.340603-08 benchmark f A100 \N t \N \N \N {} +2456 860 2025-02-26 12:50:27.406788-08 2025-02-26 14:27:11.783431-08 leaderboard f A100 0.0030875686666666665 t \N \N \N {} +2457 860 2025-02-26 13:17:23.373759-08 2025-02-26 14:15:06.821417-08 test t A100 \N t \N \N \N {} +2458 860 2025-02-26 13:49:59.089223-08 2025-02-26 14:34:48.510454-08 benchmark t A100 \N t \N \N \N {} +2459 860 2025-02-26 12:54:53.87546-08 2025-02-26 13:27:24.625628-08 leaderboard t A100 0.003084496 t \N \N \N {} +2460 862 2025-02-26 14:25:24.115849-08 2025-02-26 14:24:38.388715-08 benchmark f A100 \N t \N \N \N {} +2461 863 2025-02-26 12:52:35.025536-08 2025-02-26 13:59:55.535093-08 benchmark f A100 \N t \N \N \N {} +3278 1144 2025-02-28 06:16:01.039147-08 2025-02-28 06:22:29.16157-08 test t A100 \N t \N \N \N {} +2467 865 2025-02-26 14:29:40.579994-08 2025-02-26 13:01:17.531306-08 benchmark t A100 \N t \N \N \N {} +2468 865 2025-02-26 14:27:27.068975-08 2025-02-26 13:15:03.336598-08 leaderboard t A100 0.0068730191799999995 t \N \N \N {} +2469 867 2025-02-26 14:38:45.220709-08 2025-02-26 13:20:49.410663-08 benchmark f H100 \N t \N \N \N {} +2470 870 2025-02-26 14:35:14.099811-08 2025-02-26 13:16:45.84219-08 test t L4 \N t \N \N \N {} +2471 870 2025-02-26 14:54:17.97184-08 2025-02-26 14:53:55.242909-08 benchmark t L4 \N t \N \N \N {} +3163 1117 2025-02-28 02:58:49.256531-08 2025-02-28 02:51:45.758919-08 test f A100 \N t \N \N \N {} +2472 870 2025-02-26 14:49:05.199571-08 2025-02-26 13:43:51.365921-08 leaderboard t L4 0.01752669466666667 t \N \N \N {} +2473 870 2025-02-26 14:29:38.067462-08 2025-02-26 13:42:20.183691-08 test t H100 \N t \N \N \N {} +2474 870 2025-02-26 14:28:13.427627-08 2025-02-26 14:43:39.03146-08 benchmark t H100 \N t \N \N \N {} +2475 870 2025-02-26 14:44:05.364863-08 2025-02-26 14:18:20.873024-08 leaderboard t H100 0.0014439003333333333 t \N \N \N {} +2476 870 2025-02-26 14:09:52.898678-08 2025-02-26 14:05:33.382355-08 test t T4 \N t \N \N \N {} +2482 870 2025-02-26 14:26:54.587486-08 2025-02-26 13:04:15.11736-08 test f A100 \N t \N \N \N {} +3391 1168 2025-02-28 09:57:12.308014-08 2025-02-28 09:07:40.869103-08 test t L4 \N f \N \N \N {} +2477 870 2025-02-26 14:11:49.901716-08 2025-02-26 13:57:28.933272-08 benchmark t T4 \N t \N \N \N {} +2478 870 2025-02-26 14:52:49.914318-08 2025-02-26 14:30:14.957771-08 leaderboard t T4 0.018135662333333333 t \N \N \N {} +2479 870 2025-02-26 13:04:33.014453-08 2025-02-26 13:28:38.801176-08 test f H100 \N t \N \N \N {} +2480 870 2025-02-26 13:05:56.396107-08 2025-02-26 13:11:26.670759-08 benchmark f H100 \N t \N \N \N {} +2487 870 2025-02-26 13:53:37.8607-08 2025-02-26 13:42:40.91502-08 leaderboard f T4 0.018169958333333333 t \N \N \N {} +3284 1147 2025-02-28 06:34:56.869895-08 2025-02-28 06:59:54.488727-08 benchmark f A100 \N t \N \N \N {} +2488 870 2025-02-26 13:55:29.990434-08 2025-02-26 13:42:48.566834-08 test t A100 \N t \N \N \N {} +2489 870 2025-02-26 13:33:21.862277-08 2025-02-26 13:18:14.133316-08 benchmark t A100 \N t \N \N \N {} +2490 870 2025-02-26 14:25:53.367799-08 2025-02-26 14:32:23.689127-08 leaderboard t A100 0.0035314313333333334 t \N \N \N {} +2491 870 2025-02-26 13:48:35.450822-08 2025-02-26 14:06:57.276021-08 test f L4 \N t \N \N \N {} +2492 870 2025-02-26 13:52:29.744988-08 2025-02-26 13:53:25.403361-08 benchmark f L4 \N t \N \N \N {} +3285 1147 2025-02-28 06:24:43.203152-08 2025-02-28 06:13:29.756699-08 benchmark f T4 \N t \N \N \N {} +2493 870 2025-02-26 13:59:31.991194-08 2025-02-26 13:59:26.579581-08 leaderboard f L4 0.017607324 t \N \N \N {} +2494 869 2025-02-26 13:53:41.469056-08 2025-02-26 14:38:54.569715-08 test f H100 \N t \N \N \N {} +2495 869 2025-02-26 13:23:26.566841-08 2025-02-26 14:00:54.453804-08 benchmark f H100 \N t \N \N \N {} +2496 869 2025-02-26 14:13:25.627702-08 2025-02-26 13:16:17.10637-08 leaderboard f H100 0.0029435355699999996 t \N \N \N {} +2497 869 2025-02-26 13:45:34.876263-08 2025-02-26 13:53:58.749847-08 test t H100 \N t \N \N \N {} +2498 869 2025-02-26 13:46:26.837661-08 2025-02-26 13:24:26.415607-08 benchmark t H100 \N t \N \N \N {} +2503 872 2025-02-26 14:19:49.973867-08 2025-02-26 13:58:04.924559-08 leaderboard t A100 0.0031960086666666665 t \N \N \N {} +2524 872 2025-02-26 14:59:20.978098-08 2025-02-26 14:04:05.964393-08 leaderboard t L4 0.01734968933333333 t \N \N \N {} +2525 873 2025-02-26 14:17:24.445616-08 2025-02-26 13:21:44.500572-08 benchmark f T4 \N t \N \N \N {} +3311 1149 2025-02-28 07:38:30.746822-08 2025-02-28 06:13:50.041675-08 benchmark f H100 \N t \N \N \N {} +2526 874 2025-02-26 14:02:11.631299-08 2025-02-26 14:54:22.504542-08 benchmark f H100 \N f \N \N \N {} +2528 875 2025-02-26 13:53:59.206204-08 2025-02-26 13:59:51.952233-08 benchmark t L4 \N t \N \N \N {} +2529 875 2025-02-26 13:45:06.978432-08 2025-02-26 14:27:23.069181-08 leaderboard t L4 0.017381977333333333 t \N \N \N {} +2530 875 2025-02-26 13:14:04.120622-08 2025-02-26 13:37:23.311234-08 test f H100 \N t \N \N \N {} +2536 875 2025-02-26 14:17:45.203727-08 2025-02-26 13:52:51.20816-08 test t A100 \N t \N \N \N {} +3398 1172 2025-02-28 10:30:25.502848-08 2025-02-28 09:32:32.438948-08 test t L4 \N f \N \N \N {} +2531 875 2025-02-26 13:27:29.331193-08 2025-02-26 13:29:35.204364-08 benchmark f H100 \N t \N \N \N {} +2538 875 2025-02-26 13:53:08.870239-08 2025-02-26 14:00:49.668667-08 leaderboard t A100 0.0032747976666666666 t \N \N \N {} +2539 875 2025-02-26 13:14:52.010055-08 2025-02-26 14:18:34.81441-08 test f T4 \N t \N \N \N {} +2540 875 2025-02-26 13:20:18.964191-08 2025-02-26 13:13:55.628835-08 benchmark f T4 \N t \N \N \N {} +2541 875 2025-02-26 14:54:26.113811-08 2025-02-26 13:43:25.579169-08 leaderboard f T4 0.018149821333333333 t \N \N \N {} +3349 1156 2025-02-28 06:29:55.568265-08 2025-02-28 08:02:02.535704-08 leaderboard f A100 0.0031082066666666664 t \N \N \N {} +2542 875 2025-02-26 14:51:18.161936-08 2025-02-26 14:37:36.137121-08 test t H100 \N t \N \N \N {} +2543 875 2025-02-26 14:35:54.200978-08 2025-02-26 13:13:31.055337-08 benchmark t H100 \N t \N \N \N {} +2544 875 2025-02-26 14:33:45.385769-08 2025-02-26 13:26:20.622935-08 leaderboard t H100 0.0014337893333333334 t \N \N \N {} +2545 875 2025-02-26 13:53:24.267369-08 2025-02-26 13:18:55.978128-08 test t T4 \N t \N \N \N {} +2546 875 2025-02-26 13:38:11.861473-08 2025-02-26 14:59:51.393224-08 benchmark t T4 \N t \N \N \N {} +2567 877 2025-02-26 15:03:59.772268-08 2025-02-26 14:40:45.912645-08 test t A100 \N t \N \N \N {} +2547 875 2025-02-26 14:32:26.84369-08 2025-02-26 13:13:58.565371-08 leaderboard t T4 0.01822390733333333 t \N \N \N {} +2552 877 2025-02-26 13:20:01.898564-08 2025-02-26 13:58:51.317431-08 benchmark f L4 \N t \N \N \N {} +2557 876 2025-02-26 15:02:00.99544-08 2025-02-26 13:29:31.499121-08 benchmark f T4 \N t \N \N \N {} +2558 877 2025-02-26 13:46:19.16509-08 2025-02-26 14:56:55.471863-08 test f A100 \N t \N \N \N {} +2562 877 2025-02-26 14:11:45.890202-08 2025-02-26 14:42:19.158457-08 benchmark f H100 \N t \N \N \N {} +2574 877 2025-02-26 14:53:25.554208-08 2025-02-26 14:16:55.579672-08 benchmark t T4 \N t \N \N \N {} +2580 880 2025-02-26 15:07:50.100053-08 2025-02-26 13:18:56.845732-08 benchmark f H100 \N t \N \N \N {} +2585 883 2025-02-26 15:03:39.651289-08 2025-02-26 14:42:53.129821-08 benchmark f T4 \N t \N \N \N {} +2586 884 2025-02-26 14:50:00.889114-08 2025-02-26 15:10:04.434058-08 test f A100 \N t \N \N \N {} +2590 885 2025-02-26 15:19:01.393865-08 2025-02-26 13:28:15.590615-08 benchmark f H100 \N t \N \N \N {} +2591 885 2025-02-26 13:26:24.818556-08 2025-02-26 13:50:12.168949-08 leaderboard f H100 0.0014732981818181819 t \N \N \N {} +2602 884 2025-02-26 14:57:40.130347-08 2025-02-26 14:36:36.467514-08 benchmark t H100 \N t \N \N \N {} +2603 884 2025-02-26 14:47:56.199118-08 2025-02-26 14:47:39.751177-08 leaderboard t H100 0.001451446 t \N \N \N {} +2608 884 2025-02-26 14:52:54.969875-08 2025-02-26 14:45:17.829971-08 benchmark t T4 \N t \N \N \N {} +2609 884 2025-02-26 14:00:42.000568-08 2025-02-26 14:07:23.322658-08 leaderboard t T4 0.01839805933333333 t \N \N \N {} +2623 885 2025-02-26 13:24:51.303162-08 2025-02-26 14:59:25.978603-08 benchmark f L4 \N t \N \N \N {} +2624 885 2025-02-26 14:46:25.13041-08 2025-02-26 14:01:16.028106-08 leaderboard f L4 0.018322243399999997 t \N \N \N {} +2632 885 2025-02-26 13:42:28.590895-08 2025-02-26 15:11:24.84455-08 benchmark f T4 \N t \N \N \N {} +2633 885 2025-02-26 14:17:07.144881-08 2025-02-26 13:42:07.341899-08 leaderboard f T4 0.017316619 t \N \N \N {} +2638 890 2025-02-26 14:38:01.800384-08 2025-02-26 14:21:29.502344-08 benchmark f H100 \N t \N \N \N {} +2639 891 2025-02-26 14:00:22.235154-08 2025-02-26 13:49:58.414404-08 benchmark f H100 \N t \N \N \N {} +2640 893 2025-02-26 14:44:28.627123-08 2025-02-26 14:56:02.319874-08 benchmark f H100 \N t \N \N \N {} +2641 892 2025-02-26 14:06:16.166538-08 2025-02-26 14:45:31.778383-08 benchmark f H100 \N t \N \N \N {} +2646 898 2025-02-26 15:10:17.627593-08 2025-02-26 14:28:58.359942-08 benchmark f H100 \N t \N \N \N {} +2647 899 2025-02-26 13:42:07.536404-08 2025-02-26 14:35:17.861549-08 benchmark f H100 \N t \N \N \N {} +2652 909 2025-02-26 15:08:14.043409-08 2025-02-26 14:36:11.735601-08 test f T4 \N f \N \N \N {} +2674 917 2025-02-26 14:23:43.794578-08 2025-02-26 14:36:33.755414-08 benchmark t T4 \N t \N \N \N {} +2686 922 2025-02-26 15:57:13.427621-08 2025-02-26 15:26:20.611904-08 test f T4 \N t \N \N \N {} +2696 925 2025-02-26 14:17:38.083972-08 2025-02-26 16:03:31.423476-08 leaderboard f A100 0.0032370253333333333 t \N \N \N {} +2713 927 2025-02-26 14:37:41.802897-08 2025-02-26 14:31:27.897832-08 test f H100 \N t \N \N \N {} +2714 927 2025-02-26 15:15:35.837289-08 2025-02-26 14:47:25.94265-08 benchmark f H100 \N t \N \N \N {} +2718 927 2025-02-26 15:59:35.075538-08 2025-02-26 15:41:14.515832-08 leaderboard t H100 0.0008215225714285714 t \N \N \N {} +2724 937 2025-02-26 16:47:17.193058-08 2025-02-26 16:23:22.453844-08 test f H100 \N t \N \N \N {} +2725 937 2025-02-26 15:50:22.227979-08 2025-02-26 16:45:26.76204-08 benchmark f H100 \N t \N \N \N {} +2726 937 2025-02-26 16:51:40.672188-08 2025-02-26 16:03:32.138312-08 leaderboard f H100 0.00079554 t \N \N \N {} +2727 937 2025-02-26 15:40:40.022004-08 2025-02-26 17:18:13.128244-08 test t H100 \N t \N \N \N {} +2728 937 2025-02-26 17:19:47.937296-08 2025-02-26 17:33:18.727563-08 benchmark t H100 \N t \N \N \N {} +2789 974 2025-02-26 20:55:25.229033-08 2025-02-26 20:10:10.409265-08 test t A100 \N t \N \N \N {} +3494 1198 2025-02-28 11:44:26.606686-08 2025-02-28 12:27:34.310314-08 test t A100 \N t \N \N \N {} +3877 1295 2025-03-01 04:30:13.818352-08 2025-03-01 03:53:15.973054-08 test f A100 \N t \N \N \N {} +2734 939 2025-02-26 16:52:57.1295-08 2025-02-26 16:57:05.775532-08 test t H100 \N t \N \N \N {} +2735 939 2025-02-26 16:51:34.603971-08 2025-02-26 16:14:14.968064-08 benchmark t H100 \N t \N \N \N {} +2736 939 2025-02-26 17:22:29.074864-08 2025-02-26 17:19:20.267253-08 leaderboard t H100 0.0004968643 t \N \N \N {} +2737 940 2025-02-26 17:36:57.572482-08 2025-02-26 17:38:12.608294-08 benchmark f H100 \N t \N \N \N {} +2738 941 2025-02-26 16:31:57.595239-08 2025-02-26 17:53:18.674166-08 benchmark f H100 \N t \N \N \N {} +2740 943 2025-02-26 18:29:20.73428-08 2025-02-26 18:00:14.467783-08 test f H100 \N f \N \N \N {} +2742 945 2025-02-26 19:02:41.462274-08 2025-02-26 17:30:22.202193-08 test f H100 \N f \N \N \N {} +2743 946 2025-02-26 18:48:22.350038-08 2025-02-26 17:37:45.056255-08 test f H100 \N t \N \N \N {} +2744 947 2025-02-26 17:40:41.628733-08 2025-02-26 18:01:07.85212-08 benchmark f H100 \N t \N \N \N {} +3497 1199 2025-02-28 10:52:33.356367-08 2025-02-28 11:45:48.622925-08 test f A100 \N t \N \N \N {} +2746 949 2025-02-26 19:17:31.915891-08 2025-02-26 17:48:54.299006-08 test f H100 \N t \N \N \N {} +3590 1229 2025-02-28 14:30:29.574017-08 2025-02-28 13:08:20.706884-08 test t H100 \N t \N \N \N {} +2748 951 2025-02-26 17:49:30.605776-08 2025-02-26 19:07:43.438503-08 benchmark f H100 \N t \N \N \N {} +2749 952 2025-02-26 18:03:40.668218-08 2025-02-26 17:46:00.26634-08 test t H100 \N t \N \N \N {} +3500 1199 2025-02-28 11:02:55.892242-08 2025-02-28 11:47:33.229851-08 test t A100 \N t \N \N \N {} +2750 952 2025-02-26 17:51:37.477609-08 2025-02-26 18:26:25.871776-08 benchmark t H100 \N t \N \N \N {} +2753 952 2025-02-26 18:36:33.794858-08 2025-02-26 18:27:26.243733-08 benchmark f H100 \N t \N \N \N {} +2754 952 2025-02-26 18:28:16.765992-08 2025-02-26 19:25:37.400542-08 leaderboard f H100 0.00005700789 t \N \N \N {} +3503 1200 2025-02-28 12:12:33.080144-08 2025-02-28 10:58:43.275181-08 test f A100 \N t \N \N \N {} +3880 1295 2025-03-01 02:41:14.083823-08 2025-03-01 02:48:54.508486-08 test t A100 \N t \N \N \N {} +2758 956 2025-02-26 19:12:28.514931-08 2025-02-26 19:22:57.495178-08 test f A100 \N f \N \N \N {} +2764 962 2025-02-26 19:58:47.609776-08 2025-02-26 20:12:54.160596-08 test f A100 \N f \N \N \N {} +2777 965 2025-02-26 19:44:07.999345-08 2025-02-26 19:22:11.183958-08 benchmark t A100 \N t \N \N \N {} +2778 965 2025-02-26 20:27:24.433649-08 2025-02-26 21:00:25.129429-08 leaderboard t A100 0.014223114 t \N \N \N {} +2779 967 2025-02-26 21:12:06.665368-08 2025-02-26 19:59:07.508513-08 test f A100 \N t \N \N \N {} +2783 971 2025-02-26 21:16:26.422875-08 2025-02-26 20:09:38.194507-08 test f A100 \N f \N \N \N {} +2784 972 2025-02-26 19:47:01.998273-08 2025-02-26 20:04:32.122274-08 test f A100 \N f \N \N \N {} +2793 975 2025-02-26 21:52:56.123062-08 2025-02-26 20:49:58.093155-08 benchmark t A100 \N t \N \N \N {} +2795 975 2025-02-26 21:40:13.75942-08 2025-02-26 21:41:56.411339-08 test f A100 \N t \N \N \N {} +2802 976 2025-02-26 20:52:48.46039-08 2025-02-26 21:31:20.714827-08 benchmark t A100 \N t \N \N \N {} +2803 976 2025-02-26 20:35:27.685284-08 2025-02-26 20:31:29.866823-08 leaderboard t A100 0.0031533973333333336 t \N \N \N {} +2808 981 2025-02-26 23:31:14.250849-08 2025-02-26 22:50:58.663532-08 test f H100 \N t \N \N \N {} +2809 981 2025-02-26 22:06:44.871153-08 2025-02-26 23:16:53.896715-08 benchmark f H100 \N t \N \N \N {} +2812 981 2025-02-26 23:27:33.898802-08 2025-02-26 22:27:35.757556-08 benchmark t H100 \N t \N \N \N {} +2813 981 2025-02-26 22:50:45.630362-08 2025-02-26 22:38:26.024532-08 leaderboard t H100 0.0014750091999999999 t \N \N \N {} +2821 983 2025-02-26 23:54:19.617224-08 2025-02-26 22:54:43.658755-08 benchmark f T4 \N t \N \N \N {} +2830 984 2025-02-26 22:48:33.644224-08 2025-02-26 23:28:42.682233-08 benchmark f L4 \N t \N \N \N {} +2831 984 2025-02-26 22:41:43.757041-08 2025-02-26 22:20:51.450588-08 leaderboard f L4 0.01786917866666667 t \N \N \N {} +2832 985 2025-02-26 22:21:51.307477-08 2025-02-26 23:57:27.92374-08 test t H100 \N f \N \N \N {} +2833 985 2025-02-26 23:24:09.942894-08 2025-02-26 23:56:01.814252-08 test f H100 \N f \N \N \N {} +2834 986 2025-02-27 00:15:23.058422-08 2025-02-26 22:20:39.444729-08 test t H100 \N f \N \N \N {} +2836 987 2025-02-26 23:41:37.112761-08 2025-02-26 23:11:03.419055-08 test t H100 \N f \N \N \N {} +2837 987 2025-02-26 23:42:42.038768-08 2025-02-26 22:42:24.323417-08 test f H100 \N f \N \N \N {} +2838 988 2025-02-27 00:16:37.194207-08 2025-02-26 23:55:57.063136-08 test t H100 \N t \N \N \N {} +2845 989 2025-02-26 23:26:00.442015-08 2025-02-27 00:38:35.106507-08 benchmark t H100 \N t \N \N \N {} +2846 989 2025-02-27 00:27:01.196646-08 2025-02-26 23:54:58.682026-08 leaderboard t H100 0.0016006286666666667 t \N \N \N {} +2854 990 2025-02-26 23:50:17.183107-08 2025-02-27 00:18:03.383314-08 benchmark f H100 \N t \N \N \N {} +2862 992 2025-02-27 00:36:55.131454-08 2025-02-27 00:15:38.507114-08 benchmark f H100 \N t \N \N \N {} +2863 992 2025-02-27 00:40:16.481455-08 2025-02-26 22:52:39.64442-08 leaderboard f H100 0.0014829375555555555 t \N \N \N {} +2870 994 2025-02-26 23:16:51.538974-08 2025-02-26 23:17:43.660758-08 test t H100 \N f \N \N \N {} +2871 994 2025-02-26 23:04:28.177098-08 2025-02-26 23:47:57.196289-08 test f H100 \N f \N \N \N {} +2872 995 2025-02-26 23:07:49.94189-08 2025-02-26 23:01:43.419125-08 test t H100 \N t \N \N \N {} +2890 1001 2025-02-27 00:11:31.886016-08 2025-02-27 00:30:39.383216-08 benchmark f T4 \N f \N \N \N {} +3098 1106 2025-02-28 01:45:37.330184-08 2025-02-28 01:23:52.672209-08 benchmark f A100 \N t \N \N \N {} +3099 1106 2025-02-28 02:14:12.999333-08 2025-02-28 01:30:27.761931-08 leaderboard f A100 0.003088477 t \N \N \N {} +6141 1883 2025-03-11 12:00:12.478656-07 2025-03-11 10:31:10.503903-07 test f T4 \N f \N \N \N {} +6313 1953 2025-03-12 19:49:16.980515-07 2025-03-12 19:36:25.891323-07 benchmark f A100 \N t \N \N \N {} +3101 1107 2025-02-28 03:01:32.467064-08 2025-02-28 01:43:34.556856-08 benchmark f A100 \N t \N \N \N {} +3104 1107 2025-02-28 02:08:46.087586-08 2025-02-28 03:09:33.504864-08 benchmark t A100 \N t \N \N \N {} +3105 1107 2025-02-28 02:13:06.321782-08 2025-02-28 02:29:03.356769-08 leaderboard t A100 0.003087981 t \N \N \N {} +6143 1884 2025-03-11 10:32:50.120634-07 2025-03-11 10:37:50.909508-07 benchmark f A100 \N t \N \N \N {} +6316 1954 2025-03-12 19:55:01.537607-07 2025-03-12 19:24:32.57323-07 leaderboard f A100 0.0013369068899999998 t \N \N \N {} +6463 2007 2025-03-14 06:32:12.249801-07 2025-03-14 06:46:07.006143-07 benchmark f A100 \N t \N \N \N {} +3107 1108 2025-02-28 02:23:54.191397-08 2025-02-28 03:14:14.624324-08 benchmark t A100 \N t \N \N \N {} +6464 2008 2025-03-14 06:36:56.11805-07 2025-03-14 07:46:19.266759-07 test f A100 \N t \N \N \N {} +3110 1108 2025-02-28 01:35:00.983298-08 2025-02-28 01:52:32.794497-08 benchmark f A100 \N t \N \N \N {} +3111 1108 2025-02-28 02:00:47.78852-08 2025-02-28 02:24:47.205564-08 leaderboard f A100 0.0030859636666666665 t \N \N \N {} +6145 1885 2025-03-11 11:00:03.267504-07 2025-03-11 12:18:13.420225-07 test t T4 \N f \N \N \N {} +6466 2008 2025-03-14 07:55:41.158642-07 2025-03-14 06:50:35.791318-07 leaderboard f A100 0.003151319 t \N \N \N {} +3113 1109 2025-02-28 03:14:09.084685-08 2025-02-28 03:13:52.452047-08 benchmark t A100 \N t \N \N \N {} +3114 1109 2025-02-28 02:43:35.282513-08 2025-02-28 02:18:55.469658-08 leaderboard t A100 0.0030930446666666664 t \N \N \N {} +6146 1886 2025-03-11 11:52:02.632791-07 2025-03-11 12:03:46.235678-07 test t T4 \N f \N \N \N {} +6320 1955 2025-03-12 20:34:48.835946-07 2025-03-12 20:33:18.170617-07 benchmark f A100 \N t \N \N \N {} +6469 2008 2025-03-14 06:40:05.438504-07 2025-03-14 06:15:33.296357-07 leaderboard t A100 0.0031540403333333335 t \N \N \N {} +3116 1109 2025-02-28 02:52:12.076786-08 2025-02-28 02:54:24.827665-08 benchmark f A100 \N t \N \N \N {} +3117 1109 2025-02-28 01:28:21.790912-08 2025-02-28 02:23:58.811177-08 leaderboard f A100 0.003085927 t \N \N \N {} +6147 1886 2025-03-11 12:54:26.920466-07 2025-03-11 11:44:08.259058-07 test f T4 \N f \N \N \N {} +6472 2009 2025-03-14 07:41:35.13284-07 2025-03-14 07:39:10.080386-07 leaderboard t A100 0.0024421096666666663 t \N \N \N {} +6148 1887 2025-03-11 12:01:43.87785-07 2025-03-11 12:26:23.003639-07 test f T4 \N f \N \N \N {} +6474 2009 2025-03-14 06:55:16.190554-07 2025-03-14 07:53:24.794099-07 benchmark f A100 \N t \N \N \N {} +3125 1111 2025-02-28 03:11:12.541581-08 2025-02-28 01:41:13.937321-08 benchmark f A100 \N t \N \N \N {} +3126 1111 2025-02-28 02:42:24.500879-08 2025-02-28 03:15:29.89987-08 leaderboard f A100 0.00316644475 t \N \N \N {} +6149 1887 2025-03-11 11:38:18.005859-07 2025-03-11 12:03:46.05906-07 test t T4 \N f \N \N \N {} +6323 1957 2025-03-13 09:54:02.414286-07 2025-03-13 09:52:30.795121-07 test t T4 \N f \N \N \N {} +6475 2009 2025-03-14 06:45:29.792426-07 2025-03-14 07:50:32.215984-07 leaderboard f A100 0.0024576166666666665 t \N \N \N {} +6597 2044 2025-03-14 13:34:41.655918-07 2025-03-14 12:08:56.842687-07 test t T4 \N f \N \N \N {} +6896 2145 2025-03-16 13:27:48.549328-07 2025-03-16 13:17:35.997934-07 benchmark f T4 \N f \N \N \N {} +6476 2010 2025-03-14 08:15:20.795494-07 2025-03-14 08:16:59.451399-07 test t A100 \N t \N \N \N {} +6477 2010 2025-03-14 08:08:47.27748-07 2025-03-14 06:59:19.727174-07 benchmark t A100 \N t \N \N \N {} +6478 2010 2025-03-14 06:48:33.525468-07 2025-03-14 08:02:14.948812-07 leaderboard t A100 0.002638912 t \N \N \N {} +3131 1112 2025-02-28 02:57:25.964429-08 2025-02-28 01:48:05.5249-08 benchmark f A100 \N t \N \N \N {} +6479 2010 2025-03-14 06:44:08.522027-07 2025-03-14 07:44:23.847039-07 test f A100 \N t \N \N \N {} +6480 2010 2025-03-14 07:56:38.205116-07 2025-03-14 07:54:00.333047-07 benchmark f A100 \N t \N \N \N {} +6152 1889 2025-03-11 13:30:29.989092-07 2025-03-11 12:48:46.455566-07 test t T4 \N t \N \N \N {} +6326 1958 2025-03-13 10:24:57.188611-07 2025-03-13 10:35:01.884206-07 test f T4 \N f \N \N \N {} +6482 2011 2025-03-14 08:02:31.143266-07 2025-03-14 06:41:16.991591-07 test f A100 \N t \N \N \N {} +6598 2045 2025-03-14 12:19:29.381029-07 2025-03-14 12:25:30.310096-07 test f A100 \N t \N \N \N {} +3137 1113 2025-02-28 03:08:43.17474-08 2025-02-28 02:45:51.821714-08 benchmark f A100 \N t \N \N \N {} +3138 1113 2025-02-28 02:20:25.9795-08 2025-02-28 02:34:44.310115-08 leaderboard f A100 0.0030921646666666664 t \N \N \N {} +6154 1889 2025-03-11 13:37:01.73686-07 2025-03-11 12:13:13.284253-07 test f T4 \N t \N \N \N {} +6327 1959 2025-03-13 10:26:24.667252-07 2025-03-13 11:45:14.442974-07 test t T4 \N f \N \N \N {} +3140 1113 2025-02-28 01:57:21.325728-08 2025-02-28 03:23:21.141542-08 benchmark t A100 \N t \N \N \N {} +6156 1890 2025-03-11 12:21:39.727111-07 2025-03-11 12:34:12.250351-07 test t T4 \N t \N \N \N {} +6157 1890 2025-03-11 13:12:21.4034-07 2025-03-11 12:39:13.895393-07 benchmark t T4 \N f \N \N \N {} +6328 1959 2025-03-13 12:08:12.60756-07 2025-03-13 12:05:32.463165-07 test f T4 \N f \N \N \N {} +6899 2148 2025-03-16 12:45:15.052291-07 2025-03-16 12:31:02.61805-07 test t T4 \N t \N \N \N {} +3143 1114 2025-02-28 01:39:46.938722-08 2025-02-28 02:11:45.255844-08 benchmark t A100 \N t \N \N \N {} +6159 1890 2025-03-11 13:25:06.856726-07 2025-03-11 12:59:34.598467-07 benchmark f T4 \N f \N \N \N {} +6329 1960 2025-03-13 11:46:43.371-07 2025-03-13 10:55:57.695267-07 test f T4 \N f \N \N \N {} +6902 2148 2025-03-16 12:34:32.187225-07 2025-03-16 12:33:04.36163-07 test f T4 \N t \N \N \N {} +3146 1114 2025-02-28 03:06:33.724337-08 2025-02-28 03:00:58.980906-08 benchmark f A100 \N t \N \N \N {} +6330 1960 2025-03-13 12:01:53.271866-07 2025-03-13 10:58:04.509016-07 test t T4 \N f \N \N \N {} +6483 2011 2025-03-14 07:37:56.702294-07 2025-03-14 07:18:30.959485-07 benchmark f A100 \N t \N \N \N {} +6331 1961 2025-03-13 12:07:41.574197-07 2025-03-13 11:12:52.328699-07 test f T4 \N f \N \N \N {} +6485 2011 2025-03-14 08:12:16.79755-07 2025-03-14 07:27:22.899891-07 test t A100 \N t \N \N \N {} +6671 2063 2025-03-14 14:46:30.836769-07 2025-03-14 14:56:13.217033-07 test t T4 \N f \N \N \N {} +6332 1961 2025-03-13 11:55:09.823065-07 2025-03-13 11:32:59.676251-07 test t T4 \N f \N \N \N {} +6486 2011 2025-03-14 07:56:31.390698-07 2025-03-14 07:47:31.714989-07 benchmark t A100 \N t \N \N \N {} +6163 1894 2025-03-11 12:16:32.845989-07 2025-03-11 13:15:29.765532-07 benchmark f T4 \N f \N \N \N {} +6333 1962 2025-03-13 12:24:14.859057-07 2025-03-13 12:20:08.73536-07 test t T4 \N f \N \N \N {} +3159 1116 2025-02-28 02:38:21.177305-08 2025-02-28 02:21:09.980476-08 leaderboard t A100 0.0030984103333333334 t \N \N \N {} +6164 1895 2025-03-11 12:39:00.764376-07 2025-03-11 12:37:33.970367-07 benchmark f T4 \N f \N \N \N {} +3162 1117 2025-02-28 03:41:30.098596-08 2025-02-28 03:20:42.331939-08 leaderboard t A100 0.0031041603333333335 t \N \N \N {} +6165 1896 2025-03-11 14:01:29.688942-07 2025-03-11 14:06:15.280779-07 benchmark f T4 \N t \N \N \N {} +6335 1963 2025-03-13 12:06:34.502766-07 2025-03-13 12:03:06.093899-07 test f T4 \N f \N \N \N {} +3169 1118 2025-02-28 03:31:50.129542-08 2025-02-28 03:36:06.764464-08 test f A100 \N t \N \N \N {} +3170 1118 2025-02-28 03:57:44.683588-08 2025-02-28 02:28:12.168907-08 benchmark f A100 \N t \N \N \N {} +3175 1119 2025-02-28 03:54:43.890351-08 2025-02-28 02:21:17.673941-08 test t A100 \N t \N \N \N {} +3176 1119 2025-02-28 03:25:21.208695-08 2025-02-28 02:53:54.097447-08 benchmark t A100 \N t \N \N \N {} +3179 1120 2025-02-28 03:19:52.64551-08 2025-02-28 04:03:22.512601-08 benchmark f A100 \N t \N \N \N {} +3180 1120 2025-02-28 02:37:44.833383-08 2025-02-28 03:49:05.485113-08 leaderboard f A100 0.0030965363333333336 t \N \N \N {} +3182 1120 2025-02-28 03:37:30.50022-08 2025-02-28 02:42:14.289029-08 benchmark t A100 \N t \N \N \N {} +3183 1120 2025-02-28 04:27:43.205983-08 2025-02-28 04:09:58.805071-08 leaderboard t A100 0.0030974113333333336 t \N \N \N {} +3188 1121 2025-02-28 05:16:45.018621-08 2025-02-28 04:16:05.328123-08 benchmark t A100 \N t \N \N \N {} +3189 1121 2025-02-28 03:32:46.838908-08 2025-02-28 04:24:02.880315-08 leaderboard t A100 0.0031272676666666663 t \N \N \N {} +6174 1899 2025-03-11 13:02:25.812706-07 2025-03-11 14:15:36.592099-07 test f T4 \N f \N \N \N {} +6340 1965 2025-03-13 12:42:09.960264-07 2025-03-13 12:51:36.764465-07 test f T4 \N f \N \N \N {} +6494 2013 2025-03-14 11:17:10.359439-07 2025-03-14 12:08:51.813768-07 test t H100 \N f \N \N \N {} +3191 1122 2025-02-28 04:31:59.29779-08 2025-02-28 04:47:11.70033-08 benchmark f A100 \N t \N \N \N {} +6175 1900 2025-03-11 14:09:24.209402-07 2025-03-11 14:27:29.662604-07 test t T4 \N f \N \N \N {} +3194 1122 2025-02-28 04:47:15.662542-08 2025-02-28 03:39:02.874427-08 benchmark t A100 \N t \N \N \N {} +3195 1122 2025-02-28 05:10:42.206283-08 2025-02-28 04:42:15.656228-08 leaderboard t A100 0.0031017193333333333 t \N \N \N {} +3196 1123 2025-02-28 05:13:41.323903-08 2025-02-28 04:55:50.819985-08 test f A100 \N t \N \N \N {} +3197 1123 2025-02-28 04:32:26.171448-08 2025-02-28 05:16:39.991067-08 benchmark f A100 \N t \N \N \N {} +3199 1123 2025-02-28 03:54:47.179034-08 2025-02-28 04:59:40.706458-08 test t A100 \N t \N \N \N {} +3208 1125 2025-02-28 05:52:39.581361-08 2025-02-28 05:55:19.83514-08 test t A100 \N t \N \N \N {} +3209 1125 2025-02-28 06:17:26.794554-08 2025-02-28 05:23:32.742402-08 benchmark t A100 \N t \N \N \N {} +3210 1125 2025-02-28 06:05:54.312077-08 2025-02-28 06:38:52.236143-08 leaderboard t A100 0.0030843956666666665 t \N \N \N {} +3211 1125 2025-02-28 06:16:05.768963-08 2025-02-28 06:25:41.001152-08 test f A100 \N t \N \N \N {} +3214 1126 2025-02-28 05:05:57.182176-08 2025-02-28 05:32:39.383884-08 test t A100 \N t \N \N \N {} +3215 1126 2025-02-28 05:52:39.479649-08 2025-02-28 06:47:24.562236-08 benchmark t A100 \N t \N \N \N {} +3216 1126 2025-02-28 05:10:50.786526-08 2025-02-28 04:55:01.414872-08 leaderboard t A100 0.0030879633333333336 t \N \N \N {} +3217 1126 2025-02-28 05:53:55.969095-08 2025-02-28 05:27:21.527994-08 test f A100 \N t \N \N \N {} +3220 1127 2025-02-28 06:54:22.094293-08 2025-02-28 06:25:04.727769-08 test t A100 \N t \N \N \N {} +3221 1127 2025-02-28 05:59:30.794599-08 2025-02-28 06:35:17.919421-08 benchmark t A100 \N t \N \N \N {} +3222 1127 2025-02-28 06:46:45.538801-08 2025-02-28 06:24:24.183739-08 leaderboard t A100 0.0031056823333333334 t \N \N \N {} +3223 1127 2025-02-28 05:37:12.475011-08 2025-02-28 05:15:06.135555-08 test f A100 \N t \N \N \N {} +3226 1128 2025-02-28 06:50:10.03441-08 2025-02-28 07:04:42.596407-08 benchmark f A100 \N t \N \N \N {} +3227 1129 2025-02-28 05:42:24.496681-08 2025-02-28 05:46:06.806343-08 benchmark f A100 \N t \N \N \N {} +3228 1131 2025-02-28 06:46:23.079469-08 2025-02-28 07:01:32.420227-08 benchmark f A100 \N t \N \N \N {} +3229 1130 2025-02-28 05:31:01.081051-08 2025-02-28 05:35:16.365184-08 test t A100 \N t \N \N \N {} +3232 1130 2025-02-28 05:56:38.654734-08 2025-02-28 06:03:33.120382-08 test f A100 \N t \N \N \N {} +3233 1130 2025-02-28 06:46:27.388033-08 2025-02-28 07:12:24.723128-08 benchmark f A100 \N t \N \N \N {} +3234 1130 2025-02-28 05:46:53.817871-08 2025-02-28 06:42:04.198827-08 leaderboard f A100 0.00310292 t \N \N \N {} +3235 1132 2025-02-28 06:21:32.053551-08 2025-02-28 07:14:16.857496-08 benchmark f A100 \N t \N \N \N {} +3236 1133 2025-02-28 07:07:04.755708-08 2025-02-28 06:46:05.801511-08 test t A100 \N t \N \N \N {} +3237 1133 2025-02-28 07:17:33.624616-08 2025-02-28 07:02:03.837416-08 benchmark t A100 \N t \N \N \N {} +3248 1135 2025-02-28 05:37:54.093967-08 2025-02-28 05:45:04.283128-08 benchmark t A100 \N t \N \N \N {} +3249 1135 2025-02-28 06:33:33.396017-08 2025-02-28 06:15:49.659969-08 leaderboard t A100 0.0031071463333333335 t \N \N \N {} +6177 1901 2025-03-11 14:17:16.056405-07 2025-03-11 12:39:24.253923-07 test t T4 \N f \N \N \N {} +6347 1967 2025-03-13 13:09:40.841714-07 2025-03-13 12:52:38.371098-07 test f T4 \N f \N \N \N {} +6496 2014 2025-03-14 11:32:31.174534-07 2025-03-14 10:45:59.582663-07 test f H100 \N f \N \N \N {} +6601 2045 2025-03-14 13:39:24.594274-07 2025-03-14 12:37:33.021169-07 test t A100 \N t \N \N \N {} +6602 2045 2025-03-14 13:09:25.371778-07 2025-03-14 12:02:22.248161-07 benchmark t A100 \N t \N \N \N {} +6603 2045 2025-03-14 12:21:14.812855-07 2025-03-14 13:54:36.938359-07 leaderboard t A100 0.0025770586666666664 t \N \N \N {} +6646 2053 2025-03-14 12:10:23.321882-07 2025-03-14 13:10:56.484171-07 leaderboard f A100 0.0024435633333333333 t \N \N \N {} +3251 1137 2025-02-28 05:50:43.617739-08 2025-02-28 06:49:39.360729-08 benchmark f A100 \N t \N \N \N {} +3257 1139 2025-02-28 06:31:44.654978-08 2025-02-28 05:38:26.805158-08 test f A100 \N t \N \N \N {} +3261 1140 2025-02-28 06:07:13.230828-08 2025-02-28 05:47:24.282991-08 benchmark f A100 \N t \N \N \N {} +3262 1141 2025-02-28 05:55:39.227255-08 2025-02-28 05:48:38.619947-08 benchmark f A100 \N t \N \N \N {} +6178 1901 2025-03-11 13:27:16.149666-07 2025-03-11 13:38:34.371907-07 test f T4 \N f \N \N \N {} +6348 1967 2025-03-13 13:02:53.563358-07 2025-03-13 13:01:27.915249-07 test t T4 \N f \N \N \N {} +6497 2014 2025-03-14 12:15:59.319003-07 2025-03-14 11:30:37.118914-07 test t H100 \N f \N \N \N {} +6604 2046 2025-03-14 13:43:57.061271-07 2025-03-14 13:07:50.209665-07 test t A100 \N t \N \N \N {} +6605 2046 2025-03-14 12:17:52.139426-07 2025-03-14 12:29:22.656383-07 benchmark t A100 \N t \N \N \N {} +6606 2046 2025-03-14 12:53:29.636257-07 2025-03-14 12:33:45.393318-07 leaderboard t A100 0.00256954 t \N \N \N {} +3264 1142 2025-02-28 07:20:46.488258-08 2025-02-28 06:11:17.956226-08 benchmark f A100 \N t \N \N \N {} +3265 1142 2025-02-28 07:11:08.133535-08 2025-02-28 06:09:41.048274-08 leaderboard f A100 0.0030866826666666666 t \N \N \N {} +6179 1902 2025-03-11 17:31:19.851556-07 2025-03-11 16:59:27.86489-07 test t T4 \N f \N \N \N {} +6349 1968 2025-03-13 12:16:15.487592-07 2025-03-13 13:01:52.748945-07 test t T4 \N f \N \N \N {} +6498 2015 2025-03-14 10:52:26.469597-07 2025-03-14 10:53:06.720164-07 test f H100 \N t \N \N \N {} +6499 2015 2025-03-14 11:34:32.855847-07 2025-03-14 10:47:26.771183-07 benchmark f H100 \N t \N \N \N {} +3267 1142 2025-02-28 07:07:31.550653-08 2025-02-28 06:39:21.840614-08 benchmark t A100 \N t \N \N \N {} +3268 1142 2025-02-28 06:19:53.446652-08 2025-02-28 05:31:10.708334-08 leaderboard t A100 0.0030884806666666665 t \N \N \N {} +6180 1902 2025-03-11 16:42:29.121174-07 2025-03-11 17:29:34.053097-07 test f T4 \N f \N \N \N {} +6350 1968 2025-03-13 11:36:52.447175-07 2025-03-13 11:56:08.746664-07 test f T4 \N f \N \N \N {} +6501 2015 2025-03-14 11:04:29.106551-07 2025-03-14 11:04:06.594233-07 test t H100 \N t \N \N \N {} +6502 2015 2025-03-14 10:46:29.195352-07 2025-03-14 11:29:56.856874-07 benchmark t H100 \N t \N \N \N {} +6503 2015 2025-03-14 11:02:59.180189-07 2025-03-14 11:37:31.767013-07 leaderboard t H100 0.006124215666666667 t \N \N \N {} +3270 1143 2025-02-28 07:20:25.006918-08 2025-02-28 06:39:34.217517-08 benchmark t A100 \N t \N \N \N {} +3271 1143 2025-02-28 06:14:53.755216-08 2025-02-28 05:57:39.774657-08 leaderboard t A100 0.0031000976666666663 t \N \N \N {} +6181 1903 2025-03-11 16:58:37.492878-07 2025-03-11 15:58:30.703546-07 test t T4 \N t \N \N \N {} +6182 1903 2025-03-11 17:18:44.559625-07 2025-03-11 15:57:14.031513-07 benchmark t T4 \N t \N \N \N {} +6183 1903 2025-03-11 16:37:25.986127-07 2025-03-11 16:28:32.33402-07 leaderboard t T4 3.1959500263333336 t \N \N \N {} +6351 1969 2025-03-13 11:55:17.679659-07 2025-03-13 12:30:30.977919-07 test t T4 \N f \N \N \N {} +3273 1143 2025-02-28 07:13:08.532252-08 2025-02-28 06:24:09.587417-08 benchmark f A100 \N t \N \N \N {} +6353 1970 2025-03-13 13:09:01.840724-07 2025-03-13 13:09:06.040381-07 test t T4 \N f \N \N \N {} +6507 2017 2025-03-14 12:15:09.410382-07 2025-03-14 12:08:16.887223-07 leaderboard t A100 0.0024410596666666665 t \N \N \N {} +3300 1148 2025-02-28 07:07:41.615297-08 2025-02-28 07:39:21.848265-08 benchmark t H100 \N t \N \N \N {} +3301 1148 2025-02-28 06:10:52.271101-08 2025-02-28 06:58:11.397489-08 leaderboard t H100 0.001857518 t \N \N \N {} +3302 1148 2025-02-28 06:00:16.905751-08 2025-02-28 06:14:31.098179-08 test t L4 \N t \N \N \N {} +3303 1148 2025-02-28 07:52:17.086381-08 2025-02-28 06:16:18.985978-08 benchmark t L4 \N t \N \N \N {} +3304 1148 2025-02-28 07:32:12.045626-08 2025-02-28 06:24:56.573657-08 leaderboard t L4 0.008826167333333334 t \N \N \N {} +3307 1148 2025-02-28 07:29:43.892232-08 2025-02-28 06:09:46.083173-08 leaderboard f L4 0.008876066 t \N \N \N {} +3308 1148 2025-02-28 06:13:12.466775-08 2025-02-28 07:21:14.017377-08 test f T4 \N t \N \N \N {} +3309 1148 2025-02-28 06:14:05.077253-08 2025-02-28 07:11:50.628783-08 benchmark f T4 \N t \N \N \N {} +3310 1148 2025-02-28 06:45:02.322758-08 2025-02-28 06:31:27.319188-08 leaderboard f T4 0.010512813666666666 t \N \N \N {} +3313 1151 2025-02-28 06:13:28.870279-08 2025-02-28 06:59:04.57498-08 test f H100 \N f \N \N \N {} +3314 1151 2025-02-28 06:53:49.334214-08 2025-02-28 07:39:29.099662-08 test f A100 \N f \N \N \N {} +3315 1151 2025-02-28 06:23:38.170481-08 2025-02-28 06:10:53.877168-08 test t A100 \N f \N \N \N {} +3320 1151 2025-02-28 07:07:06.317012-08 2025-02-28 07:47:22.348612-08 test f T4 \N f \N \N \N {} +3321 1152 2025-02-28 06:41:39.144469-08 2025-02-28 07:15:44.323297-08 test f A100 \N t \N \N \N {} +3322 1152 2025-02-28 06:33:30.747147-08 2025-02-28 07:19:24.710801-08 benchmark f A100 \N t \N \N \N {} +3323 1152 2025-02-28 06:13:17.846448-08 2025-02-28 07:16:53.101236-08 leaderboard f A100 0.003092475 t \N \N \N {} +3532 1205 2025-02-28 11:36:17.404133-08 2025-02-28 11:04:59.294896-08 test t A100 \N t \N \N \N {} +3324 1152 2025-02-28 07:18:58.437402-08 2025-02-28 06:19:11.553801-08 test t A100 \N t \N \N \N {} +3325 1152 2025-02-28 07:55:29.183414-08 2025-02-28 07:03:55.443509-08 benchmark t A100 \N t \N \N \N {} +3326 1152 2025-02-28 07:19:25.423303-08 2025-02-28 07:50:35.236223-08 leaderboard t A100 0.0031075596666666665 t \N \N \N {} +3329 1153 2025-02-28 06:25:00.374655-08 2025-02-28 07:30:25.54331-08 test t L4 \N f \N \N \N {} +3334 1153 2025-02-28 07:49:11.602135-08 2025-02-28 07:52:08.469201-08 test f L4 \N f \N \N \N {} +3335 1154 2025-02-28 07:00:59.730078-08 2025-02-28 06:49:51.977949-08 test t A100 \N t \N \N \N {} +3336 1154 2025-02-28 07:54:33.813629-08 2025-02-28 06:56:00.534875-08 benchmark t A100 \N t \N \N \N {} +3344 1155 2025-02-28 07:18:52.719651-08 2025-02-28 07:58:30.473969-08 test f A100 \N t \N \N \N {} +3345 1155 2025-02-28 06:18:08.185243-08 2025-02-28 06:59:20.358203-08 benchmark f A100 \N t \N \N \N {} +3346 1155 2025-02-28 07:02:45.365698-08 2025-02-28 06:33:19.989005-08 leaderboard f A100 0.00308959 t \N \N \N {} +3347 1156 2025-02-28 07:59:44.021356-08 2025-02-28 06:48:49.388888-08 test f A100 \N t \N \N \N {} +3350 1156 2025-02-28 06:29:45.868814-08 2025-02-28 06:37:20.655964-08 test t A100 \N t \N \N \N {} +3351 1156 2025-02-28 07:13:45.617041-08 2025-02-28 07:15:15.742271-08 benchmark t A100 \N t \N \N \N {} +3352 1156 2025-02-28 07:46:00.514913-08 2025-02-28 06:43:16.480409-08 leaderboard t A100 0.0031107886666666665 t \N \N \N {} +3354 1157 2025-02-28 06:33:28.648011-08 2025-02-28 06:43:24.152901-08 benchmark t A100 \N t \N \N \N {} +3355 1157 2025-02-28 07:45:51.654693-08 2025-02-28 07:44:31.010515-08 leaderboard t A100 0.0030852116666666663 t \N \N \N {} +6189 1905 2025-03-11 17:31:04.799276-07 2025-03-11 17:23:51.876169-07 test t T4 \N f \N \N \N {} +6355 1971 2025-03-13 13:24:12.401709-07 2025-03-13 13:32:14.413028-07 test f T4 \N f \N \N \N {} +6509 2017 2025-03-14 11:29:46.886636-07 2025-03-14 12:19:10.774689-07 benchmark f A100 \N t \N \N \N {} +3357 1157 2025-02-28 08:03:49.481801-08 2025-02-28 08:00:06.310818-08 benchmark f A100 \N t \N \N \N {} +3358 1157 2025-02-28 07:39:56.271986-08 2025-02-28 07:00:59.378014-08 leaderboard f A100 0.0030816896666666666 t \N \N \N {} +3360 1159 2025-02-28 07:39:08.622355-08 2025-02-28 08:38:10.734027-08 test f T4 \N f \N \N \N {} +3361 1160 2025-02-28 09:26:30.209671-08 2025-02-28 09:51:28.232565-08 test f L4 \N f \N \N \N {} +3362 1160 2025-02-28 08:13:42.830297-08 2025-02-28 08:36:53.136944-08 test t L4 \N f \N \N \N {} +3367 1163 2025-02-28 09:08:01.615266-08 2025-02-28 09:04:38.110604-08 test t T4 \N t \N \N \N {} +3368 1163 2025-02-28 08:47:34.73436-08 2025-02-28 09:40:07.599281-08 benchmark t T4 \N t \N \N \N {} +3370 1163 2025-02-28 10:03:37.527045-08 2025-02-28 10:11:34.393096-08 test f T4 \N f \N \N \N {} +3371 1164 2025-02-28 10:01:44.770216-08 2025-02-28 09:27:21.603234-08 test f L4 \N t \N \N \N {} +3372 1164 2025-02-28 08:47:05.293177-08 2025-02-28 10:23:25.384173-08 benchmark f L4 \N t \N \N \N {} +3377 1165 2025-02-28 09:21:40.284625-08 2025-02-28 10:30:06.106699-08 test f L4 \N t \N \N \N {} +3378 1165 2025-02-28 08:56:55.571472-08 2025-02-28 09:49:21.797242-08 benchmark f L4 \N t \N \N \N {} +3379 1165 2025-02-28 10:05:42.150638-08 2025-02-28 09:36:46.848902-08 leaderboard f L4 0.017076701333333333 t \N \N \N {} +3454 1190 2025-02-28 12:13:49.181929-08 2025-02-28 11:09:14.008399-08 test f A100 \N t \N \N \N {} +3380 1165 2025-02-28 09:57:10.477902-08 2025-02-28 08:59:43.970761-08 test t L4 \N t \N \N \N {} +3381 1165 2025-02-28 09:34:07.882518-08 2025-02-28 10:28:42.342832-08 benchmark t L4 \N t \N \N \N {} +3382 1165 2025-02-28 10:25:49.905778-08 2025-02-28 08:45:12.787062-08 leaderboard t L4 0.017087161666666666 t \N \N \N {} +3383 1166 2025-02-28 10:09:06.401999-08 2025-02-28 09:14:44.292073-08 test t L4 \N t \N \N \N {} +3384 1166 2025-02-28 09:27:54.175727-08 2025-02-28 08:41:58.508616-08 benchmark t L4 \N t \N \N \N {} +3387 1166 2025-02-28 09:50:29.259635-08 2025-02-28 10:07:29.279896-08 benchmark f L4 \N t \N \N \N {} +3388 1166 2025-02-28 09:42:07.63904-08 2025-02-28 10:09:52.762874-08 leaderboard f L4 0.01713320033333333 t \N \N \N {} +3389 1167 2025-02-28 10:08:48.950368-08 2025-02-28 09:00:40.626022-08 test t L4 \N f \N \N \N {} +3390 1167 2025-02-28 09:23:41.075458-08 2025-02-28 08:55:11.552134-08 test f L4 \N f \N \N \N {} +3544 1207 2025-02-28 12:21:19.174948-08 2025-02-28 11:46:49.287104-08 test f A100 \N t \N \N \N {} +3394 1169 2025-02-28 09:19:45.607358-08 2025-02-28 10:30:06.679512-08 test t L4 \N f \N \N \N {} +3395 1169 2025-02-28 10:06:46.387305-08 2025-02-28 09:43:30.292106-08 test f L4 \N f \N \N \N {} +3397 1171 2025-02-28 09:03:44.961516-08 2025-02-28 10:20:27.853337-08 test f L4 \N f \N \N \N {} +3405 1174 2025-02-28 10:39:36.975544-08 2025-02-28 09:52:14.8899-08 benchmark t L4 \N t \N \N \N {} +3400 1173 2025-02-28 10:29:49.463718-08 2025-02-28 10:21:22.102387-08 test f L4 \N t \N \N \N {} +3401 1173 2025-02-28 09:21:57.128462-08 2025-02-28 10:30:59.993678-08 benchmark f L4 \N t \N \N \N {} +3434 1185 2025-02-28 11:34:12.254431-08 2025-02-28 10:21:43.385984-08 test t L4 \N t \N \N \N {} +3435 1185 2025-02-28 11:18:57.057952-08 2025-02-28 11:28:37.311547-08 benchmark t L4 \N t \N \N \N {} +3439 1186 2025-02-28 10:55:26.766127-08 2025-02-28 11:07:39.44103-08 benchmark f L4 \N t \N \N \N {} +3440 1186 2025-02-28 10:10:12.112939-08 2025-02-28 11:10:32.049899-08 leaderboard f L4 0.01707861033333333 t \N \N \N {} +3441 1186 2025-02-28 09:42:05.101791-08 2025-02-28 10:27:34.733276-08 test t L4 \N t \N \N \N {} +3442 1186 2025-02-28 10:06:18.981477-08 2025-02-28 10:02:38.94075-08 benchmark t L4 \N t \N \N \N {} +3449 1188 2025-02-28 11:22:41.568508-08 2025-02-28 10:32:23.811511-08 test f A100 \N t \N \N \N {} +3450 1188 2025-02-28 10:37:43.385883-08 2025-02-28 11:17:39.7834-08 benchmark f A100 \N t \N \N \N {} +3456 1190 2025-02-28 10:41:28.207826-08 2025-02-28 12:14:51.122436-08 leaderboard f A100 0.002069882 t \N \N \N {} +3457 1190 2025-02-28 11:29:53.359486-08 2025-02-28 11:55:18.355744-08 test t A100 \N t \N \N \N {} +3458 1190 2025-02-28 10:37:33.37378-08 2025-02-28 11:30:33.125044-08 benchmark t A100 \N t \N \N \N {} +3459 1190 2025-02-28 11:02:25.492442-08 2025-02-28 11:05:52.473542-08 leaderboard t A100 0.002045183 t \N \N \N {} +6190 1905 2025-03-11 16:26:41.241921-07 2025-03-11 16:38:33.020666-07 test f T4 \N f \N \N \N {} +6510 2017 2025-03-14 12:01:53.782121-07 2025-03-14 10:58:53.397351-07 leaderboard f A100 0.0025043896666666664 t \N \N \N {} +3461 1191 2025-02-28 10:52:09.093285-08 2025-02-28 11:24:50.914766-08 benchmark f A100 \N t \N \N \N {} +3462 1191 2025-02-28 12:13:09.43073-08 2025-02-28 11:11:59.107864-08 leaderboard f A100 0.003088071 t \N \N \N {} +6191 1906 2025-03-11 16:40:31.677341-07 2025-03-11 17:30:01.999781-07 test t T4 \N f \N \N \N {} +6356 1971 2025-03-13 12:18:33.704663-07 2025-03-13 12:52:16.173084-07 test t T4 \N f \N \N \N {} +6511 2018 2025-03-14 11:30:51.790617-07 2025-03-14 12:46:33.976449-07 test t A100 \N t \N \N \N {} +6512 2018 2025-03-14 11:50:37.040812-07 2025-03-14 12:00:32.698091-07 benchmark t A100 \N t \N \N \N {} +6513 2018 2025-03-14 11:58:09.461167-07 2025-03-14 10:57:34.439949-07 leaderboard t A100 0.0024541096666666666 t \N \N \N {} +3464 1191 2025-02-28 11:06:30.414003-08 2025-02-28 10:53:55.756396-08 benchmark t A100 \N t \N \N \N {} +3465 1191 2025-02-28 10:54:02.19782-08 2025-02-28 11:07:04.11224-08 leaderboard t A100 0.0030866083333333334 t \N \N \N {} +6192 1906 2025-03-11 18:04:00.474946-07 2025-03-11 18:14:14.534699-07 test f T4 \N f \N \N \N {} +6357 1972 2025-03-13 13:34:07.292711-07 2025-03-13 14:11:37.509333-07 test t T4 \N t \N \N \N {} +6358 1972 2025-03-13 13:17:51.104355-07 2025-03-13 14:01:28.265004-07 benchmark t T4 \N f \N \N \N {} +6516 2018 2025-03-14 10:52:38.328-07 2025-03-14 12:27:11.650928-07 leaderboard f A100 0.0024775536666666667 t \N \N \N {} +3469 1193 2025-02-28 12:13:49.642175-08 2025-02-28 11:55:09.712002-08 benchmark f A100 \N t \N \N \N {} +3470 1193 2025-02-28 11:12:47.050744-08 2025-02-28 11:14:55.313376-08 leaderboard f A100 0.0031209246666666665 t \N \N \N {} +6193 1907 2025-03-11 16:49:57.729617-07 2025-03-11 17:48:28.815284-07 test f T4 \N f \N \N \N {} +6359 1972 2025-03-13 13:07:47.502676-07 2025-03-13 14:27:21.253575-07 test f T4 \N t \N \N \N {} +3472 1193 2025-02-28 10:47:16.91505-08 2025-02-28 11:18:49.513731-08 benchmark t A100 \N t \N \N \N {} +3473 1193 2025-02-28 11:05:06.759594-08 2025-02-28 12:04:22.05689-08 leaderboard t A100 0.003093471 t \N \N \N {} +6194 1907 2025-03-11 17:02:18.345331-07 2025-03-11 16:38:32.775938-07 test t T4 \N f \N \N \N {} +6361 1973 2025-03-13 13:34:46.334274-07 2025-03-13 14:01:13.147474-07 test f T4 \N t \N \N \N {} +6362 1973 2025-03-13 13:37:07.283208-07 2025-03-13 15:08:05.351976-07 benchmark f T4 \N f \N \N \N {} +6518 2020 2025-03-14 11:30:41.762022-07 2025-03-14 11:12:09.117466-07 test f A100 \N t \N \N \N {} +6519 2020 2025-03-14 11:13:39.719926-07 2025-03-14 11:25:21.536972-07 benchmark f A100 \N t \N \N \N {} +6520 2020 2025-03-14 12:06:50.863502-07 2025-03-14 12:44:38.653958-07 leaderboard f A100 0.0025181186666666665 t \N \N \N {} +3476 1196 2025-02-28 10:54:21.072266-08 2025-02-28 11:37:50.81538-08 benchmark t A100 \N t \N \N \N {} +3477 1196 2025-02-28 12:24:50.73577-08 2025-02-28 11:42:01.495948-08 leaderboard t A100 0.0030894043333333336 t \N \N \N {} +6195 1908 2025-03-11 16:33:13.825423-07 2025-03-11 17:00:19.916832-07 test t T4 \N f \N \N \N {} +3484 1195 2025-02-28 11:31:48.964942-08 2025-02-28 11:27:19.249047-08 benchmark t A100 \N f \N \N \N {} +6365 1974 2025-03-13 15:18:06.293763-07 2025-03-13 14:44:47.284631-07 test f T4 \N t \N \N \N {} +3486 1197 2025-02-28 12:26:33.136784-08 2025-02-28 11:24:39.011923-08 benchmark f A100 \N t \N \N \N {} +3487 1197 2025-02-28 11:54:54.56215-08 2025-02-28 12:26:07.45337-08 leaderboard f A100 0.0030942576666666663 t \N \N \N {} +6366 1974 2025-03-13 14:15:36.374645-07 2025-03-13 14:09:23.053095-07 benchmark f T4 \N f \N \N \N {} +6522 2020 2025-03-14 12:18:54.728315-07 2025-03-14 12:44:10.914453-07 benchmark t A100 \N t \N \N \N {} +3489 1197 2025-02-28 11:23:03.004299-08 2025-02-28 11:03:53.165609-08 benchmark t A100 \N t \N \N \N {} +3490 1197 2025-02-28 11:33:31.529975-08 2025-02-28 11:07:28.938258-08 leaderboard t A100 0.0030712816666666667 t \N \N \N {} +6197 1909 2025-03-11 18:17:26.032444-07 2025-03-11 17:19:28.72111-07 test t T4 \N f \N \N \N {} +6368 1974 2025-03-13 14:55:40.171664-07 2025-03-13 14:12:41.864308-07 benchmark t T4 \N f \N \N \N {} +6523 2020 2025-03-14 12:53:35.58379-07 2025-03-14 11:54:41.519798-07 leaderboard t A100 0.002498382 t \N \N \N {} +6524 2021 2025-03-14 12:36:39.402806-07 2025-03-14 11:08:03.626394-07 test t A100 \N t \N \N \N {} +6525 2021 2025-03-14 11:24:50.232214-07 2025-03-14 12:29:14.201976-07 benchmark t A100 \N t \N \N \N {} +6526 2021 2025-03-14 12:39:45.622642-07 2025-03-14 11:23:16.261268-07 leaderboard t A100 0.0025124666666666664 t \N \N \N {} +6612 2047 2025-03-14 13:18:33.788335-07 2025-03-14 13:24:29.736685-07 leaderboard f A100 0.0025605253333333333 t \N \N \N {} +6527 2021 2025-03-14 12:10:21.085092-07 2025-03-14 11:31:54.576597-07 test f A100 \N t \N \N \N {} +6374 1977 2025-03-13 16:02:01.798258-07 2025-03-13 15:38:46.117793-07 benchmark t T4 \N f \N \N \N {} +6536 2023 2025-03-14 12:00:31.556878-07 2025-03-14 12:46:54.788332-07 test t A100 \N t \N \N \N {} +6537 2023 2025-03-14 12:32:44.743863-07 2025-03-14 12:54:06.787263-07 benchmark t A100 \N t \N \N \N {} +6538 2023 2025-03-14 12:49:53.790312-07 2025-03-14 12:00:47.817767-07 leaderboard t A100 0.0025183806666666665 t \N \N \N {} +3507 1200 2025-02-28 12:16:30.548788-08 2025-02-28 11:43:54.947982-08 benchmark t A100 \N t \N \N \N {} +3508 1200 2025-02-28 11:28:38.825541-08 2025-02-28 11:10:09.664227-08 leaderboard t A100 0.003144041 t \N \N \N {} +6203 1912 2025-03-11 17:08:06.730232-07 2025-03-11 17:58:19.821741-07 test f T4 \N f \N \N \N {} +6377 1978 2025-03-13 15:54:45.454946-07 2025-03-13 16:33:05.355765-07 benchmark f T4 \N t \N \N \N {} +6541 2023 2025-03-14 11:06:04.095739-07 2025-03-14 11:57:33.138195-07 leaderboard f A100 0.0024532393333333334 t \N \N \N {} +6618 2048 2025-03-14 12:42:53.553414-07 2025-03-14 13:49:27.329988-07 leaderboard t A100 0.0025746103333333333 t \N \N \N {} +6648 2054 2025-03-14 12:06:53.802343-07 2025-03-14 13:37:02.957155-07 benchmark f A100 \N t \N \N \N {} +6649 2054 2025-03-14 12:36:50.625847-07 2025-03-14 12:35:25.488371-07 leaderboard f A100 0.0024409133333333334 t \N \N \N {} +3513 1201 2025-02-28 12:22:24.212195-08 2025-02-28 12:18:25.901829-08 benchmark t A100 \N t \N \N \N {} +3514 1201 2025-02-28 12:24:03.792716-08 2025-02-28 12:13:02.802944-08 leaderboard t A100 0.0031015336666666664 t \N \N \N {} +6378 1979 2025-03-13 17:23:58.186753-07 2025-03-13 16:06:55.720179-07 benchmark f T4 \N f \N \N \N {} +6543 2025 2025-03-14 12:41:00.561242-07 2025-03-14 11:46:24.982291-07 test t A100 \N t \N \N \N {} +6544 2025 2025-03-14 12:20:54.337724-07 2025-03-14 11:31:16.216919-07 benchmark t A100 \N t \N \N \N {} +6545 2025 2025-03-14 11:25:51.951167-07 2025-03-14 12:23:37.08102-07 leaderboard t A100 0.0025238436666666663 t \N \N \N {} +6620 2048 2025-03-14 12:30:34.657105-07 2025-03-14 12:13:05.701347-07 benchmark f A100 \N t \N \N \N {} +6621 2048 2025-03-14 12:33:05.301709-07 2025-03-14 13:37:25.906329-07 leaderboard f A100 0.002544002 t \N \N \N {} +3519 1202 2025-02-28 11:11:30.376958-08 2025-02-28 11:01:50.55658-08 benchmark t A100 \N t \N \N \N {} +3520 1202 2025-02-28 11:43:16.373234-08 2025-02-28 12:31:24.408715-08 leaderboard t A100 0.003084011 t \N \N \N {} +6207 1914 2025-03-11 17:36:02.063163-07 2025-03-11 18:44:14.758287-07 test f T4 \N f \N \N \N {} +6380 1980 2025-03-13 15:58:51.960023-07 2025-03-13 16:24:59.128925-07 test f T4 \N f \N \N \N {} +6546 2025 2025-03-14 12:53:00.880967-07 2025-03-14 12:23:03.100245-07 test f A100 \N t \N \N \N {} +6547 2025 2025-03-14 11:06:13.671532-07 2025-03-14 11:10:58.899444-07 benchmark f A100 \N t \N \N \N {} +6622 2049 2025-03-14 12:01:06.825573-07 2025-03-14 12:10:07.768513-07 test f A100 \N t \N \N \N {} +6208 1914 2025-03-11 17:46:09.416055-07 2025-03-11 18:43:51.806087-07 test t T4 \N f \N \N \N {} +6381 1981 2025-03-13 17:27:47.16164-07 2025-03-13 17:00:09.810894-07 test t T4 \N f \N \N \N {} +6209 1915 2025-03-11 17:02:30.670995-07 2025-03-11 18:34:35.253755-07 test f T4 \N f \N \N \N {} +6210 1915 2025-03-11 18:19:24.18935-07 2025-03-11 16:56:01.127871-07 test t T4 \N f \N \N \N {} +6212 1916 2025-03-11 18:37:26.217772-07 2025-03-11 18:26:08.828412-07 test t T4 \N f \N \N \N {} +6385 1983 2025-03-13 16:53:42.93878-07 2025-03-13 18:01:17.86942-07 test t T4 \N f \N \N \N {} +6213 1917 2025-03-11 18:49:08.446554-07 2025-03-11 18:23:51.890625-07 test t T4 \N f \N \N \N {} +6386 1983 2025-03-13 16:59:20.147802-07 2025-03-13 17:23:05.485124-07 test f T4 \N f \N \N \N {} +6556 2028 2025-03-14 13:11:22.628618-07 2025-03-14 12:15:44.55829-07 test f A100 \N t \N \N \N {} +6214 1917 2025-03-11 18:57:56.391885-07 2025-03-11 18:40:03.839455-07 test f T4 \N f \N \N \N {} +6387 1984 2025-03-13 17:51:21.236837-07 2025-03-13 16:43:28.14984-07 test t T4 \N f \N \N \N {} +6557 2029 2025-03-14 12:34:22.237304-07 2025-03-14 11:29:44.596311-07 test t T4 \N f \N \N \N {} +6627 2049 2025-03-14 13:14:24.077723-07 2025-03-14 12:11:10.400483-07 leaderboard t A100 0.0031591056666666665 t \N \N \N {} +3545 1207 2025-02-28 11:34:32.128279-08 2025-02-28 10:48:07.092517-08 benchmark f A100 \N t \N \N \N {} +6388 1984 2025-03-13 17:46:54.072499-07 2025-03-13 16:07:38.434852-07 test f T4 \N f \N \N \N {} +6558 2029 2025-03-14 13:24:40.101943-07 2025-03-14 12:23:21.049241-07 test f T4 \N f \N \N \N {} +6628 2050 2025-03-14 13:02:18.585241-07 2025-03-14 12:00:19.719197-07 test f A100 \N t \N \N \N {} +6680 2068 2025-03-14 15:33:14.472771-07 2025-03-14 16:02:51.078416-07 test f T4 \N f \N \N \N {} +3548 1208 2025-02-28 11:47:51.556932-08 2025-02-28 11:49:48.214291-08 benchmark f A100 \N t \N \N \N {} +3549 1208 2025-02-28 12:21:05.688454-08 2025-02-28 11:23:23.692653-08 leaderboard f A100 0.003099421 t \N \N \N {} +6389 1985 2025-03-13 16:57:14.22982-07 2025-03-13 17:30:21.244746-07 test t T4 \N f \N \N \N {} +7055 2184 2025-03-16 20:46:00.165975-07 2025-03-16 21:45:58.753749-07 test f L4 \N f \N \N \N {} +3551 1208 2025-02-28 12:08:51.038645-08 2025-02-28 11:50:46.059027-08 benchmark t A100 \N t \N \N \N {} +3552 1208 2025-02-28 11:11:38.66471-08 2025-02-28 12:18:11.410389-08 leaderboard t A100 0.0030721586666666665 t \N \N \N {} +6390 1985 2025-03-13 16:22:49.557696-07 2025-03-13 17:03:17.529052-07 test f T4 \N f \N \N \N {} +7056 2184 2025-03-16 21:28:43.042415-07 2025-03-16 21:41:54.202912-07 test t L4 \N f \N \N \N {} +3554 1209 2025-02-28 11:42:34.402738-08 2025-02-28 10:58:34.409829-08 benchmark t A100 \N t \N \N \N {} +3555 1209 2025-02-28 12:08:05.685399-08 2025-02-28 12:29:20.539116-08 leaderboard t A100 0.0030725803333333337 t \N \N \N {} +6391 1986 2025-03-13 17:33:05.58602-07 2025-03-13 17:03:31.170452-07 test f T4 \N f \N \N \N {} +6662 2059 2025-03-14 13:23:27.383679-07 2025-03-14 13:38:22.037388-07 test f T4 \N f \N \N \N {} +3557 1209 2025-02-28 10:54:49.669617-08 2025-02-28 12:37:28.504084-08 benchmark f A100 \N t \N \N \N {} +3558 1209 2025-02-28 12:46:50.125484-08 2025-02-28 10:53:05.190346-08 leaderboard f A100 0.003090619 t \N \N \N {} +6392 1986 2025-03-13 18:05:36.821373-07 2025-03-13 16:18:41.422227-07 test t T4 \N f \N \N \N {} +6559 2030 2025-03-14 11:43:33.554172-07 2025-03-14 11:45:17.444192-07 benchmark f A100 \N t \N \N \N {} +3560 1210 2025-02-28 12:25:24.784425-08 2025-02-28 11:26:41.263715-08 benchmark f A100 \N t \N \N \N {} +3561 1210 2025-02-28 10:49:57.408057-08 2025-02-28 10:50:18.625288-08 leaderboard f A100 0.0030909023333333336 t \N \N \N {} +6393 1987 2025-03-13 17:09:40.617657-07 2025-03-13 18:01:53.260744-07 test f T4 \N f \N \N \N {} +3563 1210 2025-02-28 11:47:01.61943-08 2025-02-28 11:03:40.113806-08 benchmark t A100 \N t \N \N \N {} +3592 1229 2025-02-28 14:16:18.378222-08 2025-02-28 14:41:43.485886-08 leaderboard t H100 0.0011789098333333332 t \N \N \N {} +3599 1231 2025-02-28 14:24:45.98762-08 2025-02-28 14:33:38.933844-08 leaderboard t H100 0.0012175512 t \N \N \N {} +3600 1232 2025-02-28 13:31:10.936218-08 2025-02-28 14:19:19.455477-08 test t A100 \N t \N \N \N {} +3601 1232 2025-02-28 14:40:09.495449-08 2025-02-28 13:28:50.540847-08 benchmark t A100 \N t \N \N \N {} +3602 1232 2025-02-28 14:11:02.148305-08 2025-02-28 13:20:25.431282-08 leaderboard t A100 0.0018670176 t \N \N \N {} +3603 1232 2025-02-28 13:32:32.0241-08 2025-02-28 14:31:24.527828-08 test f A100 \N t \N \N \N {} +3604 1232 2025-02-28 13:20:53.784469-08 2025-02-28 14:29:17.100087-08 benchmark f A100 \N t \N \N \N {} +3629 1240 2025-03-01 01:07:27.030682-08 2025-03-01 00:11:34.566732-08 test t A100 \N f \N \N \N {} +3648 1248 2025-03-01 01:50:58.464696-08 2025-03-01 00:44:11.917362-08 test t A100 \N t \N \N \N {} +3605 1232 2025-02-28 14:53:49.887413-08 2025-02-28 15:02:29.191933-08 leaderboard f A100 0.001868504 t \N \N \N {} +3606 1233 2025-02-28 14:00:44.074635-08 2025-02-28 14:09:25.557225-08 benchmark f A100 \N t \N \N \N {} +3607 1234 2025-02-28 14:42:03.73345-08 2025-02-28 15:08:49.030554-08 benchmark f A100 \N f \N \N \N {} +3608 1235 2025-02-28 15:06:39.526344-08 2025-02-28 15:15:55.847946-08 benchmark f A100 \N t \N \N \N {} +3609 1235 2025-02-28 14:36:31.855831-08 2025-02-28 14:45:08.753778-08 benchmark f H100 \N f \N \N \N {} +3610 1236 2025-02-28 14:22:25.170803-08 2025-02-28 15:09:04.222075-08 benchmark f H100 \N t \N \N \N {} +3611 1237 2025-02-28 14:59:01.171066-08 2025-02-28 15:22:44.422251-08 test t H100 \N t \N \N \N {} +3612 1237 2025-02-28 15:35:54.216346-08 2025-02-28 15:38:16.179312-08 benchmark t H100 \N t \N \N \N {} +3613 1237 2025-02-28 14:52:56.878083-08 2025-02-28 14:51:40.998403-08 leaderboard t H100 0.001161027 t \N \N \N {} +3614 1237 2025-02-28 16:12:12.194689-08 2025-02-28 16:07:13.458388-08 test f H100 \N f \N \N \N {} +3615 1238 2025-02-28 16:14:51.726429-08 2025-02-28 16:25:19.334709-08 benchmark f H100 \N t \N \N \N {} +3616 1238 2025-02-28 15:18:16.528728-08 2025-02-28 16:17:03.954782-08 benchmark f A100 \N t \N \N \N {} +3630 1240 2025-03-01 00:33:39.01249-08 2025-03-01 00:07:40.699626-08 test f A100 \N f \N \N \N {} +3617 1239 2025-02-28 15:32:38.229302-08 2025-02-28 15:57:09.263444-08 test t H100 \N t \N \N \N {} +3618 1239 2025-02-28 15:40:41.884458-08 2025-02-28 15:19:49.237295-08 benchmark t H100 \N t \N \N \N {} +3619 1239 2025-02-28 15:26:34.626489-08 2025-02-28 16:48:20.214012-08 leaderboard t H100 0.0011695108 t \N \N \N {} +3620 1239 2025-02-28 15:34:19.352077-08 2025-02-28 15:18:34.628786-08 test f A100 \N t \N \N \N {} +3621 1239 2025-02-28 15:34:17.50586-08 2025-02-28 15:42:25.985409-08 benchmark f A100 \N t \N \N \N {} +3622 1239 2025-02-28 15:22:37.115875-08 2025-02-28 15:55:56.769348-08 leaderboard f A100 0.002215739 t \N \N \N {} +3623 1239 2025-02-28 17:10:36.81778-08 2025-02-28 15:57:08.360846-08 test t A100 \N t \N \N \N {} +3625 1239 2025-02-28 16:44:19.965613-08 2025-02-28 15:49:32.46356-08 leaderboard t A100 0.0020725956666666667 t \N \N \N {} +3626 1239 2025-02-28 15:41:26.021642-08 2025-02-28 15:23:55.7378-08 test f H100 \N t \N \N \N {} +3627 1239 2025-02-28 16:33:01.867796-08 2025-02-28 15:24:47.499545-08 benchmark f H100 \N t \N \N \N {} +3628 1239 2025-02-28 16:49:49.757233-08 2025-02-28 16:35:02.023132-08 leaderboard f H100 0.00116604725 t \N \N \N {} +6234 1922 2025-03-12 00:22:49.917936-07 2025-03-12 00:13:52.488578-07 test t T4 \N f \N \N \N {} +6395 1988 2025-03-13 17:58:47.761639-07 2025-03-13 17:01:13.84194-07 test t T4 \N f \N \N \N {} +6560 2031 2025-03-14 13:05:32.105346-07 2025-03-14 11:29:34.501788-07 benchmark f A100 \N t \N \N \N {} +6629 2050 2025-03-14 12:58:46.09814-07 2025-03-14 12:52:50.872419-07 benchmark f A100 \N t \N \N \N {} +6630 2050 2025-03-14 12:08:39.259423-07 2025-03-14 13:48:19.785521-07 leaderboard f A100 0.002560023 t \N \N \N {} +3638 1243 2025-03-01 00:16:45.170175-08 2025-03-01 00:27:41.072541-08 test t A100 \N f \N \N \N {} +3639 1243 2025-02-28 23:56:11.182103-08 2025-03-01 00:14:59.572871-08 test f A100 \N f \N \N \N {} +6235 1922 2025-03-11 23:57:28.40312-07 2025-03-12 00:24:44.986697-07 test f T4 \N f \N \N \N {} +6396 1988 2025-03-13 16:20:45.984417-07 2025-03-13 16:27:56.674856-07 test f T4 \N f \N \N \N {} +6631 2050 2025-03-14 12:29:57.064337-07 2025-03-14 13:06:46.738173-07 test t A100 \N t \N \N \N {} +3641 1244 2025-03-01 01:27:10.713031-08 2025-03-01 01:18:07.544805-08 benchmark t A100 \N f \N \N \N {} +6236 1923 2025-03-12 01:04:23.501448-07 2025-03-12 00:05:29.54956-07 test t T4 \N f \N \N \N {} +6397 1989 2025-03-13 17:25:39.429146-07 2025-03-13 16:39:56.193057-07 test t T4 \N f \N \N \N {} +6561 2032 2025-03-14 12:04:24.116313-07 2025-03-14 12:06:21.091672-07 benchmark f A100 \N t \N \N \N {} +6632 2050 2025-03-14 12:39:52.486934-07 2025-03-14 13:43:25.534592-07 benchmark t A100 \N t \N \N \N {} +6633 2050 2025-03-14 13:36:06.263877-07 2025-03-14 13:20:17.386606-07 leaderboard t A100 0.002519189 t \N \N \N {} +3643 1244 2025-03-01 01:35:19.685312-08 2025-03-01 00:36:21.094893-08 benchmark f A100 \N f \N \N \N {} +3644 1245 2025-03-01 00:26:06.119792-08 2025-03-01 01:35:27.012514-08 test t A100 \N f \N \N \N {} +3645 1245 2025-03-01 00:43:41.46218-08 2025-02-28 23:55:12.934017-08 test f A100 \N f \N \N \N {} +3646 1246 2025-03-01 00:53:33.727311-08 2025-03-01 02:15:48.757393-08 benchmark f A100 \N f \N \N \N {} +3647 1247 2025-03-01 02:33:55.98627-08 2025-03-01 02:31:22.439703-08 benchmark f A100 \N t \N \N \N {} +6237 1923 2025-03-12 00:58:02.114277-07 2025-03-12 01:54:20.094067-07 test f T4 \N f \N \N \N {} +3649 1248 2025-03-01 01:25:05.925773-08 2025-03-01 01:09:17.783085-08 benchmark t A100 \N t \N \N \N {} +3651 1248 2025-03-01 02:17:03.556096-08 2025-03-01 02:09:10.130241-08 test f A100 \N t \N \N \N {} +3653 1248 2025-03-01 02:26:58.343458-08 2025-03-01 00:43:43.03041-08 leaderboard f A100 0.0031272273333333334 t \N \N \N {} +3654 1249 2025-03-01 02:12:30.998096-08 2025-03-01 00:50:24.715655-08 test f A100 \N t \N \N \N {} +3655 1249 2025-03-01 01:49:55.694137-08 2025-03-01 01:29:49.718531-08 benchmark f A100 \N t \N \N \N {} +3656 1249 2025-03-01 01:38:29.821048-08 2025-03-01 01:46:03.662966-08 leaderboard f A100 0.0031039466666666665 t \N \N \N {} +3889 1297 2025-03-01 02:52:44.650492-08 2025-03-01 03:23:37.6224-08 test t A100 \N t \N \N \N {} +3657 1249 2025-03-01 01:50:49.813103-08 2025-03-01 02:00:22.394174-08 test t A100 \N t \N \N \N {} +3669 1251 2025-03-01 01:21:22.679753-08 2025-03-01 02:00:57.562983-08 test f A100 \N t \N \N \N {} +3670 1251 2025-03-01 02:27:13.746914-08 2025-03-01 01:53:26.460343-08 benchmark f A100 \N t \N \N \N {} +3671 1251 2025-03-01 00:57:08.998138-08 2025-03-01 00:58:17.33663-08 leaderboard f A100 0.0031081036666666664 t \N \N \N {} +3672 1252 2025-03-01 01:11:34.754783-08 2025-03-01 00:52:36.138674-08 test f A100 \N t \N \N \N {} +3681 1253 2025-03-01 01:03:11.979635-08 2025-03-01 02:40:30.398743-08 test f A100 \N t \N \N \N {} +3910 1301 2025-03-01 03:46:47.874489-08 2025-03-01 03:33:33.484922-08 test f A100 \N f \N \N \N {} +3673 1252 2025-03-01 02:02:20.431435-08 2025-03-01 01:05:47.013515-08 benchmark f A100 \N t \N \N \N {} +3674 1252 2025-03-01 02:12:52.290378-08 2025-03-01 01:46:02.154693-08 leaderboard f A100 0.0031048003333333336 t \N \N \N {} +3676 1252 2025-03-01 01:03:12.897497-08 2025-03-01 01:26:39.062279-08 benchmark t A100 \N t \N \N \N {} +3677 1252 2025-03-01 01:38:06.493139-08 2025-03-01 02:09:28.677674-08 leaderboard t A100 0.0031104436666666664 t \N \N \N {} +3678 1253 2025-03-01 02:06:10.058974-08 2025-03-01 01:30:07.280628-08 test t A100 \N t \N \N \N {} +3679 1253 2025-03-01 02:03:05.966122-08 2025-03-01 00:56:13.504406-08 benchmark t A100 \N t \N \N \N {} +3680 1253 2025-03-01 02:45:57.725737-08 2025-03-01 02:34:27.95218-08 leaderboard t A100 0.0031090656666666666 t \N \N \N {} +3682 1253 2025-03-01 02:05:39.373314-08 2025-03-01 01:20:42.226463-08 benchmark f A100 \N t \N \N \N {} +3683 1253 2025-03-01 01:49:07.509476-08 2025-03-01 01:57:14.444777-08 leaderboard f A100 0.0031048203333333335 t \N \N \N {} +3690 1260 2025-03-01 01:35:12.029569-08 2025-03-01 01:45:52.929787-08 benchmark f A100 \N t \N \N \N {} +3691 1261 2025-03-01 01:38:16.278326-08 2025-03-01 01:36:09.051317-08 test t A100 \N t \N \N \N {} +3692 1261 2025-03-01 02:49:47.45514-08 2025-03-01 03:17:46.327605-08 benchmark t A100 \N t \N \N \N {} +3693 1261 2025-03-01 02:16:44.503505-08 2025-03-01 02:01:57.713906-08 leaderboard t A100 0.0030880283333333336 t \N \N \N {} +3694 1261 2025-03-01 02:18:25.475537-08 2025-03-01 02:21:41.927369-08 test f A100 \N t \N \N \N {} +3712 1264 2025-03-01 02:56:03.049752-08 2025-03-01 01:43:52.163817-08 test t A100 \N t \N \N \N {} +3697 1262 2025-03-01 03:19:09.843073-08 2025-03-01 01:30:49.179646-08 test t A100 \N t \N \N \N {} +3698 1262 2025-03-01 03:23:21.681606-08 2025-03-01 02:15:56.73891-08 benchmark t A100 \N t \N \N \N {} +3699 1262 2025-03-01 02:06:27.293399-08 2025-03-01 02:01:32.569519-08 leaderboard t A100 0.0030787253333333336 t \N \N \N {} +3700 1262 2025-03-01 03:02:04.699757-08 2025-03-01 01:27:30.191004-08 test f A100 \N t \N \N \N {} +3701 1262 2025-03-01 01:41:08.419018-08 2025-03-01 02:33:50.455395-08 benchmark f A100 \N t \N \N \N {} +3702 1262 2025-03-01 03:18:19.489873-08 2025-03-01 02:32:54.172929-08 leaderboard f A100 0.0030775323333333336 t \N \N \N {} +3919 1304 2025-03-01 05:14:59.512003-08 2025-03-01 04:15:31.250903-08 test f A100 \N t \N \N \N {} +3704 1263 2025-03-01 03:03:11.700889-08 2025-03-01 02:34:12.760558-08 benchmark f A100 \N t \N \N \N {} +3721 1266 2025-03-01 03:29:58.775718-08 2025-03-01 02:01:14.58848-08 benchmark f A100 \N t \N \N \N {} +3722 1267 2025-03-01 02:00:41.613096-08 2025-03-01 02:39:28.789934-08 test t A100 \N t \N \N \N {} +3723 1267 2025-03-01 02:08:23.198399-08 2025-03-01 02:24:17.413187-08 benchmark t A100 \N t \N \N \N {} +3724 1267 2025-03-01 02:58:51.225291-08 2025-03-01 01:53:44.033391-08 leaderboard t A100 0.0030839456666666666 t \N \N \N {} +3725 1267 2025-03-01 02:45:10.109595-08 2025-03-01 02:41:38.775413-08 test f A100 \N t \N \N \N {} +3928 1305 2025-03-01 05:09:48.841292-08 2025-03-01 05:01:55.591158-08 test f A100 \N t \N \N \N {} +3729 1268 2025-03-01 03:05:42.17627-08 2025-03-01 02:39:10.249741-08 benchmark t A100 \N t \N \N \N {} +3730 1268 2025-03-01 01:47:00.767793-08 2025-03-01 02:57:06.798112-08 leaderboard t A100 0.0031026586666666666 t \N \N \N {} +3731 1268 2025-03-01 02:41:00.300122-08 2025-03-01 03:30:29.20737-08 test f A100 \N t \N \N \N {} +3732 1268 2025-03-01 03:04:23.203277-08 2025-03-01 01:43:41.695278-08 benchmark f A100 \N t \N \N \N {} +3733 1268 2025-03-01 01:51:37.215302-08 2025-03-01 02:38:14.824507-08 leaderboard f A100 0.0030949656666666666 t \N \N \N {} +3734 1269 2025-03-01 02:58:59.331439-08 2025-03-01 02:02:26.027818-08 test t A100 \N t \N \N \N {} +3735 1269 2025-03-01 03:35:14.847212-08 2025-03-01 02:02:44.717076-08 benchmark t A100 \N t \N \N \N {} +3736 1269 2025-03-01 01:53:25.567276-08 2025-03-01 02:33:01.154365-08 leaderboard t A100 0.0031152113333333337 t \N \N \N {} +3737 1269 2025-03-01 02:30:37.175149-08 2025-03-01 02:34:29.93941-08 test f A100 \N t \N \N \N {} +3738 1269 2025-03-01 03:04:08.336127-08 2025-03-01 02:39:05.51646-08 benchmark f A100 \N t \N \N \N {} +3739 1269 2025-03-01 03:26:54.522181-08 2025-03-01 02:30:29.400292-08 leaderboard f A100 0.003116479 t \N \N \N {} +3752 1272 2025-03-01 03:35:25.32574-08 2025-03-01 03:42:34.897532-08 leaderboard t A100 0.003070418 t \N \N \N {} +3964 1321 2025-03-01 04:25:29.824066-08 2025-03-01 05:01:10.21614-08 test t A100 \N t \N \N \N {} +3753 1273 2025-03-01 01:58:03.710454-08 2025-03-01 03:21:45.806033-08 test f A100 \N t \N \N \N {} +3754 1273 2025-03-01 03:22:29.292453-08 2025-03-01 03:42:54.655064-08 benchmark f A100 \N t \N \N \N {} +3755 1273 2025-03-01 02:32:56.792637-08 2025-03-01 03:04:59.058769-08 leaderboard f A100 0.0031262986666666663 t \N \N \N {} +3768 1275 2025-03-01 02:46:08.326312-08 2025-03-01 02:22:21.610827-08 test f A100 \N t \N \N \N {} +3777 1277 2025-03-01 02:21:07.979234-08 2025-03-01 03:47:24.50701-08 test t A100 \N t \N \N \N {} +3775 1276 2025-03-01 01:55:51.302209-08 2025-03-01 02:01:49.922507-08 benchmark t A100 \N t \N \N \N {} +3776 1276 2025-03-01 02:08:35.267407-08 2025-03-01 01:54:26.716726-08 leaderboard t A100 0.0030983713333333335 t \N \N \N {} +3778 1277 2025-03-01 03:32:26.499294-08 2025-03-01 03:46:45.069319-08 benchmark t A100 \N t \N \N \N {} +3779 1277 2025-03-01 03:38:23.774189-08 2025-03-01 02:35:30.930272-08 leaderboard t A100 0.003084539 t \N \N \N {} +3780 1277 2025-03-01 03:22:55.155356-08 2025-03-01 02:43:56.628934-08 test f A100 \N t \N \N \N {} +3784 1278 2025-03-01 02:48:23.56591-08 2025-03-01 02:33:17.354544-08 benchmark t A100 \N t \N \N \N {} +3785 1278 2025-03-01 02:39:45.41231-08 2025-03-01 02:40:12.61527-08 leaderboard t A100 0.0031055783333333335 t \N \N \N {} +3789 1279 2025-03-01 02:01:53.626081-08 2025-03-01 02:47:46.086211-08 test f A100 \N t \N \N \N {} +3786 1278 2025-03-01 02:03:03.53279-08 2025-03-01 03:47:19.946226-08 test f A100 \N t \N \N \N {} +3787 1278 2025-03-01 03:29:33.298347-08 2025-03-01 03:34:20.277338-08 benchmark f A100 \N t \N \N \N {} +3788 1278 2025-03-01 03:00:05.752988-08 2025-03-01 01:56:16.762213-08 leaderboard f A100 0.003097914 t \N \N \N {} +6238 1924 2025-03-12 01:21:46.601814-07 2025-03-12 01:32:26.514257-07 test t T4 \N f \N \N \N {} +6398 1989 2025-03-13 17:08:21.233687-07 2025-03-13 17:06:06.404998-07 test f T4 \N f \N \N \N {} +6562 2033 2025-03-14 13:12:39.948827-07 2025-03-14 13:23:53.347312-07 benchmark f A100 \N t \N \N \N {} +6634 2051 2025-03-14 12:30:34.721734-07 2025-03-14 12:30:27.296794-07 test t A100 \N t \N \N \N {} +3790 1279 2025-03-01 03:32:19.769184-08 2025-03-01 03:50:26.829557-08 benchmark f A100 \N t \N \N \N {} +3791 1279 2025-03-01 02:54:07.655455-08 2025-03-01 02:19:13.748311-08 leaderboard f A100 0.0030992133333333336 t \N \N \N {} +6399 1990 2025-03-13 17:50:57.628826-07 2025-03-13 16:49:18.239868-07 test t T4 \N f \N \N \N {} +6563 2034 2025-03-14 12:07:55.631644-07 2025-03-14 12:27:16.784091-07 benchmark f A100 \N t \N \N \N {} +7059 2185 2025-03-16 22:03:27.033571-07 2025-03-16 21:36:29.376537-07 leaderboard f L4 0.00011869082456140351 t \N \N \N {} +3793 1279 2025-03-01 03:42:52.339097-08 2025-03-01 03:50:15.909886-08 benchmark t A100 \N t \N \N \N {} +3794 1279 2025-03-01 02:12:29.931818-08 2025-03-01 03:14:22.177181-08 leaderboard t A100 0.0030700836666666667 t \N \N \N {} +6240 1925 2025-03-12 01:13:55.791064-07 2025-03-12 00:29:24.037503-07 test f T4 \N t \N \N \N {} +6244 1925 2025-03-12 01:24:08.247115-07 2025-03-12 00:16:50.797071-07 benchmark t T4 \N t \N \N \N {} +6245 1925 2025-03-12 00:39:44.345705-07 2025-03-12 01:03:34.532126-07 leaderboard t T4 3.565283209 t \N \N \N {} +6401 1991 2025-03-13 18:14:55.765999-07 2025-03-13 16:54:11.131116-07 test f T4 \N t \N \N \N {} +6250 1926 2025-03-12 02:15:10.273238-07 2025-03-12 01:01:04.027885-07 benchmark f T4 \N t \N \N \N {} +6251 1926 2025-03-12 00:50:43.580637-07 2025-03-12 00:57:06.425599-07 leaderboard f T4 3.682825032 t \N \N \N {} +6404 1991 2025-03-13 18:23:53.350354-07 2025-03-13 17:41:06.459187-07 test t T4 \N t \N \N \N {} +6405 1991 2025-03-13 18:45:41.07329-07 2025-03-13 17:03:11.875615-07 benchmark t T4 \N t \N \N \N {} +3805 1281 2025-03-01 02:40:30.487878-08 2025-03-01 02:47:22.690155-08 benchmark t A100 \N t \N \N \N {} +3806 1281 2025-03-01 03:18:03.1251-08 2025-03-01 02:07:31.048245-08 leaderboard t A100 0.0030762023333333337 t \N \N \N {} +6406 1991 2025-03-13 16:56:16.674442-07 2025-03-13 17:32:22.195013-07 leaderboard t T4 0.01198132082 t \N \N \N {} +6565 2036 2025-03-14 13:26:34.659744-07 2025-03-14 12:28:45.901302-07 benchmark f A100 \N t \N \N \N {} +6635 2051 2025-03-14 12:12:47.169659-07 2025-03-14 12:30:19.462198-07 benchmark t A100 \N t \N \N \N {} +6636 2051 2025-03-14 13:22:32.747423-07 2025-03-14 13:46:45.493463-07 leaderboard t A100 0.0025760326666666666 t \N \N \N {} +3808 1283 2025-03-01 03:57:31.233254-08 2025-03-01 03:30:41.035131-08 benchmark t A100 \N t \N \N \N {} +3809 1283 2025-03-01 02:53:01.039251-08 2025-03-01 03:11:15.949496-08 leaderboard t A100 0.0030891856666666667 t \N \N \N {} +3810 1282 2025-03-01 02:47:29.668011-08 2025-03-01 03:06:52.828571-08 benchmark f A100 \N t \N \N \N {} +3811 1282 2025-03-01 02:30:35.994805-08 2025-03-01 02:24:05.263461-08 benchmark f H100 \N t \N \N \N {} +6407 1992 2025-03-13 18:08:55.397985-07 2025-03-13 17:16:33.413217-07 test t T4 \N f \N \N \N {} +6566 2037 2025-03-14 12:48:47.844486-07 2025-03-14 12:08:55.718904-07 test f A100 \N f \N \N \N {} +6637 2051 2025-03-14 12:40:08.734201-07 2025-03-14 12:40:23.426756-07 test f A100 \N t \N \N \N {} +3813 1283 2025-03-01 04:00:42.588176-08 2025-03-01 02:20:16.080762-08 benchmark f A100 \N t \N \N \N {} +3814 1283 2025-03-01 03:40:07.853236-08 2025-03-01 03:29:58.258385-08 leaderboard f A100 0.003101622 t \N \N \N {} +3815 1284 2025-03-01 02:25:33.95253-08 2025-03-01 02:28:22.525197-08 test t A100 \N f \N \N \N {} +3816 1284 2025-03-01 03:57:37.041465-08 2025-03-01 02:25:56.015805-08 test f A100 \N f \N \N \N {} +6408 1992 2025-03-13 19:09:40.136801-07 2025-03-13 17:14:34.611515-07 test f T4 \N f \N \N \N {} +6567 2037 2025-03-14 12:36:50.459533-07 2025-03-14 12:05:58.31517-07 test t A100 \N f \N \N \N {} +3818 1285 2025-03-01 02:34:29.255307-08 2025-03-01 02:41:27.552585-08 benchmark f A100 \N t \N \N \N {} +3819 1285 2025-03-01 03:02:33.900221-08 2025-03-01 02:39:16.471311-08 leaderboard f A100 0.003196064 t \N \N \N {} +6409 1993 2025-03-13 17:37:09.987891-07 2025-03-13 17:44:10.720084-07 test t T4 \N f \N \N \N {} +6568 2038 2025-03-14 13:27:33.060334-07 2025-03-14 11:46:55.719973-07 test t A100 \N f \N \N \N {} +6638 2051 2025-03-14 14:00:30.84485-07 2025-03-14 12:56:57.304408-07 benchmark f A100 \N t \N \N \N {} +6639 2051 2025-03-14 13:35:20.196957-07 2025-03-14 12:03:45.180778-07 leaderboard f A100 0.002520242 t \N \N \N {} +3821 1285 2025-03-01 03:35:11.06709-08 2025-03-01 03:50:33.861385-08 benchmark t A100 \N t \N \N \N {} +3822 1285 2025-03-01 04:05:45.187777-08 2025-03-01 03:19:06.671204-08 leaderboard t A100 0.003205114 t \N \N \N {} +6410 1993 2025-03-13 18:31:14.592561-07 2025-03-13 18:03:31.587714-07 test f T4 \N f \N \N \N {} +6569 2038 2025-03-14 11:54:10.44922-07 2025-03-14 12:14:26.005828-07 test f A100 \N f \N \N \N {} +6640 2052 2025-03-14 13:20:11.68013-07 2025-03-14 13:54:08.817793-07 benchmark f A100 \N t \N \N \N {} +6663 2059 2025-03-14 13:43:14.834484-07 2025-03-14 13:10:23.01354-07 test t T4 \N f \N \N \N {} +3824 1286 2025-03-01 03:48:51.460228-08 2025-03-01 03:05:00.868889-08 benchmark t A100 \N t \N \N \N {} +3825 1286 2025-03-01 03:13:52.902037-08 2025-03-01 03:59:39.680247-08 leaderboard t A100 0.0032052243333333336 t \N \N \N {} +6257 1933 2025-03-12 06:29:52.272103-07 2025-03-12 06:33:58.837779-07 benchmark f A100 \N t \N \N \N {} +6411 1994 2025-03-13 17:31:54.500515-07 2025-03-13 17:50:40.571354-07 test t T4 \N f \N \N \N {} +6570 2039 2025-03-14 12:43:54.474387-07 2025-03-14 11:46:53.304327-07 test f A100 \N t \N \N \N {} +6571 2039 2025-03-14 13:36:58.274258-07 2025-03-14 11:48:23.338015-07 benchmark f A100 \N t \N \N \N {} +6572 2039 2025-03-14 12:38:10.932323-07 2025-03-14 12:39:34.102441-07 leaderboard f A100 0.0025144643333333337 t \N \N \N {} +6259 1936 2025-03-12 07:33:01.179724-07 2025-03-12 08:00:02.311465-07 benchmark f A100 \N t \N \N \N {} +6413 1995 2025-03-13 18:23:54.614105-07 2025-03-13 19:13:53.587183-07 test t T4 \N f \N \N \N {} +6576 2040 2025-03-14 12:03:45.01229-07 2025-03-14 12:44:15.413186-07 test f A100 \N t \N \N \N {} +6577 2040 2025-03-14 11:54:53.53347-07 2025-03-14 12:54:34.179949-07 benchmark f A100 \N t \N \N \N {} +6578 2040 2025-03-14 13:10:32.037477-07 2025-03-14 12:17:48.109262-07 leaderboard f A100 0.002512107 t \N \N \N {} +3833 1287 2025-03-01 02:36:47.925069-08 2025-03-01 03:26:45.302835-08 benchmark t A100 \N t \N \N \N {} +3834 1287 2025-03-01 02:32:30.809129-08 2025-03-01 03:12:32.781049-08 leaderboard t A100 0.0030916056666666666 t \N \N \N {} +6260 1937 2025-03-12 07:23:06.50947-07 2025-03-12 08:23:19.792542-07 benchmark f A100 \N f \N \N \N {} +6261 1938 2025-03-12 08:35:16.313743-07 2025-03-12 08:05:05.626744-07 benchmark f A100 \N t \N \N \N {} +6415 1996 2025-03-13 17:38:08.027955-07 2025-03-13 18:18:31.168297-07 test f T4 \N f \N \N \N {} +6582 2041 2025-03-14 13:35:12.454784-07 2025-03-14 11:47:41.393176-07 test f A100 \N t \N \N \N {} +6583 2041 2025-03-14 13:00:57.639982-07 2025-03-14 12:50:35.650726-07 benchmark f A100 \N t \N \N \N {} +3839 1288 2025-03-01 02:49:11.125792-08 2025-03-01 03:21:23.235824-08 benchmark t A100 \N t \N \N \N {} +6262 1939 2025-03-12 07:09:38.373161-07 2025-03-12 08:48:50.014584-07 test f A100 \N t \N \N \N {} +6417 1997 2025-03-13 20:13:42.139063-07 2025-03-13 19:48:53.847265-07 test t T4 \N f \N \N \N {} +6268 1940 2025-03-12 08:48:23.158902-07 2025-03-12 07:34:48.110256-07 test f H100 \N t \N \N \N {} +6271 1940 2025-03-12 08:39:14.809321-07 2025-03-12 08:06:26.839197-07 test t H100 \N t \N \N \N {} +6272 1940 2025-03-12 08:53:57.603406-07 2025-03-12 08:56:46.092949-07 benchmark t H100 \N t \N \N \N {} +6273 1940 2025-03-12 08:30:27.185852-07 2025-03-12 07:53:33.36497-07 leaderboard t H100 0.00004756101351351351 t \N \N \N {} +6584 2041 2025-03-14 11:48:11.361349-07 2025-03-14 13:07:20.826594-07 leaderboard f A100 0.00251554 t \N \N \N {} +3851 1290 2025-03-01 02:42:29.071194-08 2025-03-01 03:06:56.033212-08 benchmark t A100 \N t \N \N \N {} +3852 1290 2025-03-01 03:17:02.156523-08 2025-03-01 02:24:26.38537-08 leaderboard t A100 0.0030752533333333336 t \N \N \N {} +6274 1941 2025-03-12 08:49:13.036616-07 2025-03-12 09:04:52.719316-07 benchmark f T4 \N t \N \N \N {} +6419 1998 2025-03-14 05:01:43.261969-07 2025-03-14 04:51:13.385755-07 benchmark f A100 \N t \N \N \N {} +6585 2041 2025-03-14 12:16:33.083304-07 2025-03-14 11:57:07.432446-07 test t A100 \N t \N \N \N {} +6586 2041 2025-03-14 12:55:15.401484-07 2025-03-14 13:17:33.140416-07 benchmark t A100 \N t \N \N \N {} +3854 1291 2025-03-01 03:39:45.47163-08 2025-03-01 04:08:17.677385-08 benchmark t A100 \N t \N \N \N {} +3855 1291 2025-03-01 04:06:18.714922-08 2025-03-01 04:05:24.645808-08 leaderboard t A100 0.0030727556666666667 t \N \N \N {} +6275 1942 2025-03-12 08:13:42.287391-07 2025-03-12 08:06:35.694977-07 test f T4 \N t \N \N \N {} +6276 1942 2025-03-12 08:50:04.371499-07 2025-03-12 07:30:05.01053-07 benchmark f T4 \N t \N \N \N {} +6277 1942 2025-03-12 08:59:14.826076-07 2025-03-12 08:14:40.196742-07 leaderboard f T4 0.00013039858 t \N \N \N {} +6420 1999 2025-03-14 05:43:54.110159-07 2025-03-14 04:25:26.908913-07 test f A100 \N t \N \N \N {} +3858 1291 2025-03-01 02:47:40.294969-08 2025-03-01 03:21:35.997134-08 leaderboard f A100 0.0030834156666666667 t \N \N \N {} +6278 1942 2025-03-12 07:53:39.313803-07 2025-03-12 08:52:00.346671-07 test t T4 \N t \N \N \N {} +6279 1942 2025-03-12 07:54:27.029152-07 2025-03-12 08:11:56.444947-07 benchmark t T4 \N t \N \N \N {} +6280 1942 2025-03-12 09:01:51.96126-07 2025-03-12 07:48:32.954993-07 leaderboard t T4 0.00017005301923076922 t \N \N \N {} +6588 2042 2025-03-14 12:41:32.755275-07 2025-03-14 13:27:47.933076-07 test t A100 \N t \N \N \N {} +3860 1292 2025-03-01 03:44:44.046571-08 2025-03-01 02:59:32.783521-08 benchmark f A100 \N t \N \N \N {} +3861 1292 2025-03-01 02:54:03.416387-08 2025-03-01 04:17:02.090714-08 leaderboard f A100 0.0030798846666666665 t \N \N \N {} +3864 1292 2025-03-01 03:01:12.811119-08 2025-03-01 03:57:53.126582-08 leaderboard t A100 0.0030685383333333336 t \N \N \N {} +6284 1943 2025-03-12 07:37:15.328744-07 2025-03-12 08:00:32.114323-07 test f L4 \N t \N \N \N {} +6285 1943 2025-03-12 09:15:51.836775-07 2025-03-12 08:18:34.03295-07 benchmark f L4 \N t \N \N \N {} +6286 1943 2025-03-12 07:18:39.964262-07 2025-03-12 08:42:15.886255-07 leaderboard f L4 0.00009671902499999999 t \N \N \N {} +6589 2042 2025-03-14 13:28:36.680878-07 2025-03-14 12:42:33.70216-07 benchmark t A100 \N t \N \N \N {} +3866 1293 2025-03-01 03:45:51.800351-08 2025-03-01 04:07:15.03461-08 benchmark f A100 \N t \N \N \N {} +3867 1293 2025-03-01 03:54:05.969256-08 2025-03-01 03:52:25.243297-08 leaderboard f A100 0.0030785423333333337 t \N \N \N {} +3869 1293 2025-03-01 03:05:36.772437-08 2025-03-01 03:41:52.212108-08 benchmark t A100 \N t \N \N \N {} +3870 1293 2025-03-01 03:43:37.103283-08 2025-03-01 03:30:28.463215-08 leaderboard t A100 0.0030768713333333333 t \N \N \N {} +6290 1944 2025-03-12 10:57:21.104648-07 2025-03-12 10:29:58.980609-07 test t T4 \N t \N \N \N {} +3872 1294 2025-03-01 02:49:18.306021-08 2025-03-01 03:07:55.017888-08 benchmark f A100 \N t \N \N \N {} +3873 1294 2025-03-01 03:18:49.888527-08 2025-03-01 02:34:53.156434-08 leaderboard f A100 0.0030735693333333335 t \N \N \N {} +6293 1945 2025-03-12 11:27:15.263361-07 2025-03-12 10:44:27.72857-07 test t T4 \N f \N \N \N {} +6425 1999 2025-03-14 04:01:43.503576-07 2025-03-14 04:06:07.830224-07 leaderboard t A100 0.0024771706666666666 t \N \N \N {} +6590 2042 2025-03-14 12:07:33.510979-07 2025-03-14 12:55:00.902598-07 leaderboard t A100 0.0031459183333333333 t \N \N \N {} +6642 2053 2025-03-14 13:53:44.288829-07 2025-03-14 13:50:05.960849-07 benchmark t A100 \N t \N \N \N {} +3875 1294 2025-03-01 03:43:43.782738-08 2025-03-01 04:24:15.592112-08 benchmark t A100 \N t \N \N \N {} +3878 1295 2025-03-01 04:12:08.121664-08 2025-03-01 04:27:53.16111-08 benchmark f A100 \N t \N \N \N {} +3879 1295 2025-03-01 03:26:16.469666-08 2025-03-01 03:58:31.78643-08 leaderboard f A100 0.0030710493333333333 t \N \N \N {} +6429 2000 2025-03-14 06:05:54.518003-07 2025-03-14 07:37:02.202589-07 test t H100 \N t \N \N \N {} +6430 2000 2025-03-14 06:35:29.934346-07 2025-03-14 07:22:55.35725-07 benchmark t H100 \N t \N \N \N {} +6431 2000 2025-03-14 06:03:20.051179-07 2025-03-14 06:00:31.398891-07 leaderboard t H100 0.00140618875 t \N \N \N {} +6592 2042 2025-03-14 12:08:29.246929-07 2025-03-14 13:42:58.5473-07 benchmark f A100 \N t \N \N \N {} +6593 2042 2025-03-14 12:19:09.742168-07 2025-03-14 13:19:22.260046-07 leaderboard f A100 0.0038880753333333335 t \N \N \N {} +3881 1295 2025-03-01 03:58:42.298448-08 2025-03-01 04:33:00.759565-08 benchmark t A100 \N t \N \N \N {} +3882 1295 2025-03-01 04:11:32.308003-08 2025-03-01 03:45:03.260733-08 leaderboard t A100 0.0030748093333333336 t \N \N \N {} +6432 2001 2025-03-14 06:20:47.16994-07 2025-03-14 06:30:53.126756-07 test f A100 \N t \N \N \N {} +6433 2001 2025-03-14 06:43:34.047031-07 2025-03-14 05:58:41.550022-07 benchmark f A100 \N t \N \N \N {} +6434 2001 2025-03-14 07:28:13.286534-07 2025-03-14 05:58:23.458927-07 leaderboard f A100 0.0025180643333333336 t \N \N \N {} +6594 2043 2025-03-14 12:13:34.198838-07 2025-03-14 13:41:30.590742-07 test f A100 \N f \N \N \N {} +5073 1550 2025-03-04 16:13:34.029481-08 2025-03-04 17:53:09.887209-08 test f H100 \N f \N \N \N {} +3884 1296 2025-03-01 03:12:53.853598-08 2025-03-01 03:21:33.361057-08 benchmark t A100 \N t \N \N \N {} +3885 1296 2025-03-01 02:52:45.802668-08 2025-03-01 04:33:27.422507-08 leaderboard t A100 0.003072869 t \N \N \N {} +6297 1948 2025-03-12 11:29:51.801512-07 2025-03-12 10:25:13.155691-07 test f T4 \N t \N \N \N {} +6298 1948 2025-03-12 10:35:30.201717-07 2025-03-12 10:27:15.46981-07 benchmark f T4 \N f \N \N \N {} +6435 2001 2025-03-14 07:04:41.915018-07 2025-03-14 07:08:00.946033-07 test t A100 \N t \N \N \N {} +6439 2002 2025-03-14 06:51:44.898139-07 2025-03-14 07:04:48.007592-07 benchmark t A100 \N t \N \N \N {} +6440 2002 2025-03-14 06:58:37.268782-07 2025-03-14 07:30:47.849941-07 leaderboard t A100 0.0025299696666666667 t \N \N \N {} +3890 1297 2025-03-01 04:08:21.671557-08 2025-03-01 03:21:10.925412-08 benchmark t A100 \N t \N \N \N {} +6441 2002 2025-03-14 06:48:37.342621-07 2025-03-14 07:23:19.449449-07 test f A100 \N t \N \N \N {} +6442 2002 2025-03-14 07:08:21.810282-07 2025-03-14 06:12:36.932788-07 benchmark f A100 \N t \N \N \N {} +3893 1297 2025-03-01 03:27:32.585689-08 2025-03-01 03:40:51.676057-08 benchmark f A100 \N t \N \N \N {} +3900 1299 2025-03-01 03:40:55.395999-08 2025-03-01 03:53:19.378987-08 benchmark f A100 \N f \N \N \N {} +3901 1301 2025-03-01 03:23:52.831997-08 2025-03-01 03:24:06.231506-08 test t A100 \N t \N \N \N {} +3906 1301 2025-03-01 04:15:07.380839-08 2025-03-01 03:58:42.535677-08 leaderboard f H100 0.000903244375 t \N \N \N {} +3907 1301 2025-03-01 03:58:13.621695-08 2025-03-01 03:35:37.1111-08 test t H100 \N t \N \N \N {} +3908 1301 2025-03-01 04:58:15.504982-08 2025-03-01 04:38:42.581718-08 benchmark t H100 \N t \N \N \N {} +3913 1303 2025-03-01 03:45:26.565256-08 2025-03-01 05:21:39.968163-08 test t A100 \N t \N \N \N {} +3920 1304 2025-03-01 04:39:50.463012-08 2025-03-01 05:01:19.916314-08 benchmark f A100 \N t \N \N \N {} +3921 1304 2025-03-01 04:39:17.669272-08 2025-03-01 03:55:58.328183-08 leaderboard f A100 0.0030727456666666663 t \N \N \N {} +6670 2063 2025-03-14 14:58:48.236812-07 2025-03-14 15:04:51.635193-07 test f T4 \N f \N \N \N {} +3923 1304 2025-03-01 04:23:40.082947-08 2025-03-01 04:52:37.416251-08 benchmark t A100 \N t \N \N \N {} +3924 1304 2025-03-01 05:06:09.579535-08 2025-03-01 04:30:25.040011-08 leaderboard t A100 0.0030747073333333335 t \N \N \N {} +6307 1950 2025-03-12 11:44:15.74066-07 2025-03-12 10:54:32.286656-07 test f T4 \N f \N \N \N {} +6444 2003 2025-03-14 06:32:45.509148-07 2025-03-14 07:18:07.617634-07 test t A100 \N t \N \N \N {} +6445 2003 2025-03-14 06:43:39.064772-07 2025-03-14 07:26:22.701906-07 benchmark t A100 \N t \N \N \N {} +6446 2003 2025-03-14 07:33:49.087001-07 2025-03-14 06:39:43.653159-07 leaderboard t A100 0.00251628 t \N \N \N {} +3926 1305 2025-03-01 05:27:38.253899-08 2025-03-01 04:35:13.150247-08 benchmark t A100 \N t \N \N \N {} +3927 1305 2025-03-01 04:30:10.546633-08 2025-03-01 04:32:49.219013-08 leaderboard t A100 0.003074227 t \N \N \N {} +6308 1950 2025-03-12 12:25:05.113988-07 2025-03-12 12:37:59.249778-07 test t T4 \N f \N \N \N {} +6447 2003 2025-03-14 07:25:35.263987-07 2025-03-14 05:57:18.436435-07 test f A100 \N t \N \N \N {} +6448 2003 2025-03-14 06:22:22.575249-07 2025-03-14 07:19:37.030539-07 benchmark f A100 \N t \N \N \N {} +6449 2003 2025-03-14 07:33:07.159548-07 2025-03-14 07:43:35.889465-07 leaderboard f A100 0.0025153743333333335 t \N \N \N {} +3929 1305 2025-03-01 05:24:53.102248-08 2025-03-01 05:27:25.918489-08 benchmark f A100 \N t \N \N \N {} +3930 1305 2025-03-01 04:56:51.217802-08 2025-03-01 04:41:26.360821-08 leaderboard f A100 0.0030731703333333337 t \N \N \N {} +6309 1951 2025-03-12 12:37:05.375541-07 2025-03-12 12:21:31.848581-07 test t T4 \N f \N \N \N {} +6450 2004 2025-03-14 07:29:50.054077-07 2025-03-14 06:54:48.788265-07 test f A100 \N t \N \N \N {} +6451 2004 2025-03-14 06:47:18.994761-07 2025-03-14 07:41:04.641274-07 benchmark f A100 \N t \N \N \N {} +6452 2004 2025-03-14 07:10:52.271141-07 2025-03-14 07:05:23.598203-07 leaderboard f A100 0.0025149163333333334 t \N \N \N {} +6686 2071 2025-03-14 15:07:32.035959-07 2025-03-14 16:41:36.954155-07 test t T4 \N f \N \N \N {} +6935 2159 2025-03-16 16:02:23.35434-07 2025-03-16 16:42:39.300711-07 test f T4 \N t \N \N \N {} +6311 1952 2025-03-12 11:40:24.149803-07 2025-03-12 11:50:01.838943-07 test t T4 \N f \N \N \N {} +6458 2005 2025-03-14 07:21:25.580439-07 2025-03-14 07:49:47.315286-07 leaderboard f A100 0.004558669333333333 t \N \N \N {} +6687 2071 2025-03-14 15:04:02.106962-07 2025-03-14 16:27:06.289153-07 test f T4 \N f \N \N \N {} +6883 2136 2025-03-15 15:12:53.005577-07 2025-03-15 15:45:03.98046-07 test f T4 \N t \N \N \N {} +3968 1322 2025-03-01 05:31:02.366882-08 2025-03-01 05:15:44.290903-08 benchmark t A100 \N t \N \N \N {} +3969 1322 2025-03-01 05:37:29.486548-08 2025-03-01 05:45:21.369833-08 leaderboard t A100 0.0030774476666666664 t \N \N \N {} +3970 1322 2025-03-01 05:40:01.603582-08 2025-03-01 05:45:11.281035-08 test f A100 \N t \N \N \N {} +3971 1322 2025-03-01 06:09:29.893232-08 2025-03-01 05:02:33.403442-08 benchmark f A100 \N t \N \N \N {} +3972 1322 2025-03-01 05:06:02.207814-08 2025-03-01 05:55:39.737325-08 leaderboard f A100 0.0030752193333333333 t \N \N \N {} +3973 1323 2025-03-01 05:58:27.485607-08 2025-03-01 05:32:24.84737-08 test f A100 \N t \N \N \N {} +3974 1323 2025-03-01 06:16:37.08018-08 2025-03-01 04:46:10.75807-08 benchmark f A100 \N t \N \N \N {} +3975 1323 2025-03-01 06:18:48.328453-08 2025-03-01 05:34:36.581312-08 leaderboard f A100 0.003101147 t \N \N \N {} +3976 1323 2025-03-01 06:03:28.859556-08 2025-03-01 05:45:30.216393-08 test t A100 \N t \N \N \N {} +3977 1323 2025-03-01 06:05:43.149319-08 2025-03-01 06:06:01.600121-08 benchmark t A100 \N t \N \N \N {} +3978 1323 2025-03-01 05:20:14.946-08 2025-03-01 06:09:26.3369-08 leaderboard t A100 0.0030720786666666665 t \N \N \N {} +3979 1324 2025-03-01 05:38:48.032681-08 2025-03-01 05:38:54.242522-08 test t A100 \N f \N \N \N {} +3980 1324 2025-03-01 05:03:07.857476-08 2025-03-01 05:56:18.026019-08 test f A100 \N f \N \N \N {} +3981 1325 2025-03-01 04:30:24.581687-08 2025-03-01 05:30:26.203251-08 test f A100 \N f \N \N \N {} +3982 1325 2025-03-01 04:56:35.77322-08 2025-03-01 05:06:42.117449-08 test t A100 \N f \N \N \N {} +3989 1327 2025-03-01 04:28:09.627783-08 2025-03-01 06:14:03.4485-08 test f A100 \N t \N \N \N {} +3990 1327 2025-03-01 04:35:12.535221-08 2025-03-01 06:16:55.791101-08 benchmark f A100 \N t \N \N \N {} +3998 1328 2025-03-01 05:02:22.872872-08 2025-03-01 05:53:01.111038-08 test t A100 \N t \N \N \N {} +3999 1328 2025-03-01 05:20:30.293816-08 2025-03-01 04:33:16.504511-08 benchmark t A100 \N t \N \N \N {} +4000 1328 2025-03-01 05:24:59.129999-08 2025-03-01 05:11:00.788762-08 leaderboard t A100 0.0030814433333333333 t \N \N \N {} +4006 1329 2025-03-01 05:03:38.237873-08 2025-03-01 05:49:05.125811-08 leaderboard f A100 0.0030727136666666667 t \N \N \N {} +4007 1330 2025-03-01 06:29:07.869426-08 2025-03-01 06:22:44.330161-08 test t A100 \N t \N \N \N {} +4008 1330 2025-03-01 06:01:42.053545-08 2025-03-01 04:58:15.207722-08 benchmark t A100 \N t \N \N \N {} +4009 1330 2025-03-01 05:03:24.750209-08 2025-03-01 05:57:23.562377-08 leaderboard t A100 0.0030764006666666667 t \N \N \N {} +4010 1330 2025-03-01 05:02:18.387184-08 2025-03-01 04:49:51.849261-08 test f A100 \N t \N \N \N {} +4011 1330 2025-03-01 04:34:41.853129-08 2025-03-01 05:27:20.630573-08 benchmark f A100 \N t \N \N \N {} +4012 1330 2025-03-01 04:49:25.539951-08 2025-03-01 05:57:06.277345-08 leaderboard f A100 0.0030913556666666664 t \N \N \N {} +4013 1331 2025-03-01 04:48:50.723394-08 2025-03-01 05:02:12.598261-08 test f A100 \N t \N \N \N {} +4014 1331 2025-03-01 05:46:30.40955-08 2025-03-01 04:34:39.633219-08 benchmark f A100 \N t \N \N \N {} +4015 1331 2025-03-01 05:38:54.065577-08 2025-03-01 05:25:23.854596-08 leaderboard f A100 0.0030761543333333334 t \N \N \N {} +4016 1331 2025-03-01 04:41:26.822773-08 2025-03-01 06:31:54.977434-08 test t A100 \N t \N \N \N {} +4025 1333 2025-03-01 05:50:11.13569-08 2025-03-01 06:05:47.809639-08 test f A100 \N t \N \N \N {} +4034 1334 2025-03-01 05:19:46.050467-08 2025-03-01 06:13:31.802821-08 test t A100 \N t \N \N \N {} +4042 1336 2025-03-01 06:19:43.447341-08 2025-03-01 05:51:53.054293-08 test f A100 \N t \N \N \N {} +4017 1331 2025-03-01 04:46:15.747877-08 2025-03-01 05:40:33.74129-08 benchmark t A100 \N t \N \N \N {} +4018 1331 2025-03-01 04:50:33.824408-08 2025-03-01 04:35:16.517834-08 leaderboard t A100 0.0030775386666666666 t \N \N \N {} +4019 1332 2025-03-01 06:15:51.207238-08 2025-03-01 06:21:40.29258-08 test f A100 \N t \N \N \N {} +4020 1332 2025-03-01 05:54:40.351278-08 2025-03-01 06:07:21.488972-08 benchmark f A100 \N t \N \N \N {} +4021 1332 2025-03-01 05:31:15.028107-08 2025-03-01 06:30:55.794956-08 leaderboard f A100 0.0032326943333333335 t \N \N \N {} +4029 1333 2025-03-01 04:43:11.484999-08 2025-03-01 06:18:08.597188-08 benchmark t A100 \N t \N \N \N {} +4030 1333 2025-03-01 05:38:23.948276-08 2025-03-01 05:39:32.998484-08 leaderboard t A100 0.0030748286666666667 t \N \N \N {} +4031 1334 2025-03-01 05:48:36.681273-08 2025-03-01 05:09:41.33705-08 test f A100 \N t \N \N \N {} +4032 1334 2025-03-01 05:41:35.653199-08 2025-03-01 05:12:26.846467-08 benchmark f A100 \N t \N \N \N {} +4033 1334 2025-03-01 05:50:46.305933-08 2025-03-01 05:16:24.499999-08 leaderboard f A100 0.0031010393333333335 t \N \N \N {} +4035 1334 2025-03-01 06:38:20.344372-08 2025-03-01 05:01:27.901596-08 benchmark t A100 \N t \N \N \N {} +4036 1334 2025-03-01 05:13:10.923007-08 2025-03-01 05:05:10.395318-08 leaderboard t A100 0.0030793333333333337 t \N \N \N {} +4037 1335 2025-03-01 05:22:53.382722-08 2025-03-01 05:25:53.811459-08 test t A100 \N f \N \N \N {} +4045 1337 2025-03-01 06:43:13.512286-08 2025-03-01 05:52:38.084808-08 test t A100 \N t \N \N \N {} +4046 1337 2025-03-01 06:44:42.843151-08 2025-03-01 06:01:44.654233-08 benchmark t A100 \N t \N \N \N {} +4053 1338 2025-03-01 05:49:19.310951-08 2025-03-01 05:36:21.556206-08 leaderboard t A100 0.0030786213333333333 t \N \N \N {} +4054 1338 2025-03-01 06:08:31.2478-08 2025-03-01 05:15:47.918409-08 test f A100 \N t \N \N \N {} +4055 1338 2025-03-01 06:00:55.317206-08 2025-03-01 05:42:35.691206-08 benchmark f A100 \N t \N \N \N {} +4061 1339 2025-03-01 04:55:54.708149-08 2025-03-01 06:20:05.506072-08 benchmark t A100 \N t \N \N \N {} +4062 1339 2025-03-01 06:29:35.459123-08 2025-03-01 06:23:22.438908-08 leaderboard t A100 0.0030713596666666664 t \N \N \N {} +4063 1340 2025-03-01 05:41:13.291902-08 2025-03-01 05:08:42.07891-08 test f A100 \N t \N \N \N {} +4064 1340 2025-03-01 05:33:54.804638-08 2025-03-01 06:49:34.790694-08 benchmark f A100 \N t \N \N \N {} +4065 1340 2025-03-01 06:22:42.948861-08 2025-03-01 06:31:27.337118-08 leaderboard f A100 0.003198704 t \N \N \N {} +4066 1340 2025-03-01 05:53:22.22062-08 2025-03-01 06:34:56.280809-08 test t A100 \N t \N \N \N {} +4075 1342 2025-03-01 06:23:06.476775-08 2025-03-01 06:20:15.612586-08 test f A100 \N t \N \N \N {} +4084 1343 2025-03-01 06:31:52.402656-08 2025-03-01 06:10:01.556153-08 test f A100 \N t \N \N \N {} +4109 1349 2025-03-01 05:50:58.123614-08 2025-03-01 07:02:47.190517-08 test t A100 \N t \N \N \N {} +4067 1340 2025-03-01 05:51:32.857224-08 2025-03-01 05:50:31.869282-08 benchmark t A100 \N t \N \N \N {} +4068 1340 2025-03-01 06:52:04.233379-08 2025-03-01 05:20:00.968896-08 leaderboard t A100 0.0032254873333333336 t \N \N \N {} +4076 1342 2025-03-01 05:52:15.30752-08 2025-03-01 05:16:01.171421-08 benchmark f A100 \N t \N \N \N {} +4077 1342 2025-03-01 06:48:06.395287-08 2025-03-01 06:20:52.98914-08 leaderboard f A100 0.003085711 t \N \N \N {} +4078 1342 2025-03-01 06:38:10.193601-08 2025-03-01 06:54:28.028198-08 test t A100 \N t \N \N \N {} +4085 1343 2025-03-01 06:30:45.755953-08 2025-03-01 05:42:14.015703-08 benchmark f A100 \N t \N \N \N {} +4086 1343 2025-03-01 05:14:21.820912-08 2025-03-01 06:41:27.76024-08 leaderboard f A100 0.0031491675 t \N \N \N {} +4093 1345 2025-03-01 05:54:34.018691-08 2025-03-01 06:48:03.464696-08 test f A100 \N f \N \N \N {} +4094 1345 2025-03-01 06:07:45.020264-08 2025-03-01 05:56:24.473964-08 test t A100 \N f \N \N \N {} +4101 1347 2025-03-01 06:44:45.774365-08 2025-03-01 05:10:23.773951-08 test f A100 \N f \N \N \N {} +4102 1347 2025-03-01 06:21:29.448139-08 2025-03-01 05:16:01.94432-08 test t A100 \N f \N \N \N {} +4110 1349 2025-03-01 06:50:52.832524-08 2025-03-01 06:21:34.412682-08 benchmark t A100 \N t \N \N \N {} +4111 1349 2025-03-01 05:37:38.785374-08 2025-03-01 05:39:45.670246-08 leaderboard t A100 0.0030744786666666666 t \N \N \N {} +4118 1350 2025-03-01 05:53:22.664423-08 2025-03-01 07:01:25.508428-08 test t A100 \N t \N \N \N {} +4119 1350 2025-03-01 07:06:15.020001-08 2025-03-01 05:36:11.046309-08 benchmark t A100 \N t \N \N \N {} +4120 1350 2025-03-01 06:20:34.501205-08 2025-03-01 07:07:39.442371-08 leaderboard t A100 0.003088505 t \N \N \N {} +4126 1351 2025-03-01 05:29:03.034314-08 2025-03-01 06:25:02.133816-08 leaderboard t A100 0.0030791646666666664 t \N \N \N {} +4134 1353 2025-03-01 06:04:43.547155-08 2025-03-01 05:13:34.33261-08 benchmark t A100 \N t \N \N \N {} +4135 1353 2025-03-01 07:05:13.871186-08 2025-03-01 06:41:07.602177-08 leaderboard t A100 0.003071621 t \N \N \N {} +4143 1354 2025-03-01 05:17:03.588452-08 2025-03-01 05:16:11.035298-08 benchmark t A100 \N t \N \N \N {} +4144 1354 2025-03-01 06:01:34.820165-08 2025-03-01 06:34:58.72374-08 leaderboard t A100 0.00309386 t \N \N \N {} +4165 1363 2025-03-01 09:39:07.8542-08 2025-03-01 09:06:36.459295-08 benchmark f A100 \N t \N \N \N {} +4166 1364 2025-03-01 10:45:48.93923-08 2025-03-01 10:33:36.557942-08 test t A100 \N t \N \N \N {} +4167 1364 2025-03-01 09:43:39.289368-08 2025-03-01 09:24:23.903656-08 benchmark t A100 \N t \N \N \N {} +4170 1364 2025-03-01 10:36:49.26316-08 2025-03-01 09:28:08.907873-08 benchmark f A100 \N t \N \N \N {} +4175 1366 2025-03-01 10:48:21.857968-08 2025-03-01 10:57:43.342896-08 leaderboard t A100 0.0030997733333333337 t \N \N \N {} +4176 1366 2025-03-01 10:27:28.925342-08 2025-03-01 09:25:05.826997-08 test f A100 \N t \N \N \N {} +4184 1372 2025-03-01 10:01:34.032339-08 2025-03-01 10:16:18.107861-08 benchmark f A100 \N t \N \N \N {} +4185 1373 2025-03-01 09:15:05.438331-08 2025-03-01 10:28:47.955472-08 benchmark f A100 \N t \N \N \N {} +4191 1380 2025-03-01 11:36:24.556989-08 2025-03-01 11:40:57.219225-08 benchmark f A100 \N t \N \N \N {} +4192 1381 2025-03-01 12:37:28.809727-08 2025-03-01 11:54:48.723704-08 benchmark f A100 \N f \N \N \N {} +4193 1382 2025-03-01 12:05:31.937467-08 2025-03-01 12:39:42.035292-08 benchmark f A100 \N f \N \N \N {} +4194 1383 2025-03-01 12:24:03.28573-08 2025-03-01 12:49:27.141084-08 benchmark f A100 \N f \N \N \N {} +4195 1384 2025-03-01 12:30:23.318874-08 2025-03-01 11:45:28.231808-08 benchmark f A100 \N t \N \N \N {} +4196 1385 2025-03-01 12:10:07.559639-08 2025-03-01 13:20:27.623427-08 benchmark f A100 \N t \N \N \N {} +4197 1386 2025-03-01 13:54:20.599915-08 2025-03-01 13:59:32.398597-08 test f A100 \N t \N \N \N {} +4198 1387 2025-03-01 14:14:19.137435-08 2025-03-01 13:16:59.246757-08 benchmark f A100 \N t \N \N \N {} +4204 1388 2025-03-01 12:23:09.987084-08 2025-03-01 13:22:39.985792-08 leaderboard t A100 0.496474646 t \N \N \N {} +4205 1389 2025-03-01 13:19:47.926437-08 2025-03-01 13:50:43.82625-08 test f H100 \N t \N \N \N {} +4206 1389 2025-03-01 12:57:36.960021-08 2025-03-01 13:42:33.039405-08 benchmark f H100 \N t \N \N \N {} +4224 1394 2025-03-01 16:02:06.026414-08 2025-03-01 15:58:14.959593-08 leaderboard t A100 0.0031078296666666666 t \N \N \N {} +4225 1394 2025-03-01 15:56:49.702024-08 2025-03-01 14:57:49.257679-08 test t H100 \N t \N \N \N {} +4226 1394 2025-03-01 16:23:12.233422-08 2025-03-01 16:09:26.381534-08 benchmark t H100 \N t \N \N \N {} +4227 1394 2025-03-01 15:22:20.9878-08 2025-03-01 15:51:31.402515-08 leaderboard t H100 0.00140471775 t \N \N \N {} +4230 1397 2025-03-01 17:11:50.531769-08 2025-03-01 17:06:47.495163-08 benchmark f H100 \N t \N \N \N {} +4235 1398 2025-03-01 16:50:13.330905-08 2025-03-01 16:05:18.289309-08 benchmark f H100 \N t \N \N \N {} +4240 1398 2025-03-01 15:52:44.51554-08 2025-03-01 15:40:06.215226-08 test f A100 \N f \N \N \N {} +4241 1399 2025-03-01 17:23:26.394202-08 2025-03-01 16:46:15.926611-08 benchmark f H100 \N f \N \N \N {} +4248 1402 2025-03-01 16:50:49.496325-08 2025-03-01 16:38:17.053665-08 benchmark t H100 \N t \N \N \N {} +4249 1402 2025-03-01 17:29:19.46759-08 2025-03-01 17:17:13.325643-08 leaderboard t H100 0.0013931105 t \N \N \N {} +4250 1403 2025-03-01 16:41:21.078394-08 2025-03-01 16:31:20.029427-08 test f H100 \N t \N \N \N {} +4251 1403 2025-03-01 17:01:41.343912-08 2025-03-01 17:09:49.148125-08 benchmark f H100 \N t \N \N \N {} +4252 1403 2025-03-01 16:09:08.072113-08 2025-03-01 16:30:28.83147-08 leaderboard f H100 0.00139343525 t \N \N \N {} +4415 1443 2025-03-01 23:27:54.443718-08 2025-03-01 23:44:48.408471-08 benchmark t L4 \N t \N \N \N {} +4254 1403 2025-03-01 16:28:43.322824-08 2025-03-01 16:12:28.94178-08 benchmark t H100 \N t \N \N \N {} +4255 1403 2025-03-01 16:59:30.637265-08 2025-03-01 16:54:54.864297-08 leaderboard t H100 0.0014084093333333332 t \N \N \N {} +4256 1402 2025-03-01 17:48:26.163752-08 2025-03-01 16:43:09.49428-08 test f H100 \N t \N \N \N {} +4257 1402 2025-03-01 17:31:46.113694-08 2025-03-01 16:07:29.034922-08 benchmark f H100 \N t \N \N \N {} +4258 1402 2025-03-01 16:44:05.583517-08 2025-03-01 16:25:07.353815-08 leaderboard f H100 0.0014460703333333332 t \N \N \N {} +4259 1404 2025-03-01 16:04:19.796606-08 2025-03-01 17:33:34.248936-08 test t H100 \N t \N \N \N {} +4260 1404 2025-03-01 16:49:13.841981-08 2025-03-01 17:51:00.007281-08 benchmark t H100 \N t \N \N \N {} +4261 1404 2025-03-01 17:21:32.526268-08 2025-03-01 16:39:22.981736-08 leaderboard t H100 0.00139580475 t \N \N \N {} +4262 1404 2025-03-01 16:31:24.233879-08 2025-03-01 17:38:25.925363-08 test f H100 \N t \N \N \N {} +4263 1404 2025-03-01 17:31:33.499326-08 2025-03-01 16:34:05.021142-08 benchmark f H100 \N t \N \N \N {} +4379 1433 2025-03-01 21:51:29.505052-08 2025-03-01 21:42:07.436975-08 benchmark f H100 \N t \N \N \N {} +4264 1404 2025-03-01 16:46:11.323414-08 2025-03-01 17:19:04.9435-08 leaderboard f H100 0.0014059293333333331 t \N \N \N {} +4265 1405 2025-03-01 17:04:29.433941-08 2025-03-01 17:19:54.898023-08 test f L4 \N t \N \N \N {} +4266 1405 2025-03-01 17:15:36.867761-08 2025-03-01 16:06:23.499666-08 benchmark f L4 \N t \N \N \N {} +4267 1405 2025-03-01 17:28:05.852436-08 2025-03-01 16:40:49.105619-08 leaderboard f L4 0.017067446 t \N \N \N {} +4268 1405 2025-03-01 17:14:37.360289-08 2025-03-01 16:02:42.08553-08 test t L4 \N t \N \N \N {} +4384 1437 2025-03-01 22:29:30.981358-08 2025-03-01 22:15:31.729879-08 benchmark f H100 \N f \N \N \N {} +4269 1405 2025-03-01 16:26:20.496455-08 2025-03-01 16:15:13.996431-08 benchmark t L4 \N t \N \N \N {} +4270 1405 2025-03-01 16:08:25.687965-08 2025-03-01 17:10:55.36684-08 leaderboard t L4 0.01706846266666667 t \N \N \N {} +4271 1406 2025-03-01 17:35:32.433009-08 2025-03-01 16:47:33.575921-08 test f T4 \N t \N \N \N {} +4272 1406 2025-03-01 16:29:17.767422-08 2025-03-01 16:51:54.661181-08 benchmark f T4 \N t \N \N \N {} +4273 1406 2025-03-01 16:08:36.107841-08 2025-03-01 16:30:12.375768-08 leaderboard f T4 0.009307217333333334 t \N \N \N {} +4385 1438 2025-03-01 22:33:51.295044-08 2025-03-01 22:12:55.046741-08 benchmark f H100 \N f \N \N \N {} +4274 1406 2025-03-01 17:26:10.061376-08 2025-03-01 16:28:55.30488-08 test t T4 \N f \N \N \N {} +4275 1406 2025-03-01 17:24:14.461681-08 2025-03-01 17:32:10.727373-08 test f L4 \N f \N \N \N {} +4276 1406 2025-03-01 16:51:43.765001-08 2025-03-01 17:08:41.224245-08 test t L4 \N f \N \N \N {} +4351 1425 2025-03-01 18:27:28.317296-08 2025-03-01 19:48:34.537155-08 test t A100 \N t \N \N \N {} +4360 1425 2025-03-01 18:55:29.934952-08 2025-03-01 20:05:32.79375-08 test f T4 \N t \N \N \N {} +4726 1479 2025-03-02 07:19:35.258294-08 2025-03-02 07:04:07.889836-08 test f A100 \N t \N \N \N {} +4341 1424 2025-03-01 19:12:19.828159-08 2025-03-01 20:11:36.50049-08 benchmark f H100 \N t \N \N \N {} +4342 1425 2025-03-01 19:30:03.137888-08 2025-03-01 20:13:32.937364-08 test f H100 \N t \N \N \N {} +4735 1480 2025-03-02 06:33:46.379677-08 2025-03-02 07:28:20.847048-08 test f A100 \N t \N \N \N {} +4343 1425 2025-03-01 19:07:06.678388-08 2025-03-01 19:01:17.242899-08 benchmark f H100 \N t \N \N \N {} +4344 1425 2025-03-01 19:36:47.591098-08 2025-03-01 20:05:13.137174-08 leaderboard f H100 0.0015068819090909092 t \N \N \N {} +4345 1425 2025-03-01 19:40:07.817557-08 2025-03-01 19:16:20.820213-08 test t L4 \N t \N \N \N {} +4355 1425 2025-03-01 19:40:23.407312-08 2025-03-01 18:36:43.639689-08 benchmark t T4 \N t \N \N \N {} +4356 1425 2025-03-01 19:34:42.848141-08 2025-03-01 19:21:17.019994-08 leaderboard t T4 0.019392106333333332 t \N \N \N {} +4364 1425 2025-03-01 19:44:49.950465-08 2025-03-01 19:52:00.834651-08 benchmark f L4 \N t \N \N \N {} +4365 1425 2025-03-01 18:56:32.12492-08 2025-03-01 19:36:54.329004-08 leaderboard f L4 0.018389386333333334 t \N \N \N {} +4366 1426 2025-03-01 22:15:53.709448-08 2025-03-01 21:41:09.208205-08 test f H100 \N f \N \N \N {} +4367 1426 2025-03-01 20:32:14.409145-08 2025-03-01 20:49:12.590979-08 test f A100 \N f \N \N \N {} +4368 1427 2025-03-01 22:21:41.20304-08 2025-03-01 21:10:43.83991-08 test f T4 \N f \N \N \N {} +4369 1427 2025-03-01 20:41:14.21784-08 2025-03-01 22:20:23.41131-08 test f L4 \N f \N \N \N {} +4370 1428 2025-03-01 21:22:14.660722-08 2025-03-01 21:39:17.638743-08 test f L4 \N f \N \N \N {} +4371 1429 2025-03-01 20:56:28.569982-08 2025-03-01 21:27:23.848244-08 test f L4 \N f \N \N \N {} +4375 1432 2025-03-01 21:12:25.058278-08 2025-03-01 21:46:33.33486-08 benchmark f H100 \N t \N \N \N {} +4376 1432 2025-03-01 23:10:02.333804-08 2025-03-01 21:49:56.568079-08 benchmark f L4 \N t \N \N \N {} +4377 1432 2025-03-01 22:54:44.213473-08 2025-03-01 21:21:30.556007-08 benchmark f A100 \N t \N \N \N {} +4876 1500 2025-03-03 09:35:56.137495-08 2025-03-03 09:52:48.005654-08 leaderboard t A100 0.003105324 t \N \N \N {} +4380 1434 2025-03-01 22:15:57.892727-08 2025-03-01 23:36:46.04608-08 benchmark f H100 \N t \N \N \N {} +4381 1435 2025-03-01 22:24:08.366722-08 2025-03-01 22:29:42.067258-08 benchmark f H100 \N t \N \N \N {} +4382 1436 2025-03-01 23:39:19.061091-08 2025-03-01 22:54:39.475489-08 benchmark f H100 \N f \N \N \N {} +4383 1437 2025-03-01 22:35:16.757658-08 2025-03-01 22:50:02.811266-08 benchmark f A100 \N t \N \N \N {} +4386 1439 2025-03-02 00:07:36.475999-08 2025-03-01 23:44:26.098199-08 benchmark f H100 \N t \N \N \N {} +4387 1440 2025-03-02 00:02:57.861102-08 2025-03-02 00:00:32.358477-08 test f H100 \N f \N \N \N {} +4414 1443 2025-03-01 23:54:21.846815-08 2025-03-01 23:32:53.796678-08 test t L4 \N t \N \N \N {} +4388 1440 2025-03-01 23:11:27.786009-08 2025-03-01 23:02:42.012476-08 test t A100 \N f \N \N \N {} +4393 1440 2025-03-01 23:15:26.635065-08 2025-03-01 23:08:51.999345-08 test t L4 \N f \N \N \N {} +4398 1441 2025-03-01 23:17:23.847393-08 2025-03-02 00:04:54.13285-08 test f A100 \N f \N \N \N {} +4403 1442 2025-03-01 22:53:39.143776-08 2025-03-01 23:56:44.359809-08 test t L4 \N f \N \N \N {} +4404 1442 2025-03-01 22:33:19.623771-08 2025-03-01 23:47:56.133494-08 test t H100 \N f \N \N \N {} +4405 1442 2025-03-02 00:04:31.80069-08 2025-03-01 23:26:12.331721-08 test f L4 \N f \N \N \N {} +4406 1442 2025-03-01 23:27:53.503574-08 2025-03-01 22:47:29.500397-08 test f H100 \N f \N \N \N {} +4407 1442 2025-03-02 00:07:32.568134-08 2025-03-01 23:00:08.696649-08 test t T4 \N f \N \N \N {} +4408 1442 2025-03-01 23:29:07.086459-08 2025-03-01 23:23:37.989186-08 test t A100 \N f \N \N \N {} +4409 1442 2025-03-01 22:51:12.097545-08 2025-03-01 22:56:56.425507-08 test f A100 \N f \N \N \N {} +4410 1442 2025-03-01 23:47:29.737111-08 2025-03-02 00:21:02.822694-08 test f T4 \N f \N \N \N {} +4416 1443 2025-03-01 23:58:25.027917-08 2025-03-01 23:05:04.128044-08 leaderboard t L4 0.009129896 t \N \N \N {} +4417 1443 2025-03-01 23:26:40.751758-08 2025-03-02 00:27:51.073423-08 test t A100 \N t \N \N \N {} +4423 1443 2025-03-02 00:04:09.394316-08 2025-03-02 00:13:48.201959-08 test f H100 \N t \N \N \N {} +4424 1443 2025-03-01 23:36:08.305635-08 2025-03-02 00:47:29.293128-08 benchmark f H100 \N t \N \N \N {} +4425 1443 2025-03-01 23:30:11.887008-08 2025-03-02 00:21:43.679513-08 leaderboard f H100 0.0015019263333333332 t \N \N \N {} +4437 1444 2025-03-01 23:22:12.599414-08 2025-03-02 00:09:39.877934-08 leaderboard f A100 0.0021369876666666667 t \N \N \N {} +4438 1444 2025-03-01 23:59:01.602753-08 2025-03-02 00:52:25.788903-08 test f T4 \N t \N \N \N {} +4444 1444 2025-03-01 23:27:58.287274-08 2025-03-01 23:34:31.637125-08 test t T4 \N t \N \N \N {} +4445 1444 2025-03-01 23:28:25.653915-08 2025-03-02 00:46:20.886868-08 benchmark t T4 \N t \N \N \N {} +4446 1444 2025-03-01 23:44:46.267002-08 2025-03-02 00:46:20.218889-08 leaderboard t T4 0.009888984666666666 t \N \N \N {} +4458 1444 2025-03-02 00:52:06.841349-08 2025-03-02 00:45:13.235451-08 leaderboard t L4 0.009366314666666667 t \N \N \N {} +4459 1445 2025-03-02 00:17:32.146735-08 2025-03-02 00:27:53.179825-08 test f A100 \N t \N \N \N {} +4465 1445 2025-03-01 23:25:17.275973-08 2025-03-01 23:51:30.13378-08 test f H100 \N t \N \N \N {} +4466 1445 2025-03-02 00:51:37.846267-08 2025-03-02 00:39:47.26155-08 benchmark f H100 \N t \N \N \N {} +4467 1445 2025-03-01 23:12:50.976391-08 2025-03-02 00:06:29.223376-08 leaderboard f H100 0.0010895946666666667 t \N \N \N {} +4479 1445 2025-03-02 00:18:32.388575-08 2025-03-01 23:33:06.524137-08 leaderboard f L4 0.009311576333333333 t \N \N \N {} +4480 1445 2025-03-02 00:23:10.771659-08 2025-03-02 00:25:16.79615-08 test t L4 \N t \N \N \N {} +4481 1445 2025-03-02 01:06:15.185297-08 2025-03-02 00:56:41.356199-08 benchmark t L4 \N t \N \N \N {} +4486 1447 2025-03-01 23:44:19.043343-08 2025-03-02 00:34:17.729776-08 test t A100 \N t \N \N \N {} +4500 1448 2025-03-01 23:26:04.292969-08 2025-03-02 00:24:20.318749-08 test f A100 \N t \N \N \N {} +4501 1448 2025-03-01 23:52:25.86203-08 2025-03-02 00:46:12.033575-08 benchmark f A100 \N t \N \N \N {} +4502 1448 2025-03-02 00:50:48.885087-08 2025-03-02 00:58:56.374508-08 leaderboard f A100 0.005635142019999999 t \N \N \N {} +4503 1448 2025-03-02 00:36:34.265162-08 2025-03-01 23:44:52.402347-08 test t A100 \N t \N \N \N {} +4514 1450 2025-03-02 00:08:27.757645-08 2025-03-02 00:02:03.781381-08 leaderboard t H100 0.00110452 t \N \N \N {} +4515 1450 2025-03-02 00:14:30.121063-08 2025-03-02 00:04:30.11163-08 test t L4 \N t \N \N \N {} +4516 1450 2025-03-02 00:00:00.221666-08 2025-03-01 23:29:45.13959-08 benchmark t L4 \N t \N \N \N {} +4521 1450 2025-03-02 00:02:47.686536-08 2025-03-01 23:56:26.155139-08 test f H100 \N t \N \N \N {} +4522 1450 2025-03-01 23:44:32.403338-08 2025-03-02 01:02:56.194528-08 benchmark f H100 \N t \N \N \N {} +4523 1450 2025-03-02 00:07:11.12717-08 2025-03-01 23:55:23.35683-08 leaderboard f H100 0.0010997553333333332 t \N \N \N {} +4535 1450 2025-03-02 00:15:33.808245-08 2025-03-02 00:39:03.802273-08 leaderboard f L4 0.009153131 t \N \N \N {} +4536 1451 2025-03-02 01:18:52.170604-08 2025-03-02 01:11:40.804771-08 test t H100 \N t \N \N \N {} +4900 1508 2025-03-03 12:44:25.017899-08 2025-03-03 13:29:37.01192-08 test f H100 \N t \N \N \N {} +4759 1485 2025-03-02 13:16:14.223315-08 2025-03-02 12:01:21.108531-08 benchmark f H100 \N t \N \N \N {} +4905 1511 2025-03-03 14:17:16.597745-08 2025-03-03 13:19:16.395123-08 test t T4 \N f \N \N \N {} +4623 1458 2025-03-02 06:38:45.060119-08 2025-03-02 05:30:45.356488-08 test t A100 \N t \N \N \N {} +6716 2082 2025-03-15 03:44:36.086784-07 2025-03-15 03:23:26.694084-07 test t A100 \N t \N \N \N {} +4632 1460 2025-03-02 06:51:07.426669-08 2025-03-02 06:04:55.597062-08 test t A100 \N t \N \N \N {} +6719 2082 2025-03-15 03:16:11.184399-07 2025-03-15 03:09:17.233425-07 test f A100 \N t \N \N \N {} +4641 1461 2025-03-02 05:59:49.230938-08 2025-03-02 06:17:49.715674-08 test f A100 \N t \N \N \N {} +6722 2083 2025-03-15 03:29:49.696607-07 2025-03-15 04:02:55.058125-07 test f A100 \N t \N \N \N {} +4650 1463 2025-03-02 06:51:19.039666-08 2025-03-02 05:32:00.49752-08 test t A100 \N t \N \N \N {} +6725 2083 2025-03-15 04:23:08.720635-07 2025-03-15 04:21:03.715823-07 test t A100 \N t \N \N \N {} +4659 1464 2025-03-02 07:24:45.335423-08 2025-03-02 05:47:41.040187-08 test f A100 \N t \N \N \N {} +6728 2084 2025-03-15 03:50:48.746611-07 2025-03-15 03:49:46.374297-07 test t A100 \N t \N \N \N {} +4668 1466 2025-03-02 07:09:02.875102-08 2025-03-02 05:37:12.011229-08 test t A100 \N t \N \N \N {} +6731 2084 2025-03-15 02:46:52.42645-07 2025-03-15 04:11:18.181663-07 test f A100 \N t \N \N \N {} +4620 1458 2025-03-02 07:19:02.28608-08 2025-03-02 07:06:35.957428-08 test f A100 \N t \N \N \N {} +4621 1458 2025-03-02 06:23:47.065884-08 2025-03-02 07:03:30.070237-08 benchmark f A100 \N t \N \N \N {} +4622 1458 2025-03-02 07:11:03.17852-08 2025-03-02 06:48:19.70623-08 leaderboard f A100 0.0030958753333333337 t \N \N \N {} +4624 1458 2025-03-02 06:26:45.847704-08 2025-03-02 05:52:44.331704-08 benchmark t A100 \N t \N \N \N {} +4625 1458 2025-03-02 06:51:27.902184-08 2025-03-02 06:23:56.736147-08 leaderboard t A100 0.003088996 t \N \N \N {} +4633 1460 2025-03-02 06:16:38.8786-08 2025-03-02 07:11:21.936323-08 benchmark t A100 \N t \N \N \N {} +4634 1460 2025-03-02 07:04:41.283428-08 2025-03-02 07:16:21.369857-08 leaderboard t A100 0.003092899 t \N \N \N {} +4642 1461 2025-03-02 06:09:41.284959-08 2025-03-02 06:22:42.202445-08 benchmark f A100 \N t \N \N \N {} +4643 1461 2025-03-02 06:39:57.63533-08 2025-03-02 06:46:22.813146-08 leaderboard f A100 0.0030704853333333336 t \N \N \N {} +4651 1463 2025-03-02 06:26:49.901591-08 2025-03-02 06:01:32.015488-08 benchmark t A100 \N t \N \N \N {} +4653 1463 2025-03-02 07:29:04.708213-08 2025-03-02 07:18:09.192245-08 test f A100 \N t \N \N \N {} +4660 1464 2025-03-02 05:32:51.24243-08 2025-03-02 06:13:17.003468-08 benchmark f A100 \N t \N \N \N {} +4661 1464 2025-03-02 07:16:41.081997-08 2025-03-02 05:48:25.892059-08 leaderboard f A100 0.0030758066666666667 t \N \N \N {} +4669 1466 2025-03-02 07:06:29.137455-08 2025-03-02 06:45:12.645699-08 benchmark t A100 \N t \N \N \N {} +4670 1466 2025-03-02 06:40:31.210834-08 2025-03-02 05:42:40.735546-08 leaderboard t A100 0.0030799263333333333 t \N \N \N {} +4678 1467 2025-03-02 06:32:38.464263-08 2025-03-02 06:16:47.589364-08 benchmark f A100 \N t \N \N \N {} +4680 1468 2025-03-02 06:22:28.004766-08 2025-03-02 07:35:00.897908-08 test f A100 \N t \N \N \N {} +4687 1469 2025-03-02 06:37:34.057103-08 2025-03-02 05:51:59.584284-08 benchmark f A100 \N t \N \N \N {} +4688 1469 2025-03-02 06:22:01.378836-08 2025-03-02 06:13:47.362128-08 leaderboard f A100 0.00307773 t \N \N \N {} +4696 1470 2025-03-02 05:54:10.720598-08 2025-03-02 06:58:21.045687-08 benchmark f A100 \N t \N \N \N {} +4697 1470 2025-03-02 06:56:21.605195-08 2025-03-02 07:04:27.428497-08 leaderboard f A100 0.003323739428571429 t \N \N \N {} +4704 1472 2025-03-02 06:51:21.142542-08 2025-03-02 07:19:12.17164-08 benchmark f A100 \N t \N \N \N {} +4705 1473 2025-03-02 06:59:02.14179-08 2025-03-02 06:18:41.378415-08 benchmark f A100 \N t \N \N \N {} +4706 1474 2025-03-02 06:32:28.531936-08 2025-03-02 07:28:17.021525-08 benchmark f A100 \N t \N \N \N {} +4707 1475 2025-03-02 07:43:56.768427-08 2025-03-02 07:18:05.844238-08 benchmark f A100 \N t \N \N \N {} +4709 1476 2025-03-02 07:29:45.620972-08 2025-03-02 06:53:02.850347-08 benchmark t A100 \N t \N \N \N {} +4710 1476 2025-03-02 06:48:36.930559-08 2025-03-02 07:09:21.284629-08 leaderboard t A100 0.003082332 t \N \N \N {} +4718 1477 2025-03-02 07:14:49.953547-08 2025-03-02 07:01:10.444489-08 benchmark f A100 \N t \N \N \N {} +4719 1477 2025-03-02 07:13:14.737688-08 2025-03-02 07:58:55.056874-08 leaderboard f A100 0.003080525 t \N \N \N {} +4727 1479 2025-03-02 08:08:16.530944-08 2025-03-02 06:25:59.616763-08 benchmark f A100 \N t \N \N \N {} +4736 1480 2025-03-02 07:47:58.351644-08 2025-03-02 06:49:23.461444-08 benchmark f A100 \N t \N \N \N {} +4737 1480 2025-03-02 08:02:53.103575-08 2025-03-02 08:13:30.327361-08 leaderboard f A100 0.0030852583333333336 t \N \N \N {} +4752 1485 2025-03-02 11:53:55.657606-08 2025-03-02 11:29:49.203485-08 test f A100 \N t \N \N \N {} +4753 1485 2025-03-02 11:58:43.743381-08 2025-03-02 12:04:12.552688-08 benchmark f A100 \N t \N \N \N {} +4754 1485 2025-03-02 12:59:42.33895-08 2025-03-02 12:36:29.8219-08 leaderboard f A100 0.0017007568181818182 t \N \N \N {} +4760 1485 2025-03-02 13:02:43.584587-08 2025-03-02 11:40:22.88528-08 leaderboard f H100 0.001025282 t \N \N \N {} +4761 1485 2025-03-02 11:54:09.417727-08 2025-03-02 13:05:23.177947-08 test t H100 \N t \N \N \N {} +4764 1485 2025-03-02 12:19:37.770033-08 2025-03-02 11:40:52.739277-08 test f T4 \N t \N \N \N {} +4767 1485 2025-03-02 11:34:48.399203-08 2025-03-02 11:27:10.480623-08 test f L4 \N t \N \N \N {} +4768 1485 2025-03-02 12:21:36.330234-08 2025-03-02 13:16:33.964583-08 benchmark f L4 \N t \N \N \N {} +4769 1485 2025-03-02 11:54:00.372056-08 2025-03-02 12:11:51.743238-08 leaderboard f L4 0.009055283666666665 t \N \N \N {} +4780 1486 2025-03-02 13:32:04.884765-08 2025-03-02 13:19:13.880894-08 test t L4 \N t \N \N \N {} +4788 1486 2025-03-02 12:38:25.692798-08 2025-03-02 12:40:07.438573-08 test f T4 \N t \N \N \N {} +4795 1486 2025-03-02 11:54:55.018029-08 2025-03-02 12:21:15.163724-08 leaderboard f L4 0.013281347 t \N \N \N {} +4827 1490 2025-03-02 14:04:50.955427-08 2025-03-02 14:34:08.371455-08 benchmark t A100 \N t \N \N \N {} +4828 1490 2025-03-02 14:33:00.642669-08 2025-03-02 14:35:15.849785-08 leaderboard t A100 0.0017312735 t \N \N \N {} +4829 1490 2025-03-02 15:19:06.654379-08 2025-03-02 14:54:43.856752-08 test f A100 \N t \N \N \N {} +4830 1490 2025-03-02 13:56:46.528544-08 2025-03-02 15:10:08.582329-08 benchmark f A100 \N t \N \N \N {} +4831 1490 2025-03-02 13:52:33.720668-08 2025-03-02 13:42:04.183005-08 leaderboard f A100 0.0017395216666666667 t \N \N \N {} +4832 1491 2025-03-02 13:31:50.812204-08 2025-03-02 15:31:56.835867-08 test t A100 \N t \N \N \N {} +4833 1491 2025-03-02 15:09:34.132728-08 2025-03-02 13:54:04.869362-08 benchmark t A100 \N t \N \N \N {} +4834 1491 2025-03-02 14:20:30.701842-08 2025-03-02 13:59:22.648224-08 leaderboard t A100 0.0017047336000000001 t \N \N \N {} +4835 1491 2025-03-02 14:45:24.03934-08 2025-03-02 15:10:20.533866-08 test f A100 \N t \N \N \N {} +4836 1491 2025-03-02 14:20:31.244621-08 2025-03-02 15:06:36.572076-08 benchmark f A100 \N t \N \N \N {} +4837 1491 2025-03-02 15:29:45.897744-08 2025-03-02 13:54:34.350922-08 leaderboard f A100 0.0017131206666666668 t \N \N \N {} +6758 2094 2025-03-15 03:45:32.92736-07 2025-03-15 04:07:37.272427-07 test f A100 \N t \N \N \N {} +4844 1493 2025-03-02 13:46:35.98177-08 2025-03-02 14:48:05.043588-08 test f A100 \N t \N \N \N {} +4845 1493 2025-03-02 14:00:59.163247-08 2025-03-02 14:35:29.575037-08 benchmark f A100 \N t \N \N \N {} +6761 2094 2025-03-15 03:27:08.411109-07 2025-03-15 04:01:20.079415-07 test t A100 \N t \N \N \N {} +4852 1494 2025-03-02 15:08:24.34101-08 2025-03-02 14:19:40.913664-08 leaderboard f L4 0.009482677 t \N \N \N {} +4853 1494 2025-03-02 13:50:54.659903-08 2025-03-02 13:57:25.615542-08 test t L4 \N t \N \N \N {} +4854 1494 2025-03-02 14:27:36.134951-08 2025-03-02 13:47:01.295492-08 benchmark t L4 \N t \N \N \N {} +4855 1494 2025-03-02 15:05:50.23843-08 2025-03-02 15:31:13.138166-08 leaderboard t L4 0.009465309666666666 t \N \N \N {} +4856 1494 2025-03-02 14:59:15.831079-08 2025-03-02 15:20:52.999421-08 test t A100 \N t \N \N \N {} +4857 1494 2025-03-02 14:03:52.80639-08 2025-03-02 14:22:25.374431-08 benchmark t A100 \N t \N \N \N {} +4858 1494 2025-03-02 14:26:35.800808-08 2025-03-02 14:28:06.519575-08 leaderboard t A100 0.0017436125 t \N \N \N {} +4859 1494 2025-03-02 15:29:00.03614-08 2025-03-02 14:28:22.752305-08 test f A100 \N t \N \N \N {} +4860 1494 2025-03-02 14:27:41.758642-08 2025-03-02 14:55:37.506929-08 benchmark f A100 \N t \N \N \N {} +4861 1494 2025-03-02 15:13:30.285322-08 2025-03-02 13:38:55.585292-08 leaderboard f A100 0.0017438063333333332 t \N \N \N {} +4862 1494 2025-03-02 15:33:33.758009-08 2025-03-02 14:29:38.081435-08 test f T4 \N f \N \N \N {} +4869 1495 2025-03-02 15:00:43.386949-08 2025-03-02 15:19:19.029978-08 leaderboard f A100 0.0017183623 t \N \N \N {} +4863 1494 2025-03-02 13:48:00.216726-08 2025-03-02 14:43:10.705646-08 test t T4 \N f \N \N \N {} +4864 1495 2025-03-02 14:14:21.117209-08 2025-03-02 14:50:17.471125-08 test t A100 \N t \N \N \N {} +4868 1495 2025-03-02 13:56:41.498396-08 2025-03-02 15:19:57.661184-08 benchmark f A100 \N t \N \N \N {} +4870 1496 2025-03-03 05:33:22.851475-08 2025-03-03 07:04:29.985456-08 test f T4 \N f \N \N \N {} +4872 1498 2025-03-03 10:02:30.253679-08 2025-03-03 10:00:42.925801-08 benchmark f A100 \N t \N \N \N {} +4873 1499 2025-03-03 08:49:19.901664-08 2025-03-03 10:04:25.898649-08 benchmark f A100 \N t \N \N \N {} +4874 1500 2025-03-03 10:15:34.816738-08 2025-03-03 09:04:15.56701-08 test t A100 \N t \N \N \N {} +4875 1500 2025-03-03 09:58:02.568244-08 2025-03-03 10:16:40.069607-08 benchmark t A100 \N t \N \N \N {} +4878 1500 2025-03-03 10:05:00.25862-08 2025-03-03 09:37:20.75377-08 benchmark f A100 \N t \N \N \N {} +4879 1500 2025-03-03 09:50:57.496245-08 2025-03-03 08:44:48.833862-08 leaderboard f A100 0.003117381 t \N \N \N {} +4882 1503 2025-03-03 10:01:52.486048-08 2025-03-03 10:04:44.602379-08 benchmark f A100 \N t \N \N \N {} +4883 1504 2025-03-03 09:35:17.850935-08 2025-03-03 10:09:25.356439-08 benchmark f A100 \N t \N \N \N {} +4885 1506 2025-03-03 13:09:09.154292-08 2025-03-03 13:05:14.933666-08 test f H100 \N t \N \N \N {} +4886 1506 2025-03-03 12:41:09.063313-08 2025-03-03 12:33:34.096179-08 benchmark f H100 \N t \N \N \N {} +4887 1506 2025-03-03 13:59:05.903613-08 2025-03-03 13:48:05.240853-08 leaderboard f H100 0.007589034 t \N \N \N {} +4888 1506 2025-03-03 13:41:18.845874-08 2025-03-03 13:57:20.398849-08 test t H100 \N t \N \N \N {} +4889 1506 2025-03-03 13:43:47.027609-08 2025-03-03 13:34:31.478831-08 benchmark t H100 \N t \N \N \N {} +4890 1506 2025-03-03 14:32:13.712927-08 2025-03-03 13:02:10.756259-08 leaderboard t H100 0.007640819666666667 t \N \N \N {} +4892 1507 2025-03-03 14:08:09.124545-08 2025-03-03 13:52:34.536612-08 benchmark t A100 \N t \N \N \N {} +4893 1507 2025-03-03 14:13:34.686495-08 2025-03-03 13:52:41.545312-08 leaderboard t A100 0.023358184 t \N \N \N {} +4897 1508 2025-03-03 14:08:08.277234-08 2025-03-03 12:42:40.071166-08 test t H100 \N t \N \N \N {} +4898 1508 2025-03-03 13:11:16.067265-08 2025-03-03 13:52:02.911783-08 benchmark t H100 \N t \N \N \N {} +4901 1508 2025-03-03 12:59:43.830263-08 2025-03-03 13:02:51.227677-08 benchmark f H100 \N t \N \N \N {} +4903 1510 2025-03-03 13:17:23.292196-08 2025-03-03 14:30:50.919066-08 test t T4 \N f \N \N \N {} +6766 2097 2025-03-15 03:49:11.850159-07 2025-03-15 04:26:16.901571-07 test t A100 \N t \N \N \N {} +5252 1613 2025-03-06 07:03:50.875686-08 2025-03-06 06:49:34.383024-08 leaderboard t L4 0.017109318 t \N \N \N {} +5253 1613 2025-03-06 07:08:11.029391-08 2025-03-06 07:01:52.953606-08 test f L4 \N t \N \N \N {} +4937 1521 2025-03-03 14:44:11.876868-08 2025-03-03 14:10:59.325044-08 leaderboard f A100 0.0014066483684210525 t \N \N \N {} +5254 1613 2025-03-06 07:19:32.162924-08 2025-03-06 08:10:28.115423-08 benchmark f L4 \N t \N \N \N {} +4938 1521 2025-03-03 15:24:12.517619-08 2025-03-03 14:20:06.658751-08 test t A100 \N t \N \N \N {} +4935 1521 2025-03-03 14:48:27.263394-08 2025-03-03 15:21:43.699775-08 test f A100 \N t \N \N \N {} +4936 1521 2025-03-03 14:31:08.393947-08 2025-03-03 14:45:16.89233-08 benchmark f A100 \N t \N \N \N {} +4939 1521 2025-03-03 15:07:14.225011-08 2025-03-03 14:04:11.041623-08 benchmark t A100 \N t \N \N \N {} +4940 1521 2025-03-03 15:16:14.240133-08 2025-03-03 14:50:14.153588-08 leaderboard t A100 0.001405596387755102 t \N \N \N {} +4941 1522 2025-03-03 15:05:35.069177-08 2025-03-03 14:28:42.867479-08 test f H100 \N t \N \N \N {} +4945 1522 2025-03-03 14:50:55.354936-08 2025-03-03 15:01:22.261798-08 benchmark t H100 \N t \N \N \N {} +4947 1523 2025-03-03 15:51:18.302976-08 2025-03-03 15:33:42.474228-08 benchmark f L4 \N f \N \N \N {} +4950 1524 2025-03-03 14:20:11.679881-08 2025-03-03 15:49:02.916005-08 leaderboard f A100 0.0025553103333333335 t \N \N \N {} +4951 1524 2025-03-03 14:08:29.207972-08 2025-03-03 14:22:21.528102-08 test t H100 \N t \N \N \N {} +4952 1524 2025-03-03 14:38:24.77646-08 2025-03-03 15:05:24.221642-08 benchmark t H100 \N t \N \N \N {} +4953 1524 2025-03-03 14:46:04.864855-08 2025-03-03 15:51:25.144507-08 leaderboard t H100 0.001393842 t \N \N \N {} +4954 1524 2025-03-03 14:30:39.536975-08 2025-03-03 14:17:44.438638-08 test t A100 \N t \N \N \N {} +4955 1524 2025-03-03 14:40:56.30983-08 2025-03-03 14:38:06.121372-08 benchmark t A100 \N t \N \N \N {} +4956 1524 2025-03-03 15:09:02.97402-08 2025-03-03 15:42:51.195857-08 leaderboard t A100 0.0025125443333333334 t \N \N \N {} +5003 1534 2025-03-03 23:49:46.367273-08 2025-03-03 22:04:46.20686-08 benchmark f T4 \N t \N \N \N {} +5256 1613 2025-03-06 07:45:45.096126-08 2025-03-06 08:18:22.747974-08 test t T4 \N t \N \N \N {} +4968 1533 2025-03-03 22:51:29.622688-08 2025-03-03 23:00:37.622546-08 test t A100 \N t \N \N \N {} +4969 1533 2025-03-03 21:55:51.670102-08 2025-03-03 22:25:04.572539-08 benchmark t A100 \N f \N \N \N {} +4970 1533 2025-03-03 22:42:07.031457-08 2025-03-03 23:50:25.299897-08 test f A100 \N t \N \N \N {} +4971 1533 2025-03-03 22:38:20.171762-08 2025-03-03 23:13:31.324985-08 benchmark f A100 \N f \N \N \N {} +4972 1533 2025-03-03 22:03:32.238956-08 2025-03-03 22:00:26.809337-08 test t H100 \N t \N \N \N {} +4973 1533 2025-03-03 23:37:08.619928-08 2025-03-03 22:52:08.648968-08 benchmark t H100 \N f \N \N \N {} +4975 1533 2025-03-03 22:42:28.912392-08 2025-03-03 22:08:16.911585-08 benchmark f H100 \N f \N \N \N {} +4976 1533 2025-03-03 22:57:31.735787-08 2025-03-03 22:02:40.663491-08 test t T4 \N t \N \N \N {} +4977 1533 2025-03-03 23:18:01.411543-08 2025-03-03 22:17:36.143751-08 benchmark t T4 \N f \N \N \N {} +4978 1533 2025-03-03 23:47:51.751613-08 2025-03-03 22:21:29.994607-08 test f T4 \N t \N \N \N {} +4979 1533 2025-03-03 23:38:54.802767-08 2025-03-03 23:19:22.073079-08 benchmark f T4 \N f \N \N \N {} +4980 1533 2025-03-03 22:10:43.459126-08 2025-03-03 23:21:01.183959-08 test f L4 \N t \N \N \N {} +4987 1534 2025-03-03 22:29:57.882848-08 2025-03-03 22:02:34.344571-08 test f A100 \N t \N \N \N {} +4988 1534 2025-03-03 23:17:09.194997-08 2025-03-03 22:00:28.272124-08 benchmark f A100 \N t \N \N \N {} +6772 2098 2025-03-15 05:05:11.153225-07 2025-03-15 03:30:05.714156-07 test t A100 \N t \N \N \N {} +4989 1534 2025-03-03 23:03:04.470836-08 2025-03-03 22:15:42.701149-08 leaderboard f A100 0.001491699 t \N \N \N {} +4990 1534 2025-03-03 23:01:12.271656-08 2025-03-03 23:25:12.093163-08 test f H100 \N t \N \N \N {} +4991 1534 2025-03-03 23:48:25.304808-08 2025-03-03 23:01:02.444844-08 benchmark f H100 \N t \N \N \N {} +4992 1534 2025-03-03 23:36:40.312813-08 2025-03-03 23:14:16.51261-08 leaderboard f H100 0.001046992 t \N \N \N {} +4993 1534 2025-03-03 22:43:08.623067-08 2025-03-03 23:34:08.683584-08 test f L4 \N t \N \N \N {} +4994 1534 2025-03-03 22:04:15.889744-08 2025-03-03 22:26:35.523219-08 benchmark f L4 \N t \N \N \N {} +4995 1534 2025-03-03 22:41:49.509135-08 2025-03-03 23:39:33.494802-08 leaderboard f L4 0.009082157 t \N \N \N {} +4996 1534 2025-03-03 23:19:47.000677-08 2025-03-03 23:51:43.362821-08 test t H100 \N t \N \N \N {} +5000 1534 2025-03-03 22:10:50.514317-08 2025-03-03 23:01:22.855314-08 benchmark t T4 \N t \N \N \N {} +5001 1534 2025-03-03 23:23:50.275013-08 2025-03-03 22:04:37.625675-08 leaderboard t T4 0.013584824666666667 t \N \N \N {} +5002 1534 2025-03-03 22:16:59.138409-08 2025-03-03 22:04:29.040324-08 test f T4 \N t \N \N \N {} +6775 2098 2025-03-15 04:57:02.889001-07 2025-03-15 04:18:28.535969-07 test f A100 \N t \N \N \N {} +5011 1539 2025-03-04 07:31:37.161177-08 2025-03-04 07:58:56.890059-08 test f T4 \N t \N \N \N {} +5012 1539 2025-03-04 08:16:38.896588-08 2025-03-04 07:18:18.877124-08 benchmark f T4 \N t \N \N \N {} +5013 1539 2025-03-04 08:20:34.258391-08 2025-03-04 07:24:16.514602-08 leaderboard f T4 0.009332833333333334 t \N \N \N {} +5014 1539 2025-03-04 06:44:13.355504-08 2025-03-04 07:28:03.052048-08 test t T4 \N t \N \N \N {} +5015 1539 2025-03-04 08:06:37.290401-08 2025-03-04 06:41:47.643875-08 benchmark t T4 \N t \N \N \N {} +5016 1539 2025-03-04 06:59:22.532084-08 2025-03-04 06:53:54.381226-08 leaderboard t T4 0.009299085333333333 t \N \N \N {} +5017 1540 2025-03-04 08:59:30.670557-08 2025-03-04 07:55:07.055003-08 test f A100 \N f \N \N \N {} +5027 1545 2025-03-04 14:11:42.47821-08 2025-03-04 13:44:28.274936-08 leaderboard f A100 0.00004787177777777778 t \N \N \N {} +5032 1545 2025-03-04 14:00:04.919683-08 2025-03-04 13:52:34.794499-08 benchmark f H100 \N t \N \N \N {} +5033 1545 2025-03-04 15:24:15.587724-08 2025-03-04 15:27:11.189949-08 leaderboard f H100 0.00004866111 t \N \N \N {} +5035 1545 2025-03-04 15:30:36.223103-08 2025-03-04 14:27:02.560929-08 benchmark t T4 \N t \N \N \N {} +5036 1545 2025-03-04 15:07:24.286467-08 2025-03-04 14:51:48.245831-08 leaderboard t T4 0.00023255714285714288 t \N \N \N {} +5257 1613 2025-03-06 06:53:37.987844-08 2025-03-06 08:14:43.956638-08 benchmark t T4 \N t \N \N \N {} +5037 1545 2025-03-04 14:19:47.708086-08 2025-03-04 15:37:55.178413-08 test f L4 \N f \N \N \N {} +5038 1545 2025-03-04 13:49:27.790553-08 2025-03-04 15:32:18.011451-08 test t L4 \N f \N \N \N {} +5039 1545 2025-03-04 15:35:59.738048-08 2025-03-04 14:26:26.882713-08 test f T4 \N f \N \N \N {} +5072 1549 2025-03-04 18:02:14.774593-08 2025-03-04 16:24:38.762227-08 test f H100 \N f \N \N \N {} +5075 1552 2025-03-04 16:26:06.712061-08 2025-03-04 17:05:50.91046-08 benchmark f H100 \N t \N \N \N {} +5076 1553 2025-03-04 17:19:12.024738-08 2025-03-04 17:13:44.312105-08 test t H100 \N t \N \N \N {} +5077 1553 2025-03-04 17:19:42.564719-08 2025-03-04 17:12:42.482126-08 benchmark t H100 \N t \N \N \N {} +5078 1553 2025-03-04 18:03:27.829582-08 2025-03-04 16:30:29.070429-08 leaderboard t H100 0.001479608 t \N \N \N {} +5079 1553 2025-03-04 16:43:40.87693-08 2025-03-04 17:28:08.937479-08 test f H100 \N t \N \N \N {} +5080 1553 2025-03-04 16:51:53.271335-08 2025-03-04 16:21:38.563765-08 benchmark f H100 \N t \N \N \N {} +5084 1556 2025-03-04 16:54:52.472201-08 2025-03-04 17:45:47.245193-08 test f A100 \N f \N \N \N {} +5085 1557 2025-03-04 18:28:01.276786-08 2025-03-04 17:44:42.993298-08 test f A100 \N f \N \N \N {} +5086 1558 2025-03-04 18:11:17.534507-08 2025-03-04 17:29:06.539472-08 test f A100 \N f \N \N \N {} +6790 2101 2025-03-15 03:49:21.74589-07 2025-03-15 04:16:48.818713-07 test f A100 \N t \N \N \N {} +5093 1560 2025-03-04 18:23:35.895658-08 2025-03-04 17:04:19.58105-08 test f H100 \N f \N \N \N {} +5094 1561 2025-03-04 18:05:24.892968-08 2025-03-04 18:46:27.229976-08 test f H100 \N t \N \N \N {} +5095 1562 2025-03-04 18:36:54.224297-08 2025-03-04 18:32:29.172193-08 test f A100 \N t \N \N \N {} +5096 1563 2025-03-04 18:52:59.239172-08 2025-03-04 18:15:38.074894-08 test f H100 \N t \N \N \N {} +5097 1563 2025-03-04 18:49:59.0744-08 2025-03-04 17:26:17.186125-08 benchmark f H100 \N t \N \N \N {} +5098 1563 2025-03-04 17:39:36.278703-08 2025-03-04 17:57:36.746337-08 leaderboard f H100 0.001524286 t \N \N \N {} +5099 1564 2025-03-04 18:14:59.270131-08 2025-03-04 18:51:49.99979-08 benchmark f A100 \N t \N \N \N {} +5100 1563 2025-03-04 18:12:08.43731-08 2025-03-04 17:24:24.25148-08 test t H100 \N t \N \N \N {} +5101 1563 2025-03-04 17:22:01.762045-08 2025-03-04 18:00:18.365591-08 benchmark t H100 \N t \N \N \N {} +5102 1563 2025-03-04 17:14:24.254997-08 2025-03-04 18:03:10.882073-08 leaderboard t H100 0.0015306753333333332 t \N \N \N {} +5104 1565 2025-03-04 18:29:07.767766-08 2025-03-04 17:23:19.898042-08 test t A100 \N t \N \N \N {} +5105 1565 2025-03-04 18:37:16.219751-08 2025-03-04 17:10:25.551997-08 benchmark t A100 \N t \N \N \N {} +5108 1565 2025-03-04 18:35:43.150886-08 2025-03-04 18:46:33.299334-08 benchmark f A100 \N t \N \N \N {} +5109 1565 2025-03-04 17:46:26.10294-08 2025-03-04 17:32:00.135585-08 leaderboard f A100 0.006325797333333333 t \N \N \N {} +5117 1569 2025-03-04 22:14:53.928421-08 2025-03-04 22:40:51.332237-08 test f A100 \N t \N \N \N {} +5118 1569 2025-03-04 22:24:33.021103-08 2025-03-04 21:34:40.758739-08 benchmark f A100 \N t \N \N \N {} +5119 1569 2025-03-04 22:58:48.215389-08 2025-03-04 22:50:16.105009-08 leaderboard f A100 0.0016024253333333334 t \N \N \N {} +5131 1569 2025-03-04 22:47:53.005147-08 2025-03-04 21:52:01.609325-08 leaderboard t H100 0.0010646286666666667 t \N \N \N {} +5132 1569 2025-03-04 23:21:58.974923-08 2025-03-04 21:27:57.937643-08 test f T4 \N t \N \N \N {} +5133 1569 2025-03-04 21:35:43.347052-08 2025-03-04 22:03:01.621549-08 benchmark f T4 \N t \N \N \N {} +5138 1569 2025-03-04 22:37:43.97357-08 2025-03-04 23:16:28.792385-08 test f L4 \N t \N \N \N {} +5139 1569 2025-03-04 21:55:16.016933-08 2025-03-04 23:05:51.261929-08 benchmark f L4 \N t \N \N \N {} +5140 1569 2025-03-04 23:05:20.733299-08 2025-03-04 22:29:48.217612-08 leaderboard f L4 0.009060729666666666 t \N \N \N {} +5152 1570 2025-03-04 22:28:21.72317-08 2025-03-04 23:05:56.534035-08 leaderboard f H100 0.0011025386666666667 t \N \N \N {} +5153 1571 2025-03-04 23:27:03.934327-08 2025-03-04 22:25:50.087612-08 test t A100 \N t \N \N \N {} +5171 1574 2025-03-04 22:56:14.353869-08 2025-03-04 23:19:35.268597-08 test t A100 \N f \N \N \N {} +5172 1574 2025-03-04 21:59:17.605165-08 2025-03-04 22:14:53.169362-08 test f A100 \N f \N \N \N {} +5173 1575 2025-03-04 22:04:24.384305-08 2025-03-04 22:14:55.882584-08 test f H100 \N t \N \N \N {} +5174 1575 2025-03-04 23:09:21.089194-08 2025-03-04 22:12:41.373209-08 benchmark f H100 \N t \N \N \N {} +5185 1578 2025-03-05 04:32:15.216004-08 2025-03-05 04:30:27.093417-08 benchmark f H100 \N t \N \N \N {} +5186 1578 2025-03-05 05:12:33.674089-08 2025-03-05 05:32:04.699339-08 leaderboard f H100 0.0014253056666666667 t \N \N \N {} +5187 1579 2025-03-05 05:33:08.325393-08 2025-03-05 04:46:50.693654-08 test f H100 \N t \N \N \N {} +5188 1580 2025-03-05 04:39:23.334662-08 2025-03-05 04:55:15.840654-08 test f H100 \N f \N \N \N {} +5189 1581 2025-03-05 05:09:09.30193-08 2025-03-05 04:30:58.000235-08 test f H100 \N f \N \N \N {} +5191 1583 2025-03-05 06:03:36.083056-08 2025-03-05 05:52:58.768087-08 test f H100 \N f \N \N \N {} +5192 1584 2025-03-05 04:53:13.978814-08 2025-03-05 06:01:29.534419-08 test f H100 \N f \N \N \N {} +5193 1585 2025-03-05 05:16:23.384842-08 2025-03-05 04:40:47.567792-08 test f H100 \N f \N \N \N {} +5194 1586 2025-03-05 05:55:30.771046-08 2025-03-05 04:56:37.274029-08 test f H100 \N f \N \N \N {} +5195 1587 2025-03-05 05:49:50.339689-08 2025-03-05 05:58:41.349328-08 test f H100 \N f \N \N \N {} +5241 1613 2025-03-06 08:16:58.083166-08 2025-03-06 07:04:03.546846-08 test t A100 \N t \N \N \N {} +5196 1588 2025-03-05 05:52:45.188962-08 2025-03-05 06:10:15.320706-08 test f H100 \N f \N \N \N {} +5198 1590 2025-03-05 06:35:32.564142-08 2025-03-05 05:59:51.088599-08 test f H100 \N f \N \N \N {} +5211 1604 2025-03-05 23:01:07.467624-08 2025-03-06 00:15:28.333028-08 benchmark f A100 \N t \N \N \N {} +5209 1600 2025-03-05 16:08:44.73907-08 2025-03-05 16:32:02.657585-08 test f T4 \N f \N \N \N {} +5210 1601 2025-03-05 17:19:12.616402-08 2025-03-05 16:16:23.001998-08 test f T4 \N f \N \N \N {} +5212 1605 2025-03-05 22:59:21.454362-08 2025-03-05 22:28:41.565705-08 test f A100 \N t \N \N \N {} +5213 1605 2025-03-05 23:20:06.529966-08 2025-03-05 23:11:34.874411-08 benchmark f A100 \N t \N \N \N {} +5214 1605 2025-03-05 22:21:25.7678-08 2025-03-05 22:43:21.731097-08 leaderboard f A100 0.00240375 t \N \N \N {} +5218 1606 2025-03-05 22:50:16.943212-08 2025-03-06 00:17:26.530067-08 test f H100 \N t \N \N \N {} +5219 1606 2025-03-05 23:56:01.568548-08 2025-03-05 23:45:15.745008-08 benchmark f H100 \N t \N \N \N {} +5220 1606 2025-03-05 23:02:21.7458-08 2025-03-06 00:12:54.212376-08 leaderboard f H100 0.001836295 t \N \N \N {} +5221 1606 2025-03-05 23:07:53.314931-08 2025-03-05 23:19:58.450174-08 test t H100 \N t \N \N \N {} +5222 1606 2025-03-05 22:21:37.603066-08 2025-03-05 23:33:48.373334-08 benchmark t H100 \N t \N \N \N {} +5223 1606 2025-03-05 23:56:17.695563-08 2025-03-05 23:31:36.226457-08 leaderboard t H100 0.001844128 t \N \N \N {} +5224 1608 2025-03-06 06:41:32.825228-08 2025-03-06 07:35:28.239062-08 test f T4 \N f \N \N \N {} +5226 1610 2025-03-06 07:18:42.460735-08 2025-03-06 06:35:06.778387-08 test t T4 \N t \N \N \N {} +5250 1613 2025-03-06 08:07:54.141701-08 2025-03-06 07:24:21.635977-08 test t L4 \N t \N \N \N {} +5227 1610 2025-03-06 07:38:46.614128-08 2025-03-06 06:58:40.087618-08 benchmark t T4 \N t \N \N \N {} +5228 1610 2025-03-06 07:51:50.188241-08 2025-03-06 06:58:42.083894-08 leaderboard t T4 0.017143785666666668 t \N \N \N {} +5229 1610 2025-03-06 06:31:06.739357-08 2025-03-06 06:37:17.472387-08 test f T4 \N t \N \N \N {} +5230 1610 2025-03-06 07:22:42.771965-08 2025-03-06 07:33:54.418655-08 benchmark f T4 \N t \N \N \N {} +5231 1610 2025-03-06 06:16:53.919416-08 2025-03-06 06:36:47.083912-08 leaderboard f T4 0.01720559766666667 t \N \N \N {} +5232 1611 2025-03-06 08:16:50.440029-08 2025-03-06 07:49:49.111467-08 test t T4 \N f \N \N \N {} +6784 2100 2025-03-15 04:40:09.589806-07 2025-03-15 04:00:29.456068-07 test f A100 \N t \N \N \N {} +5234 1612 2025-03-06 07:34:10.797478-08 2025-03-06 06:55:41.824879-08 benchmark f L4 \N t \N \N \N {} +5235 1612 2025-03-06 07:42:17.581429-08 2025-03-06 07:04:34.801114-08 benchmark f A100 \N t \N \N \N {} +5236 1612 2025-03-06 07:29:27.298493-08 2025-03-06 08:18:54.173232-08 benchmark f H100 \N t \N \N \N {} +5237 1612 2025-03-06 07:45:18.830048-08 2025-03-06 07:09:08.875473-08 benchmark f T4 \N t \N \N \N {} +5238 1613 2025-03-06 07:02:42.383443-08 2025-03-06 06:58:40.326617-08 test t H100 \N t \N \N \N {} +5239 1613 2025-03-06 06:58:41.561231-08 2025-03-06 07:26:52.163278-08 benchmark t H100 \N t \N \N \N {} +5240 1613 2025-03-06 08:17:32.781179-08 2025-03-06 07:19:03.090424-08 leaderboard t H100 0.0014283363333333333 t \N \N \N {} +5242 1613 2025-03-06 07:50:24.304771-08 2025-03-06 06:37:12.655596-08 benchmark t A100 \N t \N \N \N {} +5243 1613 2025-03-06 07:52:14.675021-08 2025-03-06 07:13:22.297664-08 leaderboard t A100 0.0025773366666666667 t \N \N \N {} +5260 1613 2025-03-06 08:18:47.261863-08 2025-03-06 06:59:45.373237-08 benchmark f T4 \N t \N \N \N {} +5261 1613 2025-03-06 06:55:11.400354-08 2025-03-06 07:22:29.939541-08 leaderboard f T4 0.016297266666666668 t \N \N \N {} +5262 1614 2025-03-06 07:10:48.778261-08 2025-03-06 07:33:58.158012-08 test t A100 \N t \N \N \N {} +5263 1614 2025-03-06 07:57:06.330893-08 2025-03-06 07:32:20.266324-08 benchmark t A100 \N t \N \N \N {} +5264 1614 2025-03-06 07:34:46.398663-08 2025-03-06 07:48:31.150592-08 leaderboard t A100 0.002518239 t \N \N \N {} +5265 1614 2025-03-06 08:22:10.296115-08 2025-03-06 08:10:01.676777-08 test f A100 \N t \N \N \N {} +5266 1614 2025-03-06 07:36:30.354753-08 2025-03-06 08:06:26.122608-08 benchmark f A100 \N t \N \N \N {} +6793 2101 2025-03-15 04:34:05.070783-07 2025-03-15 03:38:46.857954-07 test t A100 \N t \N \N \N {} +5267 1614 2025-03-06 08:17:25.703839-08 2025-03-06 07:44:04.763072-08 leaderboard f A100 0.0025364223333333336 t \N \N \N {} +5268 1614 2025-03-06 07:54:07.764514-08 2025-03-06 06:53:43.516588-08 test f L4 \N t \N \N \N {} +5269 1614 2025-03-06 06:49:11.954531-08 2025-03-06 08:09:48.887046-08 benchmark f L4 \N t \N \N \N {} +5270 1614 2025-03-06 07:58:12.691983-08 2025-03-06 07:16:48.763534-08 leaderboard f L4 0.01703991833333333 t \N \N \N {} +5271 1614 2025-03-06 07:29:09.154149-08 2025-03-06 06:51:28.330755-08 test t L4 \N t \N \N \N {} +5272 1614 2025-03-06 08:11:29.656544-08 2025-03-06 07:59:29.594078-08 benchmark t L4 \N t \N \N \N {} +5273 1614 2025-03-06 08:35:09.612272-08 2025-03-06 07:01:28.409958-08 leaderboard t L4 0.01704863566666667 t \N \N \N {} +5274 1614 2025-03-06 07:29:25.840467-08 2025-03-06 08:40:01.921695-08 test f H100 \N t \N \N \N {} +5275 1614 2025-03-06 07:16:27.972728-08 2025-03-06 07:22:01.065139-08 benchmark f H100 \N t \N \N \N {} +5282 1617 2025-03-06 11:49:00.194719-08 2025-03-06 12:03:16.273518-08 test t L4 \N f \N \N \N {} +5283 1616 2025-03-06 12:12:36.448946-08 2025-03-06 11:07:02.723415-08 test f T4 \N t \N \N \N {} +5284 1616 2025-03-06 12:25:43.203938-08 2025-03-06 10:58:59.548248-08 benchmark f T4 \N t \N \N \N {} +5285 1616 2025-03-06 12:14:49.086319-08 2025-03-06 11:05:00.869471-08 leaderboard f T4 0.009402661909090908 t \N \N \N {} +5288 1616 2025-03-06 11:15:57.5079-08 2025-03-06 12:34:26.281327-08 leaderboard t T4 0.009402624535714285 t \N \N \N {} +5289 1617 2025-03-06 11:04:48.91978-08 2025-03-06 11:41:19.26857-08 test f T4 \N f \N \N \N {} +5290 1617 2025-03-06 11:31:11.14058-08 2025-03-06 11:07:15.510427-08 test t T4 \N f \N \N \N {} +5291 1616 2025-03-06 11:20:35.3437-08 2025-03-06 11:41:25.791263-08 test t L4 \N t \N \N \N {} +5292 1616 2025-03-06 11:17:21.149159-08 2025-03-06 11:36:57.390953-08 benchmark t L4 \N t \N \N \N {} +5293 1616 2025-03-06 11:23:09.088018-08 2025-03-06 10:56:39.864963-08 leaderboard t L4 0.009685256699999999 t \N \N \N {} +5294 1616 2025-03-06 11:46:28.551612-08 2025-03-06 10:50:17.314532-08 test f L4 \N t \N \N \N {} +5295 1616 2025-03-06 11:20:19.815835-08 2025-03-06 11:22:30.677937-08 benchmark f L4 \N t \N \N \N {} +5296 1616 2025-03-06 11:38:50.355641-08 2025-03-06 11:50:15.455809-08 leaderboard f L4 0.009650051153846154 t \N \N \N {} +5309 1620 2025-03-06 12:17:08.411894-08 2025-03-06 11:23:40.680848-08 test t L4 \N t \N \N \N {} +6796 2102 2025-03-15 03:47:18.073866-07 2025-03-15 04:58:31.457625-07 test f A100 \N t \N \N \N {} +6799 2102 2025-03-15 03:49:39.535334-07 2025-03-15 04:47:47.163839-07 test t A100 \N t \N \N \N {} +5310 1620 2025-03-06 11:47:19.342547-08 2025-03-06 11:29:52.276452-08 benchmark t L4 \N t \N \N \N {} +5311 1620 2025-03-06 11:30:56.626007-08 2025-03-06 11:54:39.587711-08 leaderboard t L4 0.017108037 t \N \N \N {} +5315 1621 2025-03-06 12:21:24.243905-08 2025-03-06 11:09:28.475948-08 test t L4 \N f \N \N \N {} +5316 1621 2025-03-06 12:05:13.275699-08 2025-03-06 12:39:19.771477-08 test f L4 \N f \N \N \N {} +5591 1709 2025-03-09 08:38:21.814972-07 2025-03-09 08:31:41.454653-07 leaderboard f T4 0.017528556666666667 t \N \N \N {} +6819 2120 2025-03-15 09:28:09.69146-07 2025-03-15 10:53:19.142071-07 test f A100 \N t \N \N \N {} +5592 1709 2025-03-09 09:45:13.630538-07 2025-03-09 08:29:21.654913-07 test t T4 \N t \N \N \N {} +5637 1719 2025-03-09 10:37:18.14285-07 2025-03-09 09:06:06.681007-07 test f T4 \N t \N \N \N {} +5646 1720 2025-03-09 10:27:41.86582-07 2025-03-09 09:54:42.03058-07 test t T4 \N t \N \N \N {} +5665 1722 2025-03-09 09:32:47.436929-07 2025-03-09 09:26:45.271601-07 benchmark f T4 \N t \N \N \N {} +6822 2120 2025-03-15 10:43:09.477007-07 2025-03-15 09:32:55.270251-07 test t A100 \N t \N \N \N {} +6825 2121 2025-03-15 10:03:06.648384-07 2025-03-15 09:27:27.809952-07 test t A100 \N t \N \N \N {} +5666 1722 2025-03-09 09:52:43.506841-07 2025-03-09 10:58:18.20445-07 leaderboard f T4 0.016603317666666666 t \N \N \N {} +6828 2121 2025-03-15 09:33:59.003711-07 2025-03-15 10:39:36.450576-07 test f A100 \N t \N \N \N {} +5667 1724 2025-03-09 10:44:35.101417-07 2025-03-09 09:13:35.191271-07 test f A100 \N t \N \N \N {} +5655 1721 2025-03-09 10:15:26.439475-07 2025-03-09 09:13:07.504617-07 test f T4 \N t \N \N \N {} +5664 1722 2025-03-09 09:28:08.995402-07 2025-03-09 10:57:10.770407-07 test f T4 \N t \N \N \N {} +6831 2122 2025-03-15 09:55:36.80771-07 2025-03-15 09:14:46.211485-07 test t A100 \N t \N \N \N {} +5668 1724 2025-03-09 10:39:56.890456-07 2025-03-09 11:00:53.761033-07 benchmark f A100 \N t \N \N \N {} +5669 1724 2025-03-09 09:11:31.886775-07 2025-03-09 10:51:26.751854-07 leaderboard f A100 0.0026062246666666666 t \N \N \N {} +5673 1724 2025-03-09 10:17:36.739347-07 2025-03-09 09:34:01.324901-07 test t A100 \N t \N \N \N {} +5670 1725 2025-03-09 09:56:06.327259-07 2025-03-09 10:35:57.538152-07 test f H100 \N t \N \N \N {} +5682 1726 2025-03-09 09:24:13.10994-07 2025-03-09 09:44:04.129165-07 test f L4 \N t \N \N \N {} +5671 1725 2025-03-09 09:58:59.213519-07 2025-03-09 09:39:17.681959-07 benchmark f H100 \N t \N \N \N {} +6834 2122 2025-03-15 09:41:59.559997-07 2025-03-15 10:55:52.113033-07 test f A100 \N t \N \N \N {} +5672 1725 2025-03-09 09:41:17.366352-07 2025-03-09 09:41:18.800653-07 leaderboard f H100 0.0014690293333333333 t \N \N \N {} +6864 2127 2025-03-15 09:44:16.174491-07 2025-03-15 10:46:22.719823-07 test t A100 \N t \N \N \N {} +6837 2123 2025-03-15 11:04:53.025505-07 2025-03-15 10:47:38.423663-07 test f A100 \N t \N \N \N {} +5691 1728 2025-03-09 09:50:18.197376-07 2025-03-09 09:21:08.916986-07 test f A100 \N t \N \N \N {} +6840 2123 2025-03-15 09:53:02.595875-07 2025-03-15 09:54:52.862532-07 test t A100 \N t \N \N \N {} +6843 2124 2025-03-15 10:08:06.005806-07 2025-03-15 10:57:35.474858-07 test f A100 \N t \N \N \N {} +5674 1724 2025-03-09 09:30:02.411383-07 2025-03-09 09:10:48.029672-07 benchmark t A100 \N t \N \N \N {} +6846 2124 2025-03-15 09:34:42.825301-07 2025-03-15 10:21:34.275136-07 test t A100 \N t \N \N \N {} +5563 1700 2025-03-08 17:19:44.52692-08 2025-03-08 16:34:26.7346-08 test t H100 \N f \N \N \N {} +6849 2125 2025-03-15 09:45:34.633967-07 2025-03-15 09:27:25.430422-07 test f A100 \N t \N \N \N {} +5564 1700 2025-03-08 17:51:09.804861-08 2025-03-08 16:57:41.781475-08 test f H100 \N f \N \N \N {} +5547 1692 2025-03-08 16:29:46.214302-08 2025-03-08 16:20:04.501518-08 test f H100 \N f \N \N \N {} +5548 1692 2025-03-08 15:58:09.465006-08 2025-03-08 16:18:52.498393-08 test t H100 \N f \N \N \N {} +5549 1693 2025-03-08 16:52:18.575178-08 2025-03-08 17:37:26.953067-08 test t H100 \N f \N \N \N {} +5567 1702 2025-03-08 16:31:44.29566-08 2025-03-08 17:20:51.940485-08 test t H100 \N f \N \N \N {} +5554 1695 2025-03-08 16:24:17.191755-08 2025-03-08 18:01:05.802736-08 test f H100 \N f \N \N \N {} +5555 1696 2025-03-08 17:11:18.772884-08 2025-03-08 17:09:32.444232-08 test t H100 \N f \N \N \N {} +5556 1696 2025-03-08 17:57:34.2415-08 2025-03-08 17:37:04.496604-08 test f H100 \N f \N \N \N {} +5581 1707 2025-03-09 09:14:09.834023-07 2025-03-09 08:25:50.569029-07 test f T4 \N t \N \N \N {} +5585 1707 2025-03-09 09:38:59.880211-07 2025-03-09 08:38:21.050436-07 benchmark t T4 \N t \N \N \N {} +5587 1708 2025-03-09 09:24:39.948132-07 2025-03-09 08:27:28.337093-07 test f T4 \N f \N \N \N {} +5593 1709 2025-03-09 08:46:11.00316-07 2025-03-09 09:09:14.41678-07 benchmark t T4 \N t \N \N \N {} +5594 1709 2025-03-09 09:13:43.175362-07 2025-03-09 09:11:53.850526-07 leaderboard t T4 0.017543475333333333 t \N \N \N {} +5595 1710 2025-03-09 09:43:56.816043-07 2025-03-09 10:16:02.768519-07 test t T4 \N t \N \N \N {} +5596 1710 2025-03-09 08:28:21.825411-07 2025-03-09 09:26:32.812995-07 benchmark t T4 \N t \N \N \N {} +5597 1710 2025-03-09 09:14:02.814794-07 2025-03-09 09:20:14.126574-07 leaderboard t T4 0.016824632333333332 t \N \N \N {} +6858 2126 2025-03-15 10:43:39.789025-07 2025-03-15 09:34:45.620404-07 test t A100 \N t \N \N \N {} +5601 1711 2025-03-09 09:09:47.896726-07 2025-03-09 09:02:44.866972-07 test t T4 \N f \N \N \N {} +5602 1711 2025-03-09 10:23:41.056545-07 2025-03-09 09:36:00.891836-07 test f T4 \N f \N \N \N {} +5700 1729 2025-03-09 10:19:32.974097-07 2025-03-09 09:54:46.566098-07 test f H100 \N t \N \N \N {} +5607 1712 2025-03-09 10:24:03.045125-07 2025-03-09 09:51:21.11739-07 benchmark t T4 \N t \N \N \N {} +5608 1712 2025-03-09 08:34:08.27444-07 2025-03-09 10:22:37.533877-07 leaderboard t T4 0.01711488933333333 t \N \N \N {} +5613 1714 2025-03-09 09:30:39.959969-07 2025-03-09 10:16:30.712056-07 test t T4 \N t \N \N \N {} +5614 1714 2025-03-09 09:55:01.657622-07 2025-03-09 09:16:09.636467-07 benchmark t T4 \N f \N \N \N {} +5781 1751 2025-03-09 22:17:14.433037-07 2025-03-09 21:49:17.871196-07 test t L4 \N t \N \N \N {} +5629 1717 2025-03-09 08:54:48.324259-07 2025-03-09 10:08:29.038511-07 benchmark t T4 \N t \N \N \N {} +5630 1717 2025-03-09 10:42:26.967084-07 2025-03-09 09:41:58.61599-07 leaderboard t T4 0.016894572333333333 t \N \N \N {} +5631 1718 2025-03-09 09:27:03.051502-07 2025-03-09 10:10:12.243584-07 test t T4 \N t \N \N \N {} +5632 1718 2025-03-09 09:39:46.422345-07 2025-03-09 10:18:05.599135-07 benchmark t T4 \N t \N \N \N {} +5636 1718 2025-03-09 09:51:06.131421-07 2025-03-09 09:42:47.922533-07 leaderboard f T4 0.021235485666666668 t \N \N \N {} +5638 1719 2025-03-09 10:26:54.407345-07 2025-03-09 09:15:13.06042-07 benchmark f T4 \N t \N \N \N {} +5639 1719 2025-03-09 09:29:44.472627-07 2025-03-09 09:37:56.334139-07 leaderboard f T4 0.016808872333333332 t \N \N \N {} +5640 1719 2025-03-09 10:32:20.431198-07 2025-03-09 09:39:18.18504-07 test t T4 \N t \N \N \N {} +5645 1720 2025-03-09 09:55:57.197808-07 2025-03-09 09:40:52.349852-07 leaderboard f T4 0.016596006666666666 t \N \N \N {} +5647 1720 2025-03-09 10:17:27.374148-07 2025-03-09 10:50:14.82081-07 benchmark t T4 \N t \N \N \N {} +5648 1720 2025-03-09 09:16:50.615819-07 2025-03-09 10:21:27.988216-07 leaderboard t T4 0.016557458666666667 t \N \N \N {} +5649 1721 2025-03-09 09:42:37.64406-07 2025-03-09 10:22:42.1259-07 test t T4 \N t \N \N \N {} +5652 1722 2025-03-09 10:58:24.342857-07 2025-03-09 09:19:31.766123-07 test t T4 \N t \N \N \N {} +5654 1722 2025-03-09 10:21:05.107452-07 2025-03-09 09:25:54.295116-07 leaderboard t T4 0.016595996333333335 t \N \N \N {} +5656 1721 2025-03-09 09:56:16.90649-07 2025-03-09 09:16:05.410514-07 benchmark f T4 \N t \N \N \N {} +5657 1721 2025-03-09 09:29:05.785369-07 2025-03-09 10:35:50.223531-07 leaderboard f T4 0.016588513666666665 t \N \N \N {} +5658 1723 2025-03-09 09:19:55.392332-07 2025-03-09 09:53:19.722057-07 test f T4 \N t \N \N \N {} +5677 1725 2025-03-09 09:28:24.172642-07 2025-03-09 10:22:11.236381-07 benchmark t H100 \N t \N \N \N {} +5678 1725 2025-03-09 09:26:29.522614-07 2025-03-09 10:22:55.539844-07 leaderboard t H100 0.001474356 t \N \N \N {} +5679 1726 2025-03-09 10:03:52.222717-07 2025-03-09 09:33:44.331339-07 test t L4 \N t \N \N \N {} +5680 1726 2025-03-09 10:46:04.610414-07 2025-03-09 09:20:34.148072-07 benchmark t L4 \N t \N \N \N {} +5681 1726 2025-03-09 09:37:57.602752-07 2025-03-09 10:22:08.448087-07 leaderboard t L4 0.017242522333333333 t \N \N \N {} +5686 1727 2025-03-09 10:05:30.828856-07 2025-03-09 09:43:47.571121-07 benchmark f L4 \N t \N \N \N {} +5687 1727 2025-03-09 11:07:15.116394-07 2025-03-09 10:13:13.37959-07 leaderboard f L4 0.017209885666666667 t \N \N \N {} +5688 1727 2025-03-09 10:18:00.314232-07 2025-03-09 10:57:21.614871-07 test t L4 \N t \N \N \N {} +5689 1727 2025-03-09 10:01:49.26376-07 2025-03-09 09:25:21.45197-07 benchmark t L4 \N t \N \N \N {} +5690 1727 2025-03-09 10:45:23.99607-07 2025-03-09 09:43:33.892968-07 leaderboard t L4 0.017194495333333334 t \N \N \N {} +5695 1729 2025-03-09 11:12:01.892957-07 2025-03-09 10:53:34.36104-07 benchmark t H100 \N t \N \N \N {} +5696 1729 2025-03-09 10:25:09.517125-07 2025-03-09 10:31:41.836024-07 leaderboard t H100 0.00151662 t \N \N \N {} +5697 1728 2025-03-09 10:48:03.533453-07 2025-03-09 09:49:32.322572-07 test t A100 \N t \N \N \N {} +5698 1728 2025-03-09 10:47:46.442739-07 2025-03-09 10:50:32.307978-07 benchmark t A100 \N t \N \N \N {} +5699 1728 2025-03-09 11:11:38.205953-07 2025-03-09 09:57:38.117077-07 leaderboard t A100 0.0027480226666666665 t \N \N \N {} +5701 1729 2025-03-09 10:46:43.436224-07 2025-03-09 10:08:36.297082-07 benchmark f H100 \N t \N \N \N {} +5702 1729 2025-03-09 09:53:35.832862-07 2025-03-09 10:24:24.298067-07 leaderboard f H100 0.001514298 t \N \N \N {} +5790 1752 2025-03-09 21:29:49.172353-07 2025-03-09 21:20:16.735724-07 test t T4 \N t \N \N \N {} +5775 1749 2025-03-09 22:00:09.902505-07 2025-03-09 22:13:49.54977-07 test f A100 \N t \N \N \N {} +5776 1749 2025-03-09 23:05:49.965781-07 2025-03-09 22:51:52.170132-07 benchmark f A100 \N t \N \N \N {} +5777 1749 2025-03-09 21:24:00.00801-07 2025-03-09 22:47:05.154155-07 leaderboard f A100 0.002640859 t \N \N \N {} +5778 1750 2025-03-09 23:04:17.164077-07 2025-03-09 23:12:50.24992-07 test t H100 \N t \N \N \N {} +5779 1750 2025-03-09 21:43:55.396805-07 2025-03-09 22:41:37.388888-07 benchmark t H100 \N t \N \N \N {} +5782 1751 2025-03-09 21:58:32.972565-07 2025-03-09 22:18:54.173023-07 benchmark t L4 \N t \N \N \N {} +5784 1750 2025-03-09 22:33:29.942446-07 2025-03-09 22:47:34.687352-07 test f H100 \N t \N \N \N {} +5785 1750 2025-03-09 22:57:51.36389-07 2025-03-09 23:11:33.809283-07 benchmark f H100 \N t \N \N \N {} +5786 1750 2025-03-09 22:55:32.663609-07 2025-03-09 21:59:50.521514-07 leaderboard f H100 0.001517079 t \N \N \N {} +5787 1752 2025-03-09 21:22:25.235791-07 2025-03-09 22:13:25.850939-07 test f T4 \N t \N \N \N {} +5788 1752 2025-03-09 23:01:38.584323-07 2025-03-09 21:41:17.038278-07 benchmark f T4 \N t \N \N \N {} +5793 1749 2025-03-09 21:33:50.661901-07 2025-03-09 22:14:05.438845-07 test t A100 \N t \N \N \N {} +5794 1749 2025-03-09 23:11:26.826051-07 2025-03-09 22:00:03.831654-07 benchmark t A100 \N t \N \N \N {} +5795 1749 2025-03-09 21:37:34.837957-07 2025-03-09 22:12:24.604584-07 leaderboard t A100 0.0027676723333333337 t \N \N \N {} +5796 1751 2025-03-09 23:08:13.975436-07 2025-03-09 22:41:10.744916-07 test f L4 \N t \N \N \N {} +5797 1751 2025-03-09 22:51:47.852548-07 2025-03-09 21:35:35.48834-07 benchmark f L4 \N t \N \N \N {} +5798 1751 2025-03-09 22:37:22.743083-07 2025-03-09 21:36:38.099972-07 leaderboard f L4 0.017245750666666667 t \N \N \N {} +5997 1823 2025-03-10 13:44:13.361199-07 2025-03-10 14:17:08.320572-07 test f H100 \N t \N \N \N {} +6111 1849 2025-03-11 00:45:42.466684-07 2025-03-11 02:08:01.224061-07 benchmark f A100 \N f \N \N \N {} +5887 1782 2025-03-10 12:48:56.366283-07 2025-03-10 11:26:28.502927-07 test t T4 \N f \N \N \N {} +5888 1782 2025-03-10 11:20:36.181976-07 2025-03-10 11:07:40.511412-07 test f T4 \N f \N \N \N {} +6006 1824 2025-03-10 14:29:01.568504-07 2025-03-10 13:17:50.880972-07 test t L4 \N t \N \N \N {} +5931 1804 2025-03-10 13:24:49.874112-07 2025-03-10 13:12:59.214864-07 test t T4 \N f \N \N \N {} +5932 1804 2025-03-10 13:11:51.554353-07 2025-03-10 12:36:24.247696-07 test f T4 \N f \N \N \N {} +5933 1805 2025-03-10 12:33:56.206912-07 2025-03-10 12:22:23.193916-07 test t T4 \N f \N \N \N {} +5934 1805 2025-03-10 12:16:39.318057-07 2025-03-10 13:04:51.698492-07 test f T4 \N f \N \N \N {} +5885 1781 2025-03-10 10:56:08.127207-07 2025-03-10 12:40:40.827896-07 test f T4 \N f \N \N \N {} +5886 1781 2025-03-10 11:37:13.25838-07 2025-03-10 11:18:09.354031-07 test t T4 \N f \N \N \N {} +5889 1783 2025-03-10 12:26:17.975854-07 2025-03-10 11:35:06.825449-07 test t T4 \N f \N \N \N {} +5893 1785 2025-03-10 12:49:26.520475-07 2025-03-10 12:05:07.923004-07 test t T4 \N f \N \N \N {} +5894 1785 2025-03-10 11:21:13.907922-07 2025-03-10 12:13:55.484547-07 test f T4 \N f \N \N \N {} +5895 1786 2025-03-10 12:53:04.60948-07 2025-03-10 12:56:23.335782-07 test t T4 \N f \N \N \N {} +5896 1786 2025-03-10 11:52:35.543265-07 2025-03-10 12:47:37.44292-07 test f T4 \N f \N \N \N {} +5897 1787 2025-03-10 12:19:59.35342-07 2025-03-10 12:03:09.756897-07 test t T4 \N f \N \N \N {} +5898 1787 2025-03-10 12:26:38.384347-07 2025-03-10 12:38:55.659544-07 test f T4 \N f \N \N \N {} +5904 1790 2025-03-10 13:07:41.471622-07 2025-03-10 11:29:54.088642-07 test f T4 \N f \N \N \N {} +5905 1791 2025-03-10 12:11:05.954905-07 2025-03-10 11:56:04.212348-07 test t T4 \N f \N \N \N {} +5906 1791 2025-03-10 12:06:20.464482-07 2025-03-10 13:05:37.047802-07 test f T4 \N f \N \N \N {} +5936 1806 2025-03-10 12:25:19.66528-07 2025-03-10 13:25:54.809235-07 test f T4 \N f \N \N \N {} +5907 1792 2025-03-10 11:41:05.031269-07 2025-03-10 12:31:10.781699-07 test f T4 \N f \N \N \N {} +5908 1792 2025-03-10 13:24:19.84949-07 2025-03-10 12:05:30.566627-07 test t T4 \N f \N \N \N {} +5909 1793 2025-03-10 13:10:29.314729-07 2025-03-10 12:27:22.377821-07 test f T4 \N f \N \N \N {} +5910 1793 2025-03-10 11:56:32.644344-07 2025-03-10 12:01:46.701558-07 test t T4 \N f \N \N \N {} +5911 1794 2025-03-10 11:51:38.865118-07 2025-03-10 11:51:19.334553-07 test t T4 \N f \N \N \N {} +5943 1809 2025-03-10 14:18:40.194531-07 2025-03-10 13:13:02.518142-07 leaderboard f A100 0.0026386466666666664 t \N \N \N {} +5912 1794 2025-03-10 12:22:55.695011-07 2025-03-10 13:18:30.719097-07 test f T4 \N f \N \N \N {} +5913 1795 2025-03-10 12:18:06.516563-07 2025-03-10 11:38:14.754524-07 test f T4 \N f \N \N \N {} +5914 1795 2025-03-10 12:36:00.298953-07 2025-03-10 12:55:22.229312-07 test t T4 \N f \N \N \N {} +5923 1800 2025-03-10 11:55:59.030364-07 2025-03-10 12:53:27.690509-07 test f T4 \N f \N \N \N {} +5924 1800 2025-03-10 12:07:20.544617-07 2025-03-10 12:44:06.621555-07 test t T4 \N f \N \N \N {} +5925 1801 2025-03-10 12:06:30.111263-07 2025-03-10 12:53:12.669353-07 test t T4 \N f \N \N \N {} +5926 1801 2025-03-10 13:31:32.380898-07 2025-03-10 13:00:06.56229-07 test f T4 \N f \N \N \N {} +5946 1809 2025-03-10 13:29:37.323879-07 2025-03-10 13:21:30.63564-07 leaderboard t A100 0.002635759 t \N \N \N {} +5937 1807 2025-03-10 12:41:08.079867-07 2025-03-10 12:35:49.297728-07 test f T4 \N f \N \N \N {} +5938 1807 2025-03-10 13:35:12.048803-07 2025-03-10 12:54:21.189668-07 test t T4 \N f \N \N \N {} +5939 1808 2025-03-10 13:59:07.441287-07 2025-03-10 13:33:08.669422-07 test t T4 \N f \N \N \N {} +5940 1808 2025-03-10 14:15:23.166819-07 2025-03-10 13:28:24.134307-07 test f T4 \N f \N \N \N {} +5941 1809 2025-03-10 14:13:38.207591-07 2025-03-10 13:49:12.972948-07 test f A100 \N t \N \N \N {} +5942 1809 2025-03-10 13:54:14.281293-07 2025-03-10 13:15:36.646021-07 benchmark f A100 \N t \N \N \N {} +5953 1811 2025-03-10 13:30:04.564524-07 2025-03-10 12:55:33.446063-07 test t T4 \N f \N \N \N {} +5954 1811 2025-03-10 14:13:48.338956-07 2025-03-10 12:46:33.424518-07 test f T4 \N f \N \N \N {} +5955 1812 2025-03-10 12:31:35.951286-07 2025-03-10 14:17:08.111846-07 test f T4 \N f \N \N \N {} +5956 1812 2025-03-10 13:45:55.348085-07 2025-03-10 13:37:16.626985-07 test t T4 \N f \N \N \N {} +5957 1813 2025-03-10 14:19:03.474874-07 2025-03-10 12:40:48.028667-07 test f T4 \N f \N \N \N {} +5958 1813 2025-03-10 13:09:32.274357-07 2025-03-10 12:58:22.955703-07 test t T4 \N f \N \N \N {} +5960 1814 2025-03-10 13:48:21.644188-07 2025-03-10 13:41:37.383129-07 test t T4 \N f \N \N \N {} +5961 1815 2025-03-10 14:25:53.892843-07 2025-03-10 14:27:52.516911-07 test f T4 \N f \N \N \N {} +5962 1815 2025-03-10 13:39:34.9964-07 2025-03-10 14:26:34.410119-07 test t T4 \N f \N \N \N {} +5963 1816 2025-03-10 12:54:03.964782-07 2025-03-10 13:19:23.51163-07 test t T4 \N f \N \N \N {} +5965 1817 2025-03-10 14:33:23.123369-07 2025-03-10 13:20:30.62665-07 test t T4 \N f \N \N \N {} +5966 1817 2025-03-10 14:24:33.780553-07 2025-03-10 13:09:05.912817-07 test f T4 \N f \N \N \N {} +5967 1818 2025-03-10 12:58:03.382174-07 2025-03-10 13:01:07.893499-07 test f T4 \N t \N \N \N {} +5971 1818 2025-03-10 13:56:03.131162-07 2025-03-10 13:29:13.996141-07 benchmark t T4 \N t \N \N \N {} +5989 1821 2025-03-10 14:34:10.506362-07 2025-03-10 13:16:40.681009-07 benchmark t L4 \N t \N \N \N {} +5990 1821 2025-03-10 14:14:40.84149-07 2025-03-10 13:59:04.607394-07 leaderboard t L4 0.0023410633333333336 t \N \N \N {} +5998 1823 2025-03-10 14:14:38.086577-07 2025-03-10 14:04:27.238303-07 benchmark f H100 \N t \N \N \N {} +5999 1823 2025-03-10 13:02:04.67764-07 2025-03-10 13:38:00.727394-07 leaderboard f H100 0.006278458 t \N \N \N {} +6000 1823 2025-03-10 14:25:36.878836-07 2025-03-10 13:56:47.954868-07 test t H100 \N t \N \N \N {} +6001 1823 2025-03-10 14:33:56.104952-07 2025-03-10 13:27:44.07615-07 benchmark t H100 \N t \N \N \N {} +6002 1823 2025-03-10 13:55:54.754715-07 2025-03-10 14:28:21.040943-07 leaderboard t H100 0.006274267666666667 t \N \N \N {} +6007 1824 2025-03-10 14:28:35.829178-07 2025-03-10 14:56:43.794426-07 benchmark t L4 \N t \N \N \N {} +6008 1824 2025-03-10 14:47:33.073271-07 2025-03-10 13:02:30.850136-07 leaderboard t L4 0.07900226333333332 t \N \N \N {} +6009 1825 2025-03-10 13:08:30.376545-07 2025-03-10 14:58:22.56048-07 test t T4 \N t \N \N \N {} +6010 1825 2025-03-10 13:10:54.018143-07 2025-03-10 14:06:35.263338-07 benchmark t T4 \N t \N \N \N {} +6012 1825 2025-03-10 14:42:31.922476-07 2025-03-10 14:59:03.540136-07 test f T4 \N t \N \N \N {} +6093 1844 2025-03-10 22:33:26.258836-07 2025-03-10 23:34:33.63838-07 leaderboard f A100 0.023116982 t \N \N \N {} +6075 1838 2025-03-10 16:41:59.664667-07 2025-03-10 17:22:08.116083-07 test t T4 \N f \N \N \N {} +6076 1838 2025-03-10 17:26:55.475959-07 2025-03-10 17:59:15.681447-07 test f T4 \N f \N \N \N {} +6077 1839 2025-03-10 17:21:38.821552-07 2025-03-10 17:51:32.223604-07 test f T4 \N f \N \N \N {} +6078 1839 2025-03-10 16:49:21.182853-07 2025-03-10 17:57:15.491956-07 test t T4 \N f \N \N \N {} +6079 1840 2025-03-10 18:26:53.2575-07 2025-03-10 18:21:22.708514-07 test t T4 \N f \N \N \N {} +6080 1840 2025-03-10 16:58:04.162955-07 2025-03-10 16:54:29.133532-07 test f T4 \N f \N \N \N {} +6094 1844 2025-03-10 22:39:58.995586-07 2025-03-10 23:17:13.977931-07 test t A100 \N t \N \N \N {} +6081 1841 2025-03-10 17:37:46.462074-07 2025-03-10 17:23:29.564865-07 test f T4 \N f \N \N \N {} +6082 1841 2025-03-10 17:01:30.436669-07 2025-03-10 17:54:05.772936-07 test t T4 \N f \N \N \N {} +6083 1842 2025-03-10 17:25:05.119241-07 2025-03-10 17:23:54.474002-07 test t T4 \N f \N \N \N {} +6084 1842 2025-03-10 17:50:14.778236-07 2025-03-10 17:38:41.04272-07 test f T4 \N f \N \N \N {} +6085 1843 2025-03-10 23:26:01.96551-07 2025-03-10 22:45:08.618919-07 test f T4 \N t \N \N \N {} +6102 1845 2025-03-10 23:18:03.684504-07 2025-03-10 22:13:42.532972-07 leaderboard t H100 0.007583517333333333 t \N \N \N {} +6086 1843 2025-03-10 23:47:15.122747-07 2025-03-10 23:21:01.982853-07 benchmark f T4 \N t \N \N \N {} +6087 1843 2025-03-10 23:09:56.514137-07 2025-03-10 23:41:36.80451-07 leaderboard f T4 1.0437126283333333 t \N \N \N {} +6088 1843 2025-03-10 22:13:21.923671-07 2025-03-10 23:11:52.790625-07 test t T4 \N t \N \N \N {} +6089 1843 2025-03-10 23:06:17.150635-07 2025-03-10 21:59:26.386221-07 benchmark t T4 \N t \N \N \N {} +6090 1843 2025-03-10 22:53:29.015752-07 2025-03-10 22:55:46.841799-07 leaderboard t T4 0.9874019316666667 t \N \N \N {} +6091 1844 2025-03-10 22:58:35.828002-07 2025-03-10 22:04:49.298779-07 test f A100 \N t \N \N \N {} +6092 1844 2025-03-10 23:35:22.877612-07 2025-03-10 22:06:59.964007-07 benchmark f A100 \N t \N \N \N {} +6101 1845 2025-03-10 22:20:24.248784-07 2025-03-10 23:42:44.548166-07 benchmark t H100 \N t \N \N \N {} +6113 1852 2025-03-11 01:44:33.106318-07 2025-03-11 00:54:18.834861-07 test f A100 \N f \N \N \N {} +6114 1853 2025-03-11 02:21:59.844098-07 2025-03-11 00:42:46.671243-07 test f A100 \N t \N \N \N {} +6115 1854 2025-03-11 02:28:36.156334-07 2025-03-11 00:46:06.202296-07 benchmark f A100 \N t \N \N \N {} +6886 2139 2025-03-16 11:38:46.212648-07 2025-03-16 11:28:03.154972-07 test f H100 \N f \N \N \N {} +6312 1952 2025-03-12 12:28:15.470511-07 2025-03-12 11:10:36.793829-07 test f T4 \N f \N \N \N {} +6460 2005 2025-03-14 06:31:39.099707-07 2025-03-14 07:51:37.583527-07 benchmark t A100 \N t \N \N \N {} +6461 2005 2025-03-14 06:46:54.111465-07 2025-03-14 07:07:11.64108-07 leaderboard t A100 0.004546970333333333 t \N \N \N {} +6596 2044 2025-03-14 13:39:50.152918-07 2025-03-14 12:42:49.661409-07 test f T4 \N f \N \N \N {} +6887 2140 2025-03-16 11:24:30.571404-07 2025-03-16 10:17:15.135954-07 test f T4 \N t \N \N \N {} +6651 2054 2025-03-14 12:23:27.508379-07 2025-03-14 12:07:07.961699-07 benchmark t A100 \N t \N \N \N {} +6652 2054 2025-03-14 13:00:27.647397-07 2025-03-14 13:25:59.68305-07 leaderboard t A100 0.0024835623333333335 t \N \N \N {} +6653 2055 2025-03-14 13:40:44.971801-07 2025-03-14 12:14:34.48949-07 benchmark f A100 \N t \N \N \N {} +6654 2056 2025-03-14 12:23:48.644957-07 2025-03-14 13:52:44.535234-07 benchmark f A100 \N t \N \N \N {} +6657 2058 2025-03-14 13:30:54.732584-07 2025-03-14 12:53:01.468344-07 benchmark t A100 \N t \N \N \N {} +6658 2058 2025-03-14 13:18:02.16859-07 2025-03-14 13:30:04.076635-07 leaderboard t A100 0.002514722 t \N \N \N {} +6660 2058 2025-03-14 12:19:31.91691-07 2025-03-14 12:23:40.05458-07 benchmark f A100 \N t \N \N \N {} +6678 2067 2025-03-14 14:51:27.080477-07 2025-03-14 15:45:50.453303-07 test t T4 \N f \N \N \N {} +6679 2067 2025-03-14 16:00:05.640981-07 2025-03-14 15:45:05.704388-07 test f T4 \N f \N \N \N {} +6681 2068 2025-03-14 15:59:19.410815-07 2025-03-14 15:47:46.277659-07 test t T4 \N f \N \N \N {} +6682 2069 2025-03-14 14:50:20.787072-07 2025-03-14 16:19:00.44313-07 test t T4 \N f \N \N \N {} +6683 2069 2025-03-14 15:33:20.870757-07 2025-03-14 16:21:01.08373-07 test f T4 \N f \N \N \N {} +6688 2072 2025-03-14 16:45:06.05283-07 2025-03-14 15:41:49.353865-07 test f T4 \N f \N \N \N {} +6689 2072 2025-03-14 15:20:50.731476-07 2025-03-14 15:36:51.584067-07 test t T4 \N f \N \N \N {} +6888 2141 2025-03-16 11:25:39.687485-07 2025-03-16 10:00:50.121381-07 test f H100 \N t \N \N \N {} +6690 2073 2025-03-14 17:13:14.556768-07 2025-03-14 16:38:50.501123-07 test t T4 \N f \N \N \N {} +6691 2073 2025-03-14 16:16:09.058399-07 2025-03-14 16:49:34.474312-07 test f T4 \N f \N \N \N {} +6692 2074 2025-03-14 16:06:22.83602-07 2025-03-14 16:32:58.584801-07 test t T4 \N f \N \N \N {} +6695 2075 2025-03-14 16:07:22.505809-07 2025-03-14 16:41:15.832579-07 test t T4 \N f \N \N \N {} +6696 2076 2025-03-14 17:00:09.730989-07 2025-03-14 17:45:40.086325-07 test f T4 \N f \N \N \N {} +6697 2076 2025-03-14 16:27:20.361782-07 2025-03-14 17:11:01.011478-07 test t T4 \N f \N \N \N {} +6698 2077 2025-03-14 17:12:38.530745-07 2025-03-14 17:22:03.682351-07 test t T4 \N f \N \N \N {} +6699 2077 2025-03-14 17:57:47.071236-07 2025-03-14 17:32:18.120911-07 test f T4 \N f \N \N \N {} +6700 2078 2025-03-14 17:18:24.324855-07 2025-03-14 16:55:54.52059-07 test t T4 \N f \N \N \N {} +6701 2078 2025-03-14 16:20:14.880505-07 2025-03-14 16:15:25.529956-07 test f T4 \N f \N \N \N {} +6702 2079 2025-03-14 16:16:56.772414-07 2025-03-14 16:16:13.557071-07 test t T4 \N f \N \N \N {} +6703 2079 2025-03-14 16:04:46.914169-07 2025-03-14 17:47:21.761297-07 test f T4 \N f \N \N \N {} +6705 2080 2025-03-15 03:22:21.602325-07 2025-03-15 03:51:58.457278-07 benchmark t A100 \N t \N \N \N {} +6706 2080 2025-03-15 02:53:01.372004-07 2025-03-15 04:22:27.007219-07 leaderboard t A100 0.002516597 t \N \N \N {} +6712 2081 2025-03-15 02:40:30.575706-07 2025-03-15 03:31:55.583471-07 leaderboard f A100 0.0025119373333333337 t \N \N \N {} +6714 2081 2025-03-15 04:01:30.92787-07 2025-03-15 04:10:50.24876-07 benchmark t A100 \N t \N \N \N {} +6715 2081 2025-03-15 03:13:12.177222-07 2025-03-15 04:31:33.220267-07 leaderboard t A100 0.0025109593333333337 t \N \N \N {} +6717 2082 2025-03-15 04:18:09.428811-07 2025-03-15 04:24:16.122702-07 benchmark t A100 \N t \N \N \N {} +6718 2082 2025-03-15 02:49:24.552314-07 2025-03-15 02:42:15.529163-07 leaderboard t A100 0.0025148316666666667 t \N \N \N {} +6720 2082 2025-03-15 04:08:25.063554-07 2025-03-15 03:27:30.400048-07 benchmark f A100 \N t \N \N \N {} +6723 2083 2025-03-15 03:03:30.27571-07 2025-03-15 03:18:11.663245-07 benchmark f A100 \N t \N \N \N {} +6724 2083 2025-03-15 03:42:23.801801-07 2025-03-15 03:28:29.653584-07 leaderboard f A100 0.002519684 t \N \N \N {} +6726 2083 2025-03-15 04:21:45.565499-07 2025-03-15 04:16:51.069546-07 benchmark t A100 \N t \N \N \N {} +6727 2083 2025-03-15 04:07:46.449197-07 2025-03-15 04:25:25.23021-07 leaderboard t A100 0.002565036 t \N \N \N {} +6729 2084 2025-03-15 03:39:18.979856-07 2025-03-15 03:30:24.318469-07 benchmark t A100 \N t \N \N \N {} +6730 2084 2025-03-15 04:36:55.579596-07 2025-03-15 02:45:20.602588-07 leaderboard t A100 0.0025688876666666665 t \N \N \N {} +6732 2084 2025-03-15 03:14:12.489414-07 2025-03-15 02:51:03.824773-07 benchmark f A100 \N t \N \N \N {} +6733 2084 2025-03-15 03:22:20.346621-07 2025-03-15 03:11:13.814391-07 leaderboard f A100 0.00251581 t \N \N \N {} +6734 2085 2025-03-15 04:20:48.927946-07 2025-03-15 04:46:33.035612-07 benchmark f A100 \N t \N \N \N {} +6736 2086 2025-03-15 04:16:49.706067-07 2025-03-15 03:09:15.471405-07 benchmark t A100 \N t \N \N \N {} +6737 2086 2025-03-15 04:03:35.988704-07 2025-03-15 03:48:00.494296-07 leaderboard t A100 0.0025361823333333337 t \N \N \N {} +6739 2086 2025-03-15 03:15:14.837866-07 2025-03-15 04:10:52.660769-07 benchmark f A100 \N t \N \N \N {} +6740 2086 2025-03-15 03:44:14.062436-07 2025-03-15 04:17:31.002026-07 leaderboard f A100 0.0025177116666666665 t \N \N \N {} +6742 2087 2025-03-15 03:11:35.820391-07 2025-03-15 03:29:33.061134-07 benchmark t A100 \N t \N \N \N {} +6743 2087 2025-03-15 04:11:17.86842-07 2025-03-15 04:57:34.429343-07 leaderboard t A100 0.0025124136666666665 t \N \N \N {} +6745 2087 2025-03-15 04:40:18.217533-07 2025-03-15 04:45:54.791513-07 benchmark f A100 \N t \N \N \N {} +6746 2087 2025-03-15 03:26:08.981404-07 2025-03-15 03:26:01.506723-07 leaderboard f A100 0.0025170136666666666 t \N \N \N {} +6747 2088 2025-03-15 03:45:17.641686-07 2025-03-15 03:59:01.555077-07 benchmark f A100 \N t \N \N \N {} +6748 2089 2025-03-15 04:38:05.216274-07 2025-03-15 03:11:47.405569-07 benchmark f A100 \N t \N \N \N {} +6749 2090 2025-03-15 03:38:24.37619-07 2025-03-15 03:54:27.64572-07 benchmark f A100 \N t \N \N \N {} +6750 2091 2025-03-15 03:40:25.330357-07 2025-03-15 05:05:56.066665-07 benchmark f A100 \N t \N \N \N {} +6751 2092 2025-03-15 03:14:13.338645-07 2025-03-15 03:23:06.85116-07 benchmark f A100 \N t \N \N \N {} +6753 2093 2025-03-15 03:42:38.171903-07 2025-03-15 04:13:43.823972-07 benchmark f A100 \N t \N \N \N {} +6754 2093 2025-03-15 04:16:08.4764-07 2025-03-15 04:59:47.179425-07 leaderboard f A100 0.0025626343333333334 t \N \N \N {} +6756 2093 2025-03-15 03:19:26.506082-07 2025-03-15 03:33:18.400847-07 benchmark t A100 \N t \N \N \N {} +6757 2093 2025-03-15 03:50:47.54997-07 2025-03-15 04:41:08.193634-07 leaderboard t A100 0.0025123326666666667 t \N \N \N {} +6759 2094 2025-03-15 04:41:28.996534-07 2025-03-15 03:59:31.091742-07 benchmark f A100 \N t \N \N \N {} +6760 2094 2025-03-15 03:19:48.97712-07 2025-03-15 04:52:46.927488-07 leaderboard f A100 0.002587383 t \N \N \N {} +6762 2094 2025-03-15 03:21:46.748837-07 2025-03-15 03:42:35.174807-07 benchmark t A100 \N t \N \N \N {} +6763 2094 2025-03-15 04:43:41.011534-07 2025-03-15 04:19:37.015055-07 leaderboard t A100 0.0025829123333333333 t \N \N \N {} +6764 2095 2025-03-15 03:22:19.084993-07 2025-03-15 04:23:32.846181-07 benchmark f A100 \N t \N \N \N {} +6765 2096 2025-03-15 04:20:43.869135-07 2025-03-15 03:47:49.996079-07 benchmark f A100 \N t \N \N \N {} +6767 2097 2025-03-15 05:10:33.784233-07 2025-03-15 05:11:32.173991-07 benchmark t A100 \N t \N \N \N {} +6768 2097 2025-03-15 05:07:59.890466-07 2025-03-15 04:39:03.8974-07 leaderboard t A100 0.0024414366666666667 t \N \N \N {} +6770 2097 2025-03-15 04:36:24.390754-07 2025-03-15 04:02:31.398882-07 benchmark f A100 \N t \N \N \N {} +6771 2097 2025-03-15 04:12:36.697244-07 2025-03-15 03:55:22.254114-07 leaderboard f A100 0.0025765703333333334 t \N \N \N {} +6773 2098 2025-03-15 05:10:57.321488-07 2025-03-15 04:50:43.920081-07 benchmark t A100 \N t \N \N \N {} +6774 2098 2025-03-15 04:14:30.497647-07 2025-03-15 04:53:30.016091-07 leaderboard t A100 0.002577079 t \N \N \N {} +6776 2098 2025-03-15 05:09:55.996121-07 2025-03-15 03:56:57.017989-07 benchmark f A100 \N t \N \N \N {} +6777 2098 2025-03-15 04:11:54.507468-07 2025-03-15 03:43:49.234308-07 leaderboard f A100 0.002441317 t \N \N \N {} +6779 2099 2025-03-15 03:45:03.883023-07 2025-03-15 05:13:30.957419-07 benchmark f A100 \N t \N \N \N {} +6780 2099 2025-03-15 03:28:47.790669-07 2025-03-15 04:06:54.990019-07 leaderboard f A100 0.002574019 t \N \N \N {} +6782 2099 2025-03-15 05:16:01.58112-07 2025-03-15 04:49:37.793655-07 benchmark t A100 \N t \N \N \N {} +6783 2099 2025-03-15 04:17:13.541413-07 2025-03-15 05:01:49.023637-07 leaderboard t A100 0.0024406096666666666 t \N \N \N {} +6785 2100 2025-03-15 05:11:53.175254-07 2025-03-15 04:00:48.036801-07 benchmark f A100 \N t \N \N \N {} +6786 2100 2025-03-15 04:50:11.427307-07 2025-03-15 04:59:24.114334-07 leaderboard f A100 0.0025154266666666666 t \N \N \N {} +6788 2100 2025-03-15 03:58:09.913043-07 2025-03-15 04:10:04.670157-07 benchmark t A100 \N t \N \N \N {} +6789 2100 2025-03-15 04:27:52.506737-07 2025-03-15 05:09:40.763406-07 leaderboard t A100 0.0025164226666666667 t \N \N \N {} +6791 2101 2025-03-15 03:51:55.571727-07 2025-03-15 04:36:50.58781-07 benchmark f A100 \N t \N \N \N {} +6792 2101 2025-03-15 05:02:04.728096-07 2025-03-15 03:47:35.278382-07 leaderboard f A100 0.002513957 t \N \N \N {} +6794 2101 2025-03-15 04:13:17.847293-07 2025-03-15 05:12:57.162997-07 benchmark t A100 \N t \N \N \N {} +6795 2101 2025-03-15 04:32:46.623254-07 2025-03-15 04:14:33.053587-07 leaderboard t A100 0.0025157153333333336 t \N \N \N {} +6797 2102 2025-03-15 04:32:32.324876-07 2025-03-15 05:04:55.352884-07 benchmark f A100 \N t \N \N \N {} +6798 2102 2025-03-15 05:21:36.583203-07 2025-03-15 04:18:22.362049-07 leaderboard f A100 0.002519632 t \N \N \N {} +6800 2102 2025-03-15 04:46:37.876337-07 2025-03-15 05:18:37.22304-07 benchmark t A100 \N t \N \N \N {} +6801 2102 2025-03-15 04:37:43.517293-07 2025-03-15 04:33:17.157515-07 leaderboard t A100 0.0025164303333333336 t \N \N \N {} +6802 2103 2025-03-15 04:28:54.778992-07 2025-03-15 04:05:58.240583-07 benchmark f A100 \N t \N \N \N {} +6803 2104 2025-03-15 04:17:37.393026-07 2025-03-15 04:32:32.169131-07 benchmark f A100 \N t \N \N \N {} +6805 2106 2025-03-15 04:31:28.892695-07 2025-03-15 05:14:03.534628-07 benchmark f A100 \N t \N \N \N {} +6809 2110 2025-03-15 05:36:30.308746-07 2025-03-15 05:16:01.72196-07 benchmark f A100 \N f \N \N \N {} +6810 2111 2025-03-15 05:40:36.907231-07 2025-03-15 04:27:34.338625-07 benchmark f A100 \N t \N \N \N {} +6811 2112 2025-03-15 04:14:08.830754-07 2025-03-15 04:10:52.990865-07 benchmark f A100 \N t \N \N \N {} +6812 2113 2025-03-15 04:18:14.027105-07 2025-03-15 04:27:38.546365-07 benchmark f A100 \N t \N \N \N {} +6892 2142 2025-03-16 11:16:36.721079-07 2025-03-16 10:23:08.981733-07 benchmark f H100 \N f \N \N \N {} +6813 2114 2025-03-15 05:32:29.886643-07 2025-03-15 04:16:37.097189-07 benchmark f A100 \N f \N \N \N {} +6814 2115 2025-03-15 04:20:01.707962-07 2025-03-15 04:47:01.479607-07 benchmark f A100 \N t \N \N \N {} +6815 2116 2025-03-15 09:41:27.374128-07 2025-03-15 10:42:21.527333-07 benchmark f A100 \N f \N \N \N {} +6817 2118 2025-03-15 09:34:00.585802-07 2025-03-15 09:31:57.810416-07 benchmark f A100 \N t \N \N \N {} +6818 2119 2025-03-15 09:01:24.585482-07 2025-03-15 09:06:44.508444-07 benchmark f A100 \N t \N \N \N {} +6820 2120 2025-03-15 09:11:32.950497-07 2025-03-15 10:49:33.106269-07 benchmark f A100 \N t \N \N \N {} +6821 2120 2025-03-15 10:40:24.613571-07 2025-03-15 09:19:20.415657-07 leaderboard f A100 0.002443316 t \N \N \N {} +6824 2120 2025-03-15 09:48:57.993341-07 2025-03-15 10:57:27.863934-07 leaderboard t A100 0.0025090733333333333 t \N \N \N {} +6826 2121 2025-03-15 10:59:21.842552-07 2025-03-15 09:27:40.540091-07 benchmark t A100 \N t \N \N \N {} +6827 2121 2025-03-15 11:01:43.071093-07 2025-03-15 09:36:34.551049-07 leaderboard t A100 0.0025179616666666663 t \N \N \N {} +6829 2121 2025-03-15 10:06:55.697797-07 2025-03-15 09:52:44.863131-07 benchmark f A100 \N t \N \N \N {} +6830 2121 2025-03-15 10:04:37.365154-07 2025-03-15 10:01:47.995774-07 leaderboard f A100 0.00258409 t \N \N \N {} +6832 2122 2025-03-15 09:19:20.004818-07 2025-03-15 10:37:41.259917-07 benchmark t A100 \N t \N \N \N {} +6833 2122 2025-03-15 10:30:16.383706-07 2025-03-15 10:47:04.167114-07 leaderboard t A100 0.002514938 t \N \N \N {} +6835 2122 2025-03-15 09:23:17.985207-07 2025-03-15 10:19:25.750256-07 benchmark f A100 \N t \N \N \N {} +6836 2122 2025-03-15 09:33:25.125545-07 2025-03-15 09:07:15.935099-07 leaderboard f A100 0.002580424 t \N \N \N {} +6838 2123 2025-03-15 09:19:08.433444-07 2025-03-15 10:51:34.7913-07 benchmark f A100 \N t \N \N \N {} +6839 2123 2025-03-15 10:48:27.578624-07 2025-03-15 10:18:07.6516-07 leaderboard f A100 0.0025640053333333334 t \N \N \N {} +6841 2123 2025-03-15 09:57:51.450683-07 2025-03-15 11:08:21.326212-07 benchmark t A100 \N t \N \N \N {} +6842 2123 2025-03-15 10:58:54.403466-07 2025-03-15 10:55:26.439192-07 leaderboard t A100 0.0024363643333333335 t \N \N \N {} +6844 2124 2025-03-15 10:59:45.752999-07 2025-03-15 11:08:50.594752-07 benchmark f A100 \N t \N \N \N {} +6845 2124 2025-03-15 10:30:51.054594-07 2025-03-15 09:50:58.327798-07 leaderboard f A100 0.0025648486666666665 t \N \N \N {} +6847 2124 2025-03-15 10:17:13.326454-07 2025-03-15 11:08:25.750753-07 benchmark t A100 \N t \N \N \N {} +6848 2124 2025-03-15 10:50:00.948662-07 2025-03-15 09:49:49.033815-07 leaderboard t A100 0.0024406783333333335 t \N \N \N {} +6850 2125 2025-03-15 10:24:17.864597-07 2025-03-15 11:03:08.565844-07 benchmark f A100 \N t \N \N \N {} +6851 2125 2025-03-15 10:43:35.545505-07 2025-03-15 09:58:24.113231-07 leaderboard f A100 0.0025163956666666666 t \N \N \N {} +6853 2125 2025-03-15 10:00:18.694621-07 2025-03-15 09:47:13.923293-07 benchmark t A100 \N t \N \N \N {} +6854 2125 2025-03-15 09:28:35.589355-07 2025-03-15 09:48:08.961608-07 leaderboard t A100 0.0024417513333333334 t \N \N \N {} +6856 2126 2025-03-15 11:02:27.462138-07 2025-03-15 09:43:02.319651-07 benchmark f A100 \N t \N \N \N {} +6857 2126 2025-03-15 10:34:45.228313-07 2025-03-15 10:59:38.816203-07 leaderboard f A100 0.0025180366666666667 t \N \N \N {} +6859 2126 2025-03-15 10:50:57.421372-07 2025-03-15 10:57:16.908572-07 benchmark t A100 \N t \N \N \N {} +6860 2126 2025-03-15 09:48:05.47393-07 2025-03-15 11:11:47.912597-07 leaderboard t A100 0.0024680626666666663 t \N \N \N {} +6862 2127 2025-03-15 10:49:46.799765-07 2025-03-15 11:16:21.43097-07 benchmark f A100 \N t \N \N \N {} +6863 2127 2025-03-15 09:54:25.438285-07 2025-03-15 09:33:00.814065-07 leaderboard f A100 0.0025179106666666667 t \N \N \N {} +6865 2127 2025-03-15 09:30:50.965569-07 2025-03-15 10:48:04.990717-07 benchmark t A100 \N t \N \N \N {} +6866 2127 2025-03-15 10:18:20.707038-07 2025-03-15 11:07:56.251823-07 leaderboard t A100 0.00251993 t \N \N \N {} +6867 2128 2025-03-15 12:31:03.821683-07 2025-03-15 13:36:09.213881-07 test t T4 \N f \N \N \N {} +6873 2131 2025-03-15 13:22:04.183948-07 2025-03-15 14:24:48.619022-07 test f T4 \N f \N \N \N {} +6879 2134 2025-03-15 13:51:24.499821-07 2025-03-15 13:59:05.032478-07 test f T4 \N f \N \N \N {} +6880 2134 2025-03-15 13:38:48.357103-07 2025-03-15 13:25:18.378546-07 test t T4 \N f \N \N \N {} +6881 2135 2025-03-15 13:08:37.869004-07 2025-03-15 14:28:23.740745-07 test t T4 \N f \N \N \N {} +6882 2135 2025-03-15 14:19:21.411146-07 2025-03-15 13:47:56.79824-07 test f T4 \N f \N \N \N {} +6885 2138 2025-03-16 09:39:05.307836-07 2025-03-16 09:57:12.148158-07 test f H100 \N f \N \N \N {} +6898 2147 2025-03-16 12:12:19.701328-07 2025-03-16 12:45:49.124926-07 benchmark f T4 \N t \N \N \N {} +6900 2148 2025-03-16 12:59:18.78657-07 2025-03-16 12:12:58.201063-07 benchmark t T4 \N t \N \N \N {} +6901 2148 2025-03-16 13:01:51.449428-07 2025-03-16 13:37:59.705354-07 leaderboard t T4 0.04730965533333334 t \N \N \N {} +6903 2148 2025-03-16 12:58:16.904312-07 2025-03-16 12:26:52.007968-07 benchmark f T4 \N t \N \N \N {} +6904 2148 2025-03-16 13:30:00.224366-07 2025-03-16 12:52:44.892037-07 leaderboard f T4 0.04833626233333334 t \N \N \N {} +6908 2152 2025-03-16 15:44:14.854646-07 2025-03-16 16:27:57.619851-07 test t T4 \N f \N \N \N {} +6937 2159 2025-03-16 16:22:15.506089-07 2025-03-16 15:10:41.848628-07 test t T4 \N t \N \N \N {} +6909 2152 2025-03-16 16:12:17.885519-07 2025-03-16 16:07:41.651133-07 test f T4 \N f \N \N \N {} +6910 2153 2025-03-16 16:06:39.076718-07 2025-03-16 16:34:36.042625-07 test f T4 \N f \N \N \N {} +6911 2153 2025-03-16 16:28:04.258089-07 2025-03-16 15:53:40.235028-07 test t T4 \N f \N \N \N {} +6917 2156 2025-03-16 16:39:30.967226-07 2025-03-16 16:15:32.585871-07 benchmark f T4 \N t \N \N \N {} +6918 2156 2025-03-16 16:54:16.751464-07 2025-03-16 16:03:12.107749-07 leaderboard f T4 0.00024644342857142856 t \N \N \N {} +6920 2156 2025-03-16 14:58:37.646855-07 2025-03-16 15:33:11.455364-07 benchmark t T4 \N t \N \N \N {} +6921 2156 2025-03-16 15:16:42.101122-07 2025-03-16 16:12:37.070857-07 leaderboard t T4 0.00027644572222222224 t \N \N \N {} +6936 2159 2025-03-16 15:05:11.186583-07 2025-03-16 15:35:42.129511-07 benchmark f T4 \N f \N \N \N {} +6938 2159 2025-03-16 16:42:58.618407-07 2025-03-16 15:47:12.763026-07 benchmark t T4 \N f \N \N \N {} +6939 2161 2025-03-16 17:02:27.026204-07 2025-03-16 16:28:43.910502-07 test t T4 \N t \N \N \N {} +6940 2161 2025-03-16 16:06:50.499036-07 2025-03-16 15:50:15.342936-07 benchmark t T4 \N t \N \N \N {} +6944 2161 2025-03-16 16:05:29.503727-07 2025-03-16 16:16:37.57484-07 leaderboard f T4 0.00036897348214285714 t \N \N \N {} +6958 2165 2025-03-16 15:32:28.811938-07 2025-03-16 15:44:31.566608-07 test t T4 \N f \N \N \N {} +6959 2165 2025-03-16 15:57:30.46348-07 2025-03-16 17:11:51.584129-07 test f T4 \N f \N \N \N {} +6967 2168 2025-03-16 19:51:44.930831-07 2025-03-16 19:38:02.035729-07 test t T4 \N f \N \N \N {} +6968 2168 2025-03-16 19:59:18.445681-07 2025-03-16 20:46:51.847725-07 test f T4 \N f \N \N \N {} +6969 2169 2025-03-16 20:01:37.505709-07 2025-03-16 19:53:20.526714-07 test t T4 \N t \N \N \N {} +6970 2169 2025-03-16 18:55:53.752614-07 2025-03-16 19:43:27.875834-07 benchmark t T4 \N t \N \N \N {} +6972 2169 2025-03-16 20:01:43.044-07 2025-03-16 20:16:29.475064-07 test f T4 \N t \N \N \N {} +6973 2169 2025-03-16 20:12:25.898132-07 2025-03-16 20:12:59.112672-07 benchmark f T4 \N t \N \N \N {} +6975 2170 2025-03-16 20:55:02.810795-07 2025-03-16 19:05:21.943366-07 test f T4 \N t \N \N \N {} +6982 2171 2025-03-16 20:34:15.139307-07 2025-03-16 19:39:38.074903-07 benchmark t T4 \N t \N \N \N {} 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20:52:50.82818-07 2025-03-16 20:52:32.181629-07 test t T4 \N t \N \N \N {} +7020 2178 2025-03-16 20:56:15.870132-07 2025-03-16 21:37:33.135945-07 benchmark f T4 \N t \N \N \N {} +7021 2178 2025-03-16 20:46:26.93908-07 2025-03-16 20:15:43.470303-07 leaderboard f T4 0.00023776616 t \N \N \N {} +7022 2178 2025-03-16 20:29:51.881141-07 2025-03-16 20:49:15.652665-07 test t T4 \N t \N \N \N {} +7023 2178 2025-03-16 19:44:17.51868-07 2025-03-16 20:29:57.33345-07 benchmark t T4 \N t \N \N \N {} +7024 2178 2025-03-16 20:46:38.473464-07 2025-03-16 19:46:05.014633-07 leaderboard t T4 0.00023401815384615384 t \N \N \N {} +7025 2179 2025-03-16 20:43:42.53621-07 2025-03-16 20:37:35.619442-07 test t T4 \N t \N \N \N {} +7026 2179 2025-03-16 20:01:15.232212-07 2025-03-16 20:15:45.919775-07 benchmark t T4 \N t \N \N \N {} +7027 2179 2025-03-16 20:05:15.564236-07 2025-03-16 20:03:37.77276-07 leaderboard t T4 0.00023105845833333333 t \N \N \N {} +7028 2179 2025-03-16 21:07:58.380332-07 2025-03-16 20:38:16.446317-07 test f T4 \N t \N \N \N {} +7029 2179 2025-03-16 21:12:36.483147-07 2025-03-16 20:50:41.542771-07 benchmark f T4 \N t \N \N \N {} +7030 2179 2025-03-16 21:41:50.345063-07 2025-03-16 20:30:53.023148-07 leaderboard f T4 0.0002410815 t \N \N \N {} +7031 2180 2025-03-16 20:01:10.433977-07 2025-03-16 21:48:38.104837-07 test f T4 \N t \N \N \N {} +7032 2180 2025-03-16 20:42:55.307852-07 2025-03-16 19:56:48.747118-07 benchmark f T4 \N t \N \N \N {} +7033 2180 2025-03-16 19:54:54.43732-07 2025-03-16 21:43:31.624944-07 leaderboard f T4 0.0010320996666666666 t \N \N \N {} +7034 2180 2025-03-16 21:08:01.480549-07 2025-03-16 20:04:50.559362-07 test t T4 \N t \N \N \N {} +7035 2180 2025-03-16 20:17:30.013044-07 2025-03-16 21:24:22.031756-07 benchmark t T4 \N t \N \N \N {} +7036 2180 2025-03-16 21:41:47.373413-07 2025-03-16 21:32:11.525076-07 leaderboard t T4 0.0010306484 t \N \N \N {} +7037 2181 2025-03-16 21:22:46.486398-07 2025-03-16 20:29:47.484582-07 test f A100 \N t \N \N \N {} +7038 2181 2025-03-16 21:38:35.851119-07 2025-03-16 21:51:47.560305-07 benchmark f A100 \N t \N \N \N {} +7039 2181 2025-03-16 20:38:02.61894-07 2025-03-16 20:02:26.204495-07 leaderboard f A100 0.0000866505 t \N \N \N {} +7040 2181 2025-03-16 20:21:30.439741-07 2025-03-16 20:59:46.729245-07 test t A100 \N t \N \N \N {} +7041 2181 2025-03-16 21:46:26.568583-07 2025-03-16 21:18:37.064553-07 benchmark t A100 \N t \N \N \N {} +7042 2181 2025-03-16 21:48:37.803054-07 2025-03-16 20:30:42.439254-07 leaderboard t A100 0.00008592785365853658 t \N \N \N {} +7044 2182 2025-03-16 21:06:13.034065-07 2025-03-16 20:08:36.728568-07 benchmark f H100 \N t \N \N \N {} +7045 2182 2025-03-16 21:28:36.786376-07 2025-03-16 21:47:18.527666-07 leaderboard f H100 0.00005831765454545455 t \N \N \N {} +7046 2182 2025-03-16 20:56:24.442304-07 2025-03-16 21:33:14.384165-07 test t H100 \N t \N \N \N {} +7047 2182 2025-03-16 21:05:38.501639-07 2025-03-16 20:55:27.320926-07 benchmark t H100 \N t \N \N \N {} +7048 2182 2025-03-16 21:24:08.431353-07 2025-03-16 20:32:33.148155-07 leaderboard t H100 0.00006414326 t \N \N \N {} +7061 2185 2025-03-16 21:59:53.418742-07 2025-03-16 21:21:24.148854-07 benchmark t L4 \N t \N \N \N {} +7062 2185 2025-03-16 21:59:20.390159-07 2025-03-16 20:48:54.636456-07 leaderboard t L4 0.00011960029545454545 t \N \N \N {} +7063 2186 2025-03-16 20:23:16.497019-07 2025-03-16 21:55:58.531742-07 test t H100 \N t \N \N \N {} +7064 2186 2025-03-16 22:01:45.798895-07 2025-03-16 20:34:14.059263-07 benchmark t H100 \N t \N \N \N {} +7065 2186 2025-03-16 20:21:16.357956-07 2025-03-16 22:00:26.089828-07 leaderboard t H100 0.00008572011000000001 t \N \N \N {} +7066 2186 2025-03-16 21:34:57.985674-07 2025-03-16 21:07:45.295076-07 test f H100 \N t \N \N \N {} +7067 2186 2025-03-16 20:33:02.195088-07 2025-03-16 20:29:30.445574-07 benchmark f H100 \N t \N \N \N {} +7068 2186 2025-03-16 22:14:09.367859-07 2025-03-16 21:19:31.416324-07 leaderboard f H100 0.00007322206 t \N \N \N {} +7069 2187 2025-03-16 21:45:45.648373-07 2025-03-16 20:53:58.681948-07 test f A100 \N t \N \N \N {} +7070 2187 2025-03-16 21:59:03.441533-07 2025-03-16 20:58:40.170299-07 benchmark f A100 \N t \N \N \N {} +7071 2187 2025-03-16 21:35:23.332245-07 2025-03-16 21:20:36.708794-07 leaderboard f A100 0.00008178825714285713 t \N \N \N {} +7073 2187 2025-03-16 20:17:23.698815-07 2025-03-16 21:16:53.360041-07 benchmark t A100 \N t \N \N \N {} +7074 2187 2025-03-16 21:35:59.086324-07 2025-03-16 21:27:19.519665-07 leaderboard t A100 0.00010883197674418605 t \N \N \N {} +7075 2188 2025-03-16 20:50:17.210026-07 2025-03-16 21:20:29.402014-07 test f T4 \N t \N \N \N {} +7076 2188 2025-03-16 21:33:43.489412-07 2025-03-16 20:58:46.157067-07 benchmark f T4 \N f \N \N \N {} +7077 2188 2025-03-16 21:27:45.9623-07 2025-03-16 20:47:40.647623-07 test t T4 \N t \N \N \N {} +7078 2188 2025-03-16 20:46:07.02613-07 2025-03-16 20:52:10.402659-07 benchmark t T4 \N f \N \N \N {} +\. + + +-- +-- Data for Name: submission; Type: TABLE DATA; Schema: leaderboard; Owner: - +-- + +COPY leaderboard.submission (id, leaderboard_id, file_name, user_id, code_id, submission_time, done) FROM stdin; +34 340 kernel.py 456789012345678 22 2025-02-23 14:06:19.847245-08 t +35 340 trial.py 234567890123456 22 2025-02-23 10:34:59.345289-08 t +36 340 kernel.py 012345678901234 22 2025-02-23 14:10:53.942002-08 t +37 340 trial.py 789012345678901 22 2025-02-23 11:31:30.819174-08 t +38 340 submission.py 456789012345678 23 2025-02-23 12:59:26.917155-08 t +39 340 impl.py 345678901234567 24 2025-02-23 11:29:53.494709-08 t +40 340 solution.py 456789012345678 25 2025-02-23 13:01:42.352117-08 t +41 340 impl.py 345678901234567 26 2025-02-23 10:25:26.751067-08 t +47 340 impl.py 123456789012345 29 2025-02-23 13:44:32.29949-08 t +48 340 trial.py 012345678901234 30 2025-02-23 11:51:10.320644-08 t +49 340 solution.py 789012345678901 31 2025-02-23 11:14:01.554187-08 t +50 340 submission.py 567890123456789 32 2025-02-23 11:30:07.746585-08 t 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2025-02-25 04:43:04.936759-08 t +482 340 impl.py 012345678901234 367 2025-02-25 02:40:24.717191-08 t +483 340 kernel.py 012345678901234 364 2025-02-25 07:38:25.590003-08 t +484 340 kernel.py 012345678901234 368 2025-02-25 04:05:24.266333-08 t +485 340 kernel.py 234567890123456 368 2025-02-25 01:21:21.995918-08 t +486 340 impl.py 123456789012345 369 2025-02-25 05:15:42.322743-08 t +487 340 impl.py 890123456789012 370 2025-02-25 03:57:08.220114-08 t +488 340 kernel.py 890123456789012 371 2025-02-25 02:34:33.54953-08 t +489 340 kernel.py 345678901234567 372 2025-02-25 06:56:15.060357-08 t +490 340 submission.py 567890123456789 372 2025-02-25 05:18:46.28171-08 t +491 340 impl.py 567890123456789 372 2025-02-25 04:41:19.684912-08 t +492 340 kernel.py 456789012345678 373 2025-02-25 06:37:30.60971-08 t +495 340 impl.py 678901234567890 376 2025-02-25 06:51:34.935988-08 t +496 340 kernel.py 234567890123456 377 2025-02-25 04:42:39.816617-08 t +497 340 trial.py 012345678901234 378 2025-02-25 06:16:55.257084-08 t +788 340 kernel.py 456789012345678 593 2025-02-26 05:29:18.26114-08 t +2119 340 kernel.py 234567890123456 1625 2025-03-15 09:03:56.203676-07 t +2120 340 kernel.py 456789012345678 1625 2025-03-15 10:58:17.809272-07 t +2121 340 impl.py 890123456789012 1625 2025-03-15 09:51:45.663147-07 t +2122 340 solution.py 123456789012345 1625 2025-03-15 11:12:31.373245-07 t +2123 340 trial.py 345678901234567 1639 2025-03-15 12:46:17.341813-07 t +2124 340 kernel.py 567890123456789 1639 2025-03-15 09:46:35.467594-07 t +2125 340 impl.py 012345678901234 1621 2025-03-15 09:52:45.740845-07 t +2126 340 submission.py 789012345678901 1621 2025-03-15 09:58:33.640706-07 t +2127 340 solution.py 789012345678901 1623 2025-03-15 10:00:55.895166-07 t +2128 342 submission.py 234567890123456 1640 2025-03-15 14:34:13.003032-07 t +2129 342 trial.py 567890123456789 1641 2025-03-15 13:00:18.227657-07 t +2130 342 impl.py 012345678901234 1642 2025-03-15 12:29:47.042745-07 t +2131 342 submission.py 345678901234567 1643 2025-03-15 13:08:41.987409-07 t +2132 342 kernel.py 901234567890123 1644 2025-03-15 15:40:51.471691-07 t +2133 342 impl.py 789012345678901 1645 2025-03-15 15:31:01.440834-07 t +\. + + +-- +-- Data for Name: user_info; Type: TABLE DATA; Schema: leaderboard; Owner: - +-- + +COPY leaderboard.user_info (id, user_name) FROM stdin; +123456789012345 Alice +234567890123456 Bob +345678901234567 Charlie +456789012345678 Dave +567890123456789 Eve +678901234567890 Frank +789012345678901 Grace +890123456789012 Heidi +901234567890123 Ivan +012345678901234 James +\. + + +-- +-- Name: code_files_id_seq; Type: SEQUENCE SET; Schema: leaderboard; Owner: - +-- + +SELECT pg_catalog.setval('leaderboard.code_files_id_seq', 1687, true); + + +-- +-- Name: leaderboard_id_seq; Type: SEQUENCE SET; Schema: leaderboard; Owner: - +-- + +SELECT pg_catalog.setval('leaderboard.leaderboard_id_seq', 365, true); + + +-- +-- Name: runs_id_seq; Type: SEQUENCE SET; Schema: leaderboard; Owner: - +-- + +SELECT pg_catalog.setval('leaderboard.runs_id_seq', 7078, true); + + +-- +-- Name: submission_id_seq; Type: SEQUENCE SET; Schema: leaderboard; Owner: - +-- + +SELECT pg_catalog.setval('leaderboard.submission_id_seq', 2188, true); + + +-- +-- Name: code_files code_files_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.code_files + ADD CONSTRAINT code_files_pkey PRIMARY KEY (id); + + +-- +-- Name: gpu_type gpu_type_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.gpu_type + ADD CONSTRAINT gpu_type_pkey PRIMARY KEY (leaderboard_id, gpu_type); + + +-- +-- Name: leaderboard leaderboard_name_key; Type: CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.leaderboard + ADD CONSTRAINT leaderboard_name_key UNIQUE (name); + + +-- +-- Name: leaderboard leaderboard_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.leaderboard + ADD CONSTRAINT leaderboard_pkey PRIMARY KEY (id); + + +-- +-- Name: runs runs_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.runs + ADD CONSTRAINT runs_pkey PRIMARY KEY (id); + + +-- +-- Name: submission submission_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.submission + ADD CONSTRAINT submission_pkey PRIMARY KEY (id); + + +-- +-- Name: user_info user_info_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.user_info + ADD CONSTRAINT user_info_pkey PRIMARY KEY (id); + + +-- +-- Name: submission fk_user_info; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.submission + ADD CONSTRAINT fk_user_info FOREIGN KEY (user_id) REFERENCES leaderboard.user_info(id); + + +-- +-- Name: gpu_type gpu_type_leaderboard_id_fkey; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.gpu_type + ADD CONSTRAINT gpu_type_leaderboard_id_fkey FOREIGN KEY (leaderboard_id) REFERENCES leaderboard.leaderboard(id) ON DELETE CASCADE; + + +-- +-- Name: runs runs_submission_id_fkey; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.runs + ADD CONSTRAINT runs_submission_id_fkey FOREIGN KEY (submission_id) REFERENCES leaderboard.submission(id); + + +-- +-- Name: submission submission_code_id_fkey; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.submission + ADD CONSTRAINT submission_code_id_fkey FOREIGN KEY (code_id) REFERENCES leaderboard.code_files(id); + + +-- +-- Name: submission submission_leaderboard_id_fkey; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - +-- + +ALTER TABLE ONLY leaderboard.submission + ADD CONSTRAINT submission_leaderboard_id_fkey FOREIGN KEY (leaderboard_id) REFERENCES leaderboard.leaderboard(id); + + +-- +-- PostgreSQL database dump complete +-- + diff --git a/src/kernelbot/api/api_utils.py b/src/kernelbot/api/api_utils.py index 12d04ba0..1cce7dfa 100644 --- a/src/kernelbot/api/api_utils.py +++ b/src/kernelbot/api/api_utils.py @@ -1,5 +1,6 @@ +from typing import Any import requests -from fastapi import HTTPException +from fastapi import HTTPException,UploadFile from kernelbot.env import env from libkernelbot.backend import KernelBackend @@ -12,7 +13,7 @@ Text, ) from libkernelbot.submission import ProcessedSubmissionRequest, SubmissionRequest, prepare_submission -from src.libkernelbot.background_submission_manager import BackgroundSubmissionManager +from libkernelbot.leaderboard_db import LeaderboardDB async def _handle_discord_oauth(code: str, redirect_uri: str) -> tuple[str, str]: diff --git a/src/kernelbot/api/main.py b/src/kernelbot/api/main.py index 97cf7066..2eeef55b 100644 --- a/src/kernelbot/api/main.py +++ b/src/kernelbot/api/main.py @@ -396,11 +396,10 @@ async def start_detached_run( time=datetime.datetime.now(), user_name=req.user_name, ) - db.upsert_submission_job_status(sub_id, "initial", None) - + job_id = db.upsert_submission_job_status(sub_id, "initial", None) # put submission request in queue await manager.enqueue(req, mode, sub_id) - return sub_id + return sub_id,job_id @app.post("/submission/{leaderboard_name}/{gpu_type}/{submission_mode}") async def run_submission_v2( @@ -445,16 +444,12 @@ async def run_submission_v2( raise HTTPException(status_code=400, detail="Invalid GPU type") # start the submission - sub_id = await start_detached_run( + sub_id,job_id = await start_detached_run( req, submission_mode_enum, backend_instance, background_submission_manager ) return JSONResponse( status_code=202, - content={ - "id": sub_id, - "jobs_id": - - "status": "accepted"}, + content={"id": sub_id, "job_id": job_id, "status": "accepted"}, ) @app.get("/leaderboards") diff --git a/src/libkernelbot/backend.py b/src/libkernelbot/backend.py index 4cee7b8d..ed104398 100644 --- a/src/libkernelbot/backend.py +++ b/src/libkernelbot/backend.py @@ -73,7 +73,6 @@ async def submit_full( user_name=req.user_name, ) selected_gpus = [get_gpu_by_name(gpu) for gpu in req.gpus] - try: tasks = [ self.submit_leaderboard( diff --git a/src/libkernelbot/background_submission_manager.py b/src/libkernelbot/background_submission_manager.py index bb40edef..db7451b8 100644 --- a/src/libkernelbot/background_submission_manager.py +++ b/src/libkernelbot/background_submission_manager.py @@ -46,7 +46,7 @@ def __init__( self, backend: KernelBackend, min_workers: int = 2, - max_workers: int = 20, + max_workers: int = 3, idle_seconds: int = 120, ): self.backend = backend @@ -62,7 +62,7 @@ def __init__( self.max_workers = max_workers async def start(self): - logger.info("starting background submission manager") + logger.info("[Background Job] starting background submission manager") async with self._state_lock: self._accepting = True need = max(0, self.min_workers - len(self._workers)) @@ -72,7 +72,7 @@ async def start(self): self._workers.append(t) async def stop(self): - logger.info("stopping background submission manager...") + logger.info("[Background Job] stopping background submission manager...") async with self._state_lock: self._accepting = False workers = list(self._workers) @@ -82,7 +82,7 @@ async def stop(self): for t in workers: with contextlib.suppress(asyncio.CancelledError): await t - logger.info("...stopped all background submission manager") + logger.info("[Background Job] ...stopped all background submission manager") async def enqueue( self, req: ProcessedSubmissionRequest, mode: SubmissionMode, sub_id: int @@ -90,7 +90,7 @@ async def enqueue( async with self._state_lock: if not self._accepting: raise RuntimeError( - "Manager is not accepting new jobs, potentially shutting down" + "[Background Job] Background Submission Manager is not accepting new jobs right now" ) logger.info("enqueueing submission %s", sub_id) now = dt.datetime.now(dt.timezone.utc) @@ -106,6 +106,11 @@ async def enqueue( return job_id, sub_id async def _worker_loop(self): + """ + A worker will keep listening to the queue, and process the job in the queue. + If the queue is empty, it will exit after idle_seconds. + Each worker only handles one submission job at a time + """ try: while True: try: @@ -113,7 +118,7 @@ async def _worker_loop(self): self.queue.get(), timeout=self.idle_seconds ) logger.info( - "worker %r got job %s", id(asyncio.current_task()), item.sub_id + "[Background Job] worker %r got submission job %s", id(asyncio.current_task()), item.sub_id ) except asyncio.TimeoutError: async with self._state_lock: @@ -125,7 +130,7 @@ async def _worker_loop(self): try: self._workers.remove(me) logger.info( - "scale down: worker %r exiting; workers=%d", + "[Background Job] scale down: worker %r exiting; workers=%d", me.get_name() if hasattr(me, "get_name") else id(me), @@ -136,11 +141,12 @@ async def _worker_loop(self): return # scale down: exit continue - t = asyncio.create_task(self._run_one(item), name=f"job-{item.sub_id}") + + t = asyncio.create_task(self._run_one(item), name=f"submision-job-{item.sub_id}") async with self._state_lock: self._live_tasks.add(t) try: - await t # wait job to finish + await t # wait submission job to finish finally: async with self._state_lock: self._live_tasks.discard(t) @@ -158,7 +164,7 @@ async def _run_one(self, item: JobItem): sub_id = item.sub_id now = dt.datetime.now(dt.timezone.utc) - logger.info("start processing %s", sub_id) + logger.info("[Background Job] start processing submission %s", sub_id) with self.backend.db as db: db.upsert_submission_job_status( sub_id, status="running", last_heartbeat=now @@ -185,7 +191,7 @@ async def heartbeat(): timeout=HARD_TIMEOUT_SEC, ) ts = dt.datetime.now(dt.timezone.utc) - logger.info("run submission %s succeeded", sub_id) + logger.info("[Background Job] run submission %s succeeded", sub_id) with self.backend.db as db: db.upsert_submission_job_status( sub_id, status="succeeded", last_heartbeat=ts @@ -201,7 +207,7 @@ async def heartbeat(): ) except Exception as e: ts = dt.datetime.now(dt.timezone.utc) - logger.error("run submission %s failed", sub_id, exc_info=True) + logger.error("[Background Job] submission job %s failed", sub_id, exc_info=True) try: with self.backend.db as db: db.upsert_submission_job_status( @@ -225,19 +231,19 @@ async def _autoscale_up(self): need = desired - workers to_add = max(0, need) logger.info( - "autoscale: max_workers: %d," - "busy worker=%d," - "available workers %s," - "jobs_in_quue:%d," - "add=%d", + "[Background Job] logging autoscale plan:" + "plan to add %d workers. Based on:" + "max_workers: %d," + "busy:%d," + "active:%s," + "enqueue:%d," + to_add, self.max_workers, running, workers, qsize, - to_add, ) - for _ in range(to_add): - logging.info("scale up: starting a new worker") + logging.info("[Background Job] scale up: starting a new worker") t = asyncio.create_task(self._worker_loop(), name="bg-worker") self._workers.append(t) diff --git a/src/libkernelbot/leaderboard_db.py b/src/libkernelbot/leaderboard_db.py index b73a6581..83094c6a 100644 --- a/src/libkernelbot/leaderboard_db.py +++ b/src/libkernelbot/leaderboard_db.py @@ -5,7 +5,7 @@ import psycopg2 -from libkernelbot.db_types import LeaderboardItem, LeaderboardRankedEntry, RunItem, SubmissionItem +from libkernelbot.db_types import IdentityType, LeaderboardItem, LeaderboardRankedEntry, RunItem, SubmissionItem from libkernelbot.run_eval import CompileResult, RunResult, SystemInfo from libkernelbot.task import LeaderboardDefinition, LeaderboardTask from libkernelbot.utils import ( diff --git a/unit-tests/test_background_submission_manager.py b/unit-tests/test_background_submission_manager.py index d29b5186..d4032534 100644 --- a/unit-tests/test_background_submission_manager.py +++ b/unit-tests/test_background_submission_manager.py @@ -53,7 +53,7 @@ async def test_enqueue_and_run_job(mock_backend): db_context = mock_backend.db db_context.upsert_submission_job_status = mock.Mock( side_effect=lambda *a, **k: a[0] - ) # 返回 sub_id 当 job_id + ) db_context.update_heartbeat_if_active = mock.Mock() # mock submit_full From 77edcf746940c40c8d251392ba88277760b6579e Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 16:45:34 -0700 Subject: [PATCH 18/28] echo variables --- .../background_submission_manager.py | 16 ++++------------ src/libkernelbot/run_eval.py | 1 - 2 files changed, 4 insertions(+), 13 deletions(-) diff --git a/src/libkernelbot/background_submission_manager.py b/src/libkernelbot/background_submission_manager.py index db7451b8..692bc999 100644 --- a/src/libkernelbot/background_submission_manager.py +++ b/src/libkernelbot/background_submission_manager.py @@ -46,7 +46,7 @@ def __init__( self, backend: KernelBackend, min_workers: int = 2, - max_workers: int = 3, + max_workers: int = 24, idle_seconds: int = 120, ): self.backend = backend @@ -231,17 +231,9 @@ async def _autoscale_up(self): need = desired - workers to_add = max(0, need) logger.info( - "[Background Job] logging autoscale plan:" - "plan to add %d workers. Based on:" - "max_workers: %d," - "busy:%d," - "active:%s," - "enqueue:%d," - to_add, - self.max_workers, - running, - workers, - qsize, + "[Background Job] autoscale plan: add %d workers " + "(max=%d, busy=%d, active=%s, enqueue=%d)", + to_add, self.max_workers, running, workers, qsize, ) for _ in range(to_add): logging.info("[Background Job] scale up: starting a new worker") diff --git a/src/libkernelbot/run_eval.py b/src/libkernelbot/run_eval.py index 442dc605..18344dd3 100644 --- a/src/libkernelbot/run_eval.py +++ b/src/libkernelbot/run_eval.py @@ -103,7 +103,6 @@ def _create_files(files: Optional[dict[str, str]]): return for name, content in files.items(): - print(f"Creating file {name} with type {type(content)}, {content}") assert Path(name).resolve().is_relative_to(Path.cwd()) Path(name).write_text(content) From dd1c0f73a6a9044963448db33b44f3f1d1210d49 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 16:46:10 -0700 Subject: [PATCH 19/28] echo variables --- data.sql | 9194 ------------------------------------------------------ 1 file changed, 9194 deletions(-) delete mode 100644 data.sql diff --git a/data.sql b/data.sql deleted file mode 100644 index 76a4633c..00000000 --- a/data.sql +++ /dev/null @@ -1,9194 +0,0 @@ --- --- PostgreSQL database dump --- - --- Dumped from database version 16.8 (Ubuntu 16.8-1.pgdg22.04+1) --- Dumped by pg_dump version 16.8 (Ubuntu 16.8-1.pgdg22.04+1) - -SET statement_timeout = 0; -SET lock_timeout = 0; -SET idle_in_transaction_session_timeout = 0; -SET client_encoding = 'UTF8'; -SET standard_conforming_strings = on; -SELECT pg_catalog.set_config('search_path', '', false); -SET check_function_bodies = false; -SET xmloption = content; -SET client_min_messages = warning; -SET row_security = off; - --- --- Name: leaderboard; Type: SCHEMA; Schema: -; Owner: - --- - -CREATE SCHEMA leaderboard; - - -SET default_tablespace = ''; - -SET default_table_access_method = heap; - --- --- Name: code_files; Type: TABLE; Schema: leaderboard; Owner: - --- - -CREATE TABLE leaderboard.code_files ( - id integer NOT NULL, - code text NOT NULL, - hash text GENERATED ALWAYS AS (encode(sha256((code)::bytea), 'hex'::text)) STORED -); - - --- --- Name: code_files_id_seq; Type: SEQUENCE; Schema: leaderboard; Owner: - --- - -CREATE SEQUENCE leaderboard.code_files_id_seq - AS integer - START WITH 1 - INCREMENT BY 1 - NO MINVALUE - NO MAXVALUE - CACHE 1; - - --- --- Name: code_files_id_seq; Type: SEQUENCE OWNED BY; Schema: leaderboard; Owner: - --- - -ALTER SEQUENCE leaderboard.code_files_id_seq OWNED BY leaderboard.code_files.id; - - --- --- Name: gpu_type; Type: TABLE; Schema: leaderboard; Owner: - --- - -CREATE TABLE leaderboard.gpu_type ( - leaderboard_id integer NOT NULL, - gpu_type text NOT NULL -); - - --- --- Name: leaderboard; Type: TABLE; Schema: leaderboard; Owner: - --- - -CREATE TABLE leaderboard.leaderboard ( - id integer NOT NULL, - name text NOT NULL, - deadline timestamp with time zone NOT NULL, - task jsonb NOT NULL, - creator_id bigint DEFAULT '-1'::integer NOT NULL, - forum_id bigint NOT NULL, - secret_seed bigint DEFAULT floor((random() * ('2147483648'::bigint)::double precision)) NOT NULL, - description text NOT NULL -); - - --- --- Name: leaderboard_id_seq; Type: SEQUENCE; Schema: leaderboard; Owner: - --- - -CREATE SEQUENCE leaderboard.leaderboard_id_seq - AS integer - START WITH 1 - INCREMENT BY 1 - NO MINVALUE - NO MAXVALUE - CACHE 1; - - --- --- Name: leaderboard_id_seq; Type: SEQUENCE OWNED BY; Schema: leaderboard; Owner: - --- - -ALTER SEQUENCE leaderboard.leaderboard_id_seq OWNED BY leaderboard.leaderboard.id; - - --- --- Name: runs; Type: TABLE; Schema: leaderboard; Owner: - --- - -CREATE TABLE leaderboard.runs ( - id integer NOT NULL, - submission_id integer NOT NULL, - start_time timestamp with time zone NOT NULL, - end_time timestamp with time zone NOT NULL, - mode text NOT NULL, - secret boolean NOT NULL, - runner text NOT NULL, - score numeric, - passed boolean NOT NULL, - compilation jsonb, - meta jsonb, - result jsonb, - system_info jsonb NOT NULL -); - - --- --- Name: runs_id_seq; Type: SEQUENCE; Schema: leaderboard; Owner: - --- - -CREATE SEQUENCE leaderboard.runs_id_seq - AS integer - START WITH 1 - INCREMENT BY 1 - NO MINVALUE - NO MAXVALUE - CACHE 1; - - --- --- Name: runs_id_seq; Type: SEQUENCE OWNED BY; Schema: leaderboard; Owner: - --- - -ALTER SEQUENCE leaderboard.runs_id_seq OWNED BY leaderboard.runs.id; - - --- --- Name: submission; Type: TABLE; Schema: leaderboard; Owner: - --- - -CREATE TABLE leaderboard.submission ( - id integer NOT NULL, - leaderboard_id integer NOT NULL, - file_name text NOT NULL, - user_id text NOT NULL, - code_id integer NOT NULL, - submission_time timestamp with time zone NOT NULL, - done boolean DEFAULT false -); - - --- --- Name: submission_id_seq; Type: SEQUENCE; Schema: leaderboard; Owner: - --- - -CREATE SEQUENCE leaderboard.submission_id_seq - AS integer - START WITH 1 - INCREMENT BY 1 - NO MINVALUE - NO MAXVALUE - CACHE 1; - - --- --- Name: submission_id_seq; Type: SEQUENCE OWNED BY; Schema: leaderboard; Owner: - --- - -ALTER SEQUENCE leaderboard.submission_id_seq OWNED BY leaderboard.submission.id; - - --- --- Name: user_info; Type: TABLE; Schema: leaderboard; Owner: - --- - -CREATE TABLE leaderboard.user_info ( - id text NOT NULL, - user_name text -); - - --- --- Name: code_files id; Type: DEFAULT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.code_files ALTER COLUMN id SET DEFAULT nextval('leaderboard.code_files_id_seq'::regclass); - - --- --- Name: leaderboard id; Type: DEFAULT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.leaderboard ALTER COLUMN id SET DEFAULT nextval('leaderboard.leaderboard_id_seq'::regclass); - - --- --- Name: runs id; Type: DEFAULT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.runs ALTER COLUMN id SET DEFAULT nextval('leaderboard.runs_id_seq'::regclass); - - --- --- Name: submission id; Type: DEFAULT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.submission ALTER COLUMN id SET DEFAULT nextval('leaderboard.submission_id_seq'::regclass); - - --- --- Data for Name: code_files; Type: TABLE DATA; Schema: leaderboard; Owner: - --- - -COPY leaderboard.code_files (id, code) FROM stdin; -13 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -14 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -24 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -25 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -39 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -40 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -41 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -211 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -53 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -56 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -83 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -102 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -101 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1608 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -105 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -125 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -126 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -127 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -147 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -159 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -905 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -146 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1459 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -153 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -161 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -907 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -172 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -184 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -806 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -290 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -807 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1274 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -929 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -372 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -373 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1275 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -392 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1276 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1278 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -423 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1361 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -429 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -435 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -442 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -445 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1362 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1363 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1364 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1365 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1366 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1367 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1368 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1369 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1370 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -507 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -572 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -513 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -521 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -524 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -526 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -534 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -535 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1371 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -578 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1372 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1373 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1374 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1375 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1376 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -709 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -716 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -717 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1377 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1378 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -743 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -744 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1379 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1390 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1391 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1393 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -793 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -794 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -890 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -891 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -820 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -821 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1553 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1554 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1555 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1556 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1601 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1049 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1078 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1135 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1117 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1143 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1144 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1146 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1149 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1161 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1192 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1602 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1603 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1604 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1605 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1606 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1609 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1610 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1611 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1612 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1613 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1614 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1615 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1616 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1617 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -22 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1270 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1277 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1299 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1300 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1301 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1322 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1321 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1324 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1353 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1354 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1392 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1410 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1422 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1411 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -298 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -299 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -300 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -301 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -23 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1539 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1543 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -26 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1588 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1607 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -302 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -303 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -29 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -30 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1661 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1663 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -304 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -305 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -31 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -32 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -33 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -15 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -306 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -16 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -291 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -292 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -293 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -294 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -279 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -295 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -296 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -307 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -353 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -354 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -355 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -356 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -357 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -358 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -379 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -380 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -381 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -382 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -383 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -384 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -385 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -386 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -387 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -388 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -389 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -390 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -391 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -72 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -970 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -393 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -5 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -394 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -395 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -396 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -397 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -400 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -401 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -402 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -17 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -18 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -19 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -20 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -21 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -34 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -35 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -36 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -37 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -38 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -46 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -54 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -55 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -58 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -59 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -60 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -403 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -404 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -405 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -406 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -407 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -408 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -409 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -410 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -411 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -412 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -413 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -414 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -308 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -415 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -417 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -418 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -421 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -425 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -309 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -426 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -427 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -431 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -433 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -432 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -310 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -437 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -438 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -439 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -440 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -61 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -62 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -63 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -64 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -65 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -71 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -66 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -67 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -68 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -69 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -70 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1644 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -962 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -447 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -448 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -449 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -451 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -452 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -453 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -454 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -455 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -456 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1645 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -457 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -458 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -459 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -460 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -461 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -462 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -463 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -464 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -465 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -466 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -467 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -468 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -469 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -470 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -471 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -472 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -473 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -474 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -475 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -476 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -477 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -478 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -479 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -480 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -482 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -483 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -484 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -485 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -486 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -487 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -488 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -490 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -489 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -491 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -492 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -493 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -495 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -497 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -499 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -500 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -976 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -536 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -537 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -538 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -539 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -540 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -541 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -542 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -543 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -544 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -545 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -546 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -547 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -548 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -549 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -550 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -551 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -552 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -553 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -554 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -555 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -556 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -557 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -558 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -559 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -560 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -561 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -562 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -563 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -564 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -565 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -566 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -567 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -568 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -569 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -570 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -963 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -571 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -573 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -574 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -575 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -576 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -577 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -245 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -579 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -580 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -581 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -582 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -583 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -584 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -585 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -586 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -587 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -588 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -589 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -590 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -591 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -592 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -594 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -595 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -596 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -597 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -598 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -599 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -600 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -601 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -603 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -604 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -605 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -606 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -607 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -608 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -609 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -611 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -610 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -613 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -612 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -614 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -615 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -616 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -617 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -618 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -619 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -620 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -621 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -622 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -623 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -624 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -625 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -626 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -627 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -628 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -629 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -630 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -636 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -631 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -632 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -633 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -634 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -635 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -637 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -638 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -639 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -640 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -641 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -642 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -643 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -644 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -645 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -646 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -648 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -647 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -649 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -650 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -651 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -652 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -653 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -654 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -656 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -655 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -657 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -658 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -659 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -660 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -661 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -662 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -663 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -664 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -665 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -666 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -668 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -667 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -669 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -670 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -671 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -672 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -673 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -674 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -675 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -676 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -677 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -11 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -73 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -74 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -75 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -76 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -678 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -679 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -680 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -681 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -682 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -683 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -684 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -685 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -686 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -687 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -688 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -689 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -691 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -690 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -692 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -693 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -694 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -696 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -695 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -697 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -698 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -700 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -964 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -971 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -972 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -699 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -701 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -702 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -703 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -704 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -705 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -722 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -706 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -707 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -708 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -710 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -723 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -711 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -712 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -714 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -718 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -721 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -724 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -725 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -726 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -727 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -728 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -729 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -730 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -731 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -732 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -733 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -734 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -735 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -736 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -737 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -738 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -77 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -78 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -79 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -80 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -82 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -81 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -6 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -85 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -84 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -86 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -87 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -89 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -90 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -92 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -93 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -95 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -96 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -97 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -98 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -100 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -745 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -746 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -747 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -748 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -749 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -750 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -751 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -752 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -753 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -754 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -755 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -756 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -757 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -758 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -759 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -760 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -761 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -762 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -965 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -763 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -764 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -765 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -766 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -767 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -768 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -769 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -770 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -771 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -772 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -773 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -774 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -775 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -130 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -776 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -777 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -778 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -103 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -106 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -107 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -108 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -109 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -110 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -111 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -114 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -115 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -132 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -116 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -117 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -119 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -120 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -121 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -122 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -123 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -124 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -129 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -131 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -134 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -954 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -968 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -779 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -780 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -781 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -783 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -784 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -785 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -786 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -805 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -787 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -788 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -803 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -802 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -148 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -150 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -151 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -152 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -154 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -156 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -157 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -158 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -804 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -808 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -809 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -810 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -811 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -812 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -813 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -814 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -815 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -816 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -817 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -818 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -819 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -822 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -826 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -827 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -828 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -829 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -830 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -831 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -832 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -833 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -834 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -835 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -836 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -837 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -838 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -839 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -840 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -841 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -842 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -843 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -844 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -845 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -846 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -847 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -848 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -849 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -850 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -851 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -852 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -854 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -853 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -855 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -857 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -856 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -859 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -858 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -860 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -861 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -862 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -863 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -864 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -160 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -782 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -865 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -866 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -867 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -868 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -869 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -870 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -871 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -872 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -873 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -874 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -875 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -876 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -877 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -878 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -879 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -880 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -881 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -882 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -883 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -966 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -884 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -885 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -886 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -887 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -888 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -889 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -892 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -893 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -894 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -895 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -896 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -897 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -898 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -899 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -900 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -901 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -902 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -903 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -904 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -906 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -179 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -908 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -909 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -910 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -911 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -912 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -913 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -914 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -915 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -916 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -917 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -918 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -919 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -920 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -921 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -922 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -923 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -924 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -164 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -165 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -166 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -167 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -168 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -169 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -925 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -926 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -927 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -928 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -930 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -931 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -932 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -934 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -935 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -936 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -937 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -938 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -939 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -940 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -941 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -942 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -943 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -944 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -945 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -946 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -947 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -948 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -949 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -950 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -951 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -952 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -953 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -955 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -956 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -957 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -974 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -958 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -959 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -960 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -961 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -967 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -973 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -975 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -978 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -979 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -980 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -991 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -992 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -993 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -994 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -995 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -996 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -997 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -998 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -999 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1000 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1001 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1002 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1003 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1004 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1005 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1006 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1007 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1008 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1009 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1010 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1011 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1012 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1013 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1014 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1015 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1016 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -170 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -171 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -173 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -174 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -176 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -177 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -178 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1017 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1018 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1019 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1020 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1021 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1022 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1023 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1024 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1025 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1027 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1028 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1029 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1030 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1031 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1032 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1033 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1034 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1035 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1036 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1037 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1038 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1039 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1040 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1041 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1042 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1043 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1044 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1045 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1046 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1047 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1048 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1050 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1051 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1052 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1053 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1054 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1055 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1056 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1057 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1058 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1059 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1060 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1061 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1062 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1079 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1080 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1081 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1082 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1083 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1084 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1085 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -181 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -182 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -185 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -186 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -187 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -188 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -190 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -191 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -192 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -193 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -194 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -195 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -196 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1086 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1087 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1088 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1089 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1090 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1091 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1092 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1093 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1094 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1095 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1096 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1101 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1102 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1103 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1104 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1105 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1106 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1107 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1108 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1109 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1110 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1111 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1112 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1113 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1114 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1115 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1116 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1118 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1119 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1120 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1121 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1124 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1125 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1126 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1127 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1128 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1129 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -197 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1130 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1131 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1132 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1133 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1134 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1136 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1140 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1141 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1151 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1152 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1153 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1154 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1155 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1156 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1157 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1158 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1159 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1162 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1163 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1166 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1167 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1168 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1169 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1170 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1171 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1172 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1173 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1174 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1175 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1176 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -213 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1177 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1178 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1179 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1180 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1181 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1182 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1183 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1184 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1185 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1186 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1187 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1188 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1189 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1190 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1191 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1193 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1194 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1195 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1196 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1197 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1198 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1199 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1200 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1201 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1202 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -225 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1203 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -198 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -199 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1267 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1268 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1269 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1271 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1272 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1279 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1280 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1281 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1282 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1283 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1284 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1285 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1286 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1287 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1288 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1289 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1290 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1291 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1292 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1293 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1294 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1295 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1296 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1297 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1298 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1323 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1355 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1356 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1357 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1358 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1359 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1360 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -244 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1380 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1381 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1383 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1384 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1385 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1386 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1387 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1388 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1389 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1394 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1395 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1396 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1397 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -200 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -201 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -202 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -203 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -204 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -205 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -206 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -207 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -208 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -209 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -210 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -212 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1412 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1413 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1414 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1415 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1416 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1417 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1418 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -216 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1419 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1420 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1421 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1423 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -217 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1424 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1453 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1454 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1455 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -218 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1456 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1457 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1458 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -331 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1273 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1460 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1461 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1462 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1463 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -219 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1464 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1465 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1466 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1467 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -220 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1468 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1469 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1470 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1471 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -221 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1472 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1473 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1474 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1475 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -222 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1476 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1477 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1478 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1479 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -223 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1480 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1481 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1482 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -214 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -215 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -224 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1483 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1487 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1488 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1489 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1490 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1491 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1497 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1498 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1545 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1546 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1499 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1500 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1501 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1502 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1547 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1548 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1503 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1504 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1505 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1506 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1549 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1550 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1507 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1509 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1510 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1511 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -226 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1512 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1513 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1514 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1515 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -227 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1516 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1517 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1518 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1519 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -228 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1520 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1521 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1522 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1523 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -229 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1524 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1525 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1526 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1527 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -230 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1528 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1529 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1530 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1531 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -231 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1532 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1533 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1534 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1535 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -232 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1536 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1537 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1538 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1540 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1541 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1542 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1544 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -233 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -234 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -235 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -236 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -237 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -238 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -239 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -240 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -241 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -242 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -243 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1551 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1552 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1557 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1558 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1559 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1560 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1561 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1562 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1563 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1564 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1565 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1566 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -10 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1567 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1568 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1569 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1570 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1571 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1572 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1573 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1574 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1575 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1576 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1577 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1578 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -246 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -247 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -248 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -249 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -250 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -251 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -252 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -253 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -254 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -255 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -256 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -257 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -969 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -977 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -258 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -259 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -260 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1579 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1580 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1581 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1582 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1583 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1584 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1585 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1586 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1587 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1589 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1590 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1591 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1592 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1593 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1594 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1595 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1596 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1597 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1598 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1599 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1600 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -311 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -312 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -313 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -314 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -315 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -316 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -317 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -318 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -319 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -320 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -321 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -322 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -323 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -324 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -325 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -326 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -327 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -328 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -329 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -330 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1618 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1619 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1620 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1621 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -332 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -333 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -334 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -335 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -336 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -337 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -338 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -339 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -340 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -341 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1622 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1623 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1624 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1625 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1626 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1627 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1628 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1629 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1630 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1631 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1632 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1633 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1634 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1646 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1647 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1648 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1649 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1650 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1651 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1652 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1653 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1654 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1655 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -1656 from utils import make_match_reference\nimport torch\nimport torch.nn.functional as F\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of 2D convolution using PyTorch.\n Args:\n data: Tuple of (input tensor, kernel tensor)\n Returns:\n Output tensor after convolution\n """\n input_tensor, kernel = data\n return F.conv2d(\n input_tensor, \n kernel,\n\n # No padding and no striding\n # TODO: Can revisit this in future problems\n stride=1,\n padding=0\n )\n\n\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\n """\n Generates random input and kernel tensors.\n Returns:\n Tuple of (input tensor, kernel tensor)\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate input tensor: [batch, in_channels, height, width]\n input_tensor = torch.randn(\n batch, channels, size, size,\n device='cuda', \n dtype=torch.float32, \n generator=gen\n ).contiguous()\n \n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\n # Here we use same number of output channels as input channels for simplicity\n kernel = torch.randn(\n channels, channels, kernelsize, kernelsize,\n device='cuda',\n dtype=torch.float32,\n generator=gen\n ).contiguous()\n \n return (input_tensor, kernel)\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)\n -359 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1657 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1658 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1659 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1660 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1664 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1665 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1667 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1669 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -343 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1670 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1671 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1672 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1673 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -344 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -1674 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1675 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1676 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1677 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -345 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1678 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1679 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1680 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1681 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -346 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1682 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1683 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1684 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1685 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -342 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -347 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -348 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -349 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -350 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -351 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -352 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1686 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1687 from utils import verbose_allequal\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of histogram using PyTorch.\n Args:\n data: tensor of shape (size,)\n Returns:\n Tensor containing bin counts\n """\n # Count values in each bin\n return torch.bincount(data, minlength=256)\n\n\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\n """\n Generates random input tensor for histogram.\n\n Args:\n size: Size of the input tensor (must be multiple of 16)\n contention: float in [0, 100], specifying the percentage of identical values\n seed: Random seed\n Returns:\n The input tensor with values in [0, 255]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n \n # Generate integer values between 0 and 256\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\n\n # make one value appear quite often, increasing the chance for atomic contention\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\n data[evil_loc] = evil_value\n\n return data.contiguous()\n\n\ndef check_implementation(data, output):\n expected = ref_kernel(data)\n reasons = verbose_allequal(output, expected)\n\n if len(reasons) > 0:\n return "mismatch found! custom implementation doesn't match reference: " + " ".join(reasons)\n\n return ''\n\n -1635 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1636 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1637 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1642 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1638 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -261 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -262 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -263 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -264 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -265 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -266 from utils import match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of inclusive prefix sum using PyTorch.\n Args:\n data: Input tensor to compute prefix sum on\n Returns:\n Tensor containing the inclusive prefix sum\n """\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random input tensor.\n Returns:\n Tensor to compute prefix sum on\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\n\n\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\n# The tolerance is scaled by the square root of the input size\ndef check_implementation(data: input_t, output: output_t) -> str:\n # Then get the size for scaling the tolerance\n n = data.numel()\n \n scale_factor = n ** 0.5 # Square root of input size\n rtol = 1e-5 * scale_factor\n atol = 1e-5 * scale_factor\n\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\n -267 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -268 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -269 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -270 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -271 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -272 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -273 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -274 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -275 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -276 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -277 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -278 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -280 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -281 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -282 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -283 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -284 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -285 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -286 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -287 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -288 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -289 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -297 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1643 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -360 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -361 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -362 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -363 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -364 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -365 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -366 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -367 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -368 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -369 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -370 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -371 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -376 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -377 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -378 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -593 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1639 from utils import make_match_reference\nimport torch\nfrom task import input_t, output_t\n\n\ndef ref_kernel(data: input_t) -> output_t:\n """\n Reference implementation of RGB to grayscale conversion using PyTorch.\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\n \n Args:\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\n Returns:\n Grayscale tensor of shape (H, W) with values in [0, 1]\n """\n # Standard RGB to Grayscale coefficients\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \n device=data.device, \n dtype=data.dtype)\n return torch.sum(data * weights, dim=-1)\n\n\ndef generate_input(size: int, seed: int) -> input_t:\n """\n Generates random RGB image tensor of specified size.\n Returns:\n Tensor of shape (size, size, 3) with values in [0, 1]\n """\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n return torch.rand(size, size, 3, \n device='cuda', \n dtype=torch.float32, \n generator=gen).contiguous()\n\n\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\n -1640 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -1641 import torch\nfrom task import input_t, output_t\nfrom utils import make_match_reference\n\n\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\n gen = torch.Generator(device='cuda')\n gen.manual_seed(seed)\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\n a.uniform_(0, 1, generator=gen)\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\n b.uniform_(0, 1, generator=gen)\n return (a, b)\n\n\ndef ref_kernel(data: input_t) -> output_t:\n a, b = data\n return a @ b\n\n\ncheck_implementation = make_match_reference(ref_kernel)\n -\. - - --- --- Data for Name: gpu_type; Type: TABLE DATA; Schema: leaderboard; Owner: - --- - -COPY leaderboard.gpu_type (leaderboard_id, gpu_type) FROM stdin; -339 H100 -339 A100 -339 T4 -339 L4 -340 H100 -340 A100 -340 T4 -340 L4 -341 H100 -341 A100 -341 T4 -341 L4 -342 H100 -342 A100 -342 T4 -342 L4 -343 H100 -343 A100 -343 T4 -343 L4 -\. - - --- --- Data for Name: leaderboard; Type: TABLE DATA; Schema: leaderboard; Owner: - --- - -COPY leaderboard.leaderboard (id, name, deadline, task, creator_id, forum_id, secret_seed, description) FROM stdin; -339 conv2d 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "from typing import TypedDict, TypeVar, Tuple\\nimport torch\\n\\ninput_t = TypeVar(\\"input_t\\", bound=Tuple[torch.Tensor, torch.Tensor])\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor)\\n\\n\\nclass TestSpec(TypedDict):\\n size: int\\n kernelsize: int\\n channels: int\\n batch: int\\n seed: int ", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "from utils import make_match_reference\\nimport torch\\nimport torch.nn.functional as F\\nfrom task import input_t, output_t\\n\\nclass DisableCuDNNTF32:\\n def __init__(self):\\n self.allow_tf32 = torch.backends.cudnn.allow_tf32\\n self.deterministic = torch.backends.cudnn.deterministic\\n pass\\n\\n def __enter__(self):\\n torch.backends.cudnn.allow_tf32 = False\\n torch.backends.cudnn.deterministic = True\\n return self\\n\\n def __exit__(self, exc_type, exc_value, traceback):\\n torch.backends.cudnn.allow_tf32 = self.allow_tf32\\n torch.backends.cudnn.deterministic = self.deterministic\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n \\"\\"\\"\\n Reference implementation of 2D convolution using PyTorch.\\n Args:\\n data: Tuple of (input tensor, kernel tensor)\\n Returns:\\n Output tensor after convolution\\n \\"\\"\\"\\n with DisableCuDNNTF32():\\n input_tensor, kernel = data\\n return F.conv2d(\\n input_tensor, \\n kernel,\\n\\n # No padding and no striding\\n # TODO: Can revisit this in future problems\\n stride=1,\\n padding=0\\n )\\n\\n\\ndef generate_input(size: int, kernelsize: int, channels: int, batch: int, seed: int) -> input_t:\\n \\"\\"\\"\\n Generates random input and kernel tensors.\\n Returns:\\n Tuple of (input tensor, kernel tensor)\\n \\"\\"\\"\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n \\n # Generate input tensor: [batch, in_channels, height, width]\\n input_tensor = torch.randn(\\n batch, channels, size, size,\\n device='cuda', \\n dtype=torch.float32, \\n generator=gen\\n ).contiguous()\\n \\n # Generate kernel tensor: [out_channels, in_channels, kernel_height, kernel_width]\\n # Here we use same number of output channels as input channels for simplicity\\n kernel = torch.randn(\\n channels, channels, kernelsize, kernelsize,\\n device='cuda',\\n dtype=torch.float32,\\n generator=gen\\n ).contiguous()\\n \\n return (input_tensor, kernel)\\n\\n\\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-3, atol=1e-3)", "submission.py": "@SUBMISSION@"}, "tests": [{"seed": 4242, "size": 32, "batch": 1, "channels": 16, "kernelsize": 4}, {"seed": 5236, "size": 32, "batch": 2, "channels": 16, "kernelsize": 4}, {"seed": 1001, "size": 64, "batch": 1, "channels": 32, "kernelsize": 4}, {"seed": 5531, "size": 64, "batch": 2, "channels": 32, "kernelsize": 8}, {"seed": 9173, "size": 128, "batch": 1, "channels": 64, "kernelsize": 8}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"seed": 54352, "size": 128, "batch": 4, "channels": 64, "kernelsize": 8}, {"seed": 93246, "size": 128, "batch": 4, "channels": 64, "kernelsize": 16}, {"seed": 6256, "size": 256, "batch": 2, "channels": 128, "kernelsize": 16}, {"seed": 8841, "size": 256, "batch": 2, "channels": 128, "kernelsize": 16}, {"seed": 6252, "size": 256, "batch": 1, "channels": 128, "kernelsize": 32}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279714277527582 1828004782 Implement a 2D convolution kernel that matches the reference implementation.\nThe kernel should perform 2D convolution with the given specifications\nWe will benchmark different sizes, kernel sizes, channels and batch sizes but they will all \nbe even numbers with the exception of batch size which can sometimes be 1\nWe assume no padding and striding and instead vary the size of the input and kernel,\nnumber of channels, and batch size.\n\nInput: Tuple of (input_tensor, kernel)\n - input_tensor: 4D tensor of shape (batch, channels, height, width) with arbitrary values\n - kernel: 4D tensor of shape (channels, channels, kernelsize, kernelsize) with arbitrary values\nOutput: 4D tensor of shape (batch, channels, height-kernelsize+1, width-kernelsize+1) with convolved values\n -340 grayscale 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "from typing import TypedDict, TypeVar\\nimport torch\\n\\ninput_t = TypeVar(\\"input_t\\", bound=torch.Tensor) # Input will be (H, W, 3) RGB tensor\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor) # Output will be (H, W) grayscale tensor\\n\\nclass TestSpec(TypedDict):\\n size: int # Size of the square image (H=W)\\n seed: int ", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "from utils import make_match_reference\\nimport torch\\nfrom task import input_t, output_t\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n \\"\\"\\"\\n Reference implementation of RGB to grayscale conversion using PyTorch.\\n Uses the standard coefficients: Y = 0.2989 R + 0.5870 G + 0.1140 B\\n \\n Args:\\n data: RGB tensor of shape (H, W, 3) with values in [0, 1]\\n Returns:\\n Grayscale tensor of shape (H, W) with values in [0, 1]\\n \\"\\"\\"\\n # Standard RGB to Grayscale coefficients\\n weights = torch.tensor([0.2989, 0.5870, 0.1140], \\n device=data.device, \\n dtype=data.dtype)\\n return torch.sum(data * weights, dim=-1)\\n\\n\\ndef generate_input(size: int, seed: int) -> input_t:\\n \\"\\"\\"\\n Generates random RGB image tensor of specified size.\\n Returns:\\n Tensor of shape (size, size, 3) with values in [0, 1]\\n \\"\\"\\"\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n return torch.rand(size, size, 3, \\n device='cuda', \\n dtype=torch.float32, \\n generator=gen).contiguous()\\n\\n\\ncheck_implementation = make_match_reference(ref_kernel, rtol=1e-4, atol=1e-4)\\n", "submission.py": "@SUBMISSION@"}, "tests": [{"seed": 1001, "size": 128}, {"seed": 5531, "size": 256}, {"seed": 9173, "size": 512}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"seed": 54352, "size": 512}, {"seed": 93246, "size": 1024}, {"seed": 6256, "size": 2048}, {"seed": 8841, "size": 4096}, {"seed": 6252, "size": 8192}, {"seed": 54352, "size": 16384}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279718383878165 1067465556 Implement an RGB to grayscale conversion kernel that matches the reference implementation.\nThe kernel should convert square RGB images with even sizes to grayscale using the standard coefficients:\nY = 0.2989 R + 0.5870 G + 0.1140 B\n\nInput: RGB tensor of shape (H, W, 3) with values in [0, 1]\nOutput: Grayscale tensor of shape (H, W) with values in [0, 1]\n -341 histogram 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "from typing import TypedDict, TypeVar\\nimport torch\\n\\ninput_t = TypeVar(\\"input_t\\", bound=torch.Tensor)\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor)\\n\\nclass TestSpec(TypedDict):\\n size: int\\n seed: int\\n contention: int\\n\\n", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "from utils import verbose_allequal\\nimport torch\\nfrom task import input_t, output_t\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n \\"\\"\\"\\n Reference implementation of histogram using PyTorch.\\n Args:\\n data: tensor of shape (size,)\\n Returns:\\n Tensor containing bin counts\\n \\"\\"\\"\\n # Count values in each bin\\n return torch.bincount(data, minlength=256)\\n\\n\\ndef generate_input(size: int, contention: float, seed: int) -> input_t:\\n \\"\\"\\"\\n Generates random input tensor for histogram.\\n\\n Args:\\n size: Size of the input tensor (must be multiple of 16)\\n contention: float in [0, 100], specifying the percentage of identical values\\n seed: Random seed\\n Returns:\\n The input tensor with values in [0, 255]\\n \\"\\"\\"\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n \\n # Generate integer values between 0 and 256\\n data = torch.randint(0, 256, (size,), device='cuda', dtype=torch.uint8, generator=gen)\\n\\n # make one value appear quite often, increasing the chance for atomic contention\\n evil_value = torch.randint(0, 256, (), device='cuda', dtype=torch.uint8, generator=gen)\\n evil_loc = torch.rand((size,), device='cuda', dtype=torch.float32, generator=gen) < (contention / 100.0)\\n data[evil_loc] = evil_value\\n\\n return data.contiguous()\\n\\n\\ndef check_implementation(data, output):\\n expected = ref_kernel(data)\\n reasons = verbose_allequal(output, expected)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n", "submission.py": "@SUBMISSION@"}, "tests": [{"seed": 9991, "size": 5120, "contention": 10}, {"seed": 2105, "size": 7840, "contention": 10}, {"seed": 9999, "size": 30080, "contention": 10}, {"seed": 4254, "size": 30080, "contention": 90}, {"seed": 1212, "size": 100000, "contention": 10}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"seed": 6252, "size": 1310720, "contention": 10}, {"seed": 8841, "size": 2621440, "contention": 10}, {"seed": 3411, "size": 2621440, "contention": 40}, {"seed": 8753, "size": 2621440, "contention": 90}, {"seed": 6252, "size": 5242880, "contention": 10}, {"seed": 8841, "size": 10485760, "contention": 10}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279720996929577 186241933 Implement a histogram kernel that counts the number of elements falling into each bin across the specified range.\nThe minimum and maximum values of the range are fixed to 0 and 100 respectively.\nAll sizes are multiples of 16 and the number of bins is set to the size of the input tensor divided by 16.\n\nInput:\n - data: a tensor of shape (size,)\n -342 matmul 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "import torch\\nfrom typing import TypeVar, TypedDict\\n\\ninput_t = TypeVar(\\"input_t\\", bound=tuple[torch.Tensor, torch.Tensor])\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor)\\n\\nclass TestSpec(TypedDict):\\n m: int\\n n: int\\n k: int\\n seed: int\\n", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "import torch\\nfrom task import input_t, output_t\\nfrom utils import make_match_reference\\n\\n\\ndef generate_input(m: int, n: int, k: int, seed: int) -> input_t:\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n a = torch.empty(m, k, device='cuda', dtype=torch.float16)\\n a.uniform_(0, 1, generator=gen)\\n b = torch.empty(k, n, device='cuda', dtype=torch.float16)\\n b.uniform_(0, 1, generator=gen)\\n return (a, b)\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n a, b = data\\n return a @ b\\n\\n\\ncheck_implementation = make_match_reference(ref_kernel)\\n", "submission.py": "@SUBMISSION@"}, "tests": [{"k": 64, "m": 64, "n": 64, "seed": 53124}, {"k": 128, "m": 128, "n": 128, "seed": 3321}, {"k": 256, "m": 256, "n": 256, "seed": 1200}, {"k": 32, "m": 32, "n": 512, "seed": 32523}, {"k": 64, "m": 64, "n": 1024, "seed": 4327}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"k": 128, "m": 128, "n": 128, "seed": 43214}, {"k": 256, "m": 256, "n": 256, "seed": 423011}, {"k": 512, "m": 512, "n": 512, "seed": 123456}, {"k": 1024, "m": 1024, "n": 1024, "seed": 1029}, {"k": 2048, "m": 2048, "n": 2048, "seed": 75342}, {"k": 1024, "m": 1024, "n": 1536, "seed": 321}, {"k": 2048, "m": 2048, "n": 3072, "seed": 32412}, {"k": 4096, "m": 4096, "n": 5120, "seed": 123456}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279724021026939 818466997 Implement a custom matmul function that matches the reference implementation.\nThe function should handle a tuple of input tensors and apply matmul\nThe shapes of all outer and inner dimensions of tensors are multiples of 16\n -343 prefixsum 2025-06-29 17:00:00-07 {"lang": "py", "seed": null, "files": {"eval.py": "import dataclasses\\nimport re\\nimport time\\nimport os\\nimport sys\\nimport math\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nimport torch.cuda\\n\\nfrom utils import set_seed\\ntry:\\n from task import TestSpec\\nexcept ImportError:\\n TestSpec = dict\\n\\nfrom submission import custom_kernel\\nfrom reference import check_implementation, generate_input\\n\\nWARMUP_RUNS = 10\\nTIMED_RUNS = 100\\n\\n\\nclass PopcornOutput:\\n def __init__(self, fd: int):\\n self.file = os.fdopen(fd, 'w')\\n \\n def __enter__(self):\\n return self\\n \\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self.file.close()\\n \\n def print(self, *args, **kwargs):\\n print(*args, **kwargs, file=self.file, flush=True)\\n \\n def log(self, key, value):\\n self.print(f\\"{key}: {value}\\")\\n\\n\\n@dataclasses.dataclass\\nclass TestCase:\\n args: dict\\n spec: str\\n\\n\\ndef get_test_cases(file_name: str) -> list[TestCase]:\\n try:\\n content = Path(file_name).read_text()\\n except Exception as E:\\n print(f\\"Could not open test file`{file_name}`: {E}\\", file=sys.stderr)\\n exit(113)\\n\\n tests = []\\n lines = content.splitlines()\\n match = r\\"\\\\s*([a-zA-Z]+):\\\\s*([a-zA-Z]+|[+-]?[0-9]+)\\\\s*\\"\\n for line in lines:\\n parts = line.split(\\";\\")\\n case = {}\\n for part in parts:\\n matched = re.match(match, part)\\n if not re.fullmatch(match, part):\\n print(f\\"invalid test case: '{line}': '{part}'\\", file=sys.stderr)\\n exit(113)\\n key = matched[1]\\n val = matched[2]\\n try:\\n val = int(val)\\n except ValueError:\\n pass\\n\\n case[key] = val\\n tests.append(TestCase(spec=line, args=case))\\n\\n return tests\\n\\n\\ndef warm_up(test: TestCase):\\n data = generate_input(**test.args)\\n start = time.perf_counter()\\n while time.perf_counter() - start < 0.2:\\n custom_kernel(data)\\n torch.cuda.synchronize()\\n\\n\\n@dataclasses.dataclass\\nclass Stats:\\n runs: int\\n mean: float\\n std: float\\n err: float\\n best: float\\n worst: float\\n\\n\\ndef calculate_stats(durations: list[int]):\\n \\"\\"\\"\\n Calculate statistical data from a list of durations.\\n\\n @param durations: A list of durations in nanoseconds.\\n @return: A Stats object containing the number of runs, mean, standard deviation, error, best, and worst durations.\\n \\"\\"\\"\\n runs = len(durations)\\n total = sum(durations)\\n best = min(durations)\\n worst = max(durations)\\n\\n avg = total / runs\\n variance = sum(map(lambda x: (x - avg)**2, durations))\\n std = math.sqrt(variance / (runs - 1))\\n err = std / math.sqrt(runs)\\n\\n return Stats(runs=runs, mean=avg, std=std, err=err, best=float(best),\\n worst=float(worst))\\n\\n\\ndef run_testing(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes the actual test case code and checks for correctness.\\n\\n @param logger: A PopcornOutput object used for logging test results.\\n @param tests: A list of TestCase objects representing the test cases to be executed.\\n @return: An integer representing the exit status: 0 if all tests pass, otherwise 112.\\n \\"\\"\\"\\n passed = True\\n logger.log(\\"test-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"test.{idx}.spec\\", test.spec)\\n\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n submission_output = custom_kernel(data)\\n torch.cuda.synchronize()\\n error = check_implementation(data, submission_output)\\n if error:\\n logger.log(f\\"test.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"test.{idx}.error\\", error)\\n passed = False\\n else:\\n logger.log(f\\"test.{idx}.status\\", \\"pass\\")\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef benchmark(test: TestCase, recheck: bool, max_repeats: int, max_time_ns: float) -> Stats | Any:\\n \\"\\"\\"\\n For a particular test case, check correctness (if applicable) and grab runtime results.\\n\\n @param test: TestCase object.\\n @param recheck: Flag for whether to explicitly check functional correctness.\\n @param max_repeats: Number of trials to repeat.\\n @param max_time_ns: Timeout time in nanoseconds.\\n @return: A Stats object for this particular benchmark case or an error if the test fails.\\n \\"\\"\\"\\n durations = []\\n # generate input data once\\n data = generate_input(**test.args)\\n # first, one obligatory correctness check; also triggers triton compile for the given shape\\n output = custom_kernel(data)\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n # now, do multiple timing runs without further correctness testing\\n # there is an upper bound of 100 runs, and a lower bound of 3 runs;\\n # otherwise, we repeat until we either measure at least 10 full seconds,\\n # or the relative error of the mean is below 1%.\\n\\n for i in range(max_repeats):\\n if recheck:\\n data = generate_input(**test.args)\\n torch.cuda.synchronize()\\n start = time.perf_counter_ns()\\n output = custom_kernel(data)\\n torch.cuda.synchronize()\\n end = time.perf_counter_ns()\\n\\n if recheck:\\n error = check_implementation(data, output)\\n if error:\\n return error\\n\\n del output\\n durations.append(end-start)\\n\\n if i > 1:\\n stats = calculate_stats(durations)\\n if stats.err / stats.mean < 0.01 or stats.mean * stats.runs > max_time_ns:\\n break\\n\\n return calculate_stats(durations)\\n\\n\\ndef run_benchmarking(logger: PopcornOutput, tests: list[TestCase]):\\n \\"\\"\\"\\n Executes benchmarking code for a CUDA Kernel and logs runtimes.\\n\\n @param logger: A PopcornOutput object used for logging benchmark results.\\n @param tests: A list of TestCase objects representing the test cases to be benchmarked.\\n @return: An integer representing the exit status: 0 if all benchmarks pass, otherwise 112.\\n \\"\\"\\"\\n warm_up(tests[0])\\n passed = True\\n logger.log(\\"benchmark-count\\", len(tests))\\n for idx, test in enumerate(tests):\\n logger.log(f\\"benchmark.{idx}.spec\\", test.spec)\\n result = benchmark(test, False, 100, 10e9)\\n if isinstance(result, Stats):\\n for field in dataclasses.fields(Stats):\\n logger.log(f\\"benchmark.{idx}.{field.name}\\", getattr(result, field.name))\\n else:\\n passed = False\\n logger.log(f\\"benchmark.{idx}.status\\", \\"fail\\")\\n logger.log(f\\"benchmark.{idx}.error\\", result)\\n\\n if passed:\\n logger.log(\\"check\\", \\"pass\\")\\n return 0\\n else:\\n logger.log(\\"check\\", \\"fail\\")\\n return 112\\n\\n\\ndef main():\\n fd = os.getenv(\\"POPCORN_FD\\")\\n if not fd:\\n return 111\\n\\n if len(sys.argv) < 3:\\n return 2\\n\\n mode = sys.argv[1]\\n tests = get_test_cases(sys.argv[2])\\n\\n with PopcornOutput(int(fd)) as logger:\\n seed = os.getenv(\\"POPCORN_SEED\\")\\n seed = int(seed) if seed else 42\\n set_seed(seed)\\n\\n if mode == \\"test\\":\\n return run_testing(logger, tests)\\n\\n if mode == \\"benchmark\\":\\n return run_benchmarking(logger, tests)\\n \\n if mode == \\"leaderboard\\":\\n warm_up(tests[0])\\n result = benchmark(tests[-1], True, 100, 30e9)\\n if isinstance(result, Stats):\\n logger.log(\\"benchmark-count\\", 1)\\n logger.log(f\\"benchmark.0.spec\\", tests[-1].spec)\\n logger.log(f\\"benchmark.0.runs\\", result.runs)\\n logger.log(f\\"benchmark.0.mean\\", result.mean)\\n logger.log(f\\"benchmark.0.std\\", result.std)\\n logger.log(f\\"benchmark.0.err\\", result.err)\\n logger.log(\\"check\\", \\"pass\\")\\n else:\\n logger.log(\\"test-count\\", 1)\\n logger.log(\\"test.0.status\\", \\"fail\\")\\n logger.log(\\"test.0.error\\", str(result)) #TODO: Make sure result implements __str__?\\n \\n else:\\n # TODO: Implement script and profile mode\\n return 2\\n\\n\\nif __name__ == \\"__main__\\":\\n sys.exit(main())", "task.py": "from typing import TypedDict, TypeVar\\nimport torch\\n\\ninput_t = TypeVar(\\"input_t\\", bound=torch.Tensor)\\noutput_t = TypeVar(\\"output_t\\", bound=torch.Tensor)\\n\\nclass TestSpec(TypedDict):\\n size: int\\n seed: int ", "utils.py": "import random\\nimport numpy as np\\nimport torch\\n\\n\\ndef set_seed(seed=42):\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n if torch.cuda.is_available():\\n torch.cuda.manual_seed(seed)\\n torch.cuda.manual_seed_all(seed)\\n\\n\\ndef get_device(use_cuda: bool = True) -> torch.device:\\n \\"\\"\\"Get the appropriate device (GPU or CPU).\\"\\"\\"\\n if use_cuda:\\n if torch.cuda.is_available():\\n return torch.device(\\"cuda\\")\\n elif torch.backends.mps.is_available():\\n return torch.device(\\"mps\\")\\n else:\\n print(\\"No compatible GPU found. Falling back to CPU.\\")\\n return torch.device(\\"cpu\\")\\n\\n\\n# Adapted from https://github.com/linkedin/Liger-Kernel/blob/main/test/utils.py\\n@torch.no_grad()\\ndef verbose_allclose(\\n received: torch.Tensor,\\n expected: torch.Tensor,\\n rtol=1e-05,\\n atol=1e-08,\\n max_print=5\\n) -> list[str]:\\n \\"\\"\\"\\n Assert that two tensors are element-wise equal within a tolerance, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n rtol (float): Relative tolerance; relative to expected\\n atol (float): Absolute tolerance.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Raises:\\n AssertionError: If the tensors are not all close within the given tolerance.\\n \\"\\"\\"\\n # Check if the shapes of the tensors match\\n if received.shape != expected.shape:\\n return [\\"SIZE MISMATCH\\"]\\n\\n # Calculate the difference between the tensors\\n diff = torch.abs(received - expected)\\n\\n # Determine the tolerance\\n tolerance = atol + rtol * torch.abs(expected)\\n\\n # Find tolerance mismatched elements\\n tol_mismatched = diff > tolerance\\n\\n # Find nan mismatched elements\\n nan_mismatched = torch.logical_xor(torch.isnan(received), torch.isnan(expected))\\n\\n # Find +inf mismatched elements\\n posinf_mismatched = torch.logical_xor(torch.isposinf(received), torch.isposinf(expected))\\n # Find -inf mismatched elements\\n neginf_mismatched = torch.logical_xor(torch.isneginf(received), torch.isneginf(expected))\\n\\n # Find all mismatched elements\\n mismatched = torch.logical_or(\\n torch.logical_or(tol_mismatched, nan_mismatched),\\n torch.logical_or(posinf_mismatched, neginf_mismatched),\\n )\\n\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\n@torch.no_grad()\\ndef verbose_allequal(received: torch.Tensor, expected: torch.Tensor, max_print: int=5):\\n \\"\\"\\"\\n Assert that two tensors are element-wise perfectly equal, providing detailed information about mismatches.\\n\\n Parameters:\\n received (torch.Tensor): Tensor we actually got.\\n expected (torch.Tensor): Tensor we expected to receive.\\n max_print (int): Maximum number of mismatched elements to print.\\n\\n Returns:\\n Empty string if tensors are equal, otherwise detailed error information\\n \\"\\"\\"\\n mismatched = torch.not_equal(received, expected)\\n mismatched_indices = torch.nonzero(mismatched)\\n\\n # Count the number of mismatched elements\\n num_mismatched = mismatched.count_nonzero().item()\\n\\n # Generate detailed information if there are mismatches\\n if num_mismatched >= 1:\\n mismatch_details = [f\\"Number of mismatched elements: {num_mismatched}\\"]\\n\\n for index in mismatched_indices[:max_print]:\\n i = tuple(index.tolist())\\n mismatch_details.append(f\\"ERROR AT {i}: {received[i]} {expected[i]}\\")\\n if num_mismatched > max_print:\\n mismatch_details.append(f\\"... and {num_mismatched - max_print} more mismatched elements.\\")\\n return mismatch_details\\n\\n return []\\n\\n\\ndef match_reference(data, output, reference: callable, rtol=1e-05, atol=1e-08):\\n \\"\\"\\"\\n Convenient \\"default\\" implementation for tasks' `check_implementation` function.\\n \\"\\"\\"\\n expected = reference(data)\\n reasons = verbose_allclose(output, expected, rtol=rtol, atol=atol)\\n\\n if len(reasons) > 0:\\n return \\"mismatch found! custom implementation doesn't match reference: \\" + \\" \\".join(reasons)\\n\\n return ''\\n\\n\\ndef make_match_reference(reference: callable, **kwargs):\\n def wrapped(data, output):\\n return match_reference(data, output, reference=reference, **kwargs)\\n return wrapped\\n", "reference.py": "from utils import match_reference\\nimport torch\\nfrom task import input_t, output_t\\n\\n\\ndef ref_kernel(data: input_t) -> output_t:\\n \\"\\"\\"\\n Reference implementation of inclusive prefix sum using PyTorch.\\n Args:\\n data: Input tensor to compute prefix sum on\\n Returns:\\n Tensor containing the inclusive prefix sum\\n \\"\\"\\"\\n return torch.cumsum(data.to(torch.float64), dim=0).to(torch.float64)\\n\\n\\ndef generate_input(size: int, seed: int) -> input_t:\\n \\"\\"\\"\\n Generates random input tensor.\\n Returns:\\n Tensor to compute prefix sum on\\n \\"\\"\\"\\n gen = torch.Generator(device='cuda')\\n gen.manual_seed(seed)\\n return torch.randn(size, device='cuda', dtype=torch.float32, generator=gen).contiguous()\\n\\n\\n# This algorithm is very sensitive to the tolerance and the error is magnified by the input size\\n# The tolerance is scaled by the square root of the input size\\ndef check_implementation(data: input_t, output: output_t) -> str:\\n # Then get the size for scaling the tolerance\\n n = data.numel()\\n \\n scale_factor = n ** 0.5 # Square root of input size\\n rtol = 1e-5 * scale_factor\\n atol = 1e-5 * scale_factor\\n\\n return match_reference(data, output, reference=ref_kernel, rtol=rtol, atol=atol)\\n", "submission.py": "@SUBMISSION@"}, "tests": [{"seed": 4242, "size": 1023}, {"seed": 5236, "size": 1024}, {"seed": 1001, "size": 1025}, {"seed": 5531, "size": 2048}, {"seed": 9173, "size": 4096}], "config": {"main": "eval.py"}, "libraries": [], "benchmarks": [{"seed": 12345, "size": 262144}, {"seed": 67890, "size": 524288}, {"seed": 13579, "size": 1048576}, {"seed": 24680, "size": 2097152}, {"seed": 35791, "size": 4194304}, {"seed": 46802, "size": 8388608}, {"seed": 57913, "size": 16777216}, {"seed": 68024, "size": 33554432}, {"seed": 79135, "size": 67108864}, {"seed": 80246, "size": 134217728}, {"seed": 91357, "size": 268435456}], "ranking_by": "last", "test_timeout": 180, "ranked_timeout": 180, "benchmark_timeout": 180} 838132355075014667 1343279728202874890 2028323019 Implement an inclusive prefix sum (scan) kernel that matches the reference implementation.\nThe kernel should compute the cumulative sum of all elements up to each position.\nBecause of numerical instability, the tolerance is scaled by the square root of the input size.\n\nInput:\n- `data`: A 1D tensor of size `n`\nOutput:\n- `output`: A 1D tensor of size `n`\n -\. - - --- --- Data for Name: runs; Type: TABLE DATA; Schema: leaderboard; Owner: - --- - -COPY leaderboard.runs (id, submission_id, start_time, end_time, mode, secret, runner, score, passed, compilation, meta, result, system_info) FROM stdin; -124 19 2025-02-23 10:35:45.16001-08 2025-02-23 09:11:28.790124-08 test f H100 \N t \N \N \N {} -125 19 2025-02-23 10:03:43.960257-08 2025-02-23 10:50:24.236204-08 benchmark f H100 \N t \N \N \N {} -126 19 2025-02-23 09:41:46.565827-08 2025-02-23 09:15:24.849129-08 leaderboard f H100 0.006118796 t \N \N \N {} -204 36 2025-02-23 12:17:55.397587-08 2025-02-23 12:19:38.507867-08 benchmark f A100 \N t \N \N \N {} -946 278 2025-02-24 14:44:53.850686-08 2025-02-24 14:00:11.854228-08 test f L4 \N t \N \N \N {} -205 36 2025-02-23 12:02:10.362107-08 2025-02-23 11:12:04.114552-08 benchmark f L4 \N t \N \N \N {} -206 36 2025-02-23 11:00:03.586181-08 2025-02-23 11:24:17.467616-08 benchmark f T4 \N t \N \N \N {} -207 37 2025-02-23 11:17:23.516624-08 2025-02-23 11:31:53.132068-08 test t H100 \N t \N \N \N {} -5225 1609 2025-03-06 07:56:36.142637-08 2025-03-06 06:19:05.979588-08 benchmark f T4 \N f \N \N \N {} -208 37 2025-02-23 11:26:57.890844-08 2025-02-23 11:52:24.509388-08 benchmark t H100 \N t \N \N \N {} -209 37 2025-02-23 12:35:01.552626-08 2025-02-23 11:29:30.650285-08 leaderboard t H100 0.012353844333333334 t \N \N \N {} -210 37 2025-02-23 11:15:04.963959-08 2025-02-23 11:05:51.028191-08 test f A100 \N t \N \N \N {} -3564 1210 2025-02-28 11:17:29.229928-08 2025-02-28 10:53:05.57324-08 leaderboard t A100 0.0030784606666666666 t \N \N \N {} -211 37 2025-02-23 12:02:46.792172-08 2025-02-23 11:06:28.086792-08 benchmark f A100 \N t \N \N \N {} -212 37 2025-02-23 12:10:16.775337-08 2025-02-23 11:11:59.763695-08 leaderboard f A100 0.018901413333333332 t \N \N \N {} -213 37 2025-02-23 11:16:17.316289-08 2025-02-23 11:02:40.657232-08 test f L4 \N t \N \N \N {} -214 37 2025-02-23 11:03:49.41155-08 2025-02-23 11:59:30.243303-08 benchmark f L4 \N t \N \N \N {} -215 37 2025-02-23 12:39:40.437807-08 2025-02-23 11:20:57.604522-08 leaderboard f L4 0.018757093833333332 t \N \N \N {} -217 37 2025-02-23 12:34:49.874051-08 2025-02-23 11:12:01.835801-08 benchmark t T4 \N t \N \N \N {} -218 37 2025-02-23 12:40:43.031117-08 2025-02-23 11:29:51.197423-08 leaderboard t T4 0.03179523833333333 t \N \N \N {} -219 37 2025-02-23 11:14:46.019525-08 2025-02-23 11:54:59.698254-08 test t A100 \N t \N \N \N {} -221 37 2025-02-23 11:25:33.405411-08 2025-02-23 12:18:02.508921-08 leaderboard t A100 0.018848922333333334 t \N \N \N {} -222 37 2025-02-23 11:58:16.947847-08 2025-02-23 12:21:51.3522-08 test t L4 \N t \N \N \N {} -223 37 2025-02-23 10:58:25.479183-08 2025-02-23 11:47:34.593766-08 benchmark t L4 \N t \N \N \N {} -224 37 2025-02-23 10:49:41.805569-08 2025-02-23 11:56:17.24445-08 leaderboard t L4 0.01939468366666667 t \N \N \N {} -226 37 2025-02-23 12:22:30.275578-08 2025-02-23 11:48:36.047752-08 benchmark f T4 \N t \N \N \N {} -227 37 2025-02-23 12:40:05.515776-08 2025-02-23 10:58:56.313103-08 leaderboard f T4 0.030903404 t \N \N \N {} -228 37 2025-02-23 11:04:15.184419-08 2025-02-23 11:00:17.817738-08 test f H100 \N t \N \N \N {} -229 37 2025-02-23 12:35:53.114693-08 2025-02-23 10:57:25.119918-08 benchmark f H100 \N t \N \N \N {} -230 37 2025-02-23 11:23:12.875657-08 2025-02-23 10:51:41.667717-08 leaderboard f H100 0.012353275333333334 t \N \N \N {} -231 38 2025-02-23 11:50:25.46655-08 2025-02-23 11:45:34.668208-08 test f T4 \N f \N \N \N {} -232 39 2025-02-23 12:32:41.932731-08 2025-02-23 10:59:56.084677-08 test f T4 \N f \N \N \N {} -259 54 2025-02-23 13:17:30.33764-08 2025-02-23 13:46:28.669109-08 test t T4 \N t \N \N \N {} -233 40 2025-02-23 11:29:02.58055-08 2025-02-23 12:36:20.107203-08 test f T4 \N f \N \N \N {} -234 41 2025-02-23 11:36:32.996867-08 2025-02-23 12:51:08.480576-08 test f T4 \N f \N \N \N {} -240 47 2025-02-23 11:50:55.177428-08 2025-02-23 12:30:28.807619-08 test f T4 \N f \N \N \N {} -241 48 2025-02-23 13:23:28.109642-08 2025-02-23 11:57:04.295155-08 test f T4 \N f \N \N \N {} -242 49 2025-02-23 11:53:10.397813-08 2025-02-23 12:03:03.783486-08 test f T4 \N f \N \N \N {} -1276 427 2025-02-24 22:41:37.072571-08 2025-02-24 23:30:11.670458-08 test t H100 \N t \N \N \N {} -246 53 2025-02-23 13:05:55.652535-08 2025-02-23 13:14:14.362137-08 benchmark f T4 \N t \N \N \N {} -254 54 2025-02-23 11:58:10.69175-08 2025-02-23 12:34:56.201415-08 benchmark f L4 \N t \N \N \N {} -255 54 2025-02-23 12:05:59.80503-08 2025-02-23 12:05:35.639007-08 leaderboard f L4 0.01764701466666667 t \N \N \N {} -256 54 2025-02-23 13:16:11.769925-08 2025-02-23 13:35:56.018887-08 test f A100 \N t \N \N \N {} -257 54 2025-02-23 12:06:54.552569-08 2025-02-23 12:27:58.339221-08 benchmark f A100 \N t \N \N \N {} -258 54 2025-02-23 11:52:48.189654-08 2025-02-23 13:04:58.541644-08 leaderboard f A100 0.0032710223333333333 t \N \N \N {} -260 54 2025-02-23 13:00:18.36019-08 2025-02-23 12:08:18.086439-08 benchmark t T4 \N t \N \N \N {} -261 54 2025-02-23 12:48:01.153003-08 2025-02-23 13:08:30.274831-08 leaderboard t T4 0.017266412 t \N \N \N {} -262 54 2025-02-23 12:11:01.579205-08 2025-02-23 11:58:34.491578-08 test t H100 \N t \N \N \N {} -263 54 2025-02-23 13:16:28.788531-08 2025-02-23 13:00:17.556954-08 benchmark t H100 \N t \N \N \N {} -264 54 2025-02-23 12:04:25.422389-08 2025-02-23 13:13:19.248136-08 leaderboard t H100 0.001458645875 t \N \N \N {} -265 54 2025-02-23 12:31:12.419543-08 2025-02-23 13:09:45.606598-08 test f T4 \N t \N \N \N {} -266 54 2025-02-23 12:40:46.985253-08 2025-02-23 13:34:24.451784-08 benchmark f T4 \N t \N \N \N {} -267 54 2025-02-23 13:25:58.537497-08 2025-02-23 12:54:43.007543-08 leaderboard f T4 0.01723258033333333 t \N \N \N {} -269 54 2025-02-23 12:20:20.316186-08 2025-02-23 13:13:12.18701-08 benchmark t L4 \N t \N \N \N {} -270 54 2025-02-23 13:11:14.571589-08 2025-02-23 12:58:35.784449-08 leaderboard t L4 0.017926125272727272 t \N \N \N {} -271 54 2025-02-23 12:33:04.814186-08 2025-02-23 12:38:40.126485-08 test f H100 \N t \N \N \N {} -272 54 2025-02-23 13:50:03.91953-08 2025-02-23 13:25:15.447661-08 benchmark f H100 \N t \N \N \N {} -273 54 2025-02-23 13:10:29.552929-08 2025-02-23 13:26:17.441886-08 leaderboard f H100 0.0010422076999999999 t \N \N \N {} -274 55 2025-02-23 13:01:35.147956-08 2025-02-23 13:02:44.097619-08 test f T4 \N t \N \N \N {} -275 56 2025-02-23 12:44:20.029345-08 2025-02-23 14:21:48.402951-08 test f T4 \N f \N \N \N {} -276 57 2025-02-23 12:54:37.579254-08 2025-02-23 13:41:28.569725-08 test f T4 \N t \N \N \N {} -314 77 2025-02-23 14:22:31.876974-08 2025-02-23 13:58:03.675484-08 test t H100 \N t \N \N \N {} -345 91 2025-02-23 18:28:50.540756-08 2025-02-23 18:30:32.69747-08 test f H100 \N f \N \N \N {} -277 58 2025-02-23 14:27:11.52301-08 2025-02-23 12:39:45.802969-08 benchmark f H100 \N t \N \N \N {} -278 59 2025-02-23 14:36:38.592486-08 2025-02-23 14:35:07.207052-08 test f T4 \N f \N \N \N {} -279 60 2025-02-23 13:55:19.127155-08 2025-02-23 14:12:12.400517-08 benchmark f T4 \N f \N \N \N {} -280 61 2025-02-23 13:18:07.578575-08 2025-02-23 14:55:51.403557-08 test f T4 \N f \N \N \N {} -320 78 2025-02-23 14:11:00.190481-08 2025-02-23 15:16:48.880961-08 test f H100 \N t \N \N \N {} -321 78 2025-02-23 14:03:20.036503-08 2025-02-23 14:23:37.916867-08 benchmark f H100 \N t \N \N \N {} -322 78 2025-02-23 15:36:20.056643-08 2025-02-23 15:34:57.100941-08 leaderboard f H100 0.001428631625 t \N \N \N {} -324 79 2025-02-23 15:02:01.218849-08 2025-02-23 15:47:08.188349-08 benchmark f H100 \N t \N \N \N {} -325 79 2025-02-23 14:41:07.330032-08 2025-02-23 15:38:15.89614-08 leaderboard f H100 0.0014191957142857144 t \N \N \N {} -326 79 2025-02-23 14:41:13.012481-08 2025-02-23 15:16:33.618694-08 test t H100 \N t \N \N \N {} -328 79 2025-02-23 14:27:28.406633-08 2025-02-23 15:30:01.543516-08 leaderboard t H100 0.0014211721666666666 t \N \N \N {} -329 80 2025-02-23 15:07:57.673532-08 2025-02-23 14:03:31.284187-08 test t H100 \N t \N \N \N {} -330 80 2025-02-23 13:57:07.60172-08 2025-02-23 13:55:16.031154-08 benchmark t H100 \N t \N \N \N {} -331 80 2025-02-23 14:45:52.647508-08 2025-02-23 14:51:00.631073-08 leaderboard t H100 0.0014186101666666668 t \N \N \N {} -333 80 2025-02-23 15:21:15.153944-08 2025-02-23 14:32:10.166808-08 benchmark f H100 \N t \N \N \N {} -334 80 2025-02-23 15:11:41.57439-08 2025-02-23 14:59:52.063767-08 leaderboard f H100 0.0014430232 t \N \N \N {} -4353 1425 2025-03-01 19:49:30.705637-08 2025-03-01 19:52:46.274107-08 leaderboard t A100 0.003362815 t \N \N \N {} -341 87 2025-02-23 18:32:19.084216-08 2025-02-23 18:24:30.655121-08 test f L4 \N f \N \N \N {} -342 88 2025-02-23 18:14:30.40463-08 2025-02-23 17:59:25.451214-08 test f H100 \N f \N \N \N {} -343 89 2025-02-23 17:38:34.140327-08 2025-02-23 17:53:30.915891-08 test f H100 \N f \N \N \N {} -344 90 2025-02-23 18:21:47.199663-08 2025-02-23 17:59:54.220257-08 test f H100 \N f \N \N \N {} -409 115 2025-02-23 19:35:25.852763-08 2025-02-23 18:57:39.125632-08 test f T4 \N t \N \N \N {} -575 162 2025-02-24 04:19:29.964217-08 2025-02-24 03:34:20.053499-08 benchmark t L4 \N t \N \N \N {} -346 92 2025-02-23 17:54:16.477409-08 2025-02-23 18:50:22.606457-08 test f H100 \N f \N \N \N {} -347 93 2025-02-23 18:25:21.117234-08 2025-02-23 18:07:32.76191-08 test t H100 \N t \N \N \N {} -348 93 2025-02-23 18:44:24.03074-08 2025-02-23 17:50:35.187274-08 benchmark t H100 \N t \N \N \N {} -349 93 2025-02-23 18:52:30.803751-08 2025-02-23 18:08:51.653673-08 leaderboard t H100 0.011944822666666665 t \N \N \N {} -350 93 2025-02-23 17:33:45.219692-08 2025-02-23 18:09:19.103073-08 test f H100 \N t \N \N \N {} -127 19 2025-02-23 09:52:43.386488-08 2025-02-23 09:04:44.918935-08 test t H100 \N t \N \N \N {} -128 19 2025-02-23 10:19:49.22057-08 2025-02-23 10:18:08.314956-08 benchmark t H100 \N t \N \N \N {} -129 19 2025-02-23 09:19:50.98444-08 2025-02-23 09:12:23.909584-08 leaderboard t H100 0.006109189 t \N \N \N {} -351 93 2025-02-23 18:02:58.08203-08 2025-02-23 19:27:48.608126-08 benchmark f H100 \N t \N \N \N {} -352 93 2025-02-23 17:35:41.478368-08 2025-02-23 17:43:26.596456-08 leaderboard f H100 0.0121398512 t \N \N \N {} -353 94 2025-02-23 18:01:28.888692-08 2025-02-23 18:25:10.085679-08 test t H100 \N t \N \N \N {} -355 94 2025-02-23 19:24:29.747081-08 2025-02-23 17:41:44.084312-08 leaderboard t H100 0.011895692333333334 t \N \N \N {} -356 94 2025-02-23 18:29:05.574286-08 2025-02-23 19:30:40.100513-08 test f H100 \N t \N \N \N {} -357 94 2025-02-23 19:00:06.086828-08 2025-02-23 19:27:29.302545-08 benchmark f H100 \N t \N \N \N {} -360 96 2025-02-23 18:52:27.921108-08 2025-02-23 19:32:10.278467-08 benchmark f H100 \N t \N \N \N {} -362 97 2025-02-23 18:43:11.382309-08 2025-02-23 18:32:53.162246-08 benchmark t H100 \N t \N \N \N {} -363 97 2025-02-23 18:08:22.457997-08 2025-02-23 18:01:35.677657-08 leaderboard t H100 0.0015710386666666668 t \N \N \N {} -364 97 2025-02-23 18:00:30.642191-08 2025-02-23 19:31:22.809425-08 test f H100 \N t \N \N \N {} -365 97 2025-02-23 19:12:44.951064-08 2025-02-23 17:52:24.96366-08 benchmark f H100 \N t \N \N \N {} -366 97 2025-02-23 18:34:04.98843-08 2025-02-23 19:32:44.684641-08 leaderboard f H100 0.0015707563333333333 t \N \N \N {} -370 99 2025-02-23 19:47:48.997469-08 2025-02-23 19:43:46.393992-08 leaderboard t H100 0.0014419603333333333 t \N \N \N {} -419 123 2025-02-23 20:03:41.74647-08 2025-02-23 19:48:21.795119-08 test f L4 \N t \N \N \N {} -372 99 2025-02-23 19:38:48.275786-08 2025-02-23 18:15:04.56295-08 benchmark f H100 \N t \N \N \N {} -373 99 2025-02-23 19:12:46.215077-08 2025-02-23 18:59:54.371176-08 leaderboard f H100 0.001431599 t \N \N \N {} -374 100 2025-02-23 19:55:52.569939-08 2025-02-23 19:27:50.142717-08 benchmark f H100 \N t \N \N \N {} -375 101 2025-02-23 18:47:52.05672-08 2025-02-23 18:49:29.177973-08 benchmark f H100 \N f \N \N \N {} -376 103 2025-02-23 19:50:51.240681-08 2025-02-23 19:03:51.348191-08 benchmark f T4 \N t \N \N \N {} -402 107 2025-02-23 19:56:20.721626-08 2025-02-23 19:44:53.872926-08 test f H100 \N f \N \N \N {} -381 104 2025-02-23 18:11:03.43025-08 2025-02-23 18:08:57.029995-08 test t A100 \N t \N \N \N {} -382 104 2025-02-23 18:46:23.802647-08 2025-02-23 18:27:06.148219-08 benchmark t A100 \N t \N \N \N {} -383 104 2025-02-23 20:01:50.376892-08 2025-02-23 19:53:06.069857-08 leaderboard t A100 0.0017416949090909091 t \N \N \N {} -384 104 2025-02-23 18:19:47.990746-08 2025-02-23 19:28:16.772556-08 test f A100 \N t \N \N \N {} -386 104 2025-02-23 18:12:18.644326-08 2025-02-23 19:34:19.590964-08 leaderboard f A100 0.001762813 t \N \N \N {} -387 104 2025-02-23 19:49:03.622017-08 2025-02-23 18:55:44.844842-08 test t H100 \N t \N \N \N {} -388 104 2025-02-23 19:24:20.239368-08 2025-02-23 19:08:05.533919-08 benchmark t H100 \N t \N \N \N {} -389 104 2025-02-23 20:02:50.775118-08 2025-02-23 19:19:38.596389-08 leaderboard t H100 0.001051997 t \N \N \N {} -390 104 2025-02-23 19:55:47.925265-08 2025-02-23 18:30:53.620534-08 test f T4 \N t \N \N \N {} -391 104 2025-02-23 19:19:44.079247-08 2025-02-23 19:10:46.577196-08 benchmark f T4 \N t \N \N \N {} -392 104 2025-02-23 19:52:00.493107-08 2025-02-23 19:03:38.169538-08 leaderboard f T4 0.009289433666666666 t \N \N \N {} -393 104 2025-02-23 18:24:57.464605-08 2025-02-23 20:02:29.61335-08 test f L4 \N t \N \N \N {} -582 167 2025-02-24 03:48:55.014484-08 2025-02-24 05:19:01.867475-08 test f H100 \N f \N \N \N {} -3574 1220 2025-02-28 13:45:47.445988-08 2025-02-28 13:55:19.067931-08 benchmark f A100 \N t \N \N \N {} -605 172 2025-02-24 05:12:35.084465-08 2025-02-24 03:53:41.553176-08 leaderboard f T4 0.017926890833333334 t \N \N \N {} -607 172 2025-02-24 05:35:11.303477-08 2025-02-24 04:35:10.682311-08 benchmark f L4 \N t \N \N \N {} -608 172 2025-02-24 05:14:02.764614-08 2025-02-24 05:32:57.132512-08 leaderboard f L4 0.017603741333333332 t \N \N \N {} -616 174 2025-02-24 04:08:35.184962-08 2025-02-24 04:11:19.105749-08 benchmark f A100 \N t \N \N \N {} -617 174 2025-02-24 04:45:50.307802-08 2025-02-24 04:49:07.034774-08 leaderboard f A100 0.003163628 t \N \N \N {} -618 174 2025-02-24 05:05:10.997982-08 2025-02-24 04:21:07.467177-08 test t H100 \N t \N \N \N {} -619 174 2025-02-24 04:49:07.394184-08 2025-02-24 05:36:35.84385-08 benchmark t H100 \N t \N \N \N {} -620 174 2025-02-24 03:56:43.871806-08 2025-02-24 05:06:06.267042-08 leaderboard t H100 0.0014537886666666667 t \N \N \N {} -621 174 2025-02-24 04:11:26.341666-08 2025-02-24 05:44:14.091621-08 test t A100 \N t \N \N \N {} -622 174 2025-02-24 05:03:36.072136-08 2025-02-24 04:51:58.063144-08 benchmark t A100 \N t \N \N \N {} -623 174 2025-02-24 04:20:01.163633-08 2025-02-24 03:57:19.043219-08 leaderboard t A100 0.0031645186666666666 t \N \N \N {} -690 217 2025-02-24 07:33:26.518857-08 2025-02-24 07:07:15.672567-08 test f A100 \N f \N \N \N {} -630 174 2025-02-24 04:46:32.413079-08 2025-02-24 05:37:46.353976-08 test f T4 \N t \N \N \N {} -631 174 2025-02-24 04:08:35.774251-08 2025-02-24 04:13:23.290244-08 benchmark f T4 \N t \N \N \N {} -632 174 2025-02-24 05:26:47.831146-08 2025-02-24 05:29:05.035813-08 leaderboard f T4 0.02047257833333333 t \N \N \N {} -3277 1144 2025-02-28 05:40:20.632509-08 2025-02-28 06:23:09.782086-08 leaderboard f A100 0.0030818813333333334 t \N \N \N {} -680 211 2025-02-24 06:16:43.935048-08 2025-02-24 05:24:09.037312-08 benchmark t H100 \N t \N \N \N {} -681 211 2025-02-24 06:20:50.131308-08 2025-02-24 06:19:33.50419-08 leaderboard t H100 0.00343199925 t \N \N \N {} -682 211 2025-02-24 06:04:20.742817-08 2025-02-24 06:51:37.437888-08 test f H100 \N t \N \N \N {} -725 231 2025-02-24 08:33:34.394553-08 2025-02-24 07:52:30.592827-08 test f T4 \N t \N \N \N {} -683 211 2025-02-24 05:48:41.277846-08 2025-02-24 06:20:29.451233-08 benchmark f H100 \N t \N \N \N {} -684 211 2025-02-24 05:40:28.316963-08 2025-02-24 05:24:29.585484-08 leaderboard f H100 0.0034547503333333335 t \N \N \N {} -685 212 2025-02-24 07:16:11.487533-08 2025-02-24 05:49:31.110402-08 benchmark f H100 \N t \N \N \N {} -707 225 2025-02-24 07:34:13.86609-08 2025-02-24 09:25:38.673749-08 leaderboard t A100 0.010140606949999999 t \N \N \N {} -713 229 2025-02-24 08:36:51.707477-08 2025-02-24 09:19:35.205825-08 leaderboard t A100 0.004042308666666667 t \N \N \N {} -715 229 2025-02-24 08:59:14.990632-08 2025-02-24 08:40:06.19623-08 benchmark f A100 \N t \N \N \N {} -1002 289 2025-02-24 16:17:52.085682-08 2025-02-24 15:49:52.549542-08 leaderboard t T4 0.017548657333333332 t \N \N \N {} -1004 289 2025-02-24 15:49:21.702779-08 2025-02-24 15:56:44.290148-08 benchmark f H100 \N t \N \N \N {} -1005 289 2025-02-24 16:30:57.115003-08 2025-02-24 16:53:11.026432-08 leaderboard f H100 0.0014277393999999998 t \N \N \N {} -1006 289 2025-02-24 17:08:54.259849-08 2025-02-24 16:01:18.083591-08 test f T4 \N t \N \N \N {} -1012 289 2025-02-24 15:50:44.700014-08 2025-02-24 16:13:26.646399-08 test t L4 \N t \N \N \N {} -2411 812 2025-02-26 11:55:00.140651-08 2025-02-26 12:25:31.245914-08 test t T4 \N f \N \N \N {} -1474 491 2025-02-25 04:38:47.881649-08 2025-02-25 06:09:17.695829-08 test f A100 \N t \N \N \N {} -1477 492 2025-02-25 05:11:27.321383-08 2025-02-25 05:35:34.773378-08 test t H100 \N t \N \N \N {} -1507 504 2025-02-25 07:20:39.230369-08 2025-02-25 07:49:03.830451-08 test f A100 \N t \N \N \N {} -1018 290 2025-02-24 17:13:55.431038-08 2025-02-24 16:41:27.422869-08 test f A100 \N f \N \N \N {} -1019 290 2025-02-24 16:59:40.514483-08 2025-02-24 15:54:21.408908-08 test t H100 \N f \N \N \N {} -1020 290 2025-02-24 16:35:12.120375-08 2025-02-24 17:02:25.401972-08 test t A100 \N f \N \N \N {} -1329 443 2025-02-25 01:04:39.585597-08 2025-02-25 02:46:51.917328-08 benchmark f A100 \N t \N \N \N {} -1330 443 2025-02-25 01:21:21.17285-08 2025-02-25 02:34:21.089915-08 leaderboard f A100 0.0031880928 t \N \N \N {} -1032 291 2025-02-24 17:02:48.537924-08 2025-02-24 16:42:54.273152-08 test t H100 \N t \N \N \N {} -1033 291 2025-02-24 17:18:28.789252-08 2025-02-24 17:23:51.01374-08 benchmark t H100 \N t \N \N \N {} -6109 1847 2025-03-11 00:39:56.642025-07 2025-03-11 00:31:13.424537-07 benchmark f A100 \N f \N \N \N {} -1034 291 2025-02-24 17:27:21.544186-08 2025-02-24 16:17:02.165474-08 leaderboard t H100 0.0014878653333333333 t \N \N \N {} -1035 291 2025-02-24 16:34:37.159795-08 2025-02-24 16:06:45.714048-08 test t L4 \N t \N \N \N {} -1047 291 2025-02-24 17:36:54.22773-08 2025-02-24 17:03:45.859127-08 test f L4 \N t \N \N \N {} -1480 492 2025-02-25 06:19:11.982018-08 2025-02-25 06:17:28.318413-08 test f H100 \N t \N \N \N {} -1036 291 2025-02-24 17:54:34.398457-08 2025-02-24 16:41:28.568693-08 benchmark t L4 \N t \N \N \N {} -1037 291 2025-02-24 16:38:12.362104-08 2025-02-24 16:19:38.183305-08 leaderboard t L4 0.017247674666666667 t \N \N \N {} -1038 291 2025-02-24 16:24:02.713537-08 2025-02-24 16:41:50.09925-08 test f H100 \N t \N \N \N {} -1039 291 2025-02-24 17:42:26.593225-08 2025-02-24 17:17:44.711243-08 benchmark f H100 \N t \N \N \N {} -1040 291 2025-02-24 17:17:51.955383-08 2025-02-24 17:24:28.626898-08 leaderboard f H100 0.001511652 t \N \N \N {} -1379 462 2025-02-25 04:30:12.6402-08 2025-02-25 03:17:09.15611-08 benchmark f H100 \N t \N \N \N {} -1042 291 2025-02-24 17:37:57.142995-08 2025-02-24 17:18:33.682684-08 benchmark t T4 \N t \N \N \N {} -1043 291 2025-02-24 17:05:51.220243-08 2025-02-24 17:10:08.13623-08 leaderboard t T4 0.017371874333333332 t \N \N \N {} -1044 291 2025-02-24 17:06:11.858432-08 2025-02-24 16:33:50.751909-08 test f T4 \N t \N \N \N {} -1045 291 2025-02-24 16:16:44.982001-08 2025-02-24 16:31:49.425344-08 benchmark f T4 \N t \N \N \N {} -1046 291 2025-02-24 17:12:14.548228-08 2025-02-24 16:41:40.82905-08 leaderboard f T4 0.017367958 t \N \N \N {} -1380 463 2025-02-25 03:45:25.261332-08 2025-02-25 04:20:54.913035-08 benchmark f H100 \N t \N \N \N {} -2380 802 2025-02-26 12:30:03.429377-08 2025-02-26 11:34:34.394213-08 benchmark f A100 \N f \N \N \N {} -1048 291 2025-02-24 16:19:24.170092-08 2025-02-24 17:06:26.862153-08 benchmark f L4 \N t \N \N \N {} -1049 291 2025-02-24 15:56:27.762751-08 2025-02-24 17:05:19.405224-08 leaderboard f L4 0.017399107333333334 t \N \N \N {} -1050 292 2025-02-24 17:52:15.055926-08 2025-02-24 17:44:34.698152-08 test f A100 \N t \N \N \N {} -1051 292 2025-02-24 16:22:47.842013-08 2025-02-24 17:43:41.94016-08 benchmark f A100 \N t \N \N \N {} -1052 292 2025-02-24 17:08:31.329041-08 2025-02-24 16:04:23.906272-08 leaderboard f A100 0.0031465863333333334 t \N \N \N {} -1381 464 2025-02-25 04:30:12.200923-08 2025-02-25 04:46:17.323875-08 test f H100 \N t \N \N \N {} -1510 504 2025-02-25 07:50:24.939578-08 2025-02-25 07:14:47.363201-08 test t A100 \N t \N \N \N {} -1053 292 2025-02-24 17:46:58.491665-08 2025-02-24 17:43:14.530104-08 test t A100 \N t \N \N \N {} -1054 292 2025-02-24 16:28:07.677678-08 2025-02-24 17:17:05.064075-08 benchmark t A100 \N t \N \N \N {} -1055 292 2025-02-24 17:20:28.611387-08 2025-02-24 16:25:56.736329-08 leaderboard t A100 0.003155266 t \N \N \N {} -1056 292 2025-02-24 16:22:44.412346-08 2025-02-24 16:35:31.252286-08 test t L4 \N t \N \N \N {} -1057 292 2025-02-24 17:51:06.9878-08 2025-02-24 16:57:56.1698-08 benchmark t L4 \N t \N \N \N {} -1384 464 2025-02-25 04:01:57.357806-08 2025-02-25 04:32:07.486918-08 test t H100 \N t \N \N \N {} -1485 495 2025-02-25 07:02:18.828551-08 2025-02-25 05:31:13.315806-08 test t A100 \N f \N \N \N {} -1058 292 2025-02-24 16:12:14.175483-08 2025-02-24 16:19:24.281562-08 leaderboard t L4 0.017181366666666666 t \N \N \N {} -1059 292 2025-02-24 16:45:08.313765-08 2025-02-24 16:24:12.371964-08 test f H100 \N t \N \N \N {} -1060 292 2025-02-24 16:57:23.927277-08 2025-02-24 17:45:53.131411-08 benchmark f H100 \N t \N \N \N {} -1387 465 2025-02-25 03:17:52.9791-08 2025-02-25 04:50:46.975041-08 test t H100 \N t \N \N \N {} -1063 292 2025-02-24 17:43:12.741558-08 2025-02-24 16:54:56.492661-08 benchmark t T4 \N t \N \N \N {} -1064 292 2025-02-24 16:36:30.649022-08 2025-02-24 16:31:15.555557-08 leaderboard t T4 0.016662345666666665 t \N \N \N {} -6110 1848 2025-03-11 02:24:40.439817-07 2025-03-11 01:49:27.022226-07 benchmark f A100 \N f \N \N \N {} -1065 292 2025-02-24 17:01:31.487312-08 2025-02-24 16:08:17.982794-08 test f T4 \N t \N \N \N {} -1066 292 2025-02-24 17:34:24.686206-08 2025-02-24 16:15:54.00558-08 benchmark f T4 \N t \N \N \N {} -1067 292 2025-02-24 16:25:45.909582-08 2025-02-24 17:35:16.325804-08 leaderboard f T4 0.016692271666666664 t \N \N \N {} -1089 300 2025-02-24 17:15:39.013548-08 2025-02-24 18:07:57.788624-08 test f A100 \N t \N \N \N {} -1390 465 2025-02-25 04:07:56.363414-08 2025-02-25 03:08:55.772199-08 test f H100 \N t \N \N \N {} -1069 292 2025-02-24 16:33:24.005735-08 2025-02-24 17:00:37.977793-08 benchmark f L4 \N t \N \N \N {} -1070 292 2025-02-24 16:11:38.035638-08 2025-02-24 16:35:28.298125-08 leaderboard f L4 0.01725526833333333 t \N \N \N {} -1072 292 2025-02-24 17:49:21.420511-08 2025-02-24 17:39:52.392614-08 benchmark t H100 \N t \N \N \N {} -1073 292 2025-02-24 17:39:03.403534-08 2025-02-24 16:41:35.838976-08 leaderboard t H100 0.00143393225 t \N \N \N {} -1074 293 2025-02-24 16:28:54.883242-08 2025-02-24 17:35:23.368271-08 test f H100 \N t \N \N \N {} -1075 294 2025-02-24 17:12:03.460856-08 2025-02-24 17:11:25.091501-08 benchmark f H100 \N t \N \N \N {} -1076 295 2025-02-24 17:45:00.760378-08 2025-02-24 18:03:30.527064-08 test f H100 \N t \N \N \N {} -3899 1300 2025-03-01 04:59:30.357371-08 2025-03-01 05:19:06.000963-08 benchmark f H100 \N t \N \N \N {} -1083 297 2025-02-24 18:12:12.906661-08 2025-02-24 16:31:51.879538-08 test f A100 \N t \N \N \N {} -1087 300 2025-02-24 17:00:42.01574-08 2025-02-24 17:08:59.120585-08 benchmark t A100 \N t \N \N \N {} -1088 300 2025-02-24 16:54:53.189438-08 2025-02-24 17:03:28.128475-08 leaderboard t A100 0.014111941 t \N \N \N {} -1090 300 2025-02-24 18:18:13.575515-08 2025-02-24 17:44:59.217622-08 benchmark f A100 \N t \N \N \N {} -1101 304 2025-02-24 17:40:45.55189-08 2025-02-24 17:01:05.496887-08 test t H100 \N t \N \N \N {} -1102 304 2025-02-24 18:50:24.116927-08 2025-02-24 18:27:17.991007-08 benchmark t H100 \N t \N \N \N {} -1103 304 2025-02-24 18:56:45.352747-08 2025-02-24 17:39:36.014601-08 leaderboard t H100 0.0036164646666666665 t \N \N \N {} -1104 304 2025-02-24 17:14:31.119701-08 2025-02-24 18:21:08.729398-08 test f T4 \N t \N \N \N {} -1108 304 2025-02-24 17:14:00.044903-08 2025-02-24 17:35:39.490324-08 benchmark t T4 \N t \N \N \N {} -1109 304 2025-02-24 18:01:06.49365-08 2025-02-24 17:46:19.984475-08 leaderboard t T4 0.02827727433333333 t \N \N \N {} -1110 304 2025-02-24 18:55:51.538374-08 2025-02-24 18:30:10.634801-08 test f H100 \N t \N \N \N {} -1112 304 2025-02-24 17:35:18.104482-08 2025-02-24 17:19:54.790912-08 leaderboard f H100 0.003638923 t \N \N \N {} -1115 304 2025-02-24 17:17:06.648489-08 2025-02-24 18:55:20.002642-08 leaderboard t L4 0.027880181666666667 t \N \N \N {} -1116 304 2025-02-24 18:01:36.171802-08 2025-02-24 17:39:40.210678-08 test f L4 \N t \N \N \N {} -1117 304 2025-02-24 17:16:05.293543-08 2025-02-24 17:09:24.002204-08 benchmark f L4 \N t \N \N \N {} -1149 322 2025-02-24 20:09:50.646883-08 2025-02-24 20:09:27.027041-08 benchmark f A100 \N t \N \N \N {} -1150 323 2025-02-24 19:44:17.381273-08 2025-02-24 18:40:51.316233-08 test f H100 \N f \N \N \N {} -1513 505 2025-02-25 06:40:15.410176-08 2025-02-25 07:37:48.715184-08 test f A100 \N t \N \N \N {} -1151 324 2025-02-24 18:48:04.557654-08 2025-02-24 19:42:55.295313-08 benchmark f A100 \N t \N \N \N {} -1152 325 2025-02-24 19:59:18.76177-08 2025-02-24 18:45:50.840944-08 benchmark f A100 \N f \N \N \N {} -1153 326 2025-02-24 18:52:24.053553-08 2025-02-24 19:29:33.646058-08 benchmark f A100 \N t \N \N \N {} -1154 327 2025-02-24 20:15:12.831932-08 2025-02-24 18:46:28.80301-08 benchmark f A100 \N t \N \N \N {} -1165 333 2025-02-24 19:59:10.567094-08 2025-02-24 19:43:21.331396-08 benchmark f A100 \N f \N \N \N {} -1156 329 2025-02-24 20:00:37.855323-08 2025-02-24 19:49:02.655715-08 test f H100 \N t \N \N \N {} -1157 329 2025-02-24 19:16:48.278346-08 2025-02-24 18:35:28.873095-08 benchmark f H100 \N t \N \N \N {} -1158 329 2025-02-24 19:19:35.133313-08 2025-02-24 19:39:48.264665-08 leaderboard f H100 0.0015907006666666668 t \N \N \N {} -1159 329 2025-02-24 20:06:20.184176-08 2025-02-24 19:17:10.273748-08 test t H100 \N t \N \N \N {} -1395 467 2025-02-25 04:01:56.308441-08 2025-02-25 03:40:45.970012-08 test f A100 \N f \N \N \N {} -1162 330 2025-02-24 20:15:53.048254-08 2025-02-24 19:40:58.672546-08 benchmark f A100 \N t \N \N \N {} -1163 331 2025-02-24 19:39:33.765253-08 2025-02-24 20:18:32.015656-08 benchmark f A100 \N f \N \N \N {} -1164 332 2025-02-24 19:57:11.008009-08 2025-02-24 19:45:28.206315-08 test f A100 \N f \N \N \N {} -1396 468 2025-02-25 03:56:27.738846-08 2025-02-25 04:21:51.589822-08 test f A100 \N t \N \N \N {} -1170 338 2025-02-24 20:00:52.228565-08 2025-02-24 19:06:41.353851-08 benchmark f A100 \N f \N \N \N {} -1185 353 2025-02-24 20:23:22.920373-08 2025-02-24 20:31:23.388252-08 benchmark f A100 \N f \N \N \N {} -1172 340 2025-02-24 20:30:32.647454-08 2025-02-24 19:20:53.458575-08 benchmark f A100 \N t \N \N \N {} -1173 341 2025-02-24 19:18:26.427276-08 2025-02-24 20:25:12.965728-08 benchmark f A100 \N t \N \N \N {} -1174 342 2025-02-24 19:16:50.848989-08 2025-02-24 20:26:36.909914-08 benchmark f A100 \N f \N \N \N {} -1399 468 2025-02-25 03:54:55.679208-08 2025-02-25 04:15:06.87232-08 test t A100 \N t \N \N \N {} -1516 505 2025-02-25 07:18:07.594758-08 2025-02-25 07:03:51.966033-08 test t A100 \N t \N \N \N {} -1178 346 2025-02-24 20:46:35.583472-08 2025-02-24 19:47:02.016449-08 benchmark f A100 \N f \N \N \N {} -1179 347 2025-02-24 20:01:38.196152-08 2025-02-24 19:54:49.601721-08 benchmark f A100 \N f \N \N \N {} -1402 469 2025-02-25 03:40:45.982253-08 2025-02-25 03:31:06.961948-08 test f A100 \N t \N \N \N {} -1182 350 2025-02-24 19:20:16.154751-08 2025-02-24 20:48:25.71245-08 benchmark f A100 \N t \N \N \N {} -1183 351 2025-02-24 20:45:34.464741-08 2025-02-24 20:18:14.635844-08 benchmark f H100 \N t \N \N \N {} -1184 352 2025-02-24 20:01:05.338551-08 2025-02-24 20:52:19.857137-08 test f A100 \N f \N \N \N {} -1405 469 2025-02-25 04:58:27.346875-08 2025-02-25 04:51:26.433509-08 test t A100 \N t \N \N \N {} -1519 506 2025-02-25 06:47:13.929749-08 2025-02-25 06:42:22.498667-08 test t H100 \N t \N \N \N {} -1188 358 2025-02-24 19:37:31.122358-08 2025-02-24 20:18:57.150066-08 benchmark f A100 \N f \N \N \N {} -1189 360 2025-02-24 21:06:56.386392-08 2025-02-24 19:27:17.523239-08 benchmark f A100 \N f \N \N \N {} -1190 363 2025-02-24 20:45:31.277208-08 2025-02-24 20:01:17.753967-08 benchmark f A100 \N f \N \N \N {} -1408 470 2025-02-25 03:59:58.320768-08 2025-02-25 04:28:37.828048-08 test t A100 \N t \N \N \N {} -1193 365 2025-02-24 19:43:43.570899-08 2025-02-24 19:39:23.906226-08 benchmark f A100 \N t \N \N \N {} -6140 1882 2025-03-11 10:55:44.95308-07 2025-03-11 11:05:57.417109-07 test t T4 \N f \N \N \N {} -1194 365 2025-02-24 20:08:47.222036-08 2025-02-24 19:30:56.207701-08 leaderboard f A100 0.01920636041666667 t \N \N \N {} -1195 365 2025-02-24 19:51:58.749971-08 2025-02-24 19:45:07.726733-08 test t A100 \N t \N \N \N {} -1196 365 2025-02-24 19:38:48.837266-08 2025-02-24 19:34:14.850955-08 benchmark t A100 \N t \N \N \N {} -1197 365 2025-02-24 21:18:06.454034-08 2025-02-24 20:41:46.182401-08 leaderboard t A100 0.023418701 t \N \N \N {} -1411 470 2025-02-25 03:23:48.300357-08 2025-02-25 04:14:39.867996-08 test f A100 \N t \N \N \N {} -1201 369 2025-02-24 19:57:06.809954-08 2025-02-24 19:38:21.922123-08 benchmark f A100 \N f \N \N \N {} -1202 370 2025-02-24 20:42:28.895887-08 2025-02-24 20:44:35.166507-08 benchmark f A100 \N f \N \N \N {} -1205 373 2025-02-24 20:31:46.327908-08 2025-02-24 19:52:32.554037-08 benchmark f A100 \N f \N \N \N {} -1251 412 2025-02-24 21:58:13.257224-08 2025-02-24 20:59:40.847628-08 benchmark f A100 \N t \N \N \N {} -1252 413 2025-02-24 21:02:53.363826-08 2025-02-24 21:32:32.418367-08 test f A100 \N t \N \N \N {} -1253 413 2025-02-24 21:45:44.88428-08 2025-02-24 22:03:57.186711-08 benchmark f A100 \N t \N \N \N {} -1254 413 2025-02-24 22:46:22.934333-08 2025-02-24 22:52:04.198254-08 leaderboard f A100 0.0008381501 t \N \N \N {} -1261 416 2025-02-24 22:58:19.495213-08 2025-02-24 21:04:11.500619-08 benchmark f H100 \N t \N \N \N {} -1262 416 2025-02-24 21:17:50.838915-08 2025-02-24 22:04:53.900724-08 leaderboard f H100 0.00182382483 t \N \N \N {} -1267 418 2025-02-24 23:14:01.289913-08 2025-02-24 23:08:07.113865-08 test f H100 \N f \N \N \N {} -1268 419 2025-02-24 22:02:19.904283-08 2025-02-24 22:45:33.136654-08 test f H100 \N f \N \N \N {} -1269 420 2025-02-24 22:11:27.988828-08 2025-02-24 22:20:38.794633-08 test f L4 \N f \N \N \N {} -1270 421 2025-02-24 22:44:39.054132-08 2025-02-24 23:23:37.763687-08 test f H100 \N f \N \N \N {} -1285 431 2025-02-25 00:40:59.712906-08 2025-02-25 00:18:36.054996-08 benchmark f A100 \N t \N \N \N {} -1286 432 2025-02-25 00:31:05.092152-08 2025-02-25 00:20:51.202659-08 test f A100 \N f \N \N \N {} -1287 432 2025-02-25 00:49:38.550792-08 2025-02-25 00:46:23.249092-08 test t A100 \N f \N \N \N {} -1288 433 2025-02-25 00:12:52.435038-08 2025-02-25 00:43:33.913224-08 test t A100 \N f \N \N \N {} -1289 433 2025-02-25 00:41:52.056466-08 2025-02-25 01:46:10.51578-08 test f A100 \N f \N \N \N {} -1290 434 2025-02-25 02:07:55.489435-08 2025-02-25 00:38:51.721901-08 test f A100 \N f \N \N \N {} -1300 437 2025-02-25 01:42:35.344955-08 2025-02-25 00:18:58.076587-08 test f A100 \N t \N \N \N {} -1301 438 2025-02-25 00:52:08.957799-08 2025-02-25 01:01:30.640546-08 test f A100 \N t \N \N \N {} -1306 438 2025-02-25 02:17:28.708296-08 2025-02-25 00:43:42.436098-08 leaderboard t A100 0.00319022725 t \N \N \N {} -1307 439 2025-02-25 00:35:26.857477-08 2025-02-25 00:40:59.59712-08 test t A100 \N t \N \N \N {} -1312 439 2025-02-25 02:25:43.593928-08 2025-02-25 00:33:11.310668-08 leaderboard f A100 0.018465732104166667 t \N \N \N {} -1313 440 2025-02-25 00:40:22.163117-08 2025-02-25 02:26:51.813453-08 test t A100 \N f \N \N \N {} -1345 447 2025-02-25 01:34:33.910168-08 2025-02-25 03:08:17.172342-08 test f A100 \N t \N \N \N {} -1314 440 2025-02-25 01:49:18.063405-08 2025-02-25 01:27:01.624065-08 test f A100 \N f \N \N \N {} -1315 441 2025-02-25 02:02:15.063829-08 2025-02-25 01:35:47.494284-08 test t A100 \N t \N \N \N {} -1316 441 2025-02-25 01:37:10.608604-08 2025-02-25 01:46:29.023721-08 benchmark t A100 \N f \N \N \N {} -1335 445 2025-02-25 02:43:54.759243-08 2025-02-25 01:44:21.434113-08 leaderboard f A100 0.0030836436666666664 t \N \N \N {} -1336 445 2025-02-25 02:54:21.021597-08 2025-02-25 01:51:35.634885-08 test t A100 \N t \N \N \N {} -1346 448 2025-02-25 02:33:47.252483-08 2025-02-25 02:35:35.888545-08 test f H100 \N t \N \N \N {} -1434 476 2025-02-25 04:00:22.852373-08 2025-02-25 04:04:45.593426-08 test t A100 \N f \N \N \N {} -1339 446 2025-02-25 02:36:06.477421-08 2025-02-25 03:07:01.814655-08 test f H100 \N t \N \N \N {} -1340 446 2025-02-25 01:30:46.736236-08 2025-02-25 01:35:56.032794-08 benchmark f H100 \N t \N \N \N {} -1341 446 2025-02-25 02:50:19.546464-08 2025-02-25 03:05:51.854793-08 leaderboard f H100 0.001413967 t \N \N \N {} -1342 446 2025-02-25 01:57:52.59555-08 2025-02-25 03:11:12.62416-08 test t H100 \N t \N \N \N {} -1343 446 2025-02-25 02:54:39.59271-08 2025-02-25 01:59:29.726735-08 benchmark t H100 \N t \N \N \N {} -1344 446 2025-02-25 02:39:25.860644-08 2025-02-25 02:13:36.717111-08 leaderboard t H100 0.001406711 t \N \N \N {} -1347 448 2025-02-25 02:43:41.717721-08 2025-02-25 03:26:33.446347-08 benchmark f H100 \N t \N \N \N {} -1348 448 2025-02-25 02:24:25.941676-08 2025-02-25 01:44:32.522029-08 leaderboard f H100 0.0014466804444444445 t \N \N \N {} -1349 448 2025-02-25 02:46:47.800926-08 2025-02-25 02:27:20.46965-08 test t H100 \N t \N \N \N {} -1350 448 2025-02-25 03:06:45.439062-08 2025-02-25 02:01:51.335675-08 benchmark t H100 \N t \N \N \N {} -1351 448 2025-02-25 03:30:09.49455-08 2025-02-25 03:12:41.990012-08 leaderboard t H100 0.0014756132222222221 t \N \N \N {} -1352 449 2025-02-25 03:33:12.39421-08 2025-02-25 02:23:52.923798-08 benchmark f H100 \N f \N \N \N {} -1435 476 2025-02-25 04:55:28.037485-08 2025-02-25 03:57:08.314649-08 test f A100 \N f \N \N \N {} -1555 513 2025-02-25 07:41:24.90562-08 2025-02-25 07:02:07.563164-08 test t A100 \N t \N \N \N {} -2909 1016 2025-02-27 00:36:25.1576-08 2025-02-27 00:50:15.121869-08 benchmark f L4 \N t \N \N \N {} -2910 1016 2025-02-27 00:33:26.148508-08 2025-02-27 01:43:46.236307-08 leaderboard f L4 0.017180370666666667 t \N \N \N {} -3041 1088 2025-02-27 14:56:34.384661-08 2025-02-27 15:37:21.179849-08 benchmark f A100 \N t \N \N \N {} -3369 1163 2025-02-28 10:09:00.6989-08 2025-02-28 09:38:40.385253-08 leaderboard t T4 0.01692768366666667 t \N \N \N {} -1400 468 2025-02-25 04:45:59.134192-08 2025-02-25 03:14:40.329073-08 benchmark t A100 \N t \N \N \N {} -1401 468 2025-02-25 03:36:58.192612-08 2025-02-25 04:35:55.042696-08 leaderboard t A100 0.003100401 t \N \N \N {} -2919 1023 2025-02-27 02:46:56.557824-08 2025-02-27 03:30:51.397045-08 test t A100 \N t \N \N \N {} -2920 1023 2025-02-27 02:54:26.545327-08 2025-02-27 03:57:32.864122-08 benchmark t A100 \N t \N \N \N {} -2921 1023 2025-02-27 04:31:43.865249-08 2025-02-27 03:08:56.104014-08 leaderboard t A100 0.00009803284313725489 t \N \N \N {} -3120 1110 2025-02-28 01:35:44.444997-08 2025-02-28 03:00:13.974579-08 leaderboard t A100 0.00310022 t \N \N \N {} -3559 1210 2025-02-28 11:35:52.32336-08 2025-02-28 10:56:38.662439-08 test f A100 \N t \N \N \N {} -1403 469 2025-02-25 04:31:28.4595-08 2025-02-25 03:33:06.99222-08 benchmark f A100 \N t \N \N \N {} -2381 803 2025-02-26 11:33:33.487376-08 2025-02-26 12:57:20.418702-08 benchmark f A100 \N f \N \N \N {} -2427 833 2025-02-26 13:42:17.786461-08 2025-02-26 12:30:47.6523-08 benchmark f H100 \N t \N \N \N {} -3009 1074 2025-02-27 13:15:17.144176-08 2025-02-27 12:29:52.339984-08 leaderboard t A100 0.008153932666666667 t \N \N \N {} -2952 1039 2025-02-27 09:30:20.903906-08 2025-02-27 09:13:37.115048-08 benchmark f A100 \N t \N \N \N {} -3010 1074 2025-02-27 13:59:33.48938-08 2025-02-27 13:27:19.116278-08 test f A100 \N t \N \N \N {} -3011 1074 2025-02-27 14:02:47.636628-08 2025-02-27 13:32:31.614474-08 benchmark f A100 \N t \N \N \N {} -3012 1074 2025-02-27 13:15:20.480402-08 2025-02-27 12:20:44.401517-08 leaderboard f A100 0.008157396666666667 t \N \N \N {} -3085 1104 2025-02-28 01:59:18.600676-08 2025-02-28 02:22:56.347954-08 test f A100 \N t \N \N \N {} -3094 1106 2025-02-28 02:25:58.750131-08 2025-02-28 03:06:28.944884-08 test t A100 \N t \N \N \N {} -3100 1107 2025-02-28 01:26:20.879799-08 2025-02-28 02:25:12.920428-08 test f A100 \N t \N \N \N {} -1478 492 2025-02-25 04:23:20.449898-08 2025-02-25 04:58:19.421573-08 benchmark t H100 \N t \N \N \N {} -1479 492 2025-02-25 04:51:19.751745-08 2025-02-25 05:39:17.407689-08 leaderboard t H100 0.0014122803333333333 t \N \N \N {} -2953 1040 2025-02-27 09:36:18.815464-08 2025-02-27 08:40:35.471157-08 test f A100 \N t \N \N \N {} -2954 1040 2025-02-27 08:26:37.101243-08 2025-02-27 09:01:30.532817-08 benchmark f A100 \N t \N \N \N {} -2955 1040 2025-02-27 09:41:18.891429-08 2025-02-27 08:04:00.37954-08 leaderboard f A100 0.003193513 t \N \N \N {} -3013 1075 2025-02-27 13:11:51.024355-08 2025-02-27 13:17:55.679639-08 test f A100 \N f \N \N \N {} -3103 1107 2025-02-28 03:07:10.741712-08 2025-02-28 02:19:44.946765-08 test t A100 \N t \N \N \N {} -3225 1127 2025-02-28 05:28:19.551054-08 2025-02-28 05:56:14.735613-08 leaderboard f A100 0.0030895356666666663 t \N \N \N {} -1481 492 2025-02-25 04:54:31.873992-08 2025-02-25 04:22:13.298402-08 benchmark f H100 \N t \N \N \N {} -1482 492 2025-02-25 06:14:51.357942-08 2025-02-25 06:18:57.491972-08 leaderboard f H100 0.001430011 t \N \N \N {} -1524 506 2025-02-25 06:33:04.067626-08 2025-02-25 07:23:14.188914-08 leaderboard f H100 0.0014538050909090909 t \N \N \N {} -1525 507 2025-02-25 07:34:25.667422-08 2025-02-25 06:18:53.214067-08 test t A100 \N f \N \N \N {} -1526 507 2025-02-25 08:04:00.305064-08 2025-02-25 07:53:47.234207-08 test f A100 \N f \N \N \N {} -1527 508 2025-02-25 06:51:53.729029-08 2025-02-25 06:49:15.739902-08 benchmark f A100 \N f \N \N \N {} -1528 509 2025-02-25 07:57:51.169726-08 2025-02-25 08:03:52.639663-08 benchmark f A100 \N t \N \N \N {} -3160 1117 2025-02-28 01:57:36.630356-08 2025-02-28 02:15:23.444492-08 test t A100 \N t \N \N \N {} -1531 512 2025-02-25 07:27:21.471591-08 2025-02-25 07:32:23.967466-08 test t A100 \N t \N \N \N {} -130 20 2025-02-23 09:47:46.962051-08 2025-02-23 10:49:22.323496-08 test f H100 \N t \N \N \N {} -1532 512 2025-02-25 07:07:12.686135-08 2025-02-25 06:51:42.064245-08 benchmark t A100 \N t \N \N \N {} -1533 512 2025-02-25 07:35:12.160809-08 2025-02-25 06:23:12.621053-08 leaderboard t A100 0.0033605953333333337 t \N \N \N {} -1534 512 2025-02-25 07:40:57.831066-08 2025-02-25 07:21:40.9266-08 test f A100 \N t \N \N \N {} -1535 512 2025-02-25 07:49:49.262012-08 2025-02-25 07:10:31.438503-08 benchmark f A100 \N t \N \N \N {} -1536 512 2025-02-25 06:32:25.921122-08 2025-02-25 07:25:06.894207-08 leaderboard f A100 0.0033895406666666667 t \N \N \N {} -1537 512 2025-02-25 06:24:52.920212-08 2025-02-25 06:36:53.942491-08 test t H100 \N t \N \N \N {} -1538 512 2025-02-25 06:30:33.771888-08 2025-02-25 07:19:58.296436-08 benchmark t H100 \N t \N \N \N {} -1558 513 2025-02-25 07:20:20.352133-08 2025-02-25 07:48:40.674871-08 test f A100 \N t \N \N \N {} -3263 1142 2025-02-28 06:26:36.269643-08 2025-02-28 06:34:10.748084-08 test f A100 \N t \N \N \N {} -2020 694 2025-02-25 14:25:32.099879-08 2025-02-25 14:05:01.976545-08 test f H100 \N f \N \N \N {} -1576 516 2025-02-25 07:10:56.831342-08 2025-02-25 07:01:47.567348-08 test f A100 \N t \N \N \N {} -1577 516 2025-02-25 08:23:01.443137-08 2025-02-25 07:25:44.603998-08 benchmark f A100 \N t \N \N \N {} -1578 516 2025-02-25 08:13:13.657148-08 2025-02-25 07:31:55.453583-08 leaderboard f A100 0.0031859834 t \N \N \N {} -1580 518 2025-02-25 08:07:50.629405-08 2025-02-25 07:17:48.243079-08 benchmark f T4 \N t \N \N \N {} -1582 519 2025-02-25 07:03:06.655132-08 2025-02-25 07:54:35.221308-08 benchmark t H100 \N t \N \N \N {} -1583 519 2025-02-25 08:20:50.0298-08 2025-02-25 08:34:10.546647-08 leaderboard t H100 0.0014114776666666667 t \N \N \N {} -1584 519 2025-02-25 08:01:59.762518-08 2025-02-25 08:40:03.53953-08 test f H100 \N t \N \N \N {} -1585 519 2025-02-25 06:48:35.700356-08 2025-02-25 07:22:01.937122-08 benchmark f H100 \N t \N \N \N {} -1586 519 2025-02-25 07:48:14.082024-08 2025-02-25 07:23:02.722501-08 leaderboard f H100 0.0014109543333333332 t \N \N \N {} -1587 520 2025-02-25 06:50:50.074767-08 2025-02-25 06:52:07.567587-08 test f A100 \N t \N \N \N {} -1588 521 2025-02-25 07:04:03.01671-08 2025-02-25 08:36:16.326265-08 benchmark f A100 \N t \N \N \N {} -1603 532 2025-02-25 08:01:48.392638-08 2025-02-25 07:55:14.350257-08 test t A100 \N t \N \N \N {} -1604 532 2025-02-25 08:04:01.52357-08 2025-02-25 08:48:53.380739-08 benchmark t A100 \N t \N \N \N {} -1605 532 2025-02-25 08:07:29.187473-08 2025-02-25 07:30:38.019107-08 leaderboard t A100 0.0030833873333333335 t \N \N \N {} -1606 533 2025-02-25 07:13:57.228578-08 2025-02-25 09:02:45.282064-08 test t H100 \N t \N \N \N {} -1607 533 2025-02-25 08:46:17.241842-08 2025-02-25 08:46:34.390308-08 benchmark t H100 \N t \N \N \N {} -1608 533 2025-02-25 07:15:43.847706-08 2025-02-25 07:17:56.713485-08 leaderboard t H100 0.0014808887368421052 t \N \N \N {} -1614 536 2025-02-25 07:40:05.156643-08 2025-02-25 08:50:38.71485-08 benchmark f H100 \N t \N \N \N {} -1615 537 2025-02-25 08:20:34.71729-08 2025-02-25 08:00:07.524271-08 test f A100 \N t \N \N \N {} -1626 539 2025-02-25 08:31:18.319762-08 2025-02-25 07:36:37.562316-08 benchmark t H100 \N t \N \N \N {} -1627 539 2025-02-25 07:45:22.622109-08 2025-02-25 07:26:16.545192-08 leaderboard t H100 0.0014020573333333333 t \N \N \N {} -1628 540 2025-02-25 08:39:12.579698-08 2025-02-25 08:21:54.313518-08 test f H100 \N t \N \N \N {} -1629 540 2025-02-25 09:06:18.861595-08 2025-02-25 09:06:08.76981-08 benchmark f H100 \N t \N \N \N {} -1630 540 2025-02-25 07:42:40.881925-08 2025-02-25 09:24:44.220023-08 leaderboard f H100 0.0014151046666666667 t \N \N \N {} -1631 540 2025-02-25 09:11:16.420021-08 2025-02-25 08:59:13.13714-08 test t H100 \N t \N \N \N {} -1683 557 2025-02-25 08:58:37.853947-08 2025-02-25 09:17:12.880606-08 test f A100 \N t \N \N \N {} -1642 543 2025-02-25 09:29:08.780743-08 2025-02-25 07:49:54.770844-08 benchmark t A100 \N t \N \N \N {} -1643 543 2025-02-25 08:08:43.291503-08 2025-02-25 07:45:38.670153-08 leaderboard t A100 0.003091805 t \N \N \N {} -1644 543 2025-02-25 08:16:39.851047-08 2025-02-25 09:04:53.563147-08 test f A100 \N t \N \N \N {} -1645 543 2025-02-25 08:33:33.890389-08 2025-02-25 09:25:02.242877-08 benchmark f A100 \N t \N \N \N {} -1647 544 2025-02-25 08:10:30.369306-08 2025-02-25 07:43:45.870981-08 test f A100 \N f \N \N \N {} -1648 544 2025-02-25 07:53:54.16523-08 2025-02-25 08:33:04.35208-08 test t A100 \N f \N \N \N {} -1649 544 2025-02-25 08:33:21.506269-08 2025-02-25 08:07:38.323301-08 test t T4 \N f \N \N \N {} -1650 544 2025-02-25 09:15:52.74522-08 2025-02-25 08:12:39.641754-08 test f T4 \N f \N \N \N {} -1651 545 2025-02-25 08:25:31.259943-08 2025-02-25 08:02:07.523474-08 test f A100 \N t \N \N \N {} -1976 679 2025-02-25 12:53:34.216219-08 2025-02-25 13:15:06.928515-08 test t T4 \N t \N \N \N {} -1710 571 2025-02-25 09:19:18.026341-08 2025-02-25 09:52:30.405634-08 test t T4 \N f \N \N \N {} -1711 571 2025-02-25 08:33:43.295723-08 2025-02-25 10:06:35.053909-08 test f T4 \N f \N \N \N {} -1726 579 2025-02-25 10:12:23.270121-08 2025-02-25 09:22:50.331776-08 test f A100 \N f \N \N \N {} -1734 585 2025-02-25 08:29:31.038561-08 2025-02-25 08:26:58.367928-08 test f A100 \N f \N \N \N {} -1735 583 2025-02-25 08:35:16.843418-08 2025-02-25 09:05:45.380553-08 test t H100 \N t \N \N \N {} -1736 583 2025-02-25 10:11:22.418499-08 2025-02-25 09:25:54.155768-08 benchmark t H100 \N t \N \N \N {} -1737 583 2025-02-25 08:51:06.601652-08 2025-02-25 09:10:23.575069-08 leaderboard t H100 0.0000772660625 t \N \N \N {} -2810 981 2025-02-26 23:47:22.315813-08 2025-02-26 23:04:15.576922-08 leaderboard f H100 0.00149895725 t \N \N \N {} -3817 1285 2025-03-01 03:36:02.152194-08 2025-03-01 03:19:46.959566-08 test f A100 \N t \N \N \N {} -4806 1487 2025-03-02 14:19:46.759466-08 2025-03-02 12:30:42.83975-08 test f T4 \N t \N \N \N {} -1784 613 2025-02-25 11:14:58.236656-08 2025-02-25 10:52:55.040195-08 test f A100 \N t \N \N \N {} -1785 614 2025-02-25 10:45:46.882457-08 2025-02-25 10:39:52.677212-08 benchmark f H100 \N t \N \N \N {} -2355 791 2025-02-26 08:55:33.742418-08 2025-02-26 07:47:49.701265-08 test f H100 \N f \N \N \N {} -1786 615 2025-02-25 10:56:29.193673-08 2025-02-25 09:37:14.897702-08 benchmark f H100 \N f \N \N \N {} -1787 616 2025-02-25 10:39:54.716283-08 2025-02-25 10:38:23.837561-08 test f A100 \N t \N \N \N {} -1788 616 2025-02-25 10:53:22.333552-08 2025-02-25 10:30:34.088241-08 benchmark f A100 \N t \N \N \N {} -1789 616 2025-02-25 11:11:25.986403-08 2025-02-25 10:31:45.03736-08 leaderboard f A100 0.000205736 t \N \N \N {} -1790 616 2025-02-25 09:41:53.090321-08 2025-02-25 10:12:39.131059-08 test t A100 \N t \N \N \N {} -1796 616 2025-02-25 09:49:54.963655-08 2025-02-25 11:04:32.101396-08 test t T4 \N t \N \N \N {} -2775 966 2025-02-26 20:12:59.070745-08 2025-02-26 19:39:08.158654-08 leaderboard f A100 0.014324531 t \N \N \N {} -1792 616 2025-02-25 10:01:38.707534-08 2025-02-25 11:11:50.587762-08 leaderboard t A100 0.00017068230769230768 t \N \N \N {} -1793 616 2025-02-25 10:03:00.704862-08 2025-02-25 09:45:03.774932-08 test f H100 \N t \N \N \N {} -1794 616 2025-02-25 10:53:30.74314-08 2025-02-25 11:05:56.056638-08 benchmark f H100 \N t \N \N \N {} -1795 616 2025-02-25 11:17:45.875715-08 2025-02-25 10:39:10.578077-08 leaderboard f H100 0.00012224712903225807 t \N \N \N {} -2356 791 2025-02-26 07:29:55.883929-08 2025-02-26 08:47:28.665781-08 test t H100 \N f \N \N \N {} -1798 616 2025-02-25 09:55:41.682206-08 2025-02-25 10:56:48.765016-08 leaderboard t T4 0.000549977 t \N \N \N {} -1799 616 2025-02-25 09:35:29.329871-08 2025-02-25 10:19:26.68501-08 test f T4 \N t \N \N \N {} -1800 616 2025-02-25 10:37:31.462732-08 2025-02-25 10:39:06.329009-08 benchmark f T4 \N t \N \N \N {} -1801 616 2025-02-25 10:49:46.717275-08 2025-02-25 09:34:27.810838-08 leaderboard f T4 0.0007055456666666667 t \N \N \N {} -2357 792 2025-02-26 09:14:17.578297-08 2025-02-26 08:39:13.532029-08 test t H100 \N f \N \N \N {} -1803 616 2025-02-25 10:02:17.879749-08 2025-02-25 11:05:44.711116-08 benchmark t H100 \N t \N \N \N {} -1804 616 2025-02-25 11:14:14.091418-08 2025-02-25 10:00:08.773034-08 leaderboard t H100 0.00012245449019607842 t \N \N \N {} -1805 616 2025-02-25 10:57:07.382054-08 2025-02-25 10:21:18.841312-08 test f L4 \N t \N \N \N {} -1806 616 2025-02-25 11:25:11.990406-08 2025-02-25 10:09:51.776687-08 benchmark f L4 \N t \N \N \N {} -2358 792 2025-02-26 08:54:16.016762-08 2025-02-26 08:39:38.92267-08 test f H100 \N f \N \N \N {} -1810 616 2025-02-25 11:00:52.381329-08 2025-02-25 11:23:28.247671-08 leaderboard t L4 0.00021845146153846154 t \N \N \N {} -1811 617 2025-02-25 10:55:46.819723-08 2025-02-25 10:55:23.275903-08 benchmark f H100 \N f \N \N \N {} -1812 618 2025-02-25 10:21:57.018191-08 2025-02-25 10:55:18.464595-08 benchmark f A100 \N t \N \N \N {} -2370 797 2025-02-26 10:53:12.943723-08 2025-02-26 09:33:24.539346-08 test f A100 \N f \N \N \N {} -1816 620 2025-02-25 10:05:33.623324-08 2025-02-25 10:33:30.515341-08 leaderboard t A100 0.00309566 t \N \N \N {} -1817 620 2025-02-25 11:21:18.896922-08 2025-02-25 10:03:28.563169-08 test f A100 \N t \N \N \N {} -1818 620 2025-02-25 11:29:14.645796-08 2025-02-25 09:47:07.54021-08 benchmark f A100 \N t \N \N \N {} -1819 620 2025-02-25 10:07:21.891596-08 2025-02-25 11:09:15.984274-08 leaderboard f A100 0.0030797906666666665 t \N \N \N {} -2415 817 2025-02-26 12:03:48.353023-08 2025-02-26 12:27:23.72571-08 benchmark f H100 \N f \N \N \N {} -1821 621 2025-02-25 09:54:58.090582-08 2025-02-25 11:30:23.907082-08 benchmark f A100 \N t \N \N \N {} -138 23 2025-02-23 11:54:58.067969-08 2025-02-23 12:08:04.435299-08 test f A100 \N t \N \N \N {} -147 23 2025-02-23 11:38:07.488367-08 2025-02-23 11:53:30.778579-08 test t T4 \N t \N \N \N {} -156 23 2025-02-23 10:30:22.436806-08 2025-02-23 10:14:59.268017-08 test f L4 \N t \N \N \N {} -1353 450 2025-02-25 02:01:19.511091-08 2025-02-25 01:50:23.554828-08 benchmark f H100 \N f \N \N \N {} -1822 621 2025-02-25 10:17:37.410428-08 2025-02-25 10:36:03.237286-08 leaderboard f A100 0.00011739522222222221 t \N \N \N {} -4809 1487 2025-03-02 13:45:28.37586-08 2025-03-02 12:40:42.351849-08 benchmark t L4 \N f \N \N \N {} -2226 725 2025-02-25 14:54:19.929968-08 2025-02-25 15:47:46.340338-08 benchmark f A100 \N t \N \N \N {} -2228 727 2025-02-25 20:35:49.008461-08 2025-02-25 21:27:48.607545-08 test f H100 \N f \N \N \N {} -2230 729 2025-02-25 21:17:44.501223-08 2025-02-25 20:34:01.289359-08 test f H100 \N f \N \N \N {} -2234 733 2025-02-25 21:21:37.259904-08 2025-02-25 22:02:04.25938-08 benchmark f H100 \N t \N \N \N {} -2235 734 2025-02-25 22:33:25.794507-08 2025-02-25 22:37:59.655817-08 test f H100 \N f \N \N \N {} -4810 1487 2025-03-02 14:06:07.69494-08 2025-03-02 14:05:29.275996-08 test f L4 \N t \N \N \N {} -2237 736 2025-02-25 23:19:21.988232-08 2025-02-25 23:45:39.636835-08 benchmark f A100 \N t \N \N \N {} -2238 737 2025-02-25 23:00:41.307289-08 2025-02-25 23:14:23.811188-08 benchmark f A100 \N t \N \N \N {} -2239 738 2025-02-25 23:48:44.965108-08 2025-02-25 23:42:33.619927-08 benchmark f A100 \N f \N \N \N {} -2240 739 2025-02-25 23:19:53.648595-08 2025-02-25 23:40:49.676196-08 benchmark f A100 \N f \N \N \N {} -2272 766 2025-02-26 04:44:38.752744-08 2025-02-26 03:37:57.159249-08 test f A100 \N t \N \N \N {} -2244 743 2025-02-26 00:35:45.969202-08 2025-02-26 00:32:30.806442-08 benchmark f A100 \N f \N \N \N {} -3587 1229 2025-02-28 14:32:10.95012-08 2025-02-28 14:44:22.261204-08 test f H100 \N t \N \N \N {} -2246 745 2025-02-26 00:37:33.931264-08 2025-02-26 00:40:26.964338-08 test f H100 \N f \N \N \N {} -2247 746 2025-02-26 01:55:19.252796-08 2025-02-26 00:36:07.797743-08 test f H100 \N f \N \N \N {} -2248 747 2025-02-26 01:15:55.844133-08 2025-02-26 01:53:31.828353-08 test f H100 \N f \N \N \N {} -2250 749 2025-02-26 00:25:17.403977-08 2025-02-26 00:24:56.829005-08 benchmark f H100 \N t \N \N \N {} -2256 755 2025-02-26 02:22:45.950588-08 2025-02-26 02:07:54.180517-08 benchmark f H100 \N f \N \N \N {} -2257 756 2025-02-26 03:00:14.310747-08 2025-02-26 02:46:46.937635-08 benchmark f H100 \N t \N \N \N {} -2258 757 2025-02-26 01:10:17.629744-08 2025-02-26 01:41:31.08645-08 benchmark f H100 \N t \N \N \N {} -2263 762 2025-02-26 02:18:34.242484-08 2025-02-26 02:55:39.021981-08 test t H100 \N t \N \N \N {} -2291 770 2025-02-26 04:07:08.486179-08 2025-02-26 03:34:41.389378-08 test f A100 \N t \N \N \N {} -2264 762 2025-02-26 02:58:11.327721-08 2025-02-26 03:46:33.348626-08 benchmark t H100 \N t \N \N \N {} -2266 762 2025-02-26 02:37:45.731387-08 2025-02-26 03:44:38.02645-08 test f H100 \N t \N \N \N {} -2267 762 2025-02-26 03:39:25.066238-08 2025-02-26 02:14:19.098169-08 benchmark f H100 \N t \N \N \N {} -2268 762 2025-02-26 02:23:03.127111-08 2025-02-26 02:36:35.552634-08 leaderboard f H100 0.0014381206666666667 t \N \N \N {} -2269 763 2025-02-26 02:45:57.978469-08 2025-02-26 02:49:10.932866-08 benchmark f H100 \N f \N \N \N {} -2270 765 2025-02-26 04:02:02.067979-08 2025-02-26 03:15:12.820422-08 test f A100 \N f \N \N \N {} -2271 765 2025-02-26 03:46:07.247621-08 2025-02-26 03:33:23.013529-08 test t A100 \N f \N \N \N {} -2273 766 2025-02-26 04:26:50.535168-08 2025-02-26 04:37:20.190686-08 benchmark f A100 \N t \N \N \N {} -2276 766 2025-02-26 03:57:01.450513-08 2025-02-26 04:19:48.460438-08 benchmark t A100 \N t \N \N \N {} -2277 766 2025-02-26 04:23:34.32179-08 2025-02-26 04:37:21.779651-08 leaderboard t A100 0.0008035725 t \N \N \N {} -2278 767 2025-02-26 04:59:10.716734-08 2025-02-26 04:56:31.615231-08 test f A100 \N t \N \N \N {} -2279 767 2025-02-26 04:04:53.620256-08 2025-02-26 03:40:43.285038-08 benchmark f A100 \N t \N \N \N {} -2811 981 2025-02-26 23:20:55.588139-08 2025-02-26 23:02:56.11656-08 test t H100 \N t \N \N \N {} -3820 1285 2025-03-01 02:46:38.245389-08 2025-03-01 02:36:19.519197-08 test t A100 \N t \N \N \N {} -2283 767 2025-02-26 03:48:47.510918-08 2025-02-26 04:12:24.590357-08 leaderboard t A100 0.0008112885 t \N \N \N {} -2285 769 2025-02-26 03:32:55.537334-08 2025-02-26 04:57:18.17064-08 test t A100 \N t \N \N \N {} -2286 769 2025-02-26 03:54:10.03714-08 2025-02-26 03:53:44.323045-08 benchmark t A100 \N t \N \N \N {} -2287 769 2025-02-26 04:13:17.84753-08 2025-02-26 04:35:13.277401-08 leaderboard t A100 0.0008150356 t \N \N \N {} -2288 769 2025-02-26 03:53:13.370089-08 2025-02-26 03:34:52.714707-08 test f A100 \N t \N \N \N {} -2289 769 2025-02-26 04:13:58.330006-08 2025-02-26 03:53:06.926282-08 benchmark f A100 \N t \N \N \N {} -2290 769 2025-02-26 04:11:02.675153-08 2025-02-26 04:13:02.665399-08 leaderboard f A100 0.0008101136666666666 t \N \N \N {} -2292 770 2025-02-26 03:47:42.434385-08 2025-02-26 03:50:45.275328-08 benchmark f A100 \N t \N \N \N {} -2293 770 2025-02-26 03:53:00.59453-08 2025-02-26 04:00:41.911017-08 leaderboard f A100 0.0008105805 t \N \N \N {} -2294 770 2025-02-26 04:01:53.351812-08 2025-02-26 03:50:07.775814-08 test t A100 \N t \N \N \N {} -2295 770 2025-02-26 03:11:56.96532-08 2025-02-26 04:01:17.104616-08 benchmark t A100 \N t \N \N \N {} -2296 770 2025-02-26 03:30:49.241367-08 2025-02-26 04:03:05.950494-08 leaderboard t A100 0.00080491525 t \N \N \N {} -2297 771 2025-02-26 04:33:09.624984-08 2025-02-26 04:02:12.069329-08 benchmark f A100 \N t \N \N \N {} -2298 772 2025-02-26 04:59:38.93896-08 2025-02-26 04:38:25.02308-08 benchmark f A100 \N t \N \N \N {} -2299 773 2025-02-26 04:20:34.328694-08 2025-02-26 03:54:56.809911-08 benchmark f A100 \N t \N \N \N {} -2304 774 2025-02-26 03:57:56.984318-08 2025-02-26 04:38:02.103282-08 benchmark t A100 \N t \N \N \N {} -2305 774 2025-02-26 03:44:38.635658-08 2025-02-26 05:02:38.874989-08 leaderboard t A100 0.00311144 t \N \N \N {} -2306 775 2025-02-26 04:12:31.181055-08 2025-02-26 04:59:52.882918-08 benchmark f A100 \N t \N \N \N {} -2307 776 2025-02-26 04:20:56.019237-08 2025-02-26 03:52:11.190759-08 benchmark f A100 \N t \N \N \N {} -2329 784 2025-02-26 07:42:05.262849-08 2025-02-26 07:36:19.042812-08 test f A100 \N f \N \N \N {} -2311 778 2025-02-26 03:30:09.435566-08 2025-02-26 04:06:02.956045-08 leaderboard t A100 0.003092609 t \N \N \N {} -2312 778 2025-02-26 05:23:38.077187-08 2025-02-26 03:53:55.424873-08 test f A100 \N t \N \N \N {} -2326 783 2025-02-26 08:05:37.792029-08 2025-02-26 08:12:28.335942-08 test f A100 \N t \N \N \N {} -2327 783 2025-02-26 08:39:42.131724-08 2025-02-26 08:09:32.991225-08 benchmark f A100 \N t \N \N \N {} -2328 783 2025-02-26 08:30:37.407938-08 2025-02-26 07:35:49.357033-08 leaderboard f A100 0.003227151 t \N \N \N {} -2331 785 2025-02-26 07:27:47.482468-08 2025-02-26 08:17:53.94964-08 test f A100 \N t \N \N \N {} -2332 785 2025-02-26 07:37:11.087311-08 2025-02-26 08:15:07.866294-08 benchmark f A100 \N t \N \N \N {} -2333 785 2025-02-26 07:59:24.368247-08 2025-02-26 08:20:21.480809-08 leaderboard f A100 0.004367405666666667 t \N \N \N {} -2337 786 2025-02-26 08:22:04.365539-08 2025-02-26 08:24:47.375813-08 test f A100 \N f \N \N \N {} -2338 786 2025-02-26 08:35:45.345849-08 2025-02-26 08:14:30.033798-08 test t A100 \N f \N \N \N {} -2339 787 2025-02-26 07:22:18.968697-08 2025-02-26 08:22:46.178651-08 test t A100 \N f \N \N \N {} -2346 789 2025-02-26 07:08:50.231065-08 2025-02-26 08:49:16.966065-08 test f A100 \N t \N \N \N {} -3844 1289 2025-03-01 02:58:23.301247-08 2025-03-01 03:47:22.156604-08 test t A100 \N t \N \N \N {} -2344 789 2025-02-26 08:30:43.202837-08 2025-02-26 08:53:02.953547-08 benchmark t A100 \N t \N \N \N {} -2345 789 2025-02-26 08:54:31.507554-08 2025-02-26 08:18:29.611102-08 leaderboard t A100 0.00322034475 t \N \N \N {} -2349 790 2025-02-26 07:27:15.296498-08 2025-02-26 07:45:31.770943-08 test t H100 \N t \N \N \N {} -2350 790 2025-02-26 07:27:09.22196-08 2025-02-26 08:40:58.669769-08 benchmark t H100 \N t \N \N \N {} -2351 790 2025-02-26 07:38:50.506814-08 2025-02-26 07:39:57.059049-08 leaderboard t H100 0.0015428555 t \N \N \N {} -2352 790 2025-02-26 07:14:10.99536-08 2025-02-26 08:11:26.301088-08 test f H100 \N t \N \N \N {} -2353 790 2025-02-26 07:55:02.506428-08 2025-02-26 08:30:47.285542-08 benchmark f H100 \N t \N \N \N {} -2354 790 2025-02-26 08:58:21.825582-08 2025-02-26 08:36:23.311282-08 leaderboard f H100 0.00153994575 t \N \N \N {} -2362 794 2025-02-26 09:16:23.731727-08 2025-02-26 09:45:35.701403-08 test f A100 \N f \N \N \N {} -2363 794 2025-02-26 10:05:30.642869-08 2025-02-26 10:06:11.654779-08 test f H100 \N f \N \N \N {} -2364 794 2025-02-26 08:54:28.852181-08 2025-02-26 10:36:05.947891-08 test f L4 \N f \N \N \N {} -2369 796 2025-02-26 09:04:59.633587-08 2025-02-26 08:58:58.997794-08 test f H100 \N f \N \N \N {} -3269 1143 2025-02-28 05:49:04.471481-08 2025-02-28 05:33:52.950669-08 test t A100 \N t \N \N \N {} -3272 1143 2025-02-28 07:24:05.395179-08 2025-02-28 06:21:30.549804-08 test f A100 \N t \N \N \N {} -2389 806 2025-02-26 11:59:47.526877-08 2025-02-26 13:27:08.118395-08 benchmark f H100 \N t \N \N \N {} -131 21 2025-02-23 10:54:12.413903-08 2025-02-23 11:03:24.456859-08 benchmark f H100 \N t \N \N \N {} -132 22 2025-02-23 11:18:52.406962-08 2025-02-23 11:21:40.34417-08 test f H100 \N t \N \N \N {} -133 22 2025-02-23 11:10:07.454357-08 2025-02-23 11:27:41.266157-08 benchmark f H100 \N t \N \N \N {} -134 22 2025-02-23 11:11:08.332204-08 2025-02-23 09:56:47.514659-08 leaderboard f H100 0.007636646 t \N \N \N {} -135 22 2025-02-23 11:15:29.967609-08 2025-02-23 10:36:22.679826-08 test t H100 \N t \N \N \N {} -136 22 2025-02-23 10:30:49.187464-08 2025-02-23 10:54:58.140967-08 benchmark t H100 \N t \N \N \N {} -137 22 2025-02-23 10:01:17.414414-08 2025-02-23 10:57:17.950859-08 leaderboard t H100 0.007676687 t \N \N \N {} -6139 1882 2025-03-11 10:43:46.751248-07 2025-03-11 11:39:34.207773-07 test f T4 \N f \N \N \N {} -139 23 2025-02-23 11:00:36.681319-08 2025-02-23 11:20:38.884145-08 benchmark f A100 \N t \N \N \N {} -140 23 2025-02-23 11:47:46.303484-08 2025-02-23 10:27:15.204326-08 leaderboard f A100 0.010159300357142857 t \N \N \N {} -141 23 2025-02-23 11:55:55.792415-08 2025-02-23 11:05:18.699548-08 test t A100 \N t \N \N \N {} -142 23 2025-02-23 10:45:14.3074-08 2025-02-23 10:59:33.198898-08 benchmark t A100 \N t \N \N \N {} -143 23 2025-02-23 11:17:34.462052-08 2025-02-23 10:26:24.612757-08 leaderboard t A100 0.010135129449999999 t \N \N \N {} -144 23 2025-02-23 10:09:53.746-08 2025-02-23 11:09:01.710035-08 test f H100 \N t \N \N \N {} -145 23 2025-02-23 11:23:51.944416-08 2025-02-23 11:35:42.525714-08 benchmark f H100 \N t \N \N \N {} -146 23 2025-02-23 11:15:27.676586-08 2025-02-23 12:03:10.988718-08 leaderboard f H100 0.006125231666666667 t \N \N \N {} -148 23 2025-02-23 11:13:16.041704-08 2025-02-23 11:50:47.350172-08 benchmark t T4 \N t \N \N \N {} -149 23 2025-02-23 11:28:20.433165-08 2025-02-23 10:28:06.298908-08 leaderboard t T4 0.048027397 t \N \N \N {} -150 23 2025-02-23 11:18:42.410803-08 2025-02-23 10:31:01.372052-08 test t H100 \N t \N \N \N {} -151 23 2025-02-23 11:58:30.811996-08 2025-02-23 11:21:35.60515-08 benchmark t H100 \N t \N \N \N {} -152 23 2025-02-23 11:11:00.271834-08 2025-02-23 11:00:53.563642-08 leaderboard t H100 0.006109236666666667 t \N \N \N {} -153 23 2025-02-23 10:26:41.850092-08 2025-02-23 10:39:34.799916-08 test f T4 \N t \N \N \N {} -2390 807 2025-02-26 12:40:07.882315-08 2025-02-26 13:29:00.67918-08 benchmark f A100 \N t \N \N \N {} -2391 808 2025-02-26 12:44:03.203064-08 2025-02-26 12:20:13.654261-08 benchmark f H100 \N t \N \N \N {} -2392 809 2025-02-26 11:46:24.444642-08 2025-02-26 13:37:16.547993-08 benchmark f H100 \N t \N \N \N {} -2393 811 2025-02-26 11:51:23.557392-08 2025-02-26 12:37:24.914401-08 benchmark f H100 \N f \N \N \N {} -4811 1487 2025-03-02 13:46:45.410776-08 2025-03-02 13:08:51.056877-08 benchmark f L4 \N f \N \N \N {} -3847 1290 2025-03-01 04:03:32.270689-08 2025-03-01 02:32:19.138733-08 test f A100 \N t \N \N \N {} -2394 810 2025-02-26 13:25:37.482593-08 2025-02-26 12:59:08.470332-08 benchmark f H100 \N f \N \N \N {} -2395 812 2025-02-26 13:33:28.597174-08 2025-02-26 12:57:21.570896-08 test f H100 \N t \N \N \N {} -2396 812 2025-02-26 12:34:20.532251-08 2025-02-26 12:31:27.053945-08 benchmark f H100 \N t \N \N \N {} -2397 812 2025-02-26 12:19:54.717591-08 2025-02-26 12:10:14.47041-08 leaderboard f H100 0.0032161647200000002 t \N \N \N {} -2398 812 2025-02-26 13:18:48.216215-08 2025-02-26 11:52:47.39111-08 test f A100 \N t \N \N \N {} -2401 812 2025-02-26 12:08:17.632371-08 2025-02-26 12:08:36.634538-08 test t A100 \N t \N \N \N {} -2402 812 2025-02-26 13:29:27.61967-08 2025-02-26 13:25:55.587367-08 benchmark t A100 \N t \N \N \N {} -2403 812 2025-02-26 13:21:36.142662-08 2025-02-26 12:50:20.677716-08 leaderboard t A100 0.00698412748 t \N \N \N {} -2404 812 2025-02-26 13:13:30.239955-08 2025-02-26 12:39:13.840154-08 test f L4 \N f \N \N \N {} -2405 812 2025-02-26 12:25:57.063676-08 2025-02-26 12:24:58.010138-08 test t H100 \N t \N \N \N {} -2410 812 2025-02-26 12:20:36.785307-08 2025-02-26 12:48:06.635821-08 test f T4 \N f \N \N \N {} -2435 839 2025-02-26 14:18:21.87914-08 2025-02-26 14:18:15.815334-08 benchmark t H100 \N t \N \N \N {} -2436 839 2025-02-26 14:10:30.590633-08 2025-02-26 14:11:00.415518-08 leaderboard t H100 0.0014024583333333332 t \N \N \N {} -2441 845 2025-02-26 14:15:04.422652-08 2025-02-26 14:13:19.625716-08 test t L4 \N t \N \N \N {} -2447 852 2025-02-26 14:16:39.200008-08 2025-02-26 13:25:34.941047-08 benchmark f A100 \N t \N \N \N {} -2442 845 2025-02-26 13:50:40.358584-08 2025-02-26 13:07:51.38911-08 benchmark t L4 \N t \N \N \N {} -2451 856 2025-02-26 12:43:06.254876-08 2025-02-26 13:32:18.217747-08 benchmark f A100 \N t \N \N \N {} -2452 858 2025-02-26 13:00:26.844507-08 2025-02-26 14:38:31.646993-08 test f T4 \N f \N \N \N {} -2453 859 2025-02-26 14:36:40.012139-08 2025-02-26 13:10:56.467322-08 benchmark f H100 \N t \N \N \N {} -2455 860 2025-02-26 13:23:16.833722-08 2025-02-26 14:41:34.259251-08 benchmark f A100 \N t \N \N \N {} -2462 864 2025-02-26 13:11:46.57133-08 2025-02-26 14:39:28.851851-08 test f T4 \N f \N \N \N {} -2463 865 2025-02-26 13:11:01.636695-08 2025-02-26 14:32:14.927023-08 test f A100 \N t \N \N \N {} -2464 865 2025-02-26 13:30:02.44975-08 2025-02-26 14:19:35.875255-08 benchmark f A100 \N t \N \N \N {} -2465 865 2025-02-26 13:09:06.850344-08 2025-02-26 14:04:10.639691-08 leaderboard f A100 0.0068088155999999995 t \N \N \N {} -2466 865 2025-02-26 14:08:58.695846-08 2025-02-26 12:57:46.38922-08 test t A100 \N t \N \N \N {} -3385 1166 2025-02-28 10:20:27.28906-08 2025-02-28 10:10:11.161724-08 leaderboard t L4 0.017129881333333333 t \N \N \N {} -2481 870 2025-02-26 13:59:09.266137-08 2025-02-26 13:07:02.894219-08 leaderboard f H100 0.001470785 t \N \N \N {} -2820 983 2025-02-26 23:04:13.026351-08 2025-02-26 23:16:23.959588-08 test f T4 \N t \N \N \N {} -2508 872 2025-02-26 13:21:09.133466-08 2025-02-26 13:53:51.09567-08 benchmark f A100 \N t \N \N \N {} -2509 872 2025-02-26 14:26:03.179438-08 2025-02-26 13:48:36.711846-08 leaderboard f A100 0.003223094 t \N \N \N {} -3286 1147 2025-02-28 07:23:54.538307-08 2025-02-28 06:54:57.696014-08 benchmark f L4 \N t \N \N \N {} -2511 872 2025-02-26 13:29:54.390732-08 2025-02-26 14:56:13.502248-08 benchmark f H100 \N t \N \N \N {} -2512 872 2025-02-26 14:00:24.384-08 2025-02-26 14:52:47.576219-08 leaderboard f H100 0.0014829895714285714 t \N \N \N {} -2513 872 2025-02-26 14:08:21.032655-08 2025-02-26 14:48:45.337774-08 test f T4 \N t \N \N \N {} -2514 872 2025-02-26 13:43:03.545363-08 2025-02-26 13:27:16.423676-08 benchmark f T4 \N t \N \N \N {} -2515 872 2025-02-26 14:53:04.043695-08 2025-02-26 14:31:15.017571-08 leaderboard f T4 0.016977648666666668 t \N \N \N {} -3297 1148 2025-02-28 05:56:14.732913-08 2025-02-28 06:35:38.237057-08 benchmark t T4 \N t \N \N \N {} -2516 872 2025-02-26 13:59:35.326154-08 2025-02-26 14:28:54.006791-08 test t T4 \N t \N \N \N {} -2517 872 2025-02-26 14:10:22.48064-08 2025-02-26 13:05:44.349918-08 benchmark t T4 \N t \N \N \N {} -2518 872 2025-02-26 13:38:47.072409-08 2025-02-26 13:18:53.541371-08 leaderboard t T4 0.016951106 t \N \N \N {} -2519 872 2025-02-26 14:41:30.759683-08 2025-02-26 13:51:40.998977-08 test f L4 \N t \N \N \N {} -2520 872 2025-02-26 13:15:09.29921-08 2025-02-26 13:07:54.264512-08 benchmark f L4 \N t \N \N \N {} -3181 1120 2025-02-28 02:42:01.210008-08 2025-02-28 02:43:08.325982-08 test t A100 \N t \N \N \N {} -154 23 2025-02-23 11:57:05.139267-08 2025-02-23 11:09:15.68083-08 benchmark f T4 \N t \N \N \N {} -180 27 2025-02-23 10:28:14.91215-08 2025-02-23 11:48:21.706378-08 test t T4 \N t \N \N \N {} -2521 872 2025-02-26 14:43:07.037109-08 2025-02-26 14:45:02.826917-08 leaderboard f L4 0.017294490333333332 t \N \N \N {} -2522 872 2025-02-26 14:50:44.169022-08 2025-02-26 14:42:28.113826-08 test t L4 \N t \N \N \N {} -2523 872 2025-02-26 13:56:52.894596-08 2025-02-26 13:28:48.739095-08 benchmark t L4 \N t \N \N \N {} -2527 875 2025-02-26 14:23:38.227767-08 2025-02-26 13:30:18.365385-08 test t L4 \N t \N \N \N {} -3589 1229 2025-02-28 13:21:19.11789-08 2025-02-28 14:31:58.395368-08 leaderboard f H100 0.0011892495 t \N \N \N {} -2532 875 2025-02-26 14:59:13.072405-08 2025-02-26 13:27:06.641704-08 leaderboard f H100 0.001465262 t \N \N \N {} -2533 875 2025-02-26 14:47:12.530795-08 2025-02-26 13:13:21.249202-08 test f A100 \N t \N \N \N {} -2534 875 2025-02-26 13:48:48.551741-08 2025-02-26 13:28:31.146854-08 benchmark f A100 \N t \N \N \N {} -2535 875 2025-02-26 14:39:27.031045-08 2025-02-26 13:25:27.404117-08 leaderboard f A100 0.0032578983333333335 t \N \N \N {} -3312 1150 2025-02-28 07:23:23.622707-08 2025-02-28 06:09:54.030373-08 benchmark f H100 \N t \N \N \N {} -2537 875 2025-02-26 14:32:13.549304-08 2025-02-26 14:39:07.302029-08 benchmark t A100 \N t \N \N \N {} -2548 875 2025-02-26 14:56:04.001163-08 2025-02-26 13:42:23.904365-08 test f L4 \N t \N \N \N {} -2549 875 2025-02-26 14:18:16.659538-08 2025-02-26 13:23:18.958744-08 benchmark f L4 \N t \N \N \N {} -2550 875 2025-02-26 14:22:09.462839-08 2025-02-26 14:54:20.343262-08 leaderboard f L4 0.01742031 t \N \N \N {} -2551 877 2025-02-26 13:57:57.39587-08 2025-02-26 13:12:36.270032-08 test f L4 \N t \N \N \N {} -2573 877 2025-02-26 13:16:45.52631-08 2025-02-26 14:52:12.270412-08 test t T4 \N t \N \N \N {} -3399 1172 2025-02-28 10:30:05.610744-08 2025-02-28 09:16:34.458793-08 test f L4 \N f \N \N \N {} -2553 877 2025-02-26 13:10:29.354581-08 2025-02-26 14:37:38.257685-08 leaderboard f L4 0.01741081066666667 t \N \N \N {} -2554 877 2025-02-26 13:58:10.825604-08 2025-02-26 14:00:37.261867-08 test t L4 \N t \N \N \N {} -2555 877 2025-02-26 13:48:17.730063-08 2025-02-26 14:50:07.298178-08 benchmark t L4 \N t \N \N \N {} -2556 877 2025-02-26 13:50:16.824546-08 2025-02-26 14:44:02.236307-08 leaderboard t L4 0.017350969 t \N \N \N {} -3353 1157 2025-02-28 07:00:17.816299-08 2025-02-28 08:05:40.861-08 test t A100 \N t \N \N \N {} -2559 877 2025-02-26 14:12:21.70114-08 2025-02-26 14:49:53.732127-08 benchmark f A100 \N t \N \N \N {} -2560 877 2025-02-26 13:13:01.61476-08 2025-02-26 14:52:53.675129-08 leaderboard f A100 0.00324053 t \N \N \N {} -2561 877 2025-02-26 14:17:12.652893-08 2025-02-26 14:33:51.585787-08 test f H100 \N t \N \N \N {} -3356 1157 2025-02-28 07:54:22.893197-08 2025-02-28 07:34:43.645784-08 test f A100 \N t \N \N \N {} -2563 877 2025-02-26 13:56:06.528311-08 2025-02-26 15:06:14.803986-08 leaderboard f H100 0.001457541 t \N \N \N {} -2564 877 2025-02-26 14:47:01.068968-08 2025-02-26 14:04:16.625491-08 test t H100 \N t \N \N \N {} -2565 877 2025-02-26 13:58:40.178869-08 2025-02-26 15:00:37.290202-08 benchmark t H100 \N t \N \N \N {} -2566 877 2025-02-26 14:22:11.994956-08 2025-02-26 14:57:41.541904-08 leaderboard t H100 0.00144549375 t \N \N \N {} -3359 1158 2025-02-28 06:54:49.380912-08 2025-02-28 07:18:43.60823-08 test f T4 \N f \N \N \N {} -3859 1292 2025-03-01 03:27:40.255676-08 2025-03-01 02:36:03.541477-08 test f A100 \N t \N \N \N {} -2568 877 2025-02-26 14:12:07.205591-08 2025-02-26 13:51:47.845088-08 benchmark t A100 \N t \N \N \N {} -2569 877 2025-02-26 13:57:24.028157-08 2025-02-26 14:47:07.033256-08 leaderboard t A100 0.0032292653333333334 t \N \N \N {} -2570 877 2025-02-26 14:16:41.930867-08 2025-02-26 13:09:12.487972-08 test f T4 \N t \N \N \N {} -2571 877 2025-02-26 14:36:17.713292-08 2025-02-26 14:26:39.900745-08 benchmark f T4 \N t \N \N \N {} -2572 877 2025-02-26 15:02:18.460758-08 2025-02-26 14:58:57.369637-08 leaderboard f T4 0.01811966666666667 t \N \N \N {} -3406 1174 2025-02-28 09:14:03.047294-08 2025-02-28 09:16:54.531135-08 leaderboard t L4 0.017114292333333333 t \N \N \N {} -2575 877 2025-02-26 13:12:49.74724-08 2025-02-26 14:37:18.913817-08 leaderboard t T4 0.01813283333333333 t \N \N \N {} -2576 878 2025-02-26 13:16:27.296503-08 2025-02-26 14:27:29.207894-08 benchmark f T4 \N f \N \N \N {} -2577 879 2025-02-26 14:34:25.671098-08 2025-02-26 13:45:30.958405-08 benchmark f T4 \N t \N \N \N {} -2578 881 2025-02-26 13:39:45.596644-08 2025-02-26 14:01:02.933021-08 test f T4 \N f \N \N \N {} -2579 881 2025-02-26 14:04:26.768148-08 2025-02-26 13:45:26.952914-08 test f L4 \N f \N \N \N {} -3408 1175 2025-02-28 10:29:09.88226-08 2025-02-28 09:14:06.803313-08 test f L4 \N f \N \N \N {} -3862 1292 2025-03-01 03:25:13.486827-08 2025-03-01 02:53:16.099437-08 test t A100 \N t \N \N \N {} -2581 882 2025-02-26 13:33:10.645023-08 2025-02-26 13:55:42.912415-08 test f L4 \N t \N \N \N {} -2582 883 2025-02-26 14:19:49.686171-08 2025-02-26 14:38:27.107218-08 benchmark f L4 \N t \N \N \N {} -2583 883 2025-02-26 13:51:19.440545-08 2025-02-26 14:09:42.760292-08 benchmark f H100 \N t \N \N \N {} -2584 883 2025-02-26 14:39:09.643525-08 2025-02-26 13:31:21.985198-08 benchmark f A100 \N t \N \N \N {} -3418 1178 2025-02-28 10:35:09.967262-08 2025-02-28 11:06:54.18143-08 test f L4 \N f \N \N \N {} -3865 1293 2025-03-01 02:35:22.489765-08 2025-03-01 04:14:08.608387-08 test f A100 \N t \N \N \N {} -2587 884 2025-02-26 13:22:21.621679-08 2025-02-26 13:44:27.093032-08 benchmark f A100 \N t \N \N \N {} -2588 884 2025-02-26 14:52:31.919133-08 2025-02-26 13:38:05.904058-08 leaderboard f A100 0.003262141 t \N \N \N {} -2589 885 2025-02-26 13:31:31.720937-08 2025-02-26 13:36:09.135751-08 test f H100 \N t \N \N \N {} -2607 884 2025-02-26 15:03:02.758478-08 2025-02-26 14:21:28.592486-08 test t T4 \N t \N \N \N {} -3419 1178 2025-02-28 09:14:52.41289-08 2025-02-28 09:52:57.968186-08 test t L4 \N f \N \N \N {} -2592 884 2025-02-26 14:47:15.785668-08 2025-02-26 14:38:49.447224-08 test f L4 \N t \N \N \N {} -2062 700 2025-02-25 13:08:14.95001-08 2025-02-25 15:03:46.466768-08 test f A100 \N f \N \N \N {} -4223 1394 2025-03-01 14:57:35.411891-08 2025-03-01 15:35:59.507097-08 benchmark t A100 \N t \N \N \N {} -4247 1402 2025-03-01 17:04:55.972494-08 2025-03-01 17:47:24.376431-08 test t H100 \N t \N \N \N {} -4228 1395 2025-03-01 17:18:07.852776-08 2025-03-01 15:48:31.55626-08 benchmark f A100 \N t \N \N \N {} -4229 1396 2025-03-01 16:29:41.366025-08 2025-03-01 17:24:30.840608-08 test f H100 \N t \N \N \N {} -4717 1477 2025-03-02 08:12:18.55485-08 2025-03-02 06:22:18.597758-08 test f A100 \N t \N \N \N {} -4231 1398 2025-03-01 17:25:09.232586-08 2025-03-01 17:18:31.325671-08 test t A100 \N t \N \N \N {} -4232 1398 2025-03-01 16:28:07.719269-08 2025-03-01 15:58:31.010676-08 benchmark t A100 \N t \N \N \N {} -4233 1398 2025-03-01 15:43:44.691938-08 2025-03-01 17:19:39.809228-08 leaderboard t A100 0.0031178923333333333 t \N \N \N {} -4234 1398 2025-03-01 16:39:23.354655-08 2025-03-01 15:51:38.701412-08 test f H100 \N t \N \N \N {} -4253 1403 2025-03-01 17:26:34.106395-08 2025-03-01 17:42:47.155658-08 test t H100 \N t \N \N \N {} -4236 1398 2025-03-01 16:06:23.791952-08 2025-03-01 17:26:44.856408-08 leaderboard f H100 0.0014146966666666668 t \N \N \N {} -4237 1398 2025-03-01 15:36:12.31163-08 2025-03-01 15:33:41.234566-08 test t H100 \N t \N \N \N {} -4238 1398 2025-03-01 16:56:17.454711-08 2025-03-01 17:19:38.557978-08 benchmark t H100 \N t \N \N \N {} -4239 1398 2025-03-01 16:47:15.049127-08 2025-03-01 16:11:47.093143-08 leaderboard t H100 0.0014016626666666667 t \N \N \N {} -4378 1432 2025-03-01 22:51:39.742514-08 2025-03-01 22:23:37.282462-08 benchmark f T4 \N t \N \N \N {} -4242 1399 2025-03-01 16:22:07.45004-08 2025-03-01 16:01:06.543886-08 benchmark f A100 \N f \N \N \N {} -4243 1400 2025-03-01 17:07:07.130708-08 2025-03-01 16:24:06.325488-08 benchmark f H100 \N t \N \N \N {} -4244 1400 2025-03-01 17:29:39.977791-08 2025-03-01 17:02:08.939787-08 benchmark f A100 \N f \N \N \N {} -4346 1425 2025-03-01 18:21:27.980051-08 2025-03-01 19:51:56.286091-08 benchmark t L4 \N t \N \N \N {} -4347 1425 2025-03-01 18:37:42.52065-08 2025-03-01 19:22:48.263204-08 leaderboard t L4 0.01815886933333333 t \N \N \N {} -4348 1425 2025-03-01 18:57:46.285303-08 2025-03-01 20:03:56.603557-08 test f A100 \N t \N \N \N {} -4349 1425 2025-03-01 19:34:06.836256-08 2025-03-01 19:17:45.64278-08 benchmark f A100 \N t \N \N \N {} -4350 1425 2025-03-01 19:38:27.662128-08 2025-03-01 18:55:37.052703-08 leaderboard f A100 0.0033818667999999997 t \N \N \N {} -4352 1425 2025-03-01 18:49:23.803275-08 2025-03-01 19:23:11.101795-08 benchmark t A100 \N t \N \N \N {} -4357 1425 2025-03-01 19:41:41.606451-08 2025-03-01 18:23:09.857404-08 test t H100 \N t \N \N \N {} -4358 1425 2025-03-01 20:09:08.19207-08 2025-03-01 19:19:21.809996-08 benchmark t H100 \N t \N \N \N {} -4359 1425 2025-03-01 19:59:58.903988-08 2025-03-01 19:30:42.092627-08 leaderboard t H100 0.0015028730833333333 t \N \N \N {} -4361 1425 2025-03-01 18:31:13.881994-08 2025-03-01 18:26:52.74436-08 benchmark f T4 \N t \N \N \N {} -4362 1425 2025-03-01 20:12:38.573176-08 2025-03-01 19:33:01.456733-08 leaderboard f T4 0.01936384925 t \N \N \N {} -4363 1425 2025-03-01 19:42:14.310793-08 2025-03-01 18:41:20.723626-08 test f L4 \N t \N \N \N {} -4389 1440 2025-03-02 00:03:48.502169-08 2025-03-01 23:51:41.439127-08 test f A100 \N f \N \N \N {} -4390 1440 2025-03-02 00:04:14.366803-08 2025-03-01 22:49:12.463788-08 test f L4 \N f \N \N \N {} -4391 1440 2025-03-02 00:19:06.804988-08 2025-03-01 22:28:37.519544-08 test t T4 \N f \N \N \N {} -155 23 2025-02-23 11:57:27.550237-08 2025-02-23 11:56:29.734882-08 leaderboard f T4 0.048176454 t \N \N \N {} -157 23 2025-02-23 11:12:21.468647-08 2025-02-23 10:43:21.975416-08 benchmark f L4 \N t \N \N \N {} -4392 1440 2025-03-01 22:48:12.01349-08 2025-03-01 23:58:44.155125-08 test f T4 \N f \N \N \N {} -4877 1500 2025-03-03 10:04:01.91414-08 2025-03-03 08:30:53.107105-08 test f A100 \N t \N \N \N {} -4394 1440 2025-03-01 22:21:22.693848-08 2025-03-01 22:51:41.663873-08 test t H100 \N f \N \N \N {} -4395 1441 2025-03-01 23:58:46.89894-08 2025-03-01 23:53:44.591271-08 test f H100 \N f \N \N \N {} -4396 1441 2025-03-01 22:28:23.815345-08 2025-03-01 23:22:03.532582-08 test t L4 \N f \N \N \N {} -4397 1441 2025-03-01 23:53:44.616031-08 2025-03-01 23:38:33.596759-08 test t A100 \N f \N \N \N {} -4884 1505 2025-03-03 12:56:34.606368-08 2025-03-03 12:53:18.783025-08 test t A100 \N f \N \N \N {} -4399 1441 2025-03-01 23:03:04.456378-08 2025-03-02 00:06:04.885856-08 test f T4 \N f \N \N \N {} -4400 1441 2025-03-01 23:24:40.888023-08 2025-03-01 22:59:35.615921-08 test t H100 \N f \N \N \N {} -4401 1441 2025-03-02 00:02:25.303815-08 2025-03-01 22:57:40.957396-08 test t T4 \N f \N \N \N {} -4402 1441 2025-03-01 22:31:27.972284-08 2025-03-02 00:14:26.267738-08 test f L4 \N f \N \N \N {} -5107 1565 2025-03-04 18:00:00.576218-08 2025-03-04 18:32:07.019701-08 test f A100 \N t \N \N \N {} -4411 1443 2025-03-01 23:33:39.745676-08 2025-03-02 00:11:02.528027-08 test f L4 \N t \N \N \N {} -4412 1443 2025-03-02 01:02:06.237344-08 2025-03-01 23:53:38.020406-08 benchmark f L4 \N t \N \N \N {} -4413 1443 2025-03-02 00:45:38.730106-08 2025-03-02 00:36:30.242811-08 leaderboard f L4 0.009132959666666666 t \N \N \N {} -4418 1443 2025-03-01 23:15:31.653046-08 2025-03-02 00:20:24.722124-08 benchmark t A100 \N t \N \N \N {} -4419 1443 2025-03-01 23:11:05.373713-08 2025-03-02 00:24:01.197231-08 leaderboard t A100 0.002639421 t \N \N \N {} -4420 1443 2025-03-02 00:51:28.235424-08 2025-03-01 23:57:03.643851-08 test f A100 \N t \N \N \N {} -4421 1443 2025-03-02 00:14:40.806085-08 2025-03-02 00:04:05.289386-08 benchmark f A100 \N t \N \N \N {} -4422 1443 2025-03-01 23:14:33.6022-08 2025-03-02 00:33:33.808058-08 leaderboard f A100 0.0022384285652173913 t \N \N \N {} -4426 1443 2025-03-02 00:44:05.280092-08 2025-03-02 00:15:14.019635-08 test t H100 \N t \N \N \N {} -4427 1443 2025-03-01 23:59:15.284401-08 2025-03-02 00:58:48.422971-08 benchmark t H100 \N t \N \N \N {} -4428 1443 2025-03-02 00:11:17.701257-08 2025-03-01 23:26:04.825333-08 leaderboard t H100 0.00150569175 t \N \N \N {} -4429 1443 2025-03-01 23:25:46.014191-08 2025-03-02 00:05:37.324677-08 test f T4 \N t \N \N \N {} -2776 965 2025-02-26 21:06:31.43475-08 2025-02-26 19:53:09.101962-08 test t A100 \N t \N \N \N {} -4482 1445 2025-03-01 23:50:49.419996-08 2025-03-02 00:57:24.575893-08 leaderboard t L4 0.009311355333333333 t \N \N \N {} -4483 1446 2025-03-01 23:44:24.497614-08 2025-03-02 00:35:56.595882-08 test f A100 \N t \N \N \N {} -4484 1447 2025-03-01 23:27:17.550566-08 2025-03-02 01:12:31.419414-08 test f H100 \N t \N \N \N {} -4485 1447 2025-03-02 00:26:50.566555-08 2025-03-02 00:25:59.636108-08 benchmark f H100 \N f \N \N \N {} -4487 1447 2025-03-01 23:39:57.57287-08 2025-03-02 00:18:06.18322-08 benchmark t A100 \N f \N \N \N {} -2045 697 2025-02-25 13:02:11.887058-08 2025-02-25 13:35:15.729017-08 test f T4 \N f \N \N \N {} -2047 698 2025-02-25 14:51:14.379308-08 2025-02-25 14:49:23.452588-08 test f T4 \N t \N \N \N {} -2791 974 2025-02-26 20:28:12.007924-08 2025-02-26 21:26:15.641402-08 leaderboard t A100 0.003154194 t \N \N \N {} -3509 1201 2025-02-28 11:18:27.367438-08 2025-02-28 11:48:52.303198-08 test f A100 \N t \N \N \N {} -4498 1447 2025-03-01 23:16:26.107227-08 2025-03-02 00:47:04.910932-08 test t L4 \N t \N \N \N {} -4499 1447 2025-03-02 00:01:54.884825-08 2025-03-01 23:38:49.704891-08 benchmark t L4 \N f \N \N \N {} -4891 1507 2025-03-03 12:40:00.041984-08 2025-03-03 13:14:35.944127-08 test t A100 \N t \N \N \N {} -4504 1448 2025-03-01 23:24:02.182135-08 2025-03-01 23:40:44.693368-08 benchmark t A100 \N t \N \N \N {} -4505 1448 2025-03-02 00:40:33.995417-08 2025-03-02 00:56:55.060033-08 leaderboard t A100 0.00524246372 t \N \N \N {} -4506 1449 2025-03-02 00:45:08.313002-08 2025-03-02 00:16:16.737716-08 test t H100 \N t \N \N \N {} -4507 1449 2025-03-01 23:29:42.540346-08 2025-03-01 23:39:28.57237-08 benchmark t H100 \N t \N \N \N {} -4508 1449 2025-03-02 01:19:24.4957-08 2025-03-01 23:51:56.634988-08 leaderboard t H100 0.0033840856666666665 t \N \N \N {} -4509 1449 2025-03-02 00:12:06.024757-08 2025-03-02 01:10:54.396657-08 test f H100 \N t \N \N \N {} -4510 1449 2025-03-02 01:18:17.542284-08 2025-03-02 00:19:32.525502-08 benchmark f H100 \N t \N \N \N {} -4511 1449 2025-03-01 23:57:24.368485-08 2025-03-02 00:13:37.996217-08 leaderboard f H100 0.00341148875 t \N \N \N {} -4512 1450 2025-03-02 00:45:11.616534-08 2025-03-02 00:48:49.228002-08 test t H100 \N t \N \N \N {} -4513 1450 2025-03-01 23:40:22.279655-08 2025-03-02 00:00:22.363114-08 benchmark t H100 \N t \N \N \N {} -4899 1508 2025-03-03 13:37:04.374969-08 2025-03-03 13:09:34.935444-08 leaderboard t H100 0.007641404666666667 t \N \N \N {} -4517 1450 2025-03-02 00:03:53.293716-08 2025-03-02 00:25:33.705236-08 leaderboard t L4 0.009105309 t \N \N \N {} -4518 1450 2025-03-02 00:49:38.190529-08 2025-03-02 00:22:20.570554-08 test f A100 \N t \N \N \N {} -4519 1450 2025-03-02 00:11:10.810461-08 2025-03-02 01:03:13.770329-08 benchmark f A100 \N t \N \N \N {} -4520 1450 2025-03-02 01:08:07.36635-08 2025-03-02 00:17:23.477428-08 leaderboard f A100 0.0018727816666666668 t \N \N \N {} -4524 1450 2025-03-02 01:17:14.573469-08 2025-03-02 00:17:34.147369-08 test t T4 \N t \N \N \N {} -4525 1450 2025-03-02 00:43:27.056954-08 2025-03-01 23:43:04.43667-08 benchmark t T4 \N t \N \N \N {} -4526 1450 2025-03-02 00:53:05.519328-08 2025-03-02 00:06:06.503136-08 leaderboard t T4 0.009121147333333334 t \N \N \N {} -4527 1450 2025-03-01 23:54:26.363976-08 2025-03-02 01:19:17.019076-08 test t A100 \N t \N \N \N {} -4528 1450 2025-03-02 01:18:02.912247-08 2025-03-01 23:49:49.929737-08 benchmark t A100 \N t \N \N \N {} -4529 1450 2025-03-01 23:35:24.197844-08 2025-03-02 01:11:23.44255-08 leaderboard t A100 0.0017849657777777778 t \N \N \N {} -4530 1450 2025-03-02 00:54:36.280862-08 2025-03-01 23:35:47.462129-08 test f T4 \N t \N \N \N {} -4531 1450 2025-03-02 01:00:08.84832-08 2025-03-02 00:06:08.090762-08 benchmark f T4 \N t \N \N \N {} -4532 1450 2025-03-01 23:55:16.426583-08 2025-03-02 00:04:54.110158-08 leaderboard f T4 0.009106582333333333 t \N \N \N {} -4533 1450 2025-03-02 01:11:28.694794-08 2025-03-02 00:06:59.909067-08 test f L4 \N t \N \N \N {} -4534 1450 2025-03-02 00:11:02.404367-08 2025-03-02 01:17:42.734039-08 benchmark f L4 \N t \N \N \N {} -6713 2081 2025-03-15 02:46:39.538367-07 2025-03-15 03:50:32.068412-07 test t A100 \N t \N \N \N {} -4537 1451 2025-03-02 00:52:04.139941-08 2025-03-02 00:11:49.100971-08 benchmark t H100 \N t \N \N \N {} -4538 1451 2025-03-02 00:51:58.06123-08 2025-03-02 00:02:39.242368-08 leaderboard t H100 0.001079664 t \N \N \N {} -4539 1451 2025-03-02 00:06:02.121158-08 2025-03-02 00:00:09.264632-08 test f H100 \N t \N \N \N {} -4540 1451 2025-03-02 00:23:19.306421-08 2025-03-02 00:51:05.732006-08 benchmark f H100 \N t \N \N \N {} -4541 1451 2025-03-01 23:45:53.396285-08 2025-03-02 01:03:28.077189-08 leaderboard f H100 0.0010786196666666668 t \N \N \N {} -4626 1459 2025-03-02 06:58:07.444981-08 2025-03-02 06:44:02.224251-08 test t A100 \N t \N \N \N {} -4627 1459 2025-03-02 06:21:17.548115-08 2025-03-02 06:15:21.551657-08 benchmark t A100 \N t \N \N \N {} -4628 1459 2025-03-02 07:19:36.161701-08 2025-03-02 06:24:21.742016-08 leaderboard t A100 0.0030925023333333336 t \N \N \N {} -4629 1459 2025-03-02 05:52:24.785964-08 2025-03-02 05:52:16.34119-08 test f A100 \N t \N \N \N {} -4630 1459 2025-03-02 06:37:13.167125-08 2025-03-02 05:59:45.399577-08 benchmark f A100 \N t \N \N \N {} -4631 1459 2025-03-02 05:50:19.68331-08 2025-03-02 05:29:11.389842-08 leaderboard f A100 0.003070814 t \N \N \N {} -4635 1460 2025-03-02 07:14:27.918333-08 2025-03-02 05:44:05.344296-08 test f A100 \N t \N \N \N {} -4636 1460 2025-03-02 06:43:52.831925-08 2025-03-02 07:13:44.23175-08 benchmark f A100 \N t \N \N \N {} -4637 1460 2025-03-02 06:16:46.167909-08 2025-03-02 06:00:38.936259-08 leaderboard f A100 0.0030692296666666665 t \N \N \N {} -4638 1461 2025-03-02 06:03:46.169582-08 2025-03-02 06:31:43.401529-08 test t A100 \N t \N \N \N {} -4639 1461 2025-03-02 06:35:00.966361-08 2025-03-02 05:55:09.655918-08 benchmark t A100 \N t \N \N \N {} -4640 1461 2025-03-02 05:53:37.416513-08 2025-03-02 06:54:43.324146-08 leaderboard t A100 0.003078079 t \N \N \N {} -4644 1462 2025-03-02 06:49:33.296679-08 2025-03-02 06:48:19.278536-08 test f A100 \N t \N \N \N {} -4645 1462 2025-03-02 05:51:40.609401-08 2025-03-02 05:54:32.467256-08 benchmark f A100 \N t \N \N \N {} -4646 1462 2025-03-02 07:10:09.012521-08 2025-03-02 06:39:52.753547-08 leaderboard f A100 0.0030936686666666667 t \N \N \N {} -4647 1462 2025-03-02 06:21:07.074149-08 2025-03-02 06:19:05.973852-08 test t A100 \N t \N \N \N {} -4648 1462 2025-03-02 06:22:56.946678-08 2025-03-02 06:32:18.964173-08 benchmark t A100 \N t \N \N \N {} -4649 1462 2025-03-02 06:57:59.888395-08 2025-03-02 06:30:20.934248-08 leaderboard t A100 0.0030776453333333335 t \N \N \N {} -4652 1463 2025-03-02 06:00:11.005396-08 2025-03-02 05:45:35.797748-08 leaderboard t A100 0.0030704546666666666 t \N \N \N {} -4654 1463 2025-03-02 07:23:29.318291-08 2025-03-02 06:15:05.549905-08 benchmark f A100 \N t \N \N \N {} -158 23 2025-02-23 10:46:27.961353-08 2025-02-23 10:54:07.890706-08 leaderboard f L4 0.043783646333333336 t \N \N \N {} -159 23 2025-02-23 11:24:25.855339-08 2025-02-23 11:24:36.858525-08 test t L4 \N t \N \N \N {} -160 23 2025-02-23 11:23:10.687318-08 2025-02-23 10:51:27.096721-08 benchmark t L4 \N t \N \N \N {} -161 23 2025-02-23 10:17:17.903963-08 2025-02-23 11:20:35.401154-08 leaderboard t L4 0.04378879433333334 t \N \N \N {} -162 24 2025-02-23 11:04:17.242792-08 2025-02-23 12:05:18.328255-08 test f T4 \N f \N \N \N {} -163 25 2025-02-23 11:29:26.781421-08 2025-02-23 11:38:33.754711-08 test f H100 \N t \N \N \N {} -164 25 2025-02-23 11:46:38.661863-08 2025-02-23 11:39:19.931063-08 test f A100 \N t \N \N \N {} -165 25 2025-02-23 11:44:46.673883-08 2025-02-23 12:23:21.640415-08 test f T4 \N t \N \N \N {} -4655 1463 2025-03-02 05:44:44.87667-08 2025-03-02 06:39:01.814288-08 leaderboard f A100 0.003075862 t \N \N \N {} -4656 1464 2025-03-02 06:24:06.602618-08 2025-03-02 06:25:40.742865-08 test t A100 \N t \N \N \N {} -4657 1464 2025-03-02 06:25:44.512665-08 2025-03-02 06:07:00.018787-08 benchmark t A100 \N t \N \N \N {} -4658 1464 2025-03-02 07:24:38.102391-08 2025-03-02 07:03:30.28672-08 leaderboard t A100 0.0030908703333333335 t \N \N \N {} -4662 1465 2025-03-02 06:45:59.862943-08 2025-03-02 06:27:15.188637-08 test f A100 \N t \N \N \N {} -4663 1465 2025-03-02 06:12:13.396238-08 2025-03-02 06:13:12.261244-08 benchmark f A100 \N t \N \N \N {} -4664 1465 2025-03-02 05:44:57.969997-08 2025-03-02 07:15:25.843464-08 leaderboard f A100 0.0031012606666666665 t \N \N \N {} -4665 1465 2025-03-02 07:22:35.937837-08 2025-03-02 07:15:34.451037-08 test t A100 \N t \N \N \N {} -4666 1465 2025-03-02 05:41:53.096577-08 2025-03-02 06:59:11.932007-08 benchmark t A100 \N t \N \N \N {} -4667 1465 2025-03-02 07:22:50.191152-08 2025-03-02 06:20:27.38764-08 leaderboard t A100 0.0031609141666666665 t \N \N \N {} -4671 1466 2025-03-02 06:48:49.912925-08 2025-03-02 06:36:05.088369-08 test f A100 \N t \N \N \N {} -4672 1466 2025-03-02 07:00:40.126712-08 2025-03-02 07:05:41.314194-08 benchmark f A100 \N t \N \N \N {} -4673 1466 2025-03-02 07:03:50.192296-08 2025-03-02 06:21:41.476403-08 leaderboard f A100 0.0030736896666666664 t \N \N \N {} -4674 1467 2025-03-02 07:34:10.787696-08 2025-03-02 07:29:26.411023-08 test t A100 \N t \N \N \N {} -4675 1467 2025-03-02 06:56:49.136222-08 2025-03-02 06:41:21.315023-08 benchmark t A100 \N t \N \N \N {} -4807 1487 2025-03-02 12:30:33.450976-08 2025-03-02 13:35:13.770124-08 benchmark f T4 \N f \N \N \N {} -4693 1470 2025-03-02 06:12:06.283994-08 2025-03-02 06:51:42.391905-08 benchmark t A100 \N t \N \N \N {} -4694 1470 2025-03-02 06:33:53.917995-08 2025-03-02 07:33:01.914128-08 leaderboard t A100 0.004008321333333334 t \N \N \N {} -4698 1471 2025-03-02 07:29:03.787601-08 2025-03-02 06:41:20.283599-08 test f A100 \N t \N \N \N {} -4699 1471 2025-03-02 05:48:12.197045-08 2025-03-02 06:12:43.42567-08 benchmark f A100 \N t \N \N \N {} -4700 1471 2025-03-02 06:56:34.879652-08 2025-03-02 07:44:48.551731-08 leaderboard f A100 0.0031009833333333335 t \N \N \N {} -4701 1471 2025-03-02 06:30:38.128029-08 2025-03-02 07:27:12.350551-08 test t A100 \N t \N \N \N {} -4702 1471 2025-03-02 07:44:43.71905-08 2025-03-02 07:20:57.056431-08 benchmark t A100 \N t \N \N \N {} -4703 1471 2025-03-02 05:52:40.876763-08 2025-03-02 06:18:11.15908-08 leaderboard t A100 0.003077995 t \N \N \N {} -4708 1476 2025-03-02 07:04:38.833526-08 2025-03-02 06:22:07.053844-08 test t A100 \N t \N \N \N {} -4711 1476 2025-03-02 06:32:53.010168-08 2025-03-02 08:13:38.448364-08 test f A100 \N t \N \N \N {} -4712 1476 2025-03-02 07:07:03.075398-08 2025-03-02 06:39:50.920146-08 benchmark f A100 \N t \N \N \N {} -4713 1476 2025-03-02 07:29:16.863809-08 2025-03-02 08:09:32.549873-08 leaderboard f A100 0.003081995 t \N \N \N {} -4714 1477 2025-03-02 08:12:43.521171-08 2025-03-02 07:24:21.691154-08 test t A100 \N t \N \N \N {} -4715 1477 2025-03-02 08:12:18.641473-08 2025-03-02 08:16:06.720104-08 benchmark t A100 \N t \N \N \N {} -4716 1477 2025-03-02 07:47:17.510849-08 2025-03-02 06:38:36.379617-08 leaderboard t A100 0.0030823416666666665 t \N \N \N {} -4720 1478 2025-03-02 06:43:15.887395-08 2025-03-02 07:01:09.324629-08 test f A100 \N t \N \N \N {} -4721 1478 2025-03-02 06:19:24.519466-08 2025-03-02 07:02:12.8161-08 benchmark f A100 \N t \N \N \N {} -4722 1478 2025-03-02 07:24:47.923184-08 2025-03-02 06:22:30.821228-08 leaderboard f A100 0.0030851883333333336 t \N \N \N {} -4723 1478 2025-03-02 06:21:50.565071-08 2025-03-02 06:38:29.276371-08 test t A100 \N t \N \N \N {} -4724 1478 2025-03-02 07:55:40.137891-08 2025-03-02 08:07:32.643622-08 benchmark t A100 \N t \N \N \N {} -4725 1478 2025-03-02 08:04:19.082753-08 2025-03-02 06:39:54.506806-08 leaderboard t A100 0.0030785193333333336 t \N \N \N {} -4728 1479 2025-03-02 08:14:46.908587-08 2025-03-02 07:03:37.792816-08 leaderboard f A100 0.0030867813333333334 t \N \N \N {} -4729 1479 2025-03-02 06:45:56.767886-08 2025-03-02 08:16:10.271116-08 test t A100 \N t \N \N \N {} -4730 1479 2025-03-02 07:53:43.730007-08 2025-03-02 06:49:41.785807-08 benchmark t A100 \N t \N \N \N {} -4731 1479 2025-03-02 07:11:52.419771-08 2025-03-02 07:23:15.061576-08 leaderboard t A100 0.0031140623333333335 t \N \N \N {} -4732 1480 2025-03-02 07:09:43.554747-08 2025-03-02 06:58:05.009739-08 test t A100 \N t \N \N \N {} -4733 1480 2025-03-02 07:52:32.541906-08 2025-03-02 07:38:29.663474-08 benchmark t A100 \N t \N \N \N {} -4734 1480 2025-03-02 07:44:10.008433-08 2025-03-02 08:17:23.134924-08 leaderboard t A100 0.0030920546666666666 t \N \N \N {} -4738 1481 2025-03-02 07:04:34.701596-08 2025-03-02 07:21:13.788761-08 test f A100 \N t \N \N \N {} -4739 1481 2025-03-02 06:49:36.00208-08 2025-03-02 07:31:10.469625-08 benchmark f A100 \N t \N \N \N {} -4740 1481 2025-03-02 07:47:10.568157-08 2025-03-02 08:09:45.89037-08 leaderboard f A100 0.003085828 t \N \N \N {} -4741 1481 2025-03-02 06:48:53.469551-08 2025-03-02 07:03:55.679567-08 test t A100 \N t \N \N \N {} -4742 1481 2025-03-02 06:31:13.795781-08 2025-03-02 06:49:28.737931-08 benchmark t A100 \N t \N \N \N {} -4743 1481 2025-03-02 08:15:11.812141-08 2025-03-02 06:37:00.374019-08 leaderboard t A100 0.0030922536666666664 t \N \N \N {} -4744 1482 2025-03-02 07:43:03.723058-08 2025-03-02 07:35:15.071326-08 test f T4 \N f \N \N \N {} -4745 1483 2025-03-02 07:31:56.372241-08 2025-03-02 08:05:39.164579-08 test f T4 \N t \N \N \N {} -4746 1484 2025-03-02 08:35:02.123221-08 2025-03-02 07:56:53.596064-08 test f T4 \N t \N \N \N {} -4747 1484 2025-03-02 07:06:16.362073-08 2025-03-02 07:58:54.271757-08 benchmark f T4 \N t \N \N \N {} -4748 1484 2025-03-02 07:48:44.592479-08 2025-03-02 06:54:14.32109-08 leaderboard f T4 0.07661455133333332 t \N \N \N {} -4749 1484 2025-03-02 08:43:32.528981-08 2025-03-02 07:50:46.63591-08 test t T4 \N t \N \N \N {} -4750 1484 2025-03-02 07:57:18.393313-08 2025-03-02 07:47:01.612778-08 benchmark t T4 \N t \N \N \N {} -4751 1484 2025-03-02 07:42:07.868143-08 2025-03-02 08:02:04.953244-08 leaderboard t T4 0.07684545833333332 t \N \N \N {} -6735 2086 2025-03-15 04:33:46.257775-07 2025-03-15 04:41:06.722991-07 test t A100 \N t \N \N \N {} -4755 1485 2025-03-02 11:37:55.155684-08 2025-03-02 12:30:52.129299-08 test t A100 \N t \N \N \N {} -4756 1485 2025-03-02 11:54:47.977319-08 2025-03-02 11:49:51.955954-08 benchmark t A100 \N t \N \N \N {} -4757 1485 2025-03-02 12:35:43.476034-08 2025-03-02 13:11:14.518517-08 leaderboard t A100 0.0017045449166666667 t \N \N \N {} -4758 1485 2025-03-02 12:19:44.247676-08 2025-03-02 12:41:13.158847-08 test f H100 \N t \N \N \N {} -4762 1485 2025-03-02 13:26:36.984272-08 2025-03-02 13:09:50.663429-08 benchmark t H100 \N t \N \N \N {} -4763 1485 2025-03-02 11:57:05.793115-08 2025-03-02 12:51:08.493618-08 leaderboard t H100 0.0010300953333333333 t \N \N \N {} -4765 1485 2025-03-02 13:18:29.547704-08 2025-03-02 11:53:12.069821-08 benchmark f T4 \N t \N \N \N {} -4766 1485 2025-03-02 12:34:18.620178-08 2025-03-02 12:01:46.474428-08 leaderboard f T4 0.013093859 t \N \N \N {} -4770 1485 2025-03-02 11:28:06.060656-08 2025-03-02 12:33:39.575643-08 test t L4 \N t \N \N \N {} -4771 1485 2025-03-02 12:38:32.300929-08 2025-03-02 12:35:21.086611-08 benchmark t L4 \N t \N \N \N {} -4772 1485 2025-03-02 11:38:38.993577-08 2025-03-02 12:03:06.955576-08 leaderboard t L4 0.009067796666666666 t \N \N \N {} -4782 1486 2025-03-02 13:23:24.636755-08 2025-03-02 13:02:48.120735-08 leaderboard t L4 0.01330954 t \N \N \N {} -4783 1486 2025-03-02 11:52:32.464719-08 2025-03-02 11:51:42.704104-08 test t T4 \N t \N \N \N {} -4784 1486 2025-03-02 12:54:34.061003-08 2025-03-02 13:23:05.121934-08 benchmark t T4 \N f \N \N \N {} -4785 1486 2025-03-02 13:03:39.801457-08 2025-03-02 13:50:21.033648-08 test t H100 \N t \N \N \N {} -4786 1486 2025-03-02 13:02:54.20138-08 2025-03-02 13:03:49.524039-08 benchmark t H100 \N t \N \N \N {} -4787 1486 2025-03-02 13:37:42.944284-08 2025-03-02 13:04:38.530285-08 leaderboard t H100 0.014144686666666666 t \N \N \N {} -166 25 2025-02-23 11:29:44.547374-08 2025-02-23 11:45:47.371586-08 test f L4 \N t \N \N \N {} -6741 2087 2025-03-15 03:27:43.798435-07 2025-03-15 04:38:01.97182-07 test t A100 \N t \N \N \N {} -4789 1486 2025-03-02 13:07:40.863559-08 2025-03-02 12:56:43.445817-08 benchmark f T4 \N f \N \N \N {} -4790 1486 2025-03-02 11:54:48.604048-08 2025-03-02 13:06:33.970638-08 test f H100 \N t \N \N \N {} -4791 1486 2025-03-02 13:12:20.061089-08 2025-03-02 13:06:35.664868-08 benchmark f H100 \N t \N \N \N {} -4792 1486 2025-03-02 12:46:36.012816-08 2025-03-02 13:51:06.73015-08 leaderboard f H100 0.014137576333333334 t \N \N \N {} -4793 1486 2025-03-02 11:56:43.712458-08 2025-03-02 12:12:52.126902-08 test f L4 \N t \N \N \N {} -4794 1486 2025-03-02 12:57:19.285267-08 2025-03-02 13:40:00.434892-08 benchmark f L4 \N t \N \N \N {} -6744 2087 2025-03-15 03:03:55.336213-07 2025-03-15 04:21:27.642744-07 test f A100 \N t \N \N \N {} -4796 1487 2025-03-02 13:56:03.952211-08 2025-03-02 14:17:06.867401-08 test t H100 \N t \N \N \N {} -2058 700 2025-02-25 13:35:50.559263-08 2025-02-25 14:57:49.798923-08 test t A100 \N f \N \N \N {} -4944 1522 2025-03-03 14:24:33.347813-08 2025-03-03 14:26:01.708268-08 test t H100 \N t \N \N \N {} -4946 1522 2025-03-03 15:27:11.77608-08 2025-03-03 14:42:48.21655-08 leaderboard t H100 0.00110717921 t \N \N \N {} -4948 1524 2025-03-03 14:06:57.97848-08 2025-03-03 15:53:35.131501-08 test f A100 \N t \N \N \N {} -4949 1524 2025-03-03 14:35:09.882957-08 2025-03-03 14:20:11.280882-08 benchmark f A100 \N t \N \N \N {} -6769 2097 2025-03-15 04:27:49.490385-07 2025-03-15 03:27:02.099035-07 test f A100 \N t \N \N \N {} -4957 1524 2025-03-03 14:23:59.058716-08 2025-03-03 15:25:20.637415-08 test f H100 \N t \N \N \N {} -4958 1524 2025-03-03 14:03:34.405543-08 2025-03-03 14:18:35.717577-08 benchmark f H100 \N t \N \N \N {} -4959 1524 2025-03-03 14:09:29.703995-08 2025-03-03 15:30:42.011483-08 leaderboard f H100 0.00140554825 t \N \N \N {} -5255 1613 2025-03-06 07:12:38.56695-08 2025-03-06 08:33:42.642582-08 leaderboard f L4 0.01713484 t \N \N \N {} -4981 1533 2025-03-03 23:32:30.171269-08 2025-03-03 22:37:21.705286-08 benchmark f L4 \N f \N \N \N {} -5018 1541 2025-03-04 09:09:22.632315-08 2025-03-04 09:22:15.724438-08 test f T4 \N f \N \N \N {} -4982 1533 2025-03-03 22:41:27.291617-08 2025-03-03 22:26:36.666409-08 test t L4 \N t \N \N \N {} -4983 1533 2025-03-03 22:10:42.942356-08 2025-03-03 23:52:58.998599-08 benchmark t L4 \N f \N \N \N {} -4984 1534 2025-03-03 22:26:12.301319-08 2025-03-03 22:26:15.332411-08 test t A100 \N t \N \N \N {} -4985 1534 2025-03-03 22:36:43.072753-08 2025-03-03 23:30:03.866041-08 benchmark t A100 \N t \N \N \N {} -4986 1534 2025-03-03 22:14:27.365325-08 2025-03-03 23:53:50.401219-08 leaderboard t A100 0.001491775 t \N \N \N {} -4997 1534 2025-03-03 23:13:33.43142-08 2025-03-03 22:46:25.592457-08 benchmark t H100 \N t \N \N \N {} -4998 1534 2025-03-03 23:34:45.888365-08 2025-03-03 22:22:19.090656-08 leaderboard t H100 0.001050694 t \N \N \N {} -4999 1534 2025-03-03 23:33:14.778803-08 2025-03-03 23:11:24.883939-08 test t T4 \N t \N \N \N {} -5004 1534 2025-03-03 22:52:44.48133-08 2025-03-03 22:20:58.315866-08 leaderboard f T4 0.013578895 t \N \N \N {} -5005 1534 2025-03-03 22:45:45.446291-08 2025-03-03 23:14:25.105517-08 test t L4 \N t \N \N \N {} -5006 1534 2025-03-03 23:46:05.132215-08 2025-03-03 23:00:38.449509-08 benchmark t L4 \N t \N \N \N {} -5007 1534 2025-03-03 23:25:45.105749-08 2025-03-03 22:52:16.954981-08 leaderboard t L4 0.00906405 t \N \N \N {} -5022 1545 2025-03-04 14:58:30.960919-08 2025-03-04 14:52:33.273284-08 test t A100 \N t \N \N \N {} -5023 1545 2025-03-04 14:51:10.938222-08 2025-03-04 15:28:30.503521-08 benchmark t A100 \N t \N \N \N {} -5074 1551 2025-03-04 16:47:22.476681-08 2025-03-04 17:10:56.60229-08 test f H100 \N t \N \N \N {} -5024 1545 2025-03-04 13:51:12.047062-08 2025-03-04 14:35:53.536936-08 leaderboard t A100 0.00004836930555555555 t \N \N \N {} -5025 1545 2025-03-04 14:21:17.803519-08 2025-03-04 15:31:14.61315-08 test f A100 \N t \N \N \N {} -5026 1545 2025-03-04 13:52:48.051537-08 2025-03-04 15:31:03.104837-08 benchmark f A100 \N t \N \N \N {} -5028 1545 2025-03-04 15:34:09.122729-08 2025-03-04 14:42:01.686509-08 test t H100 \N t \N \N \N {} -5034 1545 2025-03-04 14:29:25.07288-08 2025-03-04 14:33:25.56239-08 test t T4 \N t \N \N \N {} -5029 1545 2025-03-04 15:35:11.114668-08 2025-03-04 14:50:01.633693-08 benchmark t H100 \N t \N \N \N {} -5030 1545 2025-03-04 14:15:23.548104-08 2025-03-04 14:34:05.166616-08 leaderboard t H100 0.00004719919 t \N \N \N {} -5031 1545 2025-03-04 13:53:27.796904-08 2025-03-04 13:50:34.783512-08 test f H100 \N t \N \N \N {} -5157 1571 2025-03-04 23:08:49.976878-08 2025-03-04 22:42:28.36004-08 benchmark f A100 \N t \N \N \N {} -5158 1571 2025-03-04 22:35:15.21-08 2025-03-04 22:34:20.752683-08 leaderboard f A100 0.0014883393333333334 t \N \N \N {} -5159 1571 2025-03-04 23:12:18.343342-08 2025-03-04 23:12:10.298764-08 test f H100 \N t \N \N \N {} -5160 1571 2025-03-04 22:52:31.849847-08 2025-03-04 22:15:55.305164-08 benchmark f H100 \N t \N \N \N {} -5161 1571 2025-03-04 21:54:25.033079-08 2025-03-04 23:19:13.810944-08 leaderboard f H100 0.0010518306666666667 t \N \N \N {} -5162 1571 2025-03-04 23:26:27.704888-08 2025-03-04 21:45:35.062949-08 test t H100 \N t \N \N \N {} -2066 702 2025-02-25 14:01:35.67269-08 2025-02-25 14:13:03.658618-08 test f H100 \N t \N \N \N {} -2422 827 2025-02-26 13:49:48.149073-08 2025-02-26 13:42:33.751386-08 benchmark f H100 \N f \N \N \N {} -5622 1716 2025-03-09 10:46:42.607602-07 2025-03-09 09:07:56.188107-07 benchmark t T4 \N f \N \N \N {} -5623 1716 2025-03-09 09:31:22.907917-07 2025-03-09 09:56:01.467636-07 test f T4 \N t \N \N \N {} -5624 1716 2025-03-09 10:16:02.064243-07 2025-03-09 10:32:48.331759-07 benchmark f T4 \N f \N \N \N {} -5625 1717 2025-03-09 10:13:52.307292-07 2025-03-09 09:42:55.90134-07 test f T4 \N t \N \N \N {} -5626 1717 2025-03-09 10:41:28.462205-07 2025-03-09 10:30:33.37398-07 benchmark f T4 \N t \N \N \N {} -5627 1717 2025-03-09 09:58:14.43763-07 2025-03-09 09:29:59.555675-07 leaderboard f T4 0.01689148266666667 t \N \N \N {} -5628 1717 2025-03-09 08:54:15.401227-07 2025-03-09 09:01:20.665735-07 test t T4 \N t \N \N \N {} -5633 1718 2025-03-09 10:16:16.764055-07 2025-03-09 08:57:06.957134-07 leaderboard t T4 0.02135399933333333 t \N \N \N {} -5660 1723 2025-03-09 10:03:33.814323-07 2025-03-09 10:04:32.576403-07 leaderboard f T4 0.016605525666666666 t \N \N \N {} -5661 1723 2025-03-09 10:47:11.568642-07 2025-03-09 09:35:57.762614-07 test t T4 \N t \N \N \N {} -5662 1723 2025-03-09 10:00:24.122137-07 2025-03-09 09:14:18.616382-07 benchmark t T4 \N t \N \N \N {} -5683 1726 2025-03-09 09:19:42.062291-07 2025-03-09 09:47:18.455329-07 benchmark f L4 \N t \N \N \N {} -5684 1726 2025-03-09 09:35:37.887021-07 2025-03-09 10:36:27.46401-07 leaderboard f L4 0.017259138 t \N \N \N {} -5685 1727 2025-03-09 10:09:36.558925-07 2025-03-09 09:36:26.692942-07 test f L4 \N t \N \N \N {} -5692 1728 2025-03-09 09:54:55.125187-07 2025-03-09 09:30:43.343027-07 benchmark f A100 \N t \N \N \N {} -5693 1728 2025-03-09 10:44:16.533423-07 2025-03-09 10:12:57.412213-07 leaderboard f A100 0.0026361306666666663 t \N \N \N {} -5694 1729 2025-03-09 10:18:27.24795-07 2025-03-09 11:00:29.021145-07 test t H100 \N t \N \N \N {} -5780 1750 2025-03-09 22:31:41.177478-07 2025-03-09 22:32:40.296083-07 leaderboard t H100 0.0015017373333333333 t \N \N \N {} -5783 1751 2025-03-09 22:33:46.42896-07 2025-03-09 22:29:10.948791-07 leaderboard t L4 0.017186112666666666 t \N \N \N {} -5789 1752 2025-03-09 22:09:13.460918-07 2025-03-09 22:30:06.841849-07 leaderboard f T4 0.016641214666666668 t \N \N \N {} -5791 1752 2025-03-09 21:25:43.431271-07 2025-03-09 22:03:39.205218-07 benchmark t T4 \N t \N \N \N {} -5792 1752 2025-03-09 22:06:34.639731-07 2025-03-09 22:03:04.943777-07 leaderboard t T4 0.016605053666666664 t \N \N \N {} -5890 1783 2025-03-10 11:42:15.635873-07 2025-03-10 12:18:31.181049-07 test f T4 \N f \N \N \N {} -5891 1784 2025-03-10 12:42:17.820815-07 2025-03-10 12:42:44.878659-07 test f T4 \N f \N \N \N {} -5892 1784 2025-03-10 13:03:34.982484-07 2025-03-10 11:10:33.398792-07 test t T4 \N f \N \N \N {} -6112 1851 2025-03-11 02:01:12.667219-07 2025-03-11 01:10:58.721364-07 test f A100 \N f \N \N \N {} -5899 1788 2025-03-10 12:51:42.965452-07 2025-03-10 11:53:10.996306-07 test t T4 \N f \N \N \N {} -5900 1788 2025-03-10 11:22:13.20187-07 2025-03-10 11:32:35.572673-07 test f T4 \N f \N \N \N {} -5901 1789 2025-03-10 13:02:58.074539-07 2025-03-10 13:17:21.023123-07 test f T4 \N f \N \N \N {} -5902 1789 2025-03-10 12:01:52.164022-07 2025-03-10 11:27:33.462883-07 test t T4 \N f \N \N \N {} -5903 1790 2025-03-10 11:26:13.302143-07 2025-03-10 12:50:52.591159-07 test t T4 \N f \N \N \N {} -5915 1796 2025-03-10 12:15:59.801508-07 2025-03-10 13:02:17.771902-07 test t T4 \N f \N \N \N {} -5916 1796 2025-03-10 12:57:44.974492-07 2025-03-10 12:53:59.035234-07 test f T4 \N f \N \N \N {} -5944 1809 2025-03-10 13:50:04.063532-07 2025-03-10 14:02:36.910964-07 test t A100 \N t \N \N \N {} -5917 1797 2025-03-10 12:47:21.898424-07 2025-03-10 12:35:53.990658-07 test f T4 \N f \N \N \N {} -5918 1797 2025-03-10 11:54:01.992015-07 2025-03-10 13:25:49.093367-07 test t T4 \N f \N \N \N {} -5919 1798 2025-03-10 12:02:54.007464-07 2025-03-10 12:00:35.023294-07 test f T4 \N f \N \N \N {} -5920 1798 2025-03-10 11:53:48.460819-07 2025-03-10 12:54:41.600572-07 test t T4 \N f \N \N \N {} -5921 1799 2025-03-10 12:01:32.892869-07 2025-03-10 12:01:20.064834-07 test f T4 \N f \N \N \N {} -5945 1809 2025-03-10 13:30:52.742-07 2025-03-10 13:35:00.53151-07 benchmark t A100 \N t \N \N \N {} -2067 702 2025-02-25 13:08:25.421143-08 2025-02-25 13:34:01.386083-08 benchmark f H100 \N t \N \N \N {} -2068 702 2025-02-25 13:42:25.465302-08 2025-02-25 13:31:29.0514-08 leaderboard f H100 0.001931559125 t \N \N \N {} -167 26 2025-02-23 12:21:33.030404-08 2025-02-23 11:41:55.949378-08 benchmark f A100 \N t \N \N \N {} -168 26 2025-02-23 11:54:48.8754-08 2025-02-23 11:19:18.094078-08 benchmark f H100 \N t \N \N \N {} -169 26 2025-02-23 11:53:04.673811-08 2025-02-23 11:52:00.921431-08 benchmark f T4 \N t \N \N \N {} -170 26 2025-02-23 10:45:00.538357-08 2025-02-23 11:33:31.309144-08 benchmark f L4 \N t \N \N \N {} -5969 1818 2025-03-10 13:34:50.62569-07 2025-03-10 13:50:19.381297-07 leaderboard f T4 0.007938620666666667 t \N \N \N {} -5988 1821 2025-03-10 14:19:10.235543-07 2025-03-10 13:00:17.011132-07 test t L4 \N t \N \N \N {} -5972 1818 2025-03-10 14:20:22.971921-07 2025-03-10 13:57:46.364461-07 leaderboard t T4 0.006741959333333333 t \N \N \N {} -5973 1819 2025-03-10 14:09:36.845256-07 2025-03-10 12:47:23.183159-07 test f A100 \N t \N \N \N {} -5974 1819 2025-03-10 13:27:31.914549-07 2025-03-10 13:38:50.356656-07 benchmark f A100 \N t \N \N \N {} -5975 1819 2025-03-10 13:22:24.682951-07 2025-03-10 12:52:47.777634-07 leaderboard f A100 0.00066186475 t \N \N \N {} -5976 1820 2025-03-10 13:05:57.916285-07 2025-03-10 14:10:04.245483-07 test f H100 \N t \N \N \N {} -5977 1820 2025-03-10 13:06:16.389801-07 2025-03-10 13:56:50.012127-07 benchmark f H100 \N t \N \N \N {} -5980 1820 2025-03-10 14:27:30.646317-07 2025-03-10 14:22:26.85464-07 benchmark t H100 \N t \N \N \N {} -5981 1820 2025-03-10 13:34:45.928055-07 2025-03-10 14:10:03.141068-07 leaderboard t H100 0.00025382670689655173 t \N \N \N {} -5982 1821 2025-03-10 13:18:05.58891-07 2025-03-10 12:57:07.627811-07 test f L4 \N t \N \N \N {} -5983 1821 2025-03-10 13:10:21.68803-07 2025-03-10 12:59:33.456792-07 benchmark f L4 \N t \N \N \N {} -5984 1821 2025-03-10 14:27:34.743682-07 2025-03-10 13:17:42.837991-07 leaderboard f L4 0.0023442443333333337 t \N \N \N {} -5985 1819 2025-03-10 14:00:17.259921-07 2025-03-10 13:54:42.382815-07 test t A100 \N t \N \N \N {} -5986 1819 2025-03-10 13:40:20.642276-07 2025-03-10 13:23:25.385771-07 benchmark t A100 \N t \N \N \N {} -5991 1822 2025-03-10 13:01:51.312859-07 2025-03-10 13:23:25.247008-07 test f A100 \N t \N \N \N {} -5992 1822 2025-03-10 13:52:54.966043-07 2025-03-10 13:40:58.589221-07 benchmark f A100 \N t \N \N \N {} -5993 1822 2025-03-10 13:20:20.347858-07 2025-03-10 13:01:28.199599-07 leaderboard f A100 0.011864730666666667 t \N \N \N {} -5994 1822 2025-03-10 14:40:44.674945-07 2025-03-10 13:28:53.37983-07 test t A100 \N t \N \N \N {} -5995 1822 2025-03-10 14:21:29.232684-07 2025-03-10 13:52:16.353634-07 benchmark t A100 \N t \N \N \N {} -5996 1822 2025-03-10 13:03:56.315257-07 2025-03-10 13:23:43.023609-07 leaderboard t A100 0.011399361 t \N \N \N {} -6003 1824 2025-03-10 14:26:38.384984-07 2025-03-10 14:09:13.727909-07 test f L4 \N t \N \N \N {} -6004 1824 2025-03-10 13:12:15.653503-07 2025-03-10 14:52:55.190975-07 benchmark f L4 \N t \N \N \N {} -6005 1824 2025-03-10 13:13:37.254193-07 2025-03-10 13:18:36.888686-07 leaderboard f L4 0.07897275766666667 t \N \N \N {} -6011 1825 2025-03-10 13:09:55.890405-07 2025-03-10 13:48:11.961778-07 leaderboard t T4 0.07677698866666667 t \N \N \N {} -6013 1825 2025-03-10 14:28:08.622984-07 2025-03-10 14:46:31.402484-07 benchmark f T4 \N t \N \N \N {} -6014 1825 2025-03-10 14:21:16.246646-07 2025-03-10 13:28:59.68858-07 leaderboard f T4 0.07667353866666667 t \N \N \N {} -6095 1844 2025-03-10 22:07:21.414-07 2025-03-10 23:17:28.15254-07 benchmark t A100 \N t \N \N \N {} -6107 1846 2025-03-10 22:00:08.835784-07 2025-03-10 22:00:11.368406-07 benchmark f L4 \N t \N \N \N {} -6108 1846 2025-03-10 22:23:46.695176-07 2025-03-10 23:57:02.247288-07 leaderboard f L4 0.3110419582857143 t \N \N \N {} -6655 2057 2025-03-14 12:30:26.103998-07 2025-03-14 12:52:39.669941-07 benchmark f A100 \N t \N \N \N {} -6661 2058 2025-03-14 13:23:30.755804-07 2025-03-14 13:50:09.852519-07 leaderboard f A100 0.002474787 t \N \N \N {} -6672 2064 2025-03-14 15:30:52.385494-07 2025-03-14 14:26:29.449376-07 test t T4 \N f \N \N \N {} -6673 2064 2025-03-14 15:46:19.901898-07 2025-03-14 14:34:56.062065-07 test f T4 \N f \N \N \N {} -6693 2074 2025-03-14 16:21:36.880226-07 2025-03-14 17:05:48.053303-07 test f T4 \N f \N \N \N {} -6889 2142 2025-03-16 10:09:32.257151-07 2025-03-16 11:37:21.891364-07 test t H100 \N t \N \N \N {} -6694 2075 2025-03-14 16:49:43.41812-07 2025-03-14 16:35:30.091773-07 test f T4 \N f \N \N \N {} -6890 2142 2025-03-16 10:05:39.67375-07 2025-03-16 11:46:49.850371-07 benchmark t H100 \N f \N \N \N {} -6708 2080 2025-03-15 03:13:35.894439-07 2025-03-15 04:06:56.714414-07 benchmark f A100 \N t \N \N \N {} -6709 2080 2025-03-15 03:36:07.318078-07 2025-03-15 04:02:50.2109-07 leaderboard f A100 0.0025194633333333336 t \N \N \N {} -6711 2081 2025-03-15 03:52:50.095799-07 2025-03-15 03:57:17.540486-07 benchmark f A100 \N t \N \N \N {} -6721 2082 2025-03-15 02:47:16.092609-07 2025-03-15 02:57:12.920931-07 leaderboard f A100 0.0025854276666666667 t \N \N \N {} -6804 2105 2025-03-15 04:42:04.98069-07 2025-03-15 03:45:38.84867-07 benchmark f A100 \N t \N \N \N {} -6806 2107 2025-03-15 04:57:27.096967-07 2025-03-15 05:03:18.110884-07 benchmark f A100 \N t \N \N \N {} -6807 2108 2025-03-15 03:40:00.092892-07 2025-03-15 05:03:01.447944-07 benchmark f A100 \N t \N \N \N {} -6808 2109 2025-03-15 05:29:02.178375-07 2025-03-15 04:43:10.854188-07 benchmark f A100 \N t \N \N \N {} -6891 2142 2025-03-16 10:03:19.487989-07 2025-03-16 10:15:21.78931-07 test f H100 \N t \N \N \N {} -6816 2117 2025-03-15 09:06:01.891683-07 2025-03-15 09:51:02.921922-07 benchmark f A100 \N t \N \N \N {} -6893 2143 2025-03-16 12:26:21.451192-07 2025-03-16 11:55:44.286131-07 test f T4 \N f \N \N \N {} -6823 2120 2025-03-15 10:10:54.860558-07 2025-03-15 09:57:15.216128-07 benchmark t A100 \N t \N \N \N {} -6868 2128 2025-03-15 13:36:39.60935-07 2025-03-15 12:30:47.242903-07 test f T4 \N f \N \N \N {} -6869 2129 2025-03-15 12:55:04.568241-07 2025-03-15 13:27:08.036947-07 test t T4 \N f \N \N \N {} -6870 2129 2025-03-15 13:43:39.360608-07 2025-03-15 13:40:42.286936-07 test f T4 \N f \N \N \N {} -6871 2130 2025-03-15 12:56:13.688421-07 2025-03-15 13:31:59.222715-07 test t T4 \N f \N \N \N {} -6872 2130 2025-03-15 13:37:07.488274-07 2025-03-15 14:00:52.365959-07 test f T4 \N f \N \N \N {} -6874 2131 2025-03-15 14:10:20.038407-07 2025-03-15 13:06:30.274573-07 test t T4 \N f \N \N \N {} -6875 2132 2025-03-15 13:03:08.713745-07 2025-03-15 14:16:20.509728-07 test f T4 \N f \N \N \N {} -6876 2132 2025-03-15 12:46:33.250206-07 2025-03-15 14:16:03.803892-07 test t T4 \N f \N \N \N {} -6877 2133 2025-03-15 14:13:55.89837-07 2025-03-15 14:32:53.847522-07 test f T4 \N f \N \N \N {} -6878 2133 2025-03-15 13:49:17.874309-07 2025-03-15 13:42:01.322803-07 test t T4 \N f \N \N \N {} -6884 2137 2025-03-16 09:35:46.366813-07 2025-03-16 09:58:28.560263-07 test f H100 \N f \N \N \N {} -6941 2161 2025-03-16 15:54:46.742477-07 2025-03-16 16:55:32.003397-07 leaderboard t T4 0.00036617938461538463 t \N \N \N {} -6942 2161 2025-03-16 15:48:02.479891-07 2025-03-16 15:54:40.057512-07 test f T4 \N t \N \N \N {} -6943 2161 2025-03-16 16:14:50.39459-07 2025-03-16 16:50:14.655957-07 benchmark f T4 \N t \N \N \N {} -6945 2162 2025-03-16 15:42:59.335687-07 2025-03-16 16:32:27.860049-07 test t T4 \N t \N \N \N {} -6946 2162 2025-03-16 15:21:34.523769-07 2025-03-16 15:44:43.801402-07 benchmark t T4 \N t \N \N \N {} -6947 2162 2025-03-16 15:24:00.324625-07 2025-03-16 16:09:43.79608-07 leaderboard t T4 0.00028399790999999997 t \N \N \N {} -6948 2162 2025-03-16 15:07:29.788194-07 2025-03-16 16:45:42.575251-07 test f T4 \N t \N \N \N {} -6949 2162 2025-03-16 16:58:35.480846-07 2025-03-16 15:40:08.469048-07 benchmark f T4 \N t \N \N \N {} -2083 702 2025-02-25 13:37:08.890367-08 2025-02-25 14:31:19.469976-08 leaderboard f T4 0.019992128 t \N \N \N {} -2829 984 2025-02-26 23:44:55.909365-08 2025-02-26 23:59:03.36721-08 test f L4 \N t \N \N \N {} -2843 988 2025-02-27 00:16:23.988573-08 2025-02-26 23:57:40.2744-08 leaderboard f H100 0.00153285175 t \N \N \N {} -171 27 2025-02-23 10:45:11.479148-08 2025-02-23 11:31:42.340162-08 test t A100 \N t \N \N \N {} -172 27 2025-02-23 11:43:09.385926-08 2025-02-23 11:39:14.096637-08 benchmark t A100 \N t \N \N \N {} -220 37 2025-02-23 11:24:37.144954-08 2025-02-23 12:36:11.010727-08 benchmark t A100 \N t \N \N \N {} -173 27 2025-02-23 12:14:48.113644-08 2025-02-23 10:35:05.48636-08 leaderboard t A100 0.014219568666666666 t \N \N \N {} -174 27 2025-02-23 11:52:06.861806-08 2025-02-23 12:06:16.599311-08 test f A100 \N t \N \N \N {} -175 27 2025-02-23 11:14:31.938617-08 2025-02-23 10:50:54.199169-08 benchmark f A100 \N t \N \N \N {} -176 27 2025-02-23 10:55:35.824906-08 2025-02-23 11:40:46.262363-08 leaderboard f A100 0.014177354333333335 t \N \N \N {} -177 27 2025-02-23 10:54:29.927447-08 2025-02-23 11:20:37.982222-08 test f H100 \N t \N \N \N {} -178 27 2025-02-23 11:15:34.972209-08 2025-02-23 12:22:50.553085-08 benchmark f H100 \N t \N \N \N {} -179 27 2025-02-23 11:27:38.137008-08 2025-02-23 12:17:23.904583-08 leaderboard f H100 0.00628814 t \N \N \N {} -181 27 2025-02-23 12:09:44.028933-08 2025-02-23 11:26:46.80693-08 benchmark t T4 \N t \N \N \N {} -182 27 2025-02-23 11:56:25.478243-08 2025-02-23 10:36:01.655206-08 leaderboard t T4 0.07672247533333333 t \N \N \N {} -183 27 2025-02-23 11:51:05.559443-08 2025-02-23 10:39:36.577419-08 test f L4 \N t \N \N \N {} -184 27 2025-02-23 11:41:30.376423-08 2025-02-23 10:37:16.305101-08 benchmark f L4 \N t \N \N \N {} -185 27 2025-02-23 10:35:57.229029-08 2025-02-23 10:39:52.072211-08 leaderboard f L4 0.07915553666666666 t \N \N \N {} -186 27 2025-02-23 11:10:53.767744-08 2025-02-23 10:48:50.231998-08 test f T4 \N t \N \N \N {} -187 27 2025-02-23 12:02:00.098937-08 2025-02-23 11:08:01.038877-08 benchmark f T4 \N t \N \N \N {} -188 27 2025-02-23 11:11:25.926517-08 2025-02-23 10:30:09.448745-08 leaderboard f T4 0.076629145 t \N \N \N {} -189 27 2025-02-23 11:53:45.306343-08 2025-02-23 12:16:41.267247-08 test t L4 \N t \N \N \N {} -190 27 2025-02-23 11:28:09.310531-08 2025-02-23 10:48:48.140068-08 benchmark t L4 \N t \N \N \N {} -191 27 2025-02-23 12:08:23.202643-08 2025-02-23 11:17:51.689748-08 leaderboard t L4 0.07890172333333333 t \N \N \N {} -192 27 2025-02-23 10:44:11.726542-08 2025-02-23 11:02:53.940073-08 test t H100 \N t \N \N \N {} -193 27 2025-02-23 12:19:32.856515-08 2025-02-23 10:47:43.576992-08 benchmark t H100 \N t \N \N \N {} -194 27 2025-02-23 12:21:29.202301-08 2025-02-23 11:46:31.676892-08 leaderboard t H100 0.006306255 t \N \N \N {} -195 28 2025-02-23 11:13:19.67724-08 2025-02-23 10:39:48.64665-08 test f T4 \N f \N \N \N {} -196 29 2025-02-23 10:49:56.579251-08 2025-02-23 11:05:27.758434-08 test f T4 \N f \N \N \N {} -197 30 2025-02-23 12:27:41.798772-08 2025-02-23 12:02:37.751314-08 test f T4 \N f \N \N \N {} -198 31 2025-02-23 11:37:04.951166-08 2025-02-23 12:07:49.407209-08 test f T4 \N f \N \N \N {} -199 32 2025-02-23 11:58:09.474874-08 2025-02-23 10:43:38.089712-08 test f T4 \N t \N \N \N {} -200 33 2025-02-23 11:22:39.540982-08 2025-02-23 11:42:22.336345-08 benchmark f T4 \N t \N \N \N {} -201 34 2025-02-23 12:04:23.683096-08 2025-02-23 10:52:18.627224-08 test f T4 \N t \N \N \N {} -1622 539 2025-02-25 07:52:47.641192-08 2025-02-25 08:52:28.657841-08 test f H100 \N t \N \N \N {} -202 35 2025-02-23 12:24:22.817189-08 2025-02-23 10:54:19.203754-08 benchmark f T4 \N t \N \N \N {} -216 37 2025-02-23 11:14:38.746659-08 2025-02-23 11:03:54.206188-08 test t T4 \N t \N \N \N {} -243 50 2025-02-23 13:18:19.125258-08 2025-02-23 13:13:38.565546-08 test f T4 \N f \N \N \N {} -244 51 2025-02-23 13:01:35.786926-08 2025-02-23 12:24:04.575677-08 test f T4 \N f \N \N \N {} -245 52 2025-02-23 12:24:37.291631-08 2025-02-23 12:23:14.530709-08 test f T4 \N t \N \N \N {} -268 54 2025-02-23 12:25:21.411502-08 2025-02-23 12:05:19.559567-08 test t L4 \N t \N \N \N {} -247 53 2025-02-23 12:25:27.998153-08 2025-02-23 12:00:47.080967-08 benchmark f A100 \N t \N \N \N {} -248 53 2025-02-23 12:39:45.238062-08 2025-02-23 12:53:50.087404-08 benchmark f L4 \N t \N \N \N {} -249 53 2025-02-23 13:45:33.218295-08 2025-02-23 12:33:06.247895-08 benchmark f H100 \N t \N \N \N {} -250 54 2025-02-23 13:11:45.380271-08 2025-02-23 12:54:03.319507-08 test t A100 \N t \N \N \N {} -251 54 2025-02-23 12:41:48.180775-08 2025-02-23 12:18:18.458891-08 benchmark t A100 \N t \N \N \N {} -252 54 2025-02-23 13:47:01.02295-08 2025-02-23 13:12:03.617869-08 leaderboard t A100 0.00318012725 t \N \N \N {} -253 54 2025-02-23 12:36:35.151783-08 2025-02-23 12:27:40.807586-08 test f L4 \N t \N \N \N {} -3565 1211 2025-02-28 12:01:14.621003-08 2025-02-28 12:23:14.001408-08 test f H100 \N t \N \N \N {} -291 67 2025-02-23 13:52:22.554731-08 2025-02-23 15:04:28.302779-08 benchmark f H100 \N t \N \N \N {} -292 68 2025-02-23 14:13:32.52352-08 2025-02-23 13:39:23.211145-08 test f H100 \N t \N \N \N {} -294 70 2025-02-23 13:48:10.257644-08 2025-02-23 15:04:21.69425-08 test f H100 \N t \N \N \N {} -295 70 2025-02-23 13:45:01.008267-08 2025-02-23 15:19:04.053139-08 benchmark f H100 \N t \N \N \N {} -296 70 2025-02-23 14:08:32.500158-08 2025-02-23 13:34:17.476377-08 leaderboard f H100 0.0014198665 t \N \N \N {} -297 70 2025-02-23 14:22:14.947889-08 2025-02-23 14:43:43.399906-08 test t H100 \N t \N \N \N {} -395 104 2025-02-23 19:37:48.159657-08 2025-02-23 19:42:22.47971-08 leaderboard f L4 0.009219998333333333 t \N \N \N {} -396 104 2025-02-23 19:29:57.556657-08 2025-02-23 19:14:44.962181-08 test t T4 \N t \N \N \N {} -397 104 2025-02-23 19:06:51.535467-08 2025-02-23 19:09:13.584955-08 benchmark t T4 \N t \N \N \N {} -398 104 2025-02-23 19:08:03.666634-08 2025-02-23 18:31:14.289034-08 leaderboard t T4 0.009337150333333334 t \N \N \N {} -399 104 2025-02-23 18:20:58.004675-08 2025-02-23 18:12:39.887533-08 test t L4 \N t \N \N \N {} -400 104 2025-02-23 18:37:28.598432-08 2025-02-23 19:04:08.192125-08 benchmark t L4 \N t \N \N \N {} -401 104 2025-02-23 18:23:08.135641-08 2025-02-23 19:25:04.431404-08 leaderboard t L4 0.009286899666666666 t \N \N \N {} -440 137 2025-02-23 19:19:33.088698-08 2025-02-23 19:28:34.03025-08 test t A100 \N t \N \N \N {} -405 110 2025-02-23 19:36:40.687466-08 2025-02-23 19:24:36.001868-08 test f H100 \N f \N \N \N {} -406 111 2025-02-23 19:22:44.96918-08 2025-02-23 18:57:09.743491-08 test f L4 \N f \N \N \N {} -407 113 2025-02-23 18:49:58.235949-08 2025-02-23 19:26:22.885286-08 test f L4 \N f \N \N \N {} -408 112 2025-02-23 19:16:17.884334-08 2025-02-23 20:17:51.435513-08 test f H100 \N f \N \N \N {} -4354 1425 2025-03-01 20:13:46.966029-08 2025-03-01 18:52:04.589122-08 test t T4 \N t \N \N \N {} -465 144 2025-02-24 00:55:32.36629-08 2025-02-24 01:02:07.010747-08 leaderboard f A100 0.0031325836666666663 t \N \N \N {} -466 145 2025-02-24 00:32:22.702838-08 2025-02-24 01:20:22.072236-08 test f L4 \N t \N \N \N {} -467 145 2025-02-24 00:52:22.990735-08 2025-02-24 00:23:20.150431-08 benchmark f L4 \N t \N \N \N {} -468 145 2025-02-24 01:17:15.371174-08 2025-02-24 00:43:01.83229-08 leaderboard f L4 0.01690829933333333 t \N \N \N {} -472 145 2025-02-24 00:20:39.930346-08 2025-02-24 01:30:42.571148-08 test t L4 \N t \N \N \N {} -473 145 2025-02-24 01:15:01.028642-08 2025-02-24 01:45:10.923025-08 benchmark t L4 \N t \N \N \N {} -474 145 2025-02-24 01:13:01.276238-08 2025-02-24 02:03:18.528002-08 leaderboard t L4 0.016441613666666667 t \N \N \N {} -475 145 2025-02-24 00:23:20.873525-08 2025-02-24 00:29:22.877398-08 test t A100 \N t \N \N \N {} -476 145 2025-02-24 01:36:08.741349-08 2025-02-24 01:17:03.910006-08 benchmark t A100 \N t \N \N \N {} -477 145 2025-02-24 01:59:25.837709-08 2025-02-24 01:17:11.163833-08 leaderboard t A100 0.00310126 t \N \N \N {} -478 145 2025-02-24 02:01:13.502168-08 2025-02-24 01:09:26.58412-08 test t H100 \N t \N \N \N {} -298 70 2025-02-23 13:31:54.273224-08 2025-02-23 14:34:44.74164-08 benchmark t H100 \N t \N \N \N {} -299 70 2025-02-23 13:44:07.386708-08 2025-02-23 15:06:34.650489-08 leaderboard t H100 0.001028769 t \N \N \N {} -323 79 2025-02-23 15:26:33.721086-08 2025-02-23 15:20:07.296257-08 test f H100 \N t \N \N \N {} -486 145 2025-02-24 01:01:38.175295-08 2025-02-24 01:58:42.065024-08 test t T4 \N t \N \N \N {} -769 244 2025-02-24 11:02:11.786679-08 2025-02-24 09:19:00.797899-08 test f A100 \N t \N \N \N {} -479 145 2025-02-24 00:22:39.260481-08 2025-02-24 00:31:46.970049-08 benchmark t H100 \N t \N \N \N {} -480 145 2025-02-24 01:01:44.442094-08 2025-02-24 00:11:35.7708-08 leaderboard t H100 0.00142161375 t \N \N \N {} -481 145 2025-02-24 00:37:45.034628-08 2025-02-24 00:06:19.291315-08 test f H100 \N t \N \N \N {} -482 145 2025-02-24 01:23:58.17948-08 2025-02-24 00:43:49.157244-08 benchmark f H100 \N t \N \N \N {} -483 145 2025-02-24 00:09:29.066055-08 2025-02-24 00:27:00.430461-08 leaderboard f H100 0.00140814175 t \N \N \N {} -484 145 2025-02-24 02:01:37.341833-08 2025-02-24 01:22:53.107149-08 test f T4 \N t \N \N \N {} -541 157 2025-02-24 03:23:47.844322-08 2025-02-24 03:43:51.69252-08 test f A100 \N t \N \N \N {} -558 159 2025-02-24 03:52:44.512606-08 2025-02-24 05:07:17.070819-08 test f H100 \N t \N \N \N {} -542 157 2025-02-24 04:17:48.694412-08 2025-02-24 04:12:41.545476-08 benchmark f A100 \N t \N \N \N {} -549 157 2025-02-24 04:20:20.945717-08 2025-02-24 03:23:10.699715-08 leaderboard t T4 0.023143011666666668 t \N \N \N {} -1491 498 2025-02-25 07:26:44.389059-08 2025-02-25 06:14:45.396909-08 test f A100 \N f \N \N \N {} -550 157 2025-02-24 04:41:06.56919-08 2025-02-24 03:16:56.08599-08 test t A100 \N t \N \N \N {} -551 157 2025-02-24 04:37:58.696168-08 2025-02-24 03:37:13.752989-08 benchmark t A100 \N t \N \N \N {} -552 157 2025-02-24 03:31:21.654659-08 2025-02-24 04:02:58.352237-08 leaderboard t A100 0.006896261545454546 t \N \N \N {} -553 157 2025-02-24 05:03:09.664072-08 2025-02-24 05:13:14.483324-08 test t L4 \N f \N \N \N {} -554 157 2025-02-24 04:29:42.313161-08 2025-02-24 05:11:38.142222-08 test f L4 \N t \N \N \N {} -555 157 2025-02-24 04:36:10.687082-08 2025-02-24 03:31:24.036485-08 benchmark f L4 \N t \N \N \N {} -556 157 2025-02-24 03:38:18.277868-08 2025-02-24 03:25:50.851837-08 leaderboard f L4 0.018886963333333333 t \N \N \N {} -557 158 2025-02-24 04:36:41.191894-08 2025-02-24 04:18:45.993597-08 test f H100 \N t \N \N \N {} -1492 498 2025-02-25 06:34:51.735413-08 2025-02-25 05:41:39.626728-08 test t A100 \N f \N \N \N {} -559 160 2025-02-24 04:47:33.735469-08 2025-02-24 03:48:26.300529-08 test f H100 \N t \N \N \N {} -572 162 2025-02-24 05:00:12.670271-08 2025-02-24 05:05:35.448889-08 leaderboard f T4 0.018292196666666666 t \N \N \N {} -573 162 2025-02-24 03:26:01.433346-08 2025-02-24 03:56:51.262514-08 test f L4 \N f \N \N \N {} -574 162 2025-02-24 04:51:39.961542-08 2025-02-24 04:59:26.105146-08 test t L4 \N t \N \N \N {} -5259 1613 2025-03-06 06:50:43.258546-08 2025-03-06 08:19:11.165198-08 test f T4 \N t \N \N \N {} -600 172 2025-02-24 04:53:48.285554-08 2025-02-24 05:23:18.845114-08 test f A100 \N t \N \N \N {} -581 166 2025-02-24 03:35:47.72346-08 2025-02-24 03:38:46.586413-08 test f H100 \N f \N \N \N {} -606 172 2025-02-24 03:42:34.249272-08 2025-02-24 05:40:44.184189-08 test f L4 \N t \N \N \N {} -311 77 2025-02-23 14:12:30.101398-08 2025-02-23 15:24:57.946248-08 test f H100 \N t \N \N \N {} -312 77 2025-02-23 15:19:37.391023-08 2025-02-23 14:11:23.026317-08 benchmark f H100 \N t \N \N \N {} -313 77 2025-02-23 15:05:19.0137-08 2025-02-23 15:11:53.57026-08 leaderboard f H100 0.001450680857142857 t \N \N \N {} -315 77 2025-02-23 13:53:12.61876-08 2025-02-23 13:59:11.207858-08 benchmark t H100 \N t \N \N \N {} -316 77 2025-02-23 14:48:41.323897-08 2025-02-23 15:13:01.5905-08 leaderboard t H100 0.00145937425 t \N \N \N {} -317 78 2025-02-23 14:02:23.760311-08 2025-02-23 13:57:30.080076-08 test t H100 \N t \N \N \N {} -716 229 2025-02-24 08:35:17.736081-08 2025-02-24 07:54:05.005879-08 leaderboard f A100 0.004042775333333333 t \N \N \N {} -717 230 2025-02-24 09:22:18.875039-08 2025-02-24 08:00:26.274996-08 test f A100 \N t \N \N \N {} -1135 316 2025-02-24 20:02:44.708551-08 2025-02-24 18:21:09.94372-08 leaderboard t A100 0.011420428666666666 t \N \N \N {} -718 230 2025-02-24 08:33:59.269854-08 2025-02-24 07:51:14.292412-08 benchmark f A100 \N t \N \N \N {} -720 230 2025-02-24 09:24:41.117156-08 2025-02-24 08:14:51.47456-08 test t A100 \N t \N \N \N {} -721 230 2025-02-24 09:25:59.168682-08 2025-02-24 07:38:18.264898-08 benchmark t A100 \N t \N \N \N {} -722 230 2025-02-24 08:13:38.930592-08 2025-02-24 07:37:12.954266-08 leaderboard t A100 0.0032000705 t \N \N \N {} -723 231 2025-02-24 09:17:26.030333-08 2025-02-24 08:18:23.337179-08 test f H100 \N t \N \N \N {} -724 231 2025-02-24 09:02:18.510228-08 2025-02-24 08:27:34.118305-08 test f A100 \N t \N \N \N {} -1136 316 2025-02-24 18:52:30.044102-08 2025-02-24 19:36:46.191371-08 test f A100 \N t \N \N \N {} -726 231 2025-02-24 08:34:48.003809-08 2025-02-24 08:40:27.717239-08 test f L4 \N f \N \N \N {} -732 233 2025-02-24 09:29:37.727181-08 2025-02-24 08:02:29.392295-08 benchmark f A100 \N t \N \N \N {} -733 233 2025-02-24 08:20:40.863772-08 2025-02-24 07:58:20.853653-08 leaderboard f A100 0.0031231213333333336 t \N \N \N {} -734 233 2025-02-24 08:49:10.944512-08 2025-02-24 08:12:13.280323-08 test f H100 \N t \N \N \N {} -735 233 2025-02-24 08:30:07.553648-08 2025-02-24 08:58:56.261174-08 benchmark f H100 \N t \N \N \N {} -1354 451 2025-02-25 02:04:40.095144-08 2025-02-25 03:49:19.013675-08 benchmark f H100 \N t \N \N \N {} -736 233 2025-02-24 08:27:18.705589-08 2025-02-24 09:10:53.230123-08 leaderboard f H100 0.002061958 t \N \N \N {} -741 233 2025-02-24 08:13:06.765051-08 2025-02-24 07:57:46.536533-08 benchmark f T4 \N t \N \N \N {} -742 233 2025-02-24 09:28:14.411862-08 2025-02-24 07:55:13.882323-08 leaderboard f T4 0.004024178 t \N \N \N {} -743 233 2025-02-24 08:45:35.726194-08 2025-02-24 07:49:16.0139-08 test t H100 \N t \N \N \N {} -744 233 2025-02-24 08:30:17.598827-08 2025-02-24 08:45:38.138191-08 benchmark t H100 \N t \N \N \N {} -745 233 2025-02-24 08:29:13.376851-08 2025-02-24 08:41:38.50256-08 leaderboard t H100 0.0024627566666666667 t \N \N \N {} -318 78 2025-02-23 14:02:29.033294-08 2025-02-23 14:26:37.663496-08 benchmark t H100 \N t \N \N \N {} -319 78 2025-02-23 15:24:09.465856-08 2025-02-23 14:31:24.442508-08 leaderboard t H100 0.001455489 t \N \N \N {} -327 79 2025-02-23 15:44:13.226491-08 2025-02-23 13:56:26.731737-08 benchmark t H100 \N t \N \N \N {} -336 82 2025-02-23 17:25:08.638515-08 2025-02-23 17:57:21.345045-08 test f H100 \N f \N \N \N {} -337 83 2025-02-23 18:00:31.70052-08 2025-02-23 17:58:50.769159-08 test f H100 \N f \N \N \N {} -338 84 2025-02-23 18:06:57.258257-08 2025-02-23 16:51:18.239842-08 test f H100 \N f \N \N \N {} -339 85 2025-02-23 17:10:24.770217-08 2025-02-23 17:18:24.35571-08 test f H100 \N f \N \N \N {} -768 244 2025-02-24 10:02:14.922389-08 2025-02-24 10:56:52.559337-08 leaderboard t A100 0.0032697126666666665 t \N \N \N {} -340 86 2025-02-23 17:42:30.9851-08 2025-02-23 18:05:44.756059-08 test f H100 \N f \N \N \N {} -3566 1211 2025-02-28 12:19:44.674046-08 2025-02-28 11:20:06.595714-08 benchmark f H100 \N t \N \N \N {} -1356 453 2025-02-25 02:50:36.296171-08 2025-02-25 03:40:22.203999-08 benchmark f H100 \N t \N \N \N {} -777 250 2025-02-24 10:13:18.705553-08 2025-02-24 11:57:51.322708-08 test f A100 \N t \N \N \N {} -778 250 2025-02-24 11:03:04.113706-08 2025-02-24 11:50:05.562623-08 benchmark f A100 \N t \N \N \N {} -779 250 2025-02-24 11:49:40.71682-08 2025-02-24 10:20:18.789178-08 leaderboard f A100 0.0032043086666666666 t \N \N \N {} -780 250 2025-02-24 11:38:14.616988-08 2025-02-24 11:50:19.652707-08 test t A100 \N t \N \N \N {} -781 250 2025-02-24 11:06:57.166985-08 2025-02-24 11:37:32.323803-08 benchmark t A100 \N t \N \N \N {} -1358 456 2025-02-25 03:04:57.340562-08 2025-02-25 03:41:50.970915-08 benchmark f H100 \N t \N \N \N {} -782 250 2025-02-24 11:40:30.959556-08 2025-02-24 11:55:45.22426-08 leaderboard t A100 0.0031807153333333333 t \N \N \N {} -783 251 2025-02-24 10:28:34.15622-08 2025-02-24 10:26:21.513069-08 test t A100 \N t \N \N \N {} -784 251 2025-02-24 11:22:43.230478-08 2025-02-24 10:17:13.237357-08 benchmark t A100 \N t \N \N \N {} -785 251 2025-02-24 11:01:06.012176-08 2025-02-24 10:42:38.296715-08 leaderboard t A100 0.003201019 t \N \N \N {} -354 94 2025-02-23 18:07:11.918125-08 2025-02-23 18:49:09.9145-08 benchmark t H100 \N t \N \N \N {} -361 97 2025-02-23 19:03:55.153785-08 2025-02-23 19:25:28.656928-08 test t H100 \N t \N \N \N {} -367 98 2025-02-23 19:12:25.166912-08 2025-02-23 18:43:49.363313-08 benchmark f H100 \N t \N \N \N {} -368 99 2025-02-23 18:24:18.547946-08 2025-02-23 19:38:43.869117-08 test t H100 \N t \N \N \N {} -369 99 2025-02-23 19:55:02.527597-08 2025-02-23 19:45:12.210798-08 benchmark t H100 \N t \N \N \N {} -371 99 2025-02-23 19:05:59.158414-08 2025-02-23 18:08:37.209856-08 test f H100 \N t \N \N \N {} -377 102 2025-02-23 18:16:40.647606-08 2025-02-23 18:22:04.894771-08 benchmark f H100 \N t \N \N \N {} -378 104 2025-02-23 18:44:39.804161-08 2025-02-23 20:02:52.428781-08 test f H100 \N t \N \N \N {} -379 104 2025-02-23 18:34:28.483515-08 2025-02-23 19:39:00.450206-08 benchmark f H100 \N t \N \N \N {} -380 104 2025-02-23 19:35:24.292215-08 2025-02-23 20:07:53.402422-08 leaderboard f H100 0.0010334816666666667 t \N \N \N {} -385 104 2025-02-23 19:53:09.908295-08 2025-02-23 19:18:59.271408-08 benchmark f A100 \N t \N \N \N {} -394 104 2025-02-23 19:10:09.186446-08 2025-02-23 18:43:18.809569-08 benchmark f L4 \N t \N \N \N {} -403 108 2025-02-23 19:55:38.938073-08 2025-02-23 20:17:37.031218-08 test f H100 \N f \N \N \N {} -404 109 2025-02-23 19:08:16.246023-08 2025-02-23 19:56:30.430723-08 test f H100 \N f \N \N \N {} -410 114 2025-02-23 20:00:48.18605-08 2025-02-23 19:19:29.420657-08 test f H100 \N f \N \N \N {} -411 116 2025-02-23 19:34:58.202046-08 2025-02-23 20:01:11.73353-08 benchmark f T4 \N t \N \N \N {} -412 118 2025-02-23 20:37:38.267216-08 2025-02-23 19:02:18.39604-08 benchmark f T4 \N f \N \N \N {} -413 117 2025-02-23 19:24:46.341973-08 2025-02-23 18:57:45.646571-08 test f A100 \N f \N \N \N {} -414 117 2025-02-23 18:57:59.477452-08 2025-02-23 20:15:43.624804-08 test t A100 \N f \N \N \N {} -415 119 2025-02-23 18:57:34.156909-08 2025-02-23 18:54:25.73194-08 benchmark f T4 \N f \N \N \N {} -416 120 2025-02-23 20:05:08.814298-08 2025-02-23 18:59:37.20305-08 test f L4 \N t \N \N \N {} -418 122 2025-02-23 20:50:03.177865-08 2025-02-23 20:14:16.240173-08 test f L4 \N t \N \N \N {} -786 251 2025-02-24 10:26:17.480514-08 2025-02-24 10:27:14.231102-08 test t T4 \N t \N \N \N {} -792 251 2025-02-24 11:19:29.761457-08 2025-02-24 10:12:45.329053-08 test t H100 \N t \N \N \N {} -1444 484 2025-02-25 05:32:24.532424-08 2025-02-25 05:46:04.733398-08 test t A100 \N t \N \N \N {} -788 251 2025-02-24 10:59:47.100658-08 2025-02-24 11:38:00.698912-08 leaderboard t T4 0.017558684 t \N \N \N {} -789 251 2025-02-24 12:04:49.533693-08 2025-02-24 10:27:37.844537-08 test f A100 \N t \N \N \N {} -790 251 2025-02-24 12:04:40.357942-08 2025-02-24 10:51:07.619523-08 benchmark f A100 \N t \N \N \N {} -791 251 2025-02-24 11:22:21.198065-08 2025-02-24 11:52:00.418781-08 leaderboard f A100 0.003236336 t \N \N \N {} -811 253 2025-02-24 11:06:37.307391-08 2025-02-24 11:40:28.537535-08 test t A100 \N t \N \N \N {} -1447 484 2025-02-25 05:28:22.571964-08 2025-02-25 04:07:59.842135-08 test f A100 \N t \N \N \N {} -793 251 2025-02-24 11:52:18.452547-08 2025-02-24 11:37:31.847226-08 benchmark t H100 \N t \N \N \N {} -794 251 2025-02-24 10:55:56.967992-08 2025-02-24 12:02:37.363415-08 leaderboard t H100 0.001464676 t \N \N \N {} -795 251 2025-02-24 11:55:37.519662-08 2025-02-24 10:47:45.331086-08 test f H100 \N t \N \N \N {} -796 251 2025-02-24 10:51:49.810385-08 2025-02-24 10:25:51.454365-08 benchmark f H100 \N t \N \N \N {} -797 251 2025-02-24 11:15:30.778494-08 2025-02-24 11:00:25.052673-08 leaderboard f H100 0.0014206696 t \N \N \N {} -825 258 2025-02-24 13:00:16.497406-08 2025-02-24 13:34:23.466302-08 leaderboard t A100 0.0032076514 t \N \N \N {} -420 124 2025-02-23 20:02:27.111264-08 2025-02-23 20:04:53.203923-08 test f L4 \N t \N \N \N {} -422 126 2025-02-23 20:06:43.793931-08 2025-02-23 20:32:18.759967-08 test f L4 \N t \N \N \N {} -423 127 2025-02-23 20:24:10.490624-08 2025-02-23 20:17:20.346438-08 test f L4 \N f \N \N \N {} -425 129 2025-02-23 20:46:15.037395-08 2025-02-23 19:07:35.835051-08 test f L4 \N f \N \N \N {} -1450 485 2025-02-25 04:19:06.14904-08 2025-02-25 04:39:46.798027-08 test t A100 \N t \N \N \N {} -800 251 2025-02-24 10:59:21.599538-08 2025-02-24 10:25:36.051807-08 leaderboard f T4 0.01756872166666667 t \N \N \N {} -801 251 2025-02-24 10:42:44.855208-08 2025-02-24 10:38:35.745986-08 test f L4 \N t \N \N \N {} -802 251 2025-02-24 11:52:41.634738-08 2025-02-24 11:17:02.406538-08 benchmark f L4 \N t \N \N \N {} -826 258 2025-02-24 12:08:48.726753-08 2025-02-24 13:40:11.091506-08 test f A100 \N t \N \N \N {} -1453 485 2025-02-25 04:53:38.828316-08 2025-02-25 04:03:40.596895-08 test f A100 \N t \N \N \N {} -806 251 2025-02-24 10:12:06.628686-08 2025-02-24 10:32:34.248863-08 leaderboard t L4 0.017363646666666666 t \N \N \N {} -807 252 2025-02-24 10:34:25.534395-08 2025-02-24 12:10:21.1189-08 test f A100 \N f \N \N \N {} -808 252 2025-02-24 12:08:19.909711-08 2025-02-24 11:07:28.545277-08 test t A100 \N f \N \N \N {} -809 253 2025-02-24 10:56:51.671658-08 2025-02-24 12:19:25.430319-08 test f A100 \N t \N \N \N {} -810 253 2025-02-24 12:24:27.607242-08 2025-02-24 11:11:09.097006-08 benchmark f A100 \N f \N \N \N {} -821 256 2025-02-24 13:32:00.37212-08 2025-02-24 12:39:04.454955-08 test f A100 \N f \N \N \N {} -822 257 2025-02-24 11:59:03.654568-08 2025-02-24 12:25:11.023194-08 test f H100 \N t \N \N \N {} -823 258 2025-02-24 13:11:25.747263-08 2025-02-24 12:51:12.133715-08 test t A100 \N t \N \N \N {} -824 258 2025-02-24 12:23:35.99065-08 2025-02-24 12:54:48.794104-08 benchmark t A100 \N t \N \N \N {} -828 258 2025-02-24 12:28:03.710997-08 2025-02-24 12:02:38.320312-08 leaderboard f A100 0.00319396075 t \N \N \N {} -830 259 2025-02-24 13:38:53.547783-08 2025-02-24 12:05:16.740651-08 benchmark f H100 \N t \N \N \N {} -831 259 2025-02-24 12:19:41.162515-08 2025-02-24 13:02:34.045891-08 leaderboard f H100 0.0014907706363636365 t \N \N \N {} -832 259 2025-02-24 13:34:25.778466-08 2025-02-24 12:32:27.73762-08 test t H100 \N t \N \N \N {} -833 259 2025-02-24 11:57:09.771187-08 2025-02-24 13:17:24.239643-08 benchmark t H100 \N t \N \N \N {} -834 259 2025-02-24 12:53:22.94161-08 2025-02-24 13:02:51.615542-08 leaderboard t H100 0.0015141807894736843 t \N \N \N {} -837 261 2025-02-24 14:33:31.764087-08 2025-02-24 14:09:19.937583-08 benchmark f H100 \N t \N \N \N {} -838 261 2025-02-24 13:29:16.943379-08 2025-02-24 14:28:28.491199-08 benchmark f A100 \N t \N \N \N {} -965 286 2025-02-24 15:17:07.072445-08 2025-02-24 15:23:57.396796-08 test f A100 \N t \N \N \N {} -843 262 2025-02-24 14:18:36.348524-08 2025-02-24 14:09:28.031494-08 leaderboard f H100 0.00158875175 t \N \N \N {} -1456 486 2025-02-25 05:57:12.50863-08 2025-02-25 04:24:28.062012-08 test f A100 \N t \N \N \N {} -850 262 2025-02-24 13:53:27.308737-08 2025-02-24 14:16:34.938488-08 test f L4 \N t \N \N \N {} -851 262 2025-02-24 12:43:25.694004-08 2025-02-24 14:30:18.247948-08 benchmark f L4 \N t \N \N \N {} -852 262 2025-02-24 14:41:37.221242-08 2025-02-24 14:15:54.675901-08 leaderboard f L4 0.01720207333333333 t \N \N \N {} -853 262 2025-02-24 13:03:41.972489-08 2025-02-24 13:01:38.374088-08 test f A100 \N t \N \N \N {} -854 262 2025-02-24 14:31:55.855835-08 2025-02-24 13:01:31.443949-08 benchmark f A100 \N t \N \N \N {} -1359 457 2025-02-25 02:33:43.299256-08 2025-02-25 02:58:46.454768-08 benchmark f H100 \N t \N \N \N {} -860 262 2025-02-24 14:13:52.225617-08 2025-02-24 14:17:22.296848-08 test t H100 \N t \N \N \N {} -1360 458 2025-02-25 04:21:39.8674-08 2025-02-25 03:31:46.64471-08 benchmark f H100 \N t \N \N \N {} -866 264 2025-02-24 14:18:15.875273-08 2025-02-24 14:25:54.32018-08 test t A100 \N f \N \N \N {} -426 130 2025-02-23 19:07:19.532288-08 2025-02-23 19:27:05.035489-08 test f L4 \N f \N \N \N {} -427 131 2025-02-23 19:48:04.004883-08 2025-02-23 20:55:59.519835-08 test f L4 \N f \N \N \N {} -428 132 2025-02-23 19:43:42.154861-08 2025-02-23 19:58:51.977474-08 test f L4 \N t \N \N \N {} -430 134 2025-02-23 19:41:20.008317-08 2025-02-23 19:11:47.214628-08 test f L4 \N t \N \N \N {} -431 135 2025-02-23 20:41:38.742341-08 2025-02-23 20:10:24.488802-08 test t A100 \N t \N \N \N {} -867 264 2025-02-24 13:47:02.310423-08 2025-02-24 14:19:30.07954-08 test f A100 \N f \N \N \N {} -868 265 2025-02-24 13:25:41.120417-08 2025-02-24 13:19:04.569194-08 test f A100 \N f \N \N \N {} -869 265 2025-02-24 14:09:21.007191-08 2025-02-24 14:19:43.13391-08 test t A100 \N f \N \N \N {} -5676 1725 2025-03-09 09:53:25.9676-07 2025-03-09 10:54:50.998675-07 test t H100 \N t \N \N \N {} -1361 459 2025-02-25 02:30:56.124266-08 2025-02-25 02:51:08.772443-08 test t A100 \N t \N \N \N {} -871 266 2025-02-24 13:56:37.656623-08 2025-02-24 13:34:36.431618-08 test f H100 \N f \N \N \N {} -872 267 2025-02-24 13:27:30.522948-08 2025-02-24 14:25:22.159132-08 test f A100 \N t \N \N \N {} -873 267 2025-02-24 14:06:05.758615-08 2025-02-24 14:17:03.596666-08 benchmark f A100 \N t \N \N \N {} -874 267 2025-02-24 14:47:02.145718-08 2025-02-24 14:26:33.636732-08 leaderboard f A100 0.0032893633333333336 t \N \N \N {} -875 267 2025-02-24 14:02:32.569668-08 2025-02-24 13:04:10.687457-08 test t A100 \N t \N \N \N {} -876 267 2025-02-24 14:20:17.088913-08 2025-02-24 13:03:00.990794-08 benchmark t A100 \N t \N \N \N {} -877 267 2025-02-24 13:03:37.085609-08 2025-02-24 14:42:26.626312-08 leaderboard t A100 0.0032918306666666667 t \N \N \N {} -878 268 2025-02-24 13:32:17.727867-08 2025-02-24 14:42:15.893182-08 test f H100 \N t \N \N \N {} -879 268 2025-02-24 13:46:35.691848-08 2025-02-24 14:33:17.187051-08 benchmark f H100 \N t \N \N \N {} -880 268 2025-02-24 13:09:44.706773-08 2025-02-24 14:40:37.527146-08 leaderboard f H100 0.00148714025 t \N \N \N {} -881 268 2025-02-24 14:38:11.050645-08 2025-02-24 14:37:44.308056-08 test t H100 \N t \N \N \N {} -972 288 2025-02-24 16:59:51.753306-08 2025-02-24 16:32:43.265594-08 test t A100 \N t \N \N \N {} -889 270 2025-02-24 13:23:28.860312-08 2025-02-24 15:14:01.508532-08 benchmark f H100 \N t \N \N \N {} -890 270 2025-02-24 14:06:21.369663-08 2025-02-24 13:44:06.555384-08 leaderboard f H100 0.00633421 t \N \N \N {} -891 270 2025-02-24 15:11:19.3884-08 2025-02-24 14:22:05.663402-08 test t H100 \N t \N \N \N {} -892 270 2025-02-24 13:34:23.965761-08 2025-02-24 15:03:36.056155-08 benchmark t H100 \N t \N \N \N {} -897 271 2025-02-24 13:23:42.310773-08 2025-02-24 13:47:13.289788-08 benchmark f T4 \N t \N \N \N {} -432 135 2025-02-23 19:22:15.455814-08 2025-02-23 19:58:43.024263-08 benchmark t A100 \N t \N \N \N {} -433 135 2025-02-23 19:46:23.596065-08 2025-02-23 21:02:09.718637-08 leaderboard t A100 0.011416369 t \N \N \N {} -434 135 2025-02-23 20:11:32.421186-08 2025-02-23 20:54:46.274828-08 test f A100 \N t \N \N \N {} -435 135 2025-02-23 20:18:13.731355-08 2025-02-23 20:15:53.15599-08 benchmark f A100 \N t \N \N \N {} -436 135 2025-02-23 20:00:20.118277-08 2025-02-23 20:51:44.559411-08 leaderboard f A100 0.010131549619047619 t \N \N \N {} -437 137 2025-02-23 19:49:28.16914-08 2025-02-23 20:57:31.153582-08 test f A100 \N t \N \N \N {} -438 137 2025-02-23 20:09:57.38955-08 2025-02-23 20:26:38.729961-08 benchmark f A100 \N t \N \N \N {} -439 137 2025-02-23 19:51:53.206453-08 2025-02-23 20:31:39.322151-08 leaderboard f A100 0.010931205 t \N \N \N {} -441 137 2025-02-23 20:15:20.626379-08 2025-02-23 20:57:29.810715-08 benchmark t A100 \N t \N \N \N {} -442 137 2025-02-23 20:45:29.846555-08 2025-02-23 19:10:45.121007-08 leaderboard t A100 0.010102002266666667 t \N \N \N {} -576 162 2025-02-24 04:19:34.761817-08 2025-02-24 05:01:13.840464-08 leaderboard t L4 0.017179168666666668 t \N \N \N {} -577 163 2025-02-24 04:01:18.525271-08 2025-02-24 04:17:48.618121-08 test f T4 \N f \N \N \N {} -898 272 2025-02-24 14:03:42.781647-08 2025-02-24 13:55:34.397369-08 test t H100 \N t \N \N \N {} -899 272 2025-02-24 13:44:04.949972-08 2025-02-24 13:28:20.378369-08 benchmark t H100 \N t \N \N \N {} -900 272 2025-02-24 13:45:21.588357-08 2025-02-24 15:00:10.446354-08 leaderboard t H100 0.001380456 t \N \N \N {} -901 272 2025-02-24 14:59:05.351263-08 2025-02-24 15:02:57.541551-08 test f A100 \N t \N \N \N {} -3584 1228 2025-02-28 13:43:38.707851-08 2025-02-28 14:06:32.301701-08 test t A100 \N t \N \N \N {} -902 272 2025-02-24 13:41:50.302661-08 2025-02-24 14:48:59.498164-08 benchmark f A100 \N t \N \N \N {} -903 272 2025-02-24 13:24:49.213705-08 2025-02-24 13:38:04.997177-08 leaderboard f A100 0.0030942173333333334 t \N \N \N {} -904 272 2025-02-24 14:29:42.928168-08 2025-02-24 13:26:18.774967-08 test t A100 \N t \N \N \N {} -905 272 2025-02-24 14:12:54.933528-08 2025-02-24 14:48:10.033573-08 benchmark t A100 \N t \N \N \N {} -906 272 2025-02-24 14:06:49.466739-08 2025-02-24 14:26:14.363403-08 leaderboard t A100 0.0030895613333333334 t \N \N \N {} -907 272 2025-02-24 13:27:26.40339-08 2025-02-24 14:22:30.451832-08 test f T4 \N t \N \N \N {} -908 272 2025-02-24 13:50:22.416422-08 2025-02-24 14:23:23.865231-08 benchmark f T4 \N t \N \N \N {} -909 272 2025-02-24 15:02:51.400167-08 2025-02-24 14:45:31.55137-08 leaderboard f T4 0.016812591 t \N \N \N {} -910 272 2025-02-24 14:54:42.275644-08 2025-02-24 14:02:35.113798-08 test t T4 \N t \N \N \N {} -911 272 2025-02-24 14:14:55.576574-08 2025-02-24 13:55:39.639099-08 benchmark t T4 \N t \N \N \N {} -912 272 2025-02-24 14:30:30.596529-08 2025-02-24 15:11:06.343919-08 leaderboard t T4 0.016815438 t \N \N \N {} -913 272 2025-02-24 14:03:43.587782-08 2025-02-24 13:32:24.372553-08 test f H100 \N t \N \N \N {} -914 272 2025-02-24 13:58:45.530798-08 2025-02-24 14:37:34.053628-08 benchmark f H100 \N t \N \N \N {} -915 272 2025-02-24 13:44:39.285633-08 2025-02-24 14:28:08.305027-08 leaderboard f H100 0.0013929076666666668 t \N \N \N {} -916 273 2025-02-24 13:46:49.172996-08 2025-02-24 14:08:28.506582-08 test f H100 \N t \N \N \N {} -917 273 2025-02-24 13:44:56.476334-08 2025-02-24 14:40:36.885261-08 benchmark f H100 \N t \N \N \N {} -918 273 2025-02-24 14:57:37.29992-08 2025-02-24 14:13:39.653275-08 leaderboard f H100 0.00629756 t \N \N \N {} -919 272 2025-02-24 14:32:30.167492-08 2025-02-24 14:11:08.41029-08 test t L4 \N t \N \N \N {} -920 272 2025-02-24 13:47:24.679318-08 2025-02-24 13:20:10.828648-08 benchmark t L4 \N t \N \N \N {} -921 272 2025-02-24 15:14:24.435268-08 2025-02-24 15:12:42.536442-08 leaderboard t L4 0.016873888666666666 t \N \N \N {} -923 272 2025-02-24 14:50:59.333942-08 2025-02-24 14:12:16.270455-08 benchmark f L4 \N t \N \N \N {} -924 272 2025-02-24 14:47:47.523473-08 2025-02-24 13:44:27.360148-08 leaderboard f L4 0.016924354 t \N \N \N {} -925 273 2025-02-24 15:11:48.660708-08 2025-02-24 13:29:55.704446-08 test t H100 \N t \N \N \N {} -926 273 2025-02-24 15:15:14.766004-08 2025-02-24 13:19:47.409296-08 benchmark t H100 \N t \N \N \N {} -927 273 2025-02-24 14:10:49.544286-08 2025-02-24 14:57:42.082587-08 leaderboard t H100 0.004302405666666667 t \N \N \N {} -984 288 2025-02-24 15:26:18.349293-08 2025-02-24 15:44:04.650422-08 test f A100 \N t \N \N \N {} -928 274 2025-02-24 15:05:47.967562-08 2025-02-24 15:17:26.160717-08 test f H100 \N f \N \N \N {} -929 274 2025-02-24 15:04:47.728115-08 2025-02-24 14:57:41.957077-08 test t H100 \N f \N \N \N {} -3585 1228 2025-02-28 14:17:57.496054-08 2025-02-28 13:50:52.43144-08 benchmark t A100 \N t \N \N \N {} -935 278 2025-02-24 14:28:49.91519-08 2025-02-24 15:19:34.617051-08 benchmark t A100 \N t \N \N \N {} -936 278 2025-02-24 13:44:17.851045-08 2025-02-24 15:26:06.123862-08 leaderboard t A100 0.0031084026666666664 t \N \N \N {} -937 278 2025-02-24 15:01:10.720121-08 2025-02-24 14:26:25.903037-08 test f A100 \N t \N \N \N {} -990 288 2025-02-24 16:29:43.864283-08 2025-02-24 16:06:04.412307-08 test f L4 \N t \N \N \N {} -938 278 2025-02-24 15:22:13.9455-08 2025-02-24 15:34:30.906411-08 benchmark f A100 \N t \N \N \N {} -939 278 2025-02-24 15:34:14.43871-08 2025-02-24 14:09:55.104192-08 leaderboard f A100 0.003099416 t \N \N \N {} -449 138 2025-02-23 20:17:26.595429-08 2025-02-23 20:35:29.611944-08 benchmark f L4 \N t \N \N \N {} -451 139 2025-02-23 20:51:10.6817-08 2025-02-23 20:38:48.437704-08 test f A100 \N t \N \N \N {} -452 139 2025-02-23 19:49:38.747753-08 2025-02-23 19:40:42.834492-08 benchmark f A100 \N t \N \N \N {} -940 278 2025-02-24 15:09:41.101654-08 2025-02-24 13:40:05.003362-08 test f H100 \N t \N \N \N {} -941 278 2025-02-24 14:12:31.025675-08 2025-02-24 15:21:52.442215-08 benchmark f H100 \N t \N \N \N {} -942 278 2025-02-24 15:26:06.113726-08 2025-02-24 14:54:47.505262-08 leaderboard f H100 0.0013924253333333332 t \N \N \N {} -943 278 2025-02-24 13:41:17.222805-08 2025-02-24 14:40:55.819332-08 test t H100 \N t \N \N \N {} -944 278 2025-02-24 14:51:33.706683-08 2025-02-24 15:02:31.7023-08 benchmark t H100 \N t \N \N \N {} -945 278 2025-02-24 15:15:53.225485-08 2025-02-24 13:41:18.489963-08 leaderboard t H100 0.001391712 t \N \N \N {} -947 278 2025-02-24 13:47:07.517676-08 2025-02-24 14:35:18.503122-08 benchmark f L4 \N t \N \N \N {} -948 278 2025-02-24 15:32:37.786282-08 2025-02-24 14:47:49.400993-08 leaderboard f L4 0.016921183 t \N \N \N {} -950 278 2025-02-24 14:52:40.705932-08 2025-02-24 15:12:22.348896-08 benchmark t L4 \N t \N \N \N {} -951 278 2025-02-24 15:20:16.824802-08 2025-02-24 14:51:53.886502-08 leaderboard t L4 0.01689634233333333 t \N \N \N {} -952 278 2025-02-24 13:42:04.283901-08 2025-02-24 13:50:01.376078-08 test t T4 \N t \N \N \N {} -959 280 2025-02-24 14:37:57.453433-08 2025-02-24 13:55:35.067117-08 benchmark f A100 \N t \N \N \N {} -960 281 2025-02-24 15:29:16.351075-08 2025-02-24 15:15:34.238127-08 benchmark f A100 \N f \N \N \N {} -996 289 2025-02-24 17:05:53.711415-08 2025-02-24 16:12:17.469633-08 leaderboard f A100 0.0031874743333333336 t \N \N \N {} -961 282 2025-02-24 14:54:40.402838-08 2025-02-24 15:07:51.744951-08 benchmark f A100 \N t \N \N \N {} -1326 443 2025-02-25 01:31:19.548777-08 2025-02-25 02:21:57.556426-08 benchmark t A100 \N t \N \N \N {} -969 286 2025-02-24 14:27:36.98298-08 2025-02-24 14:24:18.873791-08 benchmark t A100 \N t \N \N \N {} -970 286 2025-02-24 15:45:02.969062-08 2025-02-24 15:20:58.131836-08 leaderboard t A100 0.003574085 t \N \N \N {} -971 287 2025-02-24 16:45:49.221907-08 2025-02-24 15:11:06.695278-08 benchmark f A100 \N t \N \N \N {} -1364 459 2025-02-25 03:07:07.507086-08 2025-02-25 03:28:33.611205-08 test f A100 \N t \N \N \N {} -973 288 2025-02-24 16:57:39.832425-08 2025-02-24 16:14:19.158825-08 benchmark t A100 \N t \N \N \N {} -974 288 2025-02-24 16:17:36.67277-08 2025-02-24 15:25:36.315446-08 leaderboard t A100 0.0035733863333333336 t \N \N \N {} -976 288 2025-02-24 16:12:37.835505-08 2025-02-24 16:32:25.940514-08 benchmark t H100 \N t \N \N \N {} -977 288 2025-02-24 16:02:16.632285-08 2025-02-24 15:30:08.973921-08 leaderboard t H100 0.0015792905 t \N \N \N {} -1367 460 2025-02-25 02:54:18.569745-08 2025-02-25 03:04:44.660402-08 test f H100 \N t \N \N \N {} -1462 487 2025-02-25 05:11:56.373844-08 2025-02-25 05:12:25.148355-08 test t H100 \N t \N \N \N {} -979 288 2025-02-24 16:39:20.43363-08 2025-02-24 16:31:03.690589-08 benchmark f H100 \N t \N \N \N {} -980 288 2025-02-24 15:39:09.650488-08 2025-02-24 15:08:31.848197-08 leaderboard f H100 0.001654801 t \N \N \N {} -981 288 2025-02-24 16:32:20.98845-08 2025-02-24 15:27:23.058332-08 test f T4 \N t \N \N \N {} -982 288 2025-02-24 15:11:31.029521-08 2025-02-24 15:54:36.042517-08 benchmark f T4 \N t \N \N \N {} -983 288 2025-02-24 15:57:27.675389-08 2025-02-24 16:16:35.300427-08 leaderboard f T4 0.01727558566666667 t \N \N \N {} -1370 460 2025-02-25 03:37:37.587036-08 2025-02-25 03:12:01.251999-08 test t H100 \N t \N \N \N {} -1465 487 2025-02-25 05:28:37.945849-08 2025-02-25 05:31:06.395877-08 test f H100 \N t \N \N \N {} -985 288 2025-02-24 16:00:46.319439-08 2025-02-24 16:11:47.492733-08 benchmark f A100 \N t \N \N \N {} -986 288 2025-02-24 16:16:37.007915-08 2025-02-24 16:47:35.551783-08 leaderboard f A100 0.0035907183333333333 t \N \N \N {} -987 288 2025-02-24 15:53:31.716837-08 2025-02-24 15:52:50.195056-08 test t T4 \N t \N \N \N {} -988 288 2025-02-24 15:24:51.892743-08 2025-02-24 15:37:05.023212-08 benchmark t T4 \N t \N \N \N {} -989 288 2025-02-24 15:27:07.594575-08 2025-02-24 16:56:10.768764-08 leaderboard t T4 0.017142831666666667 t \N \N \N {} -1373 461 2025-02-25 03:02:32.607966-08 2025-02-25 02:46:59.676745-08 test t H100 \N t \N \N \N {} -1469 489 2025-02-25 06:10:25.611874-08 2025-02-25 04:53:36.581953-08 test f A100 \N t \N \N \N {} -991 288 2025-02-24 16:15:35.403958-08 2025-02-24 15:42:05.66197-08 benchmark f L4 \N t \N \N \N {} -992 288 2025-02-24 15:44:25.515087-08 2025-02-24 16:58:38.135537-08 leaderboard f L4 0.017213298666666668 t \N \N \N {} -453 139 2025-02-23 20:25:09.33463-08 2025-02-23 21:00:42.630896-08 leaderboard f A100 2.3280243876666664 t \N \N \N {} -454 139 2025-02-23 19:54:51.244923-08 2025-02-23 20:51:32.367147-08 test t A100 \N t \N \N \N {} -455 139 2025-02-23 19:45:18.073547-08 2025-02-23 20:41:22.97686-08 benchmark t A100 \N t \N \N \N {} -456 139 2025-02-23 21:07:14.882985-08 2025-02-23 20:12:14.513014-08 leaderboard t A100 2.2617255652727275 t \N \N \N {} -458 142 2025-02-24 01:14:42.946921-08 2025-02-24 00:35:39.452004-08 test f A100 \N f \N \N \N {} -459 143 2025-02-24 01:11:00.689386-08 2025-02-24 00:41:21.530234-08 test f A100 \N t \N \N \N {} -460 144 2025-02-24 00:41:59.723288-08 2025-02-24 00:27:53.246315-08 test t A100 \N t \N \N \N {} -461 144 2025-02-24 00:21:52.786601-08 2025-02-24 01:33:07.398183-08 benchmark t A100 \N t \N \N \N {} -462 144 2025-02-24 01:54:02.801471-08 2025-02-24 01:19:41.256152-08 leaderboard t A100 0.003105596 t \N \N \N {} -463 144 2025-02-24 00:31:47.833871-08 2025-02-24 01:26:28.316452-08 test f A100 \N t \N \N \N {} -464 144 2025-02-24 01:15:21.059343-08 2025-02-24 01:40:04.362432-08 benchmark f A100 \N t \N \N \N {} -469 145 2025-02-24 00:21:16.048547-08 2025-02-24 01:12:36.574185-08 test f A100 \N t \N \N \N {} -470 145 2025-02-24 00:17:52.83432-08 2025-02-24 01:49:29.222442-08 benchmark f A100 \N t \N \N \N {} -471 145 2025-02-24 01:06:14.871957-08 2025-02-24 01:44:46.357832-08 leaderboard f A100 0.0030890706666666665 t \N \N \N {} -485 145 2025-02-24 01:20:54.849052-08 2025-02-24 01:08:55.25546-08 benchmark f T4 \N f \N \N \N {} -487 145 2025-02-24 00:17:54.793622-08 2025-02-24 00:31:22.4894-08 benchmark t T4 \N f \N \N \N {} -488 148 2025-02-24 03:09:35.205519-08 2025-02-24 03:27:23.971276-08 benchmark f A100 \N t \N \N \N {} -489 149 2025-02-24 03:30:55.449817-08 2025-02-24 03:37:56.567594-08 test f H100 \N t \N \N \N {} -490 150 2025-02-24 04:21:26.450468-08 2025-02-24 04:24:44.221828-08 test f H100 \N f \N \N \N {} -491 150 2025-02-24 04:05:31.466828-08 2025-02-24 03:04:56.25643-08 test t H100 \N t \N \N \N {} -993 288 2025-02-24 16:22:57.509895-08 2025-02-24 16:11:34.646111-08 test t L4 \N f \N \N \N {} -994 289 2025-02-24 16:42:29.124469-08 2025-02-24 16:15:50.980733-08 test f A100 \N t \N \N \N {} -995 289 2025-02-24 16:03:41.521804-08 2025-02-24 15:55:40.302003-08 benchmark f A100 \N t \N \N \N {} -997 289 2025-02-24 15:54:37.535241-08 2025-02-24 15:25:33.427509-08 test t A100 \N t \N \N \N {} -1327 443 2025-02-25 02:11:01.004727-08 2025-02-25 02:37:11.369093-08 leaderboard t A100 0.003228157 t \N \N \N {} -1328 443 2025-02-25 02:29:43.574743-08 2025-02-25 01:45:12.561001-08 test f A100 \N t \N \N \N {} -998 289 2025-02-24 15:30:32.784103-08 2025-02-24 15:14:53.659942-08 benchmark t A100 \N t \N \N \N {} -492 150 2025-02-24 03:09:36.145036-08 2025-02-24 03:37:47.928735-08 benchmark t H100 \N t \N \N \N {} -493 150 2025-02-24 03:50:14.789495-08 2025-02-24 04:18:31.651743-08 leaderboard t H100 0.00032365892307692306 t \N \N \N {} -494 151 2025-02-24 03:23:37.243425-08 2025-02-24 03:24:21.097123-08 test t H100 \N t \N \N \N {} -495 151 2025-02-24 04:50:10.450163-08 2025-02-24 03:28:44.586591-08 benchmark t H100 \N t \N \N \N {} -496 151 2025-02-24 04:56:39.642072-08 2025-02-24 04:24:35.925272-08 leaderboard t H100 0.00029347884745762714 t \N \N \N {} -497 151 2025-02-24 03:19:04.263395-08 2025-02-24 04:48:57.333637-08 test f H100 \N t \N \N \N {} -498 151 2025-02-24 04:44:55.465081-08 2025-02-24 04:38:58.27472-08 benchmark f H100 \N t \N \N \N {} -499 151 2025-02-24 04:22:14.523605-08 2025-02-24 03:36:00.958698-08 leaderboard f H100 0.0003125368620689655 t \N \N \N {} -5247 1613 2025-03-06 06:38:02.469655-08 2025-03-06 08:12:20.172654-08 test f H100 \N t \N \N \N {} -1322 442 2025-02-25 00:41:54.545147-08 2025-02-25 02:36:02.144792-08 test t T4 \N t \N \N \N {} -999 289 2025-02-24 15:25:33.457905-08 2025-02-24 16:27:05.872169-08 leaderboard t A100 0.0031989713333333337 t \N \N \N {} -1000 289 2025-02-24 16:48:53.415372-08 2025-02-24 16:21:48.670188-08 test t T4 \N t \N \N \N {} -1001 289 2025-02-24 17:09:32.511952-08 2025-02-24 15:33:31.453203-08 benchmark t T4 \N t \N \N \N {} -1376 461 2025-02-25 03:14:56.330738-08 2025-02-25 02:37:49.658574-08 test f H100 \N t \N \N \N {} -1471 491 2025-02-25 05:29:20.665807-08 2025-02-25 05:15:22.315477-08 test t A100 \N t \N \N \N {} -532 154 2025-02-24 04:48:16.131212-08 2025-02-24 04:44:31.007006-08 test f H100 \N t \N \N \N {} -3573 1218 2025-02-28 13:45:23.407437-08 2025-02-28 12:25:46.532879-08 test f H100 \N t \N \N \N {} -1118 304 2025-02-24 18:18:52.599968-08 2025-02-24 18:09:21.428872-08 leaderboard f L4 0.027837713666666666 t \N \N \N {} -2417 820 2025-02-26 12:21:06.882191-08 2025-02-26 13:15:22.606597-08 benchmark f H100 \N t \N \N \N {} -1123 306 2025-02-24 17:10:46.114629-08 2025-02-24 18:46:22.129619-08 benchmark f L4 \N t \N \N \N {} -1124 307 2025-02-24 17:18:47.489571-08 2025-02-24 17:33:10.036339-08 benchmark f H100 \N f \N \N \N {} -1129 312 2025-02-24 18:14:08.836383-08 2025-02-24 19:05:16.466962-08 test f T4 \N t \N \N \N {} -1130 313 2025-02-24 18:32:21.793683-08 2025-02-24 18:56:10.553395-08 test f T4 \N f \N \N \N {} -1131 314 2025-02-24 19:53:32.767284-08 2025-02-24 18:20:01.673708-08 test f A100 \N t \N \N \N {} -1132 315 2025-02-24 19:41:33.984587-08 2025-02-24 19:22:46.368176-08 test f A100 \N f \N \N \N {} -1133 316 2025-02-24 19:51:34.421459-08 2025-02-24 18:58:50.828608-08 test t A100 \N t \N \N \N {} -1134 316 2025-02-24 19:10:22.774186-08 2025-02-24 18:51:45.619893-08 benchmark t A100 \N t \N \N \N {} -1137 316 2025-02-24 19:55:11.126516-08 2025-02-24 18:17:41.336484-08 benchmark f A100 \N t \N \N \N {} -1138 316 2025-02-24 19:34:48.644475-08 2025-02-24 18:14:08.964849-08 leaderboard f A100 0.01013649895 t \N \N \N {} -1139 317 2025-02-24 19:01:55.707471-08 2025-02-24 19:26:25.81366-08 test f A100 \N f \N \N \N {} -1140 318 2025-02-24 18:34:52.552066-08 2025-02-24 19:48:38.716235-08 benchmark f A100 \N t \N \N \N {} -1141 319 2025-02-24 19:23:43.21952-08 2025-02-24 18:51:42.032684-08 test f A100 \N t \N \N \N {} -1142 320 2025-02-24 19:25:23.832847-08 2025-02-24 18:33:34.811732-08 benchmark f A100 \N t \N \N \N {} -1143 321 2025-02-24 20:03:32.364119-08 2025-02-24 18:41:04.100608-08 test f A100 \N t \N \N \N {} -1148 321 2025-02-24 19:35:02.480415-08 2025-02-24 19:46:33.415377-08 leaderboard t A100 0.008000332333333334 t \N \N \N {} -533 154 2025-02-24 04:30:12.737914-08 2025-02-24 03:41:45.719226-08 benchmark f H100 \N t \N \N \N {} -534 154 2025-02-24 04:26:39.743431-08 2025-02-24 03:49:38.668231-08 leaderboard f H100 0.00028956029310344826 t \N \N \N {} -1652 545 2025-02-25 09:28:59.147977-08 2025-02-25 08:22:34.616891-08 benchmark f A100 \N t \N \N \N {} -1653 545 2025-02-25 09:29:22.402709-08 2025-02-25 09:43:20.480827-08 leaderboard f A100 0.0031917851666666665 t \N \N \N {} -1654 545 2025-02-25 09:23:36.561531-08 2025-02-25 09:22:56.481202-08 test t A100 \N t \N \N \N {} -1655 545 2025-02-25 09:14:47.319988-08 2025-02-25 09:09:28.866828-08 benchmark t A100 \N t \N \N \N {} -1665 549 2025-02-25 09:27:34.175531-08 2025-02-25 08:31:10.590161-08 test t H100 \N t \N \N \N {} -2774 966 2025-02-26 20:38:42.929598-08 2025-02-26 19:33:54.323653-08 benchmark f A100 \N t \N \N \N {} -1671 550 2025-02-25 08:02:54.544859-08 2025-02-25 09:33:33.645968-08 test t H100 \N t \N \N \N {} -1672 550 2025-02-25 08:20:15.110251-08 2025-02-25 09:46:59.895856-08 benchmark t H100 \N t \N \N \N {} -1673 550 2025-02-25 08:16:05.976617-08 2025-02-25 08:56:29.376989-08 leaderboard t H100 0.0015150700833333332 t \N \N \N {} -1674 550 2025-02-25 09:23:16.872377-08 2025-02-25 09:42:17.075687-08 test f H100 \N t \N \N \N {} -1675 550 2025-02-25 08:00:39.040555-08 2025-02-25 08:14:08.930921-08 benchmark f H100 \N t \N \N \N {} -1677 551 2025-02-25 09:24:55.210117-08 2025-02-25 08:31:50.387183-08 benchmark f H100 \N t \N \N \N {} -4802 1487 2025-03-02 13:11:42.877171-08 2025-03-02 12:36:48.868582-08 test t A100 \N t \N \N \N {} -1704 565 2025-02-25 10:02:10.859525-08 2025-02-25 10:07:50.65938-08 test f A100 \N f \N \N \N {} -1705 566 2025-02-25 08:18:19.009138-08 2025-02-25 09:21:13.615277-08 test f A100 \N f \N \N \N {} -1706 566 2025-02-25 08:59:25.06759-08 2025-02-25 08:45:34.409254-08 test t A100 \N f \N \N \N {} -1708 570 2025-02-25 10:07:15.647675-08 2025-02-25 08:36:37.22994-08 test f T4 \N f \N \N \N {} -1709 570 2025-02-25 09:55:08.132128-08 2025-02-25 08:32:55.552216-08 test t T4 \N f \N \N \N {} -535 154 2025-02-24 04:07:21.29398-08 2025-02-24 04:13:20.791686-08 test t H100 \N t \N \N \N {} -536 154 2025-02-24 03:48:34.48216-08 2025-02-24 03:16:51.711753-08 benchmark t H100 \N t \N \N \N {} -537 154 2025-02-24 04:32:16.672746-08 2025-02-24 03:57:43.518406-08 leaderboard t H100 0.0002925063181818182 t \N \N \N {} -538 155 2025-02-24 04:35:32.646526-08 2025-02-24 05:04:46.599926-08 test f L4 \N f \N \N \N {} -539 155 2025-02-24 04:18:31.344638-08 2025-02-24 04:55:25.858103-08 test f T4 \N f \N \N \N {} -540 156 2025-02-24 03:43:29.839126-08 2025-02-24 03:21:47.390495-08 test f T4 \N t \N \N \N {} -543 157 2025-02-24 04:59:50.4166-08 2025-02-24 04:19:05.690038-08 leaderboard f A100 0.007824704333333333 t \N \N \N {} -544 157 2025-02-24 03:39:23.735651-08 2025-02-24 04:15:01.411276-08 test f T4 \N t \N \N \N {} -545 157 2025-02-24 05:04:10.421675-08 2025-02-24 04:14:54.241718-08 benchmark f T4 \N t \N \N \N {} -546 157 2025-02-24 04:57:33.857732-08 2025-02-24 04:22:04.003536-08 leaderboard f T4 0.022980689 t \N \N \N {} -547 157 2025-02-24 05:07:45.788613-08 2025-02-24 05:08:16.466658-08 test t T4 \N t \N \N \N {} -548 157 2025-02-24 03:17:36.228955-08 2025-02-24 05:13:27.73913-08 benchmark t T4 \N t \N \N \N {} -561 162 2025-02-24 05:06:05.251848-08 2025-02-24 03:48:29.825948-08 test f A100 \N t \N \N \N {} -562 162 2025-02-24 04:57:26.287277-08 2025-02-24 04:27:02.699784-08 benchmark f A100 \N t \N \N \N {} -563 162 2025-02-24 03:32:11.303899-08 2025-02-24 04:54:23.971009-08 leaderboard f A100 0.003460327 t \N \N \N {} -564 162 2025-02-24 04:54:00.853848-08 2025-02-24 04:59:22.66851-08 test t A100 \N t \N \N \N {} -565 162 2025-02-24 03:49:00.261076-08 2025-02-24 04:32:14.986087-08 benchmark t A100 \N t \N \N \N {} -566 162 2025-02-24 05:24:04.211942-08 2025-02-24 04:24:57.431418-08 leaderboard t A100 0.0034634703333333333 t \N \N \N {} -567 162 2025-02-24 04:05:26.770481-08 2025-02-24 03:41:41.45303-08 test t T4 \N t \N \N \N {} -1823 621 2025-02-25 09:37:22.310956-08 2025-02-25 10:04:30.945593-08 test f H100 \N t \N \N \N {} -1824 621 2025-02-25 09:48:47.475736-08 2025-02-25 10:36:25.122648-08 benchmark f H100 \N t \N \N \N {} -2416 818 2025-02-26 13:08:10.332573-08 2025-02-26 12:27:32.298697-08 benchmark f H100 \N f \N \N \N {} -2631 885 2025-02-26 14:51:45.736554-08 2025-02-26 14:47:20.105491-08 test f T4 \N t \N \N \N {} -1829 621 2025-02-25 09:36:01.751668-08 2025-02-25 10:06:26.482401-08 test t H100 \N t \N \N \N {} -1835 621 2025-02-25 10:55:06.644298-08 2025-02-25 10:16:25.436989-08 test t T4 \N t \N \N \N {} -1831 621 2025-02-25 09:36:43.309023-08 2025-02-25 09:39:20.008693-08 leaderboard t H100 0.00007977966666666668 t \N \N \N {} -1832 621 2025-02-25 09:37:12.560705-08 2025-02-25 11:23:33.885332-08 test f T4 \N t \N \N \N {} -1833 621 2025-02-25 10:54:16.770926-08 2025-02-25 10:13:23.913495-08 benchmark f T4 \N t \N \N \N {} -1834 621 2025-02-25 10:45:06.995356-08 2025-02-25 11:28:55.043321-08 leaderboard f T4 0.0003147605 t \N \N \N {} -1885 637 2025-02-25 11:14:01.886924-08 2025-02-25 10:33:34.875247-08 test t A100 \N t \N \N \N {} -3795 1280 2025-03-01 03:06:18.765637-08 2025-03-01 02:10:41.553886-08 test f A100 \N t \N \N \N {} -1836 621 2025-02-25 09:59:07.958405-08 2025-02-25 11:28:20.518166-08 benchmark t T4 \N t \N \N \N {} -1837 621 2025-02-25 10:14:38.896693-08 2025-02-25 10:18:05.012617-08 leaderboard t T4 0.00028145 t \N \N \N {} -568 162 2025-02-24 04:07:53.242047-08 2025-02-24 04:45:46.701262-08 benchmark t T4 \N t \N \N \N {} -569 162 2025-02-24 04:57:58.483951-08 2025-02-24 03:47:41.879786-08 leaderboard t T4 0.018000088 t \N \N \N {} -570 162 2025-02-24 04:07:02.728359-08 2025-02-24 03:30:28.466692-08 test f T4 \N t \N \N \N {} -571 162 2025-02-24 04:55:55.111211-08 2025-02-24 03:47:05.632536-08 benchmark f T4 \N t \N \N \N {} -578 164 2025-02-24 03:34:30.302743-08 2025-02-24 03:44:44.420625-08 test t H100 \N f \N \N \N {} -579 164 2025-02-24 04:40:03.835891-08 2025-02-24 05:14:49.678388-08 test f H100 \N f \N \N \N {} -580 165 2025-02-24 03:46:14.412089-08 2025-02-24 03:30:10.462639-08 test f T4 \N f \N \N \N {} -583 168 2025-02-24 03:40:59.436525-08 2025-02-24 04:13:33.24366-08 test f T4 \N f \N \N \N {} -584 169 2025-02-24 04:11:41.64701-08 2025-02-24 04:29:29.162765-08 test f T4 \N f \N \N \N {} -585 170 2025-02-24 03:50:54.355136-08 2025-02-24 04:32:48.353854-08 test f T4 \N f \N \N \N {} -609 172 2025-02-24 04:16:21.994773-08 2025-02-24 05:27:31.23999-08 test t L4 \N t \N \N \N {} -1839 621 2025-02-25 10:11:09.972425-08 2025-02-25 11:10:01.691566-08 benchmark f L4 \N t \N \N \N {} -1840 621 2025-02-25 10:32:35.014208-08 2025-02-25 10:14:06.170474-08 leaderboard f L4 0.00015338506896551724 t \N \N \N {} -1891 641 2025-02-25 12:30:16.797873-08 2025-02-25 12:50:15.869754-08 test t A100 \N t \N \N \N {} -3798 1280 2025-03-01 02:58:08.249313-08 2025-03-01 02:10:02.417766-08 test t A100 \N t \N \N \N {} -1844 622 2025-02-25 10:39:39.253611-08 2025-02-25 10:37:50.127615-08 benchmark f H100 \N t \N \N \N {} -1845 623 2025-02-25 11:18:57.219265-08 2025-02-25 10:30:49.939246-08 benchmark f H100 \N t \N \N \N {} -1851 626 2025-02-25 11:37:40.953324-08 2025-02-25 10:18:34.146973-08 test t H100 \N t \N \N \N {} -1911 658 2025-02-25 13:07:44.459666-08 2025-02-25 12:36:55.345563-08 test f L4 \N t \N \N \N {} -1854 627 2025-02-25 11:09:31.179796-08 2025-02-25 09:46:07.024596-08 test f H100 \N t \N \N \N {} -1855 627 2025-02-25 10:50:06.45193-08 2025-02-25 10:00:12.679191-08 benchmark f H100 \N t \N \N \N {} -1856 627 2025-02-25 11:18:03.678053-08 2025-02-25 09:50:41.832265-08 leaderboard f H100 0.001456563 t \N \N \N {} -1857 627 2025-02-25 10:15:45.419667-08 2025-02-25 09:46:39.110459-08 test t H100 \N t \N \N \N {} -1858 627 2025-02-25 11:37:33.070625-08 2025-02-25 10:23:48.04595-08 benchmark t H100 \N t \N \N \N {} -1859 627 2025-02-25 10:14:25.240727-08 2025-02-25 10:53:04.749149-08 leaderboard t H100 0.0014359026000000002 t \N \N \N {} -1860 628 2025-02-25 10:38:15.088855-08 2025-02-25 11:33:14.17158-08 benchmark f A100 \N t \N \N \N {} -1861 629 2025-02-25 11:24:33.838768-08 2025-02-25 11:45:59.052869-08 test f T4 \N t \N \N \N {} -1862 629 2025-02-25 10:18:02.87223-08 2025-02-25 10:01:30.670626-08 benchmark f T4 \N t \N \N \N {} -1863 629 2025-02-25 10:09:20.677026-08 2025-02-25 09:56:35.921709-08 leaderboard f T4 0.02022982983333333 t \N \N \N {} -2418 821 2025-02-26 12:32:06.906595-08 2025-02-26 12:30:33.821366-08 benchmark f H100 \N f \N \N \N {} -1872 635 2025-02-25 10:44:47.83768-08 2025-02-25 11:13:43.163043-08 leaderboard f A100 0.0031772633333333336 t \N \N \N {} -1873 635 2025-02-25 11:15:16.010215-08 2025-02-25 11:53:54.827853-08 test t A100 \N t \N \N \N {} -1875 635 2025-02-25 11:43:28.20069-08 2025-02-25 10:13:38.45603-08 leaderboard t A100 0.0032446378333333336 t \N \N \N {} -1877 636 2025-02-25 11:17:18.590101-08 2025-02-25 10:55:34.300914-08 benchmark f H100 \N t \N \N \N {} -1878 636 2025-02-25 11:59:53.786933-08 2025-02-25 11:34:35.925027-08 leaderboard f H100 0.0015205423333333333 t \N \N \N {} -588 172 2025-02-24 03:58:37.431798-08 2025-02-24 05:14:46.271612-08 test t A100 \N t \N \N \N {} -589 172 2025-02-24 05:31:54.505019-08 2025-02-24 04:23:39.720037-08 benchmark t A100 \N t \N \N \N {} -590 172 2025-02-24 03:42:59.187635-08 2025-02-24 04:26:15.997691-08 leaderboard t A100 0.003166644 t \N \N \N {} -591 172 2025-02-24 05:33:24.628818-08 2025-02-24 05:27:53.609663-08 test f H100 \N t \N \N \N {} -624 174 2025-02-24 04:00:03.344427-08 2025-02-24 04:30:10.212094-08 test f H100 \N t \N \N \N {} -592 172 2025-02-24 04:51:20.046359-08 2025-02-24 04:45:20.807066-08 benchmark f H100 \N t \N \N \N {} -1879 636 2025-02-25 10:21:35.338595-08 2025-02-25 10:41:36.288859-08 test t H100 \N t \N \N \N {} -1880 636 2025-02-25 11:47:02.581075-08 2025-02-25 11:37:54.178586-08 benchmark t H100 \N t \N \N \N {} -1881 636 2025-02-25 11:55:10.637717-08 2025-02-25 10:36:44.890626-08 leaderboard t H100 0.0014829484285714286 t \N \N \N {} -1886 637 2025-02-25 12:00:57.953939-08 2025-02-25 10:55:31.592722-08 benchmark t A100 \N t \N \N \N {} -1887 637 2025-02-25 11:56:19.406489-08 2025-02-25 11:35:39.598186-08 leaderboard t A100 0.003213315 t \N \N \N {} -1888 638 2025-02-25 12:00:43.449685-08 2025-02-25 12:07:45.096661-08 benchmark f A100 \N t \N \N \N {} -2420 823 2025-02-26 13:00:06.805549-08 2025-02-26 12:21:44.75151-08 benchmark f H100 \N t \N \N \N {} -1895 641 2025-02-25 12:54:01.264518-08 2025-02-25 11:46:30.101684-08 benchmark f A100 \N t \N \N \N {} -1896 641 2025-02-25 13:38:23.446635-08 2025-02-25 13:36:59.184489-08 leaderboard f A100 0.0030864853333333335 t \N \N \N {} -1897 642 2025-02-25 13:27:14.029692-08 2025-02-25 12:51:21.781335-08 test f T4 \N f \N \N \N {} -1912 658 2025-02-25 13:03:54.537257-08 2025-02-25 13:47:58.322026-08 benchmark f L4 \N t \N \N \N {} -1898 643 2025-02-25 13:22:18.087617-08 2025-02-25 11:54:21.931015-08 benchmark f A100 \N t \N \N \N {} -2421 825 2025-02-26 13:18:04.197511-08 2025-02-26 13:05:21.334106-08 benchmark f H100 \N t \N \N \N {} -1904 650 2025-02-25 12:31:28.402471-08 2025-02-25 13:39:04.170868-08 benchmark f A100 \N t \N \N \N {} -1905 651 2025-02-25 13:24:04.41766-08 2025-02-25 13:44:49.042952-08 benchmark f A100 \N t \N \N \N {} -1914 658 2025-02-25 13:39:12.203771-08 2025-02-25 12:35:51.134173-08 test t L4 \N t \N \N \N {} -1906 652 2025-02-25 13:04:03.79534-08 2025-02-25 12:26:24.162075-08 benchmark f A100 \N t \N \N \N {} -1907 654 2025-02-25 12:31:53.004451-08 2025-02-25 12:56:24.053648-08 test f T4 \N f \N \N \N {} -1908 653 2025-02-25 12:56:54.374943-08 2025-02-25 12:02:39.31621-08 benchmark f A100 \N t \N \N \N {} -1909 656 2025-02-25 13:47:06.574062-08 2025-02-25 13:06:17.287316-08 benchmark f A100 \N t \N \N \N {} -1910 657 2025-02-25 12:05:55.108287-08 2025-02-25 12:31:57.728641-08 benchmark f A100 \N t \N \N \N {} -3804 1281 2025-03-01 03:15:18.618566-08 2025-03-01 03:09:21.435376-08 test t A100 \N t \N \N \N {} -1915 658 2025-02-25 12:52:49.663863-08 2025-02-25 12:15:36.754739-08 benchmark t L4 \N t \N \N \N {} -1916 658 2025-02-25 13:46:15.680818-08 2025-02-25 12:14:55.778199-08 leaderboard t L4 0.017479398 t \N \N \N {} -593 172 2025-02-24 04:00:11.157726-08 2025-02-24 04:23:52.92541-08 leaderboard f H100 0.001457972 t \N \N \N {} -594 172 2025-02-24 04:24:57.48744-08 2025-02-24 05:39:56.243248-08 test t H100 \N t \N \N \N {} -595 172 2025-02-24 04:05:36.140858-08 2025-02-24 04:45:49.663541-08 benchmark t H100 \N t \N \N \N {} -596 172 2025-02-24 04:03:24.134887-08 2025-02-24 04:07:50.692444-08 leaderboard t H100 0.0014493626666666668 t \N \N \N {} -597 172 2025-02-24 04:22:04.734823-08 2025-02-24 05:20:24.419522-08 test t T4 \N t \N \N \N {} -598 172 2025-02-24 04:54:26.09647-08 2025-02-24 04:37:32.588527-08 benchmark t T4 \N t \N \N \N {} -599 172 2025-02-24 05:20:32.672091-08 2025-02-24 04:22:02.580439-08 leaderboard t T4 0.01753550875 t \N \N \N {} -601 172 2025-02-24 04:01:40.262757-08 2025-02-24 04:31:18.294476-08 benchmark f A100 \N t \N \N \N {} -602 172 2025-02-24 04:53:49.361583-08 2025-02-24 04:48:49.182764-08 leaderboard f A100 0.0031660506666666664 t \N \N \N {} -603 172 2025-02-24 03:52:06.072469-08 2025-02-24 03:59:33.848814-08 test f T4 \N t \N \N \N {} -604 172 2025-02-24 03:42:48.462511-08 2025-02-24 05:26:31.44727-08 benchmark f T4 \N t \N \N \N {} -610 172 2025-02-24 05:04:05.242805-08 2025-02-24 03:44:40.846459-08 benchmark t L4 \N t \N \N \N {} -611 172 2025-02-24 04:35:15.919214-08 2025-02-24 05:11:46.93162-08 leaderboard t L4 0.017569029 t \N \N \N {} -612 173 2025-02-24 05:24:50.95809-08 2025-02-24 04:11:33.537819-08 test f H100 \N f \N \N \N {} -613 173 2025-02-24 03:51:03.148107-08 2025-02-24 05:38:19.375618-08 test t H100 \N f \N \N \N {} -614 175 2025-02-24 04:40:13.642706-08 2025-02-24 05:40:34.3719-08 test f T4 \N f \N \N \N {} -615 174 2025-02-24 05:40:44.406358-08 2025-02-24 03:57:34.265608-08 test f A100 \N t \N \N \N {} -625 174 2025-02-24 05:28:15.27384-08 2025-02-24 04:58:32.636743-08 benchmark f H100 \N t \N \N \N {} -626 174 2025-02-24 04:27:19.875646-08 2025-02-24 05:38:11.378119-08 leaderboard f H100 0.00146166825 t \N \N \N {} -627 174 2025-02-24 04:57:35.414356-08 2025-02-24 05:31:36.9668-08 test t T4 \N t \N \N \N {} -628 174 2025-02-24 04:45:34.255845-08 2025-02-24 04:05:43.860604-08 benchmark t T4 \N t \N \N \N {} -629 174 2025-02-24 05:27:09.661991-08 2025-02-24 04:50:39.24896-08 leaderboard t T4 0.0208113558 t \N \N \N {} -1499 502 2025-02-25 06:47:02.590704-08 2025-02-25 06:27:19.767132-08 test f T4 \N t \N \N \N {} -633 174 2025-02-24 04:12:12.277241-08 2025-02-24 05:10:27.381931-08 test t L4 \N t \N \N \N {} -634 174 2025-02-24 05:00:36.484962-08 2025-02-24 04:04:08.518337-08 benchmark t L4 \N t \N \N \N {} -635 174 2025-02-24 04:34:32.791262-08 2025-02-24 04:57:05.719322-08 leaderboard t L4 0.017792222333333333 t \N \N \N {} -636 174 2025-02-24 04:08:01.422936-08 2025-02-24 05:26:59.018927-08 test f L4 \N t \N \N \N {} -637 174 2025-02-24 05:17:03.979995-08 2025-02-24 04:45:28.587227-08 benchmark f L4 \N t \N \N \N {} -638 174 2025-02-24 04:27:22.118036-08 2025-02-24 03:57:14.285261-08 leaderboard f L4 0.017829622 t \N \N \N {} -3276 1144 2025-02-28 06:55:15.001553-08 2025-02-28 05:46:00.750654-08 benchmark f A100 \N t \N \N \N {} -1961 670 2025-02-25 12:57:56.587732-08 2025-02-25 13:49:48.593819-08 benchmark f A100 \N t \N \N \N {} -642 177 2025-02-24 05:07:49.574927-08 2025-02-24 04:45:31.62306-08 test f T4 \N f \N \N \N {} -1962 671 2025-02-25 13:46:47.86222-08 2025-02-25 14:02:39.534938-08 benchmark f A100 \N t \N \N \N {} -1963 672 2025-02-25 14:00:04.67254-08 2025-02-25 13:25:13.842123-08 test t H100 \N t \N \N \N {} -1964 672 2025-02-25 12:36:59.057138-08 2025-02-25 14:02:22.120333-08 benchmark t H100 \N t \N \N \N {} -1965 672 2025-02-25 12:29:52.01167-08 2025-02-25 14:05:06.656703-08 leaderboard t H100 0.003437508 t \N \N \N {} -1966 672 2025-02-25 12:39:24.055726-08 2025-02-25 13:00:24.611271-08 test f H100 \N t \N \N \N {} -1967 672 2025-02-25 12:58:49.474139-08 2025-02-25 13:26:41.827259-08 benchmark f H100 \N t \N \N \N {} -1968 672 2025-02-25 13:26:33.936094-08 2025-02-25 12:40:21.065589-08 leaderboard f H100 0.003485398 t \N \N \N {} -2215 724 2025-02-25 14:37:54.567252-08 2025-02-25 14:43:57.444662-08 leaderboard t A100 0.010253208460000001 t \N \N \N {} -2216 724 2025-02-25 15:33:44.119406-08 2025-02-25 15:13:09.534917-08 test t H100 \N t \N \N \N {} -2253 752 2025-02-26 00:42:28.558196-08 2025-02-26 02:01:39.061078-08 benchmark f H100 \N f \N \N \N {} -2218 724 2025-02-25 15:32:48.937419-08 2025-02-25 15:25:26.740188-08 leaderboard t H100 0.00353085645 t \N \N \N {} -2219 724 2025-02-25 15:11:42.946931-08 2025-02-25 14:44:35.593924-08 test f A100 \N t \N \N \N {} -2220 724 2025-02-25 14:34:20.966564-08 2025-02-25 15:36:27.340882-08 benchmark f A100 \N t \N \N \N {} -2221 724 2025-02-25 15:58:30.291254-08 2025-02-25 14:42:07.237481-08 leaderboard f A100 0.0107430945 t \N \N \N {} -691 217 2025-02-24 07:04:53.233308-08 2025-02-24 08:10:24.296185-08 test t A100 \N f \N \N \N {} -5663 1723 2025-03-09 10:05:43.141436-07 2025-03-09 10:57:02.505632-07 leaderboard t T4 0.016583018 t \N \N \N {} -2313 778 2025-02-26 03:54:39.554659-08 2025-02-26 04:48:44.686597-08 benchmark f A100 \N t \N \N \N {} -2314 778 2025-02-26 03:35:24.258305-08 2025-02-26 04:57:01.237295-08 leaderboard f A100 0.0030904886666666665 t \N \N \N {} -2315 781 2025-02-26 08:10:42.313296-08 2025-02-26 08:01:03.24323-08 test t A100 \N t \N \N \N {} -2330 784 2025-02-26 08:48:24.701263-08 2025-02-26 06:56:52.366196-08 test t A100 \N f \N \N \N {} -2318 781 2025-02-26 08:17:57.941797-08 2025-02-26 07:48:29.179932-08 test f A100 \N t \N \N \N {} -2319 781 2025-02-26 08:04:49.559876-08 2025-02-26 07:59:49.9534-08 benchmark f A100 \N t \N \N \N {} -2320 781 2025-02-26 07:21:32.163845-08 2025-02-26 08:26:16.891069-08 leaderboard f A100 0.0031749516666666664 t \N \N \N {} -2321 782 2025-02-26 07:07:41.841142-08 2025-02-26 08:08:18.275768-08 test f T4 \N f \N \N \N {} -2322 782 2025-02-26 08:40:25.972436-08 2025-02-26 07:46:04.302799-08 test t T4 \N f \N \N \N {} -2323 783 2025-02-26 08:25:20.355357-08 2025-02-26 07:28:44.30517-08 test t A100 \N t \N \N \N {} -2334 785 2025-02-26 08:50:29.968504-08 2025-02-26 08:16:16.86245-08 test t A100 \N t \N \N \N {} -3266 1142 2025-02-28 05:40:58.845992-08 2025-02-28 06:20:23.84623-08 test t A100 \N t \N \N \N {} -692 218 2025-02-24 07:35:12.909281-08 2025-02-24 08:32:29.712533-08 test f T4 \N f \N \N \N {} -3856 1291 2025-03-01 03:58:25.966221-08 2025-03-01 02:58:47.903585-08 test f A100 \N t \N \N \N {} -2500 871 2025-02-26 14:21:21.727765-08 2025-02-26 14:48:13.334885-08 test f T4 \N t \N \N \N {} -2501 872 2025-02-26 14:53:44.720161-08 2025-02-26 14:09:15.911305-08 test t A100 \N t \N \N \N {} -2502 872 2025-02-26 14:29:17.86941-08 2025-02-26 14:42:23.807854-08 benchmark t A100 \N t \N \N \N {} -2504 872 2025-02-26 14:21:53.085089-08 2025-02-26 13:33:28.796563-08 test t H100 \N t \N \N \N {} -3396 1171 2025-02-28 08:52:34.344836-08 2025-02-28 10:12:06.257868-08 test t L4 \N f \N \N \N {} -663 194 2025-02-24 05:41:36.621215-08 2025-02-24 06:09:54.974777-08 test f T4 \N t \N \N \N {} -664 195 2025-02-24 06:08:45.709175-08 2025-02-24 05:08:22.138404-08 benchmark f H100 \N f \N \N \N {} -666 197 2025-02-24 06:56:56.061652-08 2025-02-24 06:22:23.208702-08 benchmark f H100 \N f \N \N \N {} -667 198 2025-02-24 06:34:23.314709-08 2025-02-24 05:20:01.543454-08 benchmark f H100 \N f \N \N \N {} -668 199 2025-02-24 04:59:42.957047-08 2025-02-24 05:30:49.743808-08 benchmark f H100 \N f \N \N \N {} -2598 885 2025-02-26 14:21:31.605809-08 2025-02-26 13:26:53.536131-08 test t L4 \N t \N \N \N {} -2599 885 2025-02-26 14:56:23.588162-08 2025-02-26 13:44:20.657543-08 benchmark t L4 \N t \N \N \N {} -2600 885 2025-02-26 14:30:27.342225-08 2025-02-26 14:48:35.398787-08 leaderboard t L4 0.017775642666666668 t \N \N \N {} -2601 884 2025-02-26 14:45:15.947305-08 2025-02-26 14:55:41.866073-08 test t H100 \N t \N \N \N {} -2613 884 2025-02-26 13:25:03.130497-08 2025-02-26 13:21:04.408924-08 test f T4 \N t \N \N \N {} -2604 884 2025-02-26 15:11:47.082796-08 2025-02-26 14:52:15.530642-08 test t A100 \N t \N \N \N {} -2605 884 2025-02-26 13:27:46.343561-08 2025-02-26 14:55:24.020372-08 benchmark t A100 \N t \N \N \N {} -2606 884 2025-02-26 14:44:05.568836-08 2025-02-26 13:52:58.061404-08 leaderboard t A100 0.0032559906666666666 t \N \N \N {} -2621 885 2025-02-26 14:23:14.309831-08 2025-02-26 13:26:20.529447-08 leaderboard t A100 0.00319686775 t \N \N \N {} -3421 1179 2025-02-28 10:14:03.171361-08 2025-02-28 10:16:52.20393-08 test t L4 \N f \N \N \N {} -2610 884 2025-02-26 15:13:57.604086-08 2025-02-26 13:38:33.967499-08 test f H100 \N t \N \N \N {} -2611 884 2025-02-26 14:21:20.575521-08 2025-02-26 14:28:32.061414-08 benchmark f H100 \N t \N \N \N {} -2612 884 2025-02-26 14:43:21.793286-08 2025-02-26 15:00:54.664103-08 leaderboard f H100 0.0014509983333333332 t \N \N \N {} -2622 885 2025-02-26 14:42:43.496715-08 2025-02-26 14:25:56.942011-08 test f L4 \N t \N \N \N {} -3428 1183 2025-02-28 10:08:45.167126-08 2025-02-28 11:18:19.555595-08 test t L4 \N f \N \N \N {} -2614 884 2025-02-26 15:08:00.153692-08 2025-02-26 14:14:23.980237-08 benchmark f T4 \N t \N \N \N {} -2615 884 2025-02-26 14:02:53.674937-08 2025-02-26 14:12:29.617332-08 leaderboard f T4 0.01843677733333333 t \N \N \N {} -2616 885 2025-02-26 13:36:00.637075-08 2025-02-26 13:52:46.766673-08 test t H100 \N t \N \N \N {} -2617 885 2025-02-26 14:50:02.881007-08 2025-02-26 15:06:57.135832-08 benchmark t H100 \N t \N \N \N {} -2618 885 2025-02-26 13:35:29.138108-08 2025-02-26 15:14:08.351625-08 leaderboard t H100 0.0014837716 t \N \N \N {} -2619 885 2025-02-26 13:51:02.414525-08 2025-02-26 14:11:16.314329-08 test t A100 \N t \N \N \N {} -2620 885 2025-02-26 14:21:00.02625-08 2025-02-26 13:34:27.254132-08 benchmark t A100 \N t \N \N \N {} -2625 885 2025-02-26 13:29:27.081756-08 2025-02-26 14:06:55.891402-08 test f A100 \N t \N \N \N {} -2626 885 2025-02-26 14:36:19.92372-08 2025-02-26 14:31:38.799097-08 benchmark f A100 \N t \N \N \N {} -2627 885 2025-02-26 13:30:35.621947-08 2025-02-26 14:27:23.062497-08 leaderboard f A100 0.003209451 t \N \N \N {} -2628 885 2025-02-26 14:10:08.722188-08 2025-02-26 15:18:05.698046-08 test t T4 \N t \N \N \N {} -2629 885 2025-02-26 14:59:49.529515-08 2025-02-26 13:34:26.486834-08 benchmark t T4 \N t \N \N \N {} -2630 885 2025-02-26 14:58:52.752015-08 2025-02-26 14:53:03.340521-08 leaderboard t T4 0.017220341666666666 t \N \N \N {} -2634 886 2025-02-26 14:08:14.352049-08 2025-02-26 13:46:49.236868-08 benchmark f H100 \N t \N \N \N {} -670 201 2025-02-24 05:45:11.842551-08 2025-02-24 06:28:30.253378-08 benchmark f H100 \N f \N \N \N {} -704 225 2025-02-24 08:56:12.740421-08 2025-02-24 08:34:20.349342-08 leaderboard f A100 0.011330630333333334 t \N \N \N {} -1439 480 2025-02-25 04:48:27.37121-08 2025-02-25 05:01:05.017307-08 test t A100 \N f \N \N \N {} -671 203 2025-02-24 06:24:20.549783-08 2025-02-24 05:24:34.368331-08 test f H100 \N f \N \N \N {} -673 205 2025-02-24 07:04:18.018562-08 2025-02-24 05:35:41.546773-08 test f T4 \N t \N \N \N {} -674 206 2025-02-24 06:06:45.212354-08 2025-02-24 07:06:51.567687-08 benchmark f T4 \N t \N \N \N {} -676 208 2025-02-24 06:06:40.542676-08 2025-02-24 06:41:12.566535-08 test f H100 \N t \N \N \N {} -705 225 2025-02-24 08:55:10.930728-08 2025-02-24 07:46:29.223577-08 test t A100 \N t \N \N \N {} -1502 502 2025-02-25 07:17:02.787-08 2025-02-25 07:28:38.183565-08 test t T4 \N t \N \N \N {} -677 209 2025-02-24 05:52:20.627547-08 2025-02-24 06:50:43.221253-08 benchmark f H100 \N t \N \N \N {} -678 210 2025-02-24 05:28:59.266404-08 2025-02-24 06:40:36.860883-08 benchmark f H100 \N t \N \N \N {} -679 211 2025-02-24 05:59:18.290632-08 2025-02-24 05:31:04.882898-08 test t H100 \N t \N \N \N {} -6187 1904 2025-03-11 16:30:03.874833-07 2025-03-11 16:23:04.032961-07 test f T4 \N f \N \N \N {} -2635 887 2025-02-26 14:23:20.516781-08 2025-02-26 13:30:42.901197-08 benchmark f H100 \N t \N \N \N {} -2636 888 2025-02-26 15:08:09.895226-08 2025-02-26 15:02:44.771912-08 benchmark f H100 \N t \N \N \N {} -2637 889 2025-02-26 14:20:26.49628-08 2025-02-26 14:31:54.196513-08 benchmark f H100 \N t \N \N \N {} -3429 1183 2025-02-28 10:30:24.045825-08 2025-02-28 09:58:43.334761-08 test f L4 \N f \N \N \N {} -2658 917 2025-02-26 16:04:22.640816-08 2025-02-26 15:50:33.550145-08 test t L4 \N t \N \N \N {} -2642 894 2025-02-26 15:22:35.57726-08 2025-02-26 15:19:16.529445-08 benchmark f H100 \N t \N \N \N {} -2643 895 2025-02-26 14:19:06.387984-08 2025-02-26 13:54:35.774098-08 benchmark f H100 \N t \N \N \N {} -2644 896 2025-02-26 14:32:56.05166-08 2025-02-26 15:21:02.107636-08 benchmark f H100 \N t \N \N \N {} -2645 897 2025-02-26 14:43:32.97254-08 2025-02-26 15:35:10.004299-08 benchmark f H100 \N t \N \N \N {} -3642 1244 2025-02-28 23:55:30.080534-08 2025-03-01 00:02:11.688916-08 test f A100 \N t \N \N \N {} -2648 900 2025-02-26 14:55:59.543208-08 2025-02-26 15:16:42.738329-08 benchmark f H100 \N f \N \N \N {} -2649 902 2025-02-26 15:19:37.018566-08 2025-02-26 14:33:09.313223-08 benchmark f H100 \N t \N \N \N {} -2650 905 2025-02-26 15:06:58.156118-08 2025-02-26 15:39:57.941001-08 benchmark f H100 \N t \N \N \N {} -3868 1293 2025-03-01 03:27:42.833831-08 2025-03-01 02:55:20.805453-08 test t A100 \N t \N \N \N {} -2653 910 2025-02-26 15:22:55.54094-08 2025-02-26 15:29:37.757077-08 benchmark f H100 \N f \N \N \N {} -2654 912 2025-02-26 15:40:15.529449-08 2025-02-26 14:30:04.45723-08 test f H100 \N t \N \N \N {} -2655 913 2025-02-26 15:41:19.632645-08 2025-02-26 15:25:04.114543-08 benchmark f H100 \N t \N \N \N {} -2656 914 2025-02-26 15:28:36.303476-08 2025-02-26 14:41:59.161801-08 benchmark f A100 \N t \N \N \N {} -686 213 2025-02-24 06:35:37.280408-08 2025-02-24 07:07:31.745156-08 benchmark f H100 \N t \N \N \N {} -687 214 2025-02-24 06:45:56.939468-08 2025-02-24 06:25:32.523917-08 benchmark f H100 \N t \N \N \N {} -693 219 2025-02-24 09:02:17.179137-08 2025-02-24 08:31:55.588104-08 test f T4 \N f \N \N \N {} -694 220 2025-02-24 07:57:20.923442-08 2025-02-24 08:06:53.334965-08 test f T4 \N f \N \N \N {} -6861 2127 2025-03-15 09:30:21.684959-07 2025-03-15 10:10:14.889721-07 test f A100 \N t \N \N \N {} -2657 915 2025-02-26 15:22:50.97253-08 2025-02-26 14:13:32.883512-08 benchmark f H100 \N f \N \N \N {} -2659 917 2025-02-26 15:59:10.277444-08 2025-02-26 16:06:48.272166-08 benchmark t L4 \N t \N \N \N {} -2660 917 2025-02-26 15:22:27.480576-08 2025-02-26 15:49:28.223669-08 leaderboard t L4 0.017139778 t \N \N \N {} -2661 917 2025-02-26 14:50:12.413294-08 2025-02-26 14:36:02.481211-08 test t A100 \N t \N \N \N {} -2662 917 2025-02-26 14:49:43.883464-08 2025-02-26 15:40:03.528298-08 benchmark t A100 \N t \N \N \N {} -695 221 2025-02-24 08:04:03.997077-08 2025-02-24 09:06:50.538681-08 test f T4 \N f \N \N \N {} -696 222 2025-02-24 08:59:10.271492-08 2025-02-24 07:29:33.102147-08 benchmark f A100 \N f \N \N \N {} -697 222 2025-02-24 08:07:36.901531-08 2025-02-24 07:47:31.363673-08 benchmark f T4 \N f \N \N \N {} -1440 480 2025-02-25 04:05:00.096466-08 2025-02-25 05:05:26.708798-08 test f A100 \N f \N \N \N {} -698 222 2025-02-24 08:43:36.056535-08 2025-02-24 08:16:52.237368-08 benchmark f L4 \N f \N \N \N {} -699 222 2025-02-24 08:22:16.816685-08 2025-02-24 08:37:10.387978-08 benchmark f H100 \N f \N \N \N {} -700 223 2025-02-24 09:06:11.706145-08 2025-02-24 09:03:29.685017-08 benchmark f A100 \N f \N \N \N {} -701 224 2025-02-24 08:10:04.943823-08 2025-02-24 08:28:43.870189-08 test f T4 \N f \N \N \N {} -702 225 2025-02-24 07:56:55.117396-08 2025-02-24 08:48:49.945346-08 test f A100 \N t \N \N \N {} -703 225 2025-02-24 07:41:19.688526-08 2025-02-24 07:41:37.470123-08 benchmark f A100 \N t \N \N \N {} -706 225 2025-02-24 08:37:11.189015-08 2025-02-24 09:05:40.321522-08 benchmark t A100 \N t \N \N \N {} -708 226 2025-02-24 09:25:07.198881-08 2025-02-24 07:35:03.936398-08 benchmark f A100 \N f \N \N \N {} -709 227 2025-02-24 08:24:29.897497-08 2025-02-24 09:20:50.161506-08 benchmark f A100 \N t \N \N \N {} -710 228 2025-02-24 08:50:24.79349-08 2025-02-24 08:55:39.391024-08 test f T4 \N t \N \N \N {} -711 229 2025-02-24 08:11:00.669556-08 2025-02-24 09:13:50.969935-08 test t A100 \N t \N \N \N {} -1111 304 2025-02-24 17:03:32.591659-08 2025-02-24 17:41:47.088082-08 benchmark f H100 \N t \N \N \N {} -712 229 2025-02-24 08:28:09.285744-08 2025-02-24 09:27:17.171225-08 benchmark t A100 \N t \N \N \N {} -2663 917 2025-02-26 15:02:41.87638-08 2025-02-26 15:04:14.826114-08 leaderboard t A100 0.002618883 t \N \N \N {} -3474 1194 2025-02-28 10:51:23.270197-08 2025-02-28 10:28:38.739112-08 benchmark f A100 \N f \N \N \N {} -714 229 2025-02-24 09:20:32.013179-08 2025-02-24 09:10:19.888175-08 test f A100 \N t \N \N \N {} -719 230 2025-02-24 07:43:26.148361-08 2025-02-24 09:27:29.034748-08 leaderboard f A100 0.003160102 t \N \N \N {} -728 233 2025-02-24 07:51:31.813355-08 2025-02-24 07:55:17.330164-08 test t A100 \N t \N \N \N {} -729 233 2025-02-24 07:58:04.52096-08 2025-02-24 09:14:58.005432-08 benchmark t A100 \N t \N \N \N {} -730 233 2025-02-24 09:01:40.583243-08 2025-02-24 08:17:02.267771-08 leaderboard t A100 0.0031330916666666665 t \N \N \N {} -1443 483 2025-02-25 04:08:48.170684-08 2025-02-25 04:18:24.374905-08 test f A100 \N f \N \N \N {} -731 233 2025-02-24 08:17:49.25495-08 2025-02-24 09:15:06.490991-08 test f A100 \N t \N \N \N {} -5675 1724 2025-03-09 09:09:34.316424-07 2025-03-09 09:56:58.297658-07 leaderboard t A100 0.002777401 t \N \N \N {} -2664 917 2025-02-26 15:33:18.523093-08 2025-02-26 14:23:26.27638-08 test f T4 \N t \N \N \N {} -2665 917 2025-02-26 14:41:08.119956-08 2025-02-26 14:40:45.367615-08 benchmark f T4 \N t \N \N \N {} -2666 917 2025-02-26 14:19:24.006278-08 2025-02-26 15:07:56.17656-08 leaderboard f T4 0.017432490666666668 t \N \N \N {} -2667 917 2025-02-26 14:50:03.208616-08 2025-02-26 14:15:31.2517-08 test f H100 \N t \N \N \N {} -2668 917 2025-02-26 15:41:58.425053-08 2025-02-26 14:23:19.930763-08 benchmark f H100 \N t \N \N \N {} -3184 1121 2025-02-28 04:45:28.941252-08 2025-02-28 03:32:18.191207-08 test f A100 \N t \N \N \N {} -2669 917 2025-02-26 15:11:08.928473-08 2025-02-26 15:58:37.434081-08 leaderboard f H100 0.0014553213333333332 t \N \N \N {} -2670 917 2025-02-26 15:28:35.839236-08 2025-02-26 15:52:24.038032-08 test f A100 \N t \N \N \N {} -2671 917 2025-02-26 14:52:23.197769-08 2025-02-26 15:34:23.428279-08 benchmark f A100 \N t \N \N \N {} -2672 917 2025-02-26 14:12:48.444376-08 2025-02-26 14:28:23.028924-08 leaderboard f A100 0.003219043 t \N \N \N {} -2673 917 2025-02-26 14:12:35.128126-08 2025-02-26 14:43:26.451849-08 test t T4 \N t \N \N \N {} -737 233 2025-02-24 09:16:03.275201-08 2025-02-24 09:11:08.635071-08 test t T4 \N t \N \N \N {} -738 233 2025-02-24 08:28:01.859065-08 2025-02-24 09:22:10.211632-08 benchmark t T4 \N t \N \N \N {} -739 233 2025-02-24 09:08:18.555299-08 2025-02-24 09:02:46.143397-08 leaderboard t T4 0.0036530893333333336 t \N \N \N {} -740 233 2025-02-24 08:49:10.05294-08 2025-02-24 08:07:07.637237-08 test f T4 \N t \N \N \N {} -776 249 2025-02-24 10:24:50.908208-08 2025-02-24 11:51:41.650348-08 test f A100 \N t \N \N \N {} -3898 1300 2025-03-01 04:41:21.824886-08 2025-03-01 04:35:22.271324-08 benchmark f A100 \N t \N \N \N {} -747 235 2025-02-24 09:17:44.247963-08 2025-02-24 09:28:51.71791-08 test f A100 \N f \N \N \N {} -748 235 2025-02-24 09:36:38.154313-08 2025-02-24 09:52:27.287797-08 test t A100 \N f \N \N \N {} -749 236 2025-02-24 09:03:52.762503-08 2025-02-24 09:18:03.476551-08 test f A100 \N t \N \N \N {} -750 237 2025-02-24 09:27:00.648875-08 2025-02-24 09:17:20.060425-08 test t A100 \N t \N \N \N {} -758 241 2025-02-24 10:35:45.406451-08 2025-02-24 10:49:56.940266-08 test f A100 \N f \N \N \N {} -2679 917 2025-02-26 15:58:31.502323-08 2025-02-26 15:05:12.236739-08 test f L4 \N t \N \N \N {} -3475 1196 2025-02-28 10:58:00.781332-08 2025-02-28 11:31:50.239552-08 test t A100 \N t \N \N \N {} -2675 917 2025-02-26 15:10:31.598044-08 2025-02-26 15:45:21.966872-08 leaderboard t T4 0.017423966 t \N \N \N {} -2676 917 2025-02-26 15:37:23.354452-08 2025-02-26 14:45:19.799848-08 test t H100 \N t \N \N \N {} -2677 917 2025-02-26 15:32:46.804737-08 2025-02-26 14:42:52.925066-08 benchmark t H100 \N t \N \N \N {} -2678 917 2025-02-26 15:26:19.888166-08 2025-02-26 15:42:16.647691-08 leaderboard t H100 0.0014749786666666668 t \N \N \N {} -3478 1196 2025-02-28 11:49:03.159248-08 2025-02-28 11:21:38.865185-08 test f A100 \N t \N \N \N {} -3871 1294 2025-03-01 03:25:39.575831-08 2025-03-01 03:47:44.417542-08 test f A100 \N t \N \N \N {} -2680 917 2025-02-26 14:12:15.287164-08 2025-02-26 14:56:49.444837-08 benchmark f L4 \N t \N \N \N {} -2681 917 2025-02-26 14:13:57.106393-08 2025-02-26 14:37:54.475639-08 leaderboard f L4 0.017202002666666667 t \N \N \N {} -2682 919 2025-02-26 15:07:06.306277-08 2025-02-26 14:43:54.993315-08 test f T4 \N t \N \N \N {} -2683 918 2025-02-26 14:37:18.145006-08 2025-02-26 14:58:27.554341-08 test f H100 \N f \N \N \N {} -2684 920 2025-02-26 15:32:10.569826-08 2025-02-26 15:21:11.603029-08 benchmark f H100 \N f \N \N \N {} -2690 925 2025-02-26 15:23:42.15559-08 2025-02-26 16:09:05.490772-08 leaderboard t A100 0.003207825 t \N \N \N {} -2685 921 2025-02-26 15:18:14.224991-08 2025-02-26 15:44:27.451168-08 benchmark f H100 \N f \N \N \N {} -2687 923 2025-02-26 15:03:01.412592-08 2025-02-26 15:06:11.143738-08 benchmark f H100 \N t \N \N \N {} -2688 925 2025-02-26 16:08:56.665897-08 2025-02-26 15:00:21.657629-08 test t A100 \N t \N \N \N {} -2689 925 2025-02-26 16:07:17.241061-08 2025-02-26 14:31:28.824618-08 benchmark t A100 \N t \N \N \N {} -751 237 2025-02-24 10:32:35.667519-08 2025-02-24 09:04:14.834137-08 benchmark t A100 \N t \N \N \N {} -752 237 2025-02-24 10:42:58.851055-08 2025-02-24 09:40:55.016006-08 leaderboard t A100 0.0033126033333333335 t \N \N \N {} -753 237 2025-02-24 10:22:09.7399-08 2025-02-24 10:48:42.661894-08 test f A100 \N t \N \N \N {} -754 237 2025-02-24 09:37:37.615117-08 2025-02-24 10:02:09.097882-08 benchmark f A100 \N t \N \N \N {} -755 237 2025-02-24 10:36:07.704848-08 2025-02-24 10:23:31.161901-08 leaderboard f A100 0.003277688 t \N \N \N {} -756 238 2025-02-24 09:28:50.167162-08 2025-02-24 09:49:32.625124-08 test f A100 \N f \N \N \N {} -759 242 2025-02-24 10:36:29.62006-08 2025-02-24 10:53:18.167093-08 test f A100 \N t \N \N \N {} -3485 1197 2025-02-28 11:18:37.224946-08 2025-02-28 11:49:48.964789-08 test f A100 \N t \N \N \N {} -3874 1294 2025-03-01 03:31:01.665765-08 2025-03-01 04:31:31.171928-08 test t A100 \N t \N \N \N {} -760 242 2025-02-24 09:47:29.285286-08 2025-02-24 10:30:48.947657-08 benchmark f A100 \N t \N \N \N {} -761 242 2025-02-24 10:47:46.257274-08 2025-02-24 10:32:35.015062-08 leaderboard f A100 0.0032635085 t \N \N \N {} -762 242 2025-02-24 10:33:54.353655-08 2025-02-24 09:20:51.991089-08 test t A100 \N t \N \N \N {} -763 242 2025-02-24 10:41:28.131991-08 2025-02-24 10:23:01.349186-08 benchmark t A100 \N t \N \N \N {} -764 242 2025-02-24 10:31:24.13368-08 2025-02-24 11:10:39.141474-08 leaderboard t A100 0.0032074475454545452 t \N \N \N {} -766 244 2025-02-24 11:12:28.205288-08 2025-02-24 10:55:23.216173-08 test t A100 \N t \N \N \N {} -767 244 2025-02-24 10:50:20.125343-08 2025-02-24 10:53:26.469742-08 benchmark t A100 \N t \N \N \N {} -2691 925 2025-02-26 15:03:38.341726-08 2025-02-26 14:39:04.512785-08 test f H100 \N t \N \N \N {} -770 244 2025-02-24 09:48:07.049405-08 2025-02-24 11:16:17.375008-08 benchmark f A100 \N t \N \N \N {} -771 244 2025-02-24 10:16:24.565263-08 2025-02-24 10:42:42.951293-08 leaderboard f A100 0.003206431 t \N \N \N {} -774 247 2025-02-24 11:24:50.833311-08 2025-02-24 11:11:00.480374-08 test f A100 \N t \N \N \N {} -775 248 2025-02-24 11:18:01.396675-08 2025-02-24 09:46:03.356509-08 benchmark f A100 \N t \N \N \N {} -1357 454 2025-02-25 03:22:20.667093-08 2025-02-25 02:18:17.785956-08 benchmark f H100 \N f \N \N \N {} -2692 925 2025-02-26 15:31:22.064733-08 2025-02-26 15:24:18.486092-08 benchmark f H100 \N t \N \N \N {} -2693 925 2025-02-26 15:40:09.437246-08 2025-02-26 14:33:37.515202-08 leaderboard f H100 0.0014418213333333332 t \N \N \N {} -2694 925 2025-02-26 14:21:30.999182-08 2025-02-26 14:59:14.162816-08 test f A100 \N t \N \N \N {} -2695 925 2025-02-26 14:49:05.851761-08 2025-02-26 16:02:19.46454-08 benchmark f A100 \N t \N \N \N {} -3187 1121 2025-02-28 04:13:45.106756-08 2025-02-28 05:15:10.243175-08 test t A100 \N t \N \N \N {} -2697 925 2025-02-26 15:49:51.58385-08 2025-02-26 15:15:43.400087-08 test t H100 \N t \N \N \N {} -2698 925 2025-02-26 15:20:13.529064-08 2025-02-26 14:54:28.716289-08 benchmark t H100 \N t \N \N \N {} -2699 925 2025-02-26 15:12:28.653251-08 2025-02-26 15:26:50.456366-08 leaderboard t H100 0.0014572976666666667 t \N \N \N {} -2700 925 2025-02-26 14:23:46.440832-08 2025-02-26 15:33:30.662407-08 test f T4 \N t \N \N \N {} -2706 925 2025-02-26 15:56:56.719925-08 2025-02-26 15:31:05.149568-08 test f L4 \N t \N \N \N {} -2701 925 2025-02-26 14:27:02.624231-08 2025-02-26 14:29:33.448522-08 benchmark f T4 \N t \N \N \N {} -2702 925 2025-02-26 15:37:17.423193-08 2025-02-26 14:15:41.379785-08 leaderboard f T4 0.01748257666666667 t \N \N \N {} -2703 925 2025-02-26 15:02:07.78409-08 2025-02-26 14:30:59.074601-08 test t T4 \N t \N \N \N {} -2704 925 2025-02-26 15:25:00.161914-08 2025-02-26 14:41:31.01698-08 benchmark t T4 \N t \N \N \N {} -2705 925 2025-02-26 15:45:54.49678-08 2025-02-26 15:14:52.211382-08 leaderboard t T4 0.01740308366666667 t \N \N \N {} -2712 926 2025-02-26 14:49:00.905432-08 2025-02-26 15:15:12.066984-08 test f H100 \N f \N \N \N {} -2707 925 2025-02-26 15:14:33.474068-08 2025-02-26 16:13:05.73025-08 benchmark f L4 \N t \N \N \N {} -2708 925 2025-02-26 14:20:21.316161-08 2025-02-26 14:20:47.541084-08 leaderboard f L4 0.017136907333333333 t \N \N \N {} -2709 925 2025-02-26 14:35:12.137581-08 2025-02-26 15:27:12.663189-08 test t L4 \N t \N \N \N {} -2710 925 2025-02-26 14:22:20.587234-08 2025-02-26 15:41:26.009938-08 benchmark t L4 \N t \N \N \N {} -2711 925 2025-02-26 14:28:34.108955-08 2025-02-26 15:21:09.620226-08 leaderboard t L4 0.017147484 t \N \N \N {} -2757 955 2025-02-26 19:44:57.978305-08 2025-02-26 20:19:00.046496-08 test f A100 \N f \N \N \N {} -3488 1197 2025-02-28 12:13:22.706712-08 2025-02-28 11:38:53.381675-08 test t A100 \N t \N \N \N {} -2715 927 2025-02-26 15:38:50.899973-08 2025-02-26 15:07:34.985733-08 leaderboard f H100 0.0008018275555555555 t \N \N \N {} -787 251 2025-02-24 11:51:27.707677-08 2025-02-24 11:37:57.611418-08 benchmark t T4 \N t \N \N \N {} -798 251 2025-02-24 10:21:21.926398-08 2025-02-24 11:59:58.050957-08 test f T4 \N t \N \N \N {} -799 251 2025-02-24 10:39:23.728982-08 2025-02-24 12:03:26.185356-08 benchmark f T4 \N t \N \N \N {} -803 251 2025-02-24 11:17:20.227202-08 2025-02-24 12:07:21.562687-08 leaderboard f L4 0.017377531 t \N \N \N {} -804 251 2025-02-24 10:36:19.381723-08 2025-02-24 11:38:04.632489-08 test t L4 \N t \N \N \N {} -805 251 2025-02-24 11:30:07.481337-08 2025-02-24 11:39:59.955081-08 benchmark t L4 \N t \N \N \N {} -812 253 2025-02-24 12:20:58.985139-08 2025-02-24 11:40:09.015087-08 benchmark t A100 \N f \N \N \N {} -814 255 2025-02-24 12:02:11.606734-08 2025-02-24 13:15:58.848433-08 test f H100 \N t \N \N \N {} -815 255 2025-02-24 12:02:01.586263-08 2025-02-24 12:14:04.067053-08 benchmark f H100 \N t \N \N \N {} -816 255 2025-02-24 12:48:59.000761-08 2025-02-24 12:42:09.155319-08 leaderboard f H100 0.006132886 t \N \N \N {} -817 255 2025-02-24 12:15:55.811254-08 2025-02-24 12:36:09.13928-08 test t H100 \N t \N \N \N {} -844 262 2025-02-24 13:35:47.457777-08 2025-02-24 13:57:51.505602-08 test t A100 \N t \N \N \N {} -2716 927 2025-02-26 15:52:42.380063-08 2025-02-26 14:28:51.260977-08 test t H100 \N t \N \N \N {} -2717 927 2025-02-26 16:02:37.652274-08 2025-02-26 16:05:08.637406-08 benchmark t H100 \N t \N \N \N {} -2780 968 2025-02-26 20:29:07.654188-08 2025-02-26 19:21:21.673508-08 test f A100 \N t \N \N \N {} -3491 1198 2025-02-28 12:21:58.688283-08 2025-02-28 12:05:53.605746-08 test f A100 \N t \N \N \N {} -2719 928 2025-02-26 16:32:16.602958-08 2025-02-26 16:19:31.629908-08 test f H100 \N f \N \N \N {} -2720 930 2025-02-26 15:23:05.898632-08 2025-02-26 15:07:22.998261-08 test f H100 \N f \N \N \N {} -2721 934 2025-02-26 17:22:57.87553-08 2025-02-26 15:53:13.995243-08 test f T4 \N t \N \N \N {} -2722 935 2025-02-26 16:04:13.430604-08 2025-02-26 15:30:59.454902-08 benchmark f H100 \N t \N \N \N {} -2723 936 2025-02-26 15:35:49.505504-08 2025-02-26 17:20:48.238503-08 benchmark f H100 \N t \N \N \N {} -2782 970 2025-02-26 21:07:51.669113-08 2025-02-26 20:49:24.139845-08 test f A100 \N f \N \N \N {} -2729 937 2025-02-26 17:32:52.663266-08 2025-02-26 16:47:18.507852-08 leaderboard t H100 0.0009355417142857144 t \N \N \N {} -2730 938 2025-02-26 16:48:14.702847-08 2025-02-26 16:57:10.702808-08 benchmark f H100 \N t \N \N \N {} -2731 939 2025-02-26 17:31:10.651035-08 2025-02-26 17:03:20.742136-08 test f H100 \N t \N \N \N {} -2732 939 2025-02-26 16:04:46.223011-08 2025-02-26 17:44:01.018405-08 benchmark f H100 \N t \N \N \N {} -2733 939 2025-02-26 16:09:00.549881-08 2025-02-26 16:26:03.875919-08 leaderboard f H100 0.00047589718181818185 t \N \N \N {} -2751 952 2025-02-26 18:36:20.359926-08 2025-02-26 18:56:33.112066-08 leaderboard t H100 0.00004619103 t \N \N \N {} -2752 952 2025-02-26 17:58:47.604278-08 2025-02-26 18:46:38.011565-08 test f H100 \N t \N \N \N {} -2759 957 2025-02-26 19:37:58.190081-08 2025-02-26 19:32:35.710825-08 test f A100 \N f \N \N \N {} -2760 958 2025-02-26 19:20:20.101499-08 2025-02-26 18:54:34.381861-08 test f A100 \N t \N \N \N {} -818 255 2025-02-24 12:36:47.623295-08 2025-02-24 11:44:27.318735-08 benchmark t H100 \N t \N \N \N {} -819 255 2025-02-24 12:45:56.514079-08 2025-02-24 13:03:45.699083-08 leaderboard t H100 0.006123367 t \N \N \N {} -820 256 2025-02-24 12:00:59.412291-08 2025-02-24 11:48:22.822008-08 test t A100 \N f \N \N \N {} -827 258 2025-02-24 12:14:03.313645-08 2025-02-24 13:12:29.40344-08 benchmark f A100 \N t \N \N \N {} -829 259 2025-02-24 13:27:44.987486-08 2025-02-24 12:15:45.854513-08 test f H100 \N t \N \N \N {} -835 260 2025-02-24 12:44:46.406235-08 2025-02-24 12:32:35.981113-08 test f H100 \N f \N \N \N {} -836 260 2025-02-24 11:57:43.314454-08 2025-02-24 13:37:10.081022-08 test t H100 \N f \N \N \N {} -839 261 2025-02-24 13:24:41.686947-08 2025-02-24 14:04:01.45224-08 benchmark f L4 \N t \N \N \N {} -840 261 2025-02-24 12:44:55.198063-08 2025-02-24 13:15:16.477866-08 benchmark f T4 \N t \N \N \N {} -841 262 2025-02-24 14:40:33.295376-08 2025-02-24 14:37:39.287063-08 test f H100 \N t \N \N \N {} -842 262 2025-02-24 13:32:24.520488-08 2025-02-24 12:51:42.430233-08 benchmark f H100 \N t \N \N \N {} -845 262 2025-02-24 13:16:57.772484-08 2025-02-24 13:43:01.738354-08 benchmark t A100 \N t \N \N \N {} -846 262 2025-02-24 14:40:12.521043-08 2025-02-24 12:41:50.695193-08 leaderboard t A100 0.0035759156666666666 t \N \N \N {} -847 262 2025-02-24 13:06:53.677846-08 2025-02-24 13:04:43.106693-08 test t T4 \N t \N \N \N {} -848 262 2025-02-24 13:21:20.33413-08 2025-02-24 14:07:43.524626-08 benchmark t T4 \N t \N \N \N {} -849 262 2025-02-24 13:08:55.082034-08 2025-02-24 14:11:39.783402-08 leaderboard t T4 0.017268438333333334 t \N \N \N {} -855 262 2025-02-24 13:00:14.369727-08 2025-02-24 12:58:55.821216-08 leaderboard f A100 0.0035754176666666663 t \N \N \N {} -856 263 2025-02-24 14:12:04.211732-08 2025-02-24 14:32:29.922479-08 benchmark f A100 \N f \N \N \N {} -857 262 2025-02-24 13:01:52.259649-08 2025-02-24 13:08:26.610715-08 test f T4 \N t \N \N \N {} -858 262 2025-02-24 13:09:26.79495-08 2025-02-24 14:17:18.323779-08 benchmark f T4 \N t \N \N \N {} -859 262 2025-02-24 14:15:52.323889-08 2025-02-24 14:32:11.723833-08 leaderboard f T4 0.017249578 t \N \N \N {} -1459 486 2025-02-25 04:17:57.329398-08 2025-02-25 04:19:00.137084-08 test t A100 \N t \N \N \N {} -861 262 2025-02-24 13:55:33.663644-08 2025-02-24 14:37:09.657969-08 benchmark t H100 \N t \N \N \N {} -862 262 2025-02-24 14:11:40.394727-08 2025-02-24 14:04:47.293746-08 leaderboard t H100 0.00157941125 t \N \N \N {} -863 262 2025-02-24 14:14:35.256564-08 2025-02-24 13:32:29.562338-08 test t L4 \N t \N \N \N {} -864 262 2025-02-24 12:48:02.755552-08 2025-02-24 14:13:00.945425-08 benchmark t L4 \N t \N \N \N {} -865 262 2025-02-24 12:46:48.53663-08 2025-02-24 14:29:57.105212-08 leaderboard t L4 0.017202579333333332 t \N \N \N {} -870 266 2025-02-24 14:10:29.80768-08 2025-02-24 13:31:05.267924-08 test t H100 \N f \N \N \N {} -882 268 2025-02-24 13:48:16.246206-08 2025-02-24 13:16:22.804481-08 benchmark t H100 \N t \N \N \N {} -883 268 2025-02-24 13:47:51.158916-08 2025-02-24 14:12:31.13551-08 leaderboard t H100 0.001499999 t \N \N \N {} -884 269 2025-02-24 13:36:44.335857-08 2025-02-24 13:23:48.204042-08 test f L4 \N t \N \N \N {} -885 269 2025-02-24 13:40:59.689967-08 2025-02-24 14:59:46.238789-08 test f A100 \N t \N \N \N {} -886 269 2025-02-24 14:18:48.147333-08 2025-02-24 14:54:09.931319-08 test f T4 \N t \N \N \N {} -887 269 2025-02-24 13:58:15.398454-08 2025-02-24 14:08:43.206835-08 test f H100 \N t \N \N \N {} -2761 959 2025-02-26 20:04:15.692556-08 2025-02-26 20:10:38.466984-08 test f A100 \N t \N \N \N {} -3506 1200 2025-02-28 12:29:44.776123-08 2025-02-28 11:41:55.023296-08 test t A100 \N t \N \N \N {} -2762 960 2025-02-26 19:50:11.129181-08 2025-02-26 19:57:28.353335-08 test f A100 \N t \N \N \N {} -888 270 2025-02-24 14:36:02.624025-08 2025-02-24 15:09:25.11226-08 test f H100 \N t \N \N \N {} -893 270 2025-02-24 13:25:28.447878-08 2025-02-24 14:26:32.820494-08 leaderboard t H100 0.006313673333333333 t \N \N \N {} -978 288 2025-02-24 16:16:39.684041-08 2025-02-24 16:53:33.866555-08 test f H100 \N t \N \N \N {} -894 271 2025-02-24 13:37:51.574913-08 2025-02-24 13:30:44.90995-08 benchmark f A100 \N t \N \N \N {} -895 271 2025-02-24 14:42:33.925165-08 2025-02-24 13:50:25.827947-08 benchmark f L4 \N t \N \N \N {} -896 271 2025-02-24 15:14:26.679127-08 2025-02-24 13:24:56.860101-08 benchmark f H100 \N t \N \N \N {} -2763 961 2025-02-26 20:33:39.926563-08 2025-02-26 18:53:59.629244-08 test f A100 \N f \N \N \N {} -3591 1229 2025-02-28 14:00:43.377485-08 2025-02-28 13:02:18.799382-08 benchmark t H100 \N t \N \N \N {} -922 272 2025-02-24 14:21:16.338815-08 2025-02-24 13:57:53.832712-08 test f L4 \N t \N \N \N {} -930 275 2025-02-24 14:06:04.090352-08 2025-02-24 15:32:28.313994-08 test t H100 \N f \N \N \N {} -931 275 2025-02-24 15:08:25.725574-08 2025-02-24 14:48:09.110921-08 test f H100 \N f \N \N \N {} -932 276 2025-02-24 15:08:07.755574-08 2025-02-24 14:00:19.773242-08 benchmark f H100 \N t \N \N \N {} -933 277 2025-02-24 15:11:54.355362-08 2025-02-24 14:30:44.412152-08 benchmark f H100 \N t \N \N \N {} -934 278 2025-02-24 15:11:08.217229-08 2025-02-24 13:56:46.532795-08 test t A100 \N t \N \N \N {} -2765 963 2025-02-26 21:05:46.567488-08 2025-02-26 21:01:24.814437-08 test f A100 \N t \N \N \N {} -949 278 2025-02-24 14:01:41.92456-08 2025-02-24 13:43:58.586813-08 test t L4 \N t \N \N \N {} -953 278 2025-02-24 15:30:55.085434-08 2025-02-24 15:03:27.528219-08 benchmark t T4 \N t \N \N \N {} -954 278 2025-02-24 14:13:18.813125-08 2025-02-24 14:25:32.061848-08 leaderboard t T4 0.01679078633333333 t \N \N \N {} -955 278 2025-02-24 13:55:20.752079-08 2025-02-24 14:29:46.038209-08 test f T4 \N t \N \N \N {} -956 278 2025-02-24 14:26:05.784131-08 2025-02-24 14:04:02.728948-08 benchmark f T4 \N t \N \N \N {} -957 278 2025-02-24 14:40:08.470773-08 2025-02-24 14:15:08.834032-08 leaderboard f T4 0.01680730033333333 t \N \N \N {} -958 279 2025-02-24 13:58:02.12755-08 2025-02-24 14:09:39.155243-08 test f A100 \N t \N \N \N {} -962 283 2025-02-24 15:58:39.196492-08 2025-02-24 14:11:06.156964-08 benchmark f A100 \N t \N \N \N {} -963 284 2025-02-24 14:28:10.002662-08 2025-02-24 14:33:20.990272-08 benchmark f A100 \N t \N \N \N {} -964 285 2025-02-24 14:50:05.118746-08 2025-02-24 16:06:00.21924-08 benchmark f A100 \N t \N \N \N {} -966 286 2025-02-24 15:11:06.589828-08 2025-02-24 15:54:57.512172-08 benchmark f A100 \N t \N \N \N {} -2766 964 2025-02-26 19:08:30.791267-08 2025-02-26 20:25:23.844568-08 test f A100 \N t \N \N \N {} -967 286 2025-02-24 15:50:38.06952-08 2025-02-24 14:35:41.812052-08 leaderboard f A100 0.003564924125 t \N \N \N {} -968 286 2025-02-24 15:34:26.225087-08 2025-02-24 14:31:55.790602-08 test t A100 \N t \N \N \N {} -975 288 2025-02-24 16:06:09.241772-08 2025-02-24 15:20:44.834631-08 test t H100 \N t \N \N \N {} -1003 289 2025-02-24 16:59:20.310533-08 2025-02-24 16:33:52.660533-08 test f H100 \N t \N \N \N {} -1007 289 2025-02-24 15:48:45.290987-08 2025-02-24 16:12:36.369625-08 benchmark f T4 \N t \N \N \N {} -1008 289 2025-02-24 16:43:13.03202-08 2025-02-24 16:09:32.189032-08 leaderboard f T4 0.017463506 t \N \N \N {} -1009 289 2025-02-24 15:26:10.010531-08 2025-02-24 15:31:30.134666-08 test f L4 \N t \N \N \N {} -1010 289 2025-02-24 15:34:17.620631-08 2025-02-24 15:20:56.610577-08 benchmark f L4 \N t \N \N \N {} -1011 289 2025-02-24 15:17:08.27862-08 2025-02-24 16:53:24.408023-08 leaderboard f L4 0.017437469 t \N \N \N {} -1013 289 2025-02-24 17:10:06.760302-08 2025-02-24 16:01:34.694517-08 benchmark t L4 \N t \N \N \N {} -1014 289 2025-02-24 17:02:49.759681-08 2025-02-24 16:46:54.792055-08 leaderboard t L4 0.017468976333333334 t \N \N \N {} -1015 289 2025-02-24 16:23:10.660698-08 2025-02-24 16:34:48.55816-08 test t H100 \N t \N \N \N {} -1016 289 2025-02-24 17:08:58.086549-08 2025-02-24 17:12:19.202409-08 benchmark t H100 \N t \N \N \N {} -1017 289 2025-02-24 15:29:25.320543-08 2025-02-24 16:41:10.791533-08 leaderboard t H100 0.001555808 t \N \N \N {} -1021 290 2025-02-24 17:27:27.359016-08 2025-02-24 17:10:34.608977-08 test f T4 \N f \N \N \N {} -1022 290 2025-02-24 16:04:38.081723-08 2025-02-24 16:01:10.893306-08 test t T4 \N f \N \N \N {} -1023 290 2025-02-24 17:30:53.275597-08 2025-02-24 16:15:31.909539-08 test f H100 \N f \N \N \N {} -1024 290 2025-02-24 16:19:28.643489-08 2025-02-24 17:41:16.119971-08 test t L4 \N f \N \N \N {} -2767 965 2025-02-26 20:43:06.138197-08 2025-02-26 20:38:46.941894-08 test f A100 \N t \N \N \N {} -1025 290 2025-02-24 15:50:33.00311-08 2025-02-24 17:22:52.823354-08 test f L4 \N f \N \N \N {} -1026 291 2025-02-24 16:00:27.990747-08 2025-02-24 17:05:43.520942-08 test t A100 \N t \N \N \N {} -1027 291 2025-02-24 17:29:02.565531-08 2025-02-24 17:17:14.052814-08 benchmark t A100 \N t \N \N \N {} -1028 291 2025-02-24 16:02:23.141852-08 2025-02-24 16:33:31.463874-08 leaderboard t A100 0.003320683 t \N \N \N {} -1029 291 2025-02-24 16:21:24.320655-08 2025-02-24 16:33:35.189391-08 test f A100 \N t \N \N \N {} -1030 291 2025-02-24 16:54:28.950428-08 2025-02-24 16:34:49.566872-08 benchmark f A100 \N t \N \N \N {} -1041 291 2025-02-24 16:51:50.87004-08 2025-02-24 16:52:44.22599-08 test t T4 \N t \N \N \N {} -1031 291 2025-02-24 16:57:17.908772-08 2025-02-24 17:21:40.225798-08 leaderboard f A100 0.0033077086666666667 t \N \N \N {} -1061 292 2025-02-24 16:14:47.786921-08 2025-02-24 16:02:43.643719-08 leaderboard f H100 0.0014229046666666668 t \N \N \N {} -1062 292 2025-02-24 16:05:24.343352-08 2025-02-24 16:26:39.742121-08 test t T4 \N t \N \N \N {} -1068 292 2025-02-24 16:14:27.218676-08 2025-02-24 16:12:18.471021-08 test f L4 \N t \N \N \N {} -1071 292 2025-02-24 16:21:46.18687-08 2025-02-24 17:29:33.25851-08 test t H100 \N t \N \N \N {} -1077 295 2025-02-24 17:38:05.121185-08 2025-02-24 17:38:57.063894-08 benchmark f H100 \N t \N \N \N {} -1078 295 2025-02-24 17:58:44.68097-08 2025-02-24 17:14:34.343302-08 leaderboard f H100 0.0010347983333333333 t \N \N \N {} -1079 295 2025-02-24 17:04:05.158398-08 2025-02-24 17:25:58.879946-08 test t H100 \N t \N \N \N {} -1080 295 2025-02-24 16:58:08.102388-08 2025-02-24 17:56:34.485958-08 benchmark t H100 \N t \N \N \N {} -1081 295 2025-02-24 16:55:29.797081-08 2025-02-24 17:47:07.608762-08 leaderboard t H100 0.001039122 t \N \N \N {} -1096 304 2025-02-24 18:56:04.543979-08 2025-02-24 17:04:43.557942-08 benchmark f A100 \N t \N \N \N {} -1082 296 2025-02-24 17:31:42.820566-08 2025-02-24 16:33:59.876329-08 benchmark f H100 \N f \N \N \N {} -1084 299 2025-02-24 17:39:48.424492-08 2025-02-24 17:07:52.030306-08 benchmark f A100 \N t \N \N \N {} -1085 298 2025-02-24 16:35:28.403851-08 2025-02-24 17:02:30.160259-08 benchmark f H100 \N f \N \N \N {} -1086 300 2025-02-24 17:14:06.318787-08 2025-02-24 16:50:30.934776-08 test t A100 \N t \N \N \N {} -1091 300 2025-02-24 17:29:22.731228-08 2025-02-24 17:12:13.670748-08 leaderboard f A100 0.014157763333333333 t \N \N \N {} -1092 301 2025-02-24 17:59:41.646434-08 2025-02-24 16:56:21.661999-08 benchmark f H100 \N f \N \N \N {} -1093 302 2025-02-24 18:21:23.246489-08 2025-02-24 17:07:12.364112-08 benchmark f A100 \N f \N \N \N {} -1094 303 2025-02-24 17:50:55.598954-08 2025-02-24 17:13:45.078131-08 benchmark f H100 \N t \N \N \N {} -1095 304 2025-02-24 17:38:46.646025-08 2025-02-24 17:11:44.810342-08 test f A100 \N t \N \N \N {} -1486 495 2025-02-25 05:26:36.575717-08 2025-02-25 05:35:53.585466-08 test f A100 \N f \N \N \N {} -1097 304 2025-02-24 17:03:14.22191-08 2025-02-24 17:48:13.256134-08 leaderboard f A100 0.006075353 t \N \N \N {} -3576 1223 2025-02-28 12:52:29.871321-08 2025-02-28 13:12:16.401319-08 test f H100 \N t \N \N \N {} -1098 304 2025-02-24 18:52:41.173136-08 2025-02-24 18:10:45.04049-08 test t A100 \N t \N \N \N {} -1099 304 2025-02-24 17:19:09.825043-08 2025-02-24 17:28:38.91665-08 benchmark t A100 \N t \N \N \N {} -1100 304 2025-02-24 18:23:50.783076-08 2025-02-24 18:13:43.170325-08 leaderboard t A100 0.006081086333333333 t \N \N \N {} -1105 304 2025-02-24 17:34:55.399719-08 2025-02-24 17:27:11.226139-08 benchmark f T4 \N t \N \N \N {} -1106 304 2025-02-24 18:28:11.076985-08 2025-02-24 18:13:11.8461-08 leaderboard f T4 0.028245293666666667 t \N \N \N {} -1107 304 2025-02-24 18:33:57.535639-08 2025-02-24 17:46:08.937512-08 test t T4 \N t \N \N \N {} -1113 304 2025-02-24 18:34:00.598847-08 2025-02-24 18:51:13.208664-08 test t L4 \N t \N \N \N {} -1114 304 2025-02-24 18:06:21.101459-08 2025-02-24 17:31:57.238174-08 benchmark t L4 \N t \N \N \N {} -1119 305 2025-02-24 17:42:57.925612-08 2025-02-24 17:15:14.683808-08 benchmark f H100 \N f \N \N \N {} -1120 306 2025-02-24 17:21:15.587258-08 2025-02-24 18:44:01.440783-08 benchmark f A100 \N t \N \N \N {} -1121 306 2025-02-24 18:36:34.865426-08 2025-02-24 17:49:17.471548-08 benchmark f H100 \N t \N \N \N {} -1122 306 2025-02-24 17:26:55.287166-08 2025-02-24 18:35:50.569086-08 benchmark f T4 \N t \N \N \N {} -1125 308 2025-02-24 18:34:58.15099-08 2025-02-24 17:56:55.545892-08 benchmark f H100 \N t \N \N \N {} -1126 309 2025-02-24 18:10:41.370592-08 2025-02-24 19:01:32.847335-08 test f T4 \N t \N \N \N {} -1127 310 2025-02-24 18:21:38.286944-08 2025-02-24 19:30:50.197993-08 test f T4 \N f \N \N \N {} -1128 311 2025-02-24 19:40:10.137721-08 2025-02-24 19:12:06.15593-08 test f T4 \N f \N \N \N {} -1144 321 2025-02-24 19:09:00.011949-08 2025-02-24 19:27:42.347167-08 benchmark f A100 \N t \N \N \N {} -1145 321 2025-02-24 18:28:50.942544-08 2025-02-24 20:07:48.608885-08 leaderboard f A100 0.008048783333333333 t \N \N \N {} -1146 321 2025-02-24 19:54:14.030406-08 2025-02-24 19:26:42.983807-08 test t A100 \N t \N \N \N {} -1147 321 2025-02-24 19:37:58.992398-08 2025-02-24 18:38:17.162254-08 benchmark t A100 \N t \N \N \N {} -1155 328 2025-02-24 18:59:30.528684-08 2025-02-24 18:51:38.131222-08 benchmark f A100 \N t \N \N \N {} -1160 329 2025-02-24 19:38:57.967787-08 2025-02-24 18:57:02.949354-08 benchmark t H100 \N t \N \N \N {} -1161 329 2025-02-24 19:14:33.290908-08 2025-02-24 20:08:23.310109-08 leaderboard t H100 0.0015865033333333333 t \N \N \N {} -1166 334 2025-02-24 19:50:26.32731-08 2025-02-24 20:31:43.298423-08 test f A100 \N f \N \N \N {} -1167 335 2025-02-24 19:52:47.832137-08 2025-02-24 18:40:52.747222-08 test f A100 \N f \N \N \N {} -1168 336 2025-02-24 18:58:04.317224-08 2025-02-24 20:40:01.428171-08 test f A100 \N t \N \N \N {} -1169 337 2025-02-24 19:33:06.587039-08 2025-02-24 20:20:18.024082-08 benchmark f A100 \N t \N \N \N {} -1171 339 2025-02-24 20:25:27.128083-08 2025-02-24 18:45:26.166042-08 benchmark f A100 \N t \N \N \N {} -1175 343 2025-02-24 20:01:36.447161-08 2025-02-24 20:17:57.794145-08 benchmark f A100 \N t \N \N \N {} -1176 344 2025-02-24 18:57:03.668266-08 2025-02-24 20:32:08.204872-08 benchmark f A100 \N t \N \N \N {} -1177 345 2025-02-24 19:19:48.575647-08 2025-02-24 20:35:58.402184-08 benchmark f A100 \N f \N \N \N {} -1180 348 2025-02-24 20:38:35.466753-08 2025-02-24 20:27:20.582819-08 benchmark f A100 \N t \N \N \N {} -1181 349 2025-02-24 19:50:45.13357-08 2025-02-24 19:35:45.516463-08 test f A100 \N f \N \N \N {} -1186 356 2025-02-24 20:44:38.220701-08 2025-02-24 20:35:27.553404-08 benchmark f A100 \N f \N \N \N {} -1187 357 2025-02-24 20:31:41.284738-08 2025-02-24 20:51:31.622096-08 benchmark f A100 \N t \N \N \N {} -1191 364 2025-02-24 19:38:43.168308-08 2025-02-24 20:03:40.713134-08 benchmark f A100 \N t \N \N \N {} -1192 365 2025-02-24 20:20:30.975678-08 2025-02-24 20:47:45.806082-08 test f A100 \N t \N \N \N {} -1198 366 2025-02-24 20:50:37.04848-08 2025-02-24 20:03:24.288635-08 benchmark f A100 \N f \N \N \N {} -1199 367 2025-02-24 20:01:53.464636-08 2025-02-24 20:50:55.06292-08 benchmark f A100 \N f \N \N \N {} -1200 368 2025-02-24 20:48:53.017372-08 2025-02-24 21:07:09.746977-08 benchmark f A100 \N f \N \N \N {} -1203 371 2025-02-24 21:03:40.316811-08 2025-02-24 20:06:59.770551-08 benchmark f A100 \N f \N \N \N {} -1204 372 2025-02-24 21:16:31.917525-08 2025-02-24 20:19:23.12487-08 benchmark f A100 \N f \N \N \N {} -1212 380 2025-02-24 21:30:39.643844-08 2025-02-24 21:22:48.195881-08 benchmark f A100 \N f \N \N \N {} -1213 381 2025-02-24 21:27:50.804873-08 2025-02-24 20:30:39.877648-08 benchmark f A100 \N f \N \N \N {} -1214 382 2025-02-24 21:26:22.567911-08 2025-02-24 20:42:27.609326-08 benchmark f A100 \N t \N \N \N {} -1215 383 2025-02-24 21:43:35.675505-08 2025-02-24 21:41:12.864918-08 benchmark f A100 \N f \N \N \N {} -1217 385 2025-02-24 20:39:07.414152-08 2025-02-24 21:16:56.728524-08 benchmark f A100 \N t \N \N \N {} -1218 386 2025-02-24 21:24:25.618173-08 2025-02-24 20:59:42.5791-08 benchmark f A100 \N f \N \N \N {} -1219 387 2025-02-24 20:37:38.559826-08 2025-02-24 20:50:09.030352-08 benchmark f A100 \N t \N \N \N {} -1220 388 2025-02-24 21:12:35.63089-08 2025-02-24 21:07:52.286646-08 test f H100 \N f \N \N \N {} -1221 390 2025-02-24 20:41:29.07709-08 2025-02-24 21:50:07.217387-08 test f A100 \N f \N \N \N {} -1222 391 2025-02-24 20:36:54.532658-08 2025-02-24 20:27:33.575008-08 test f A100 \N f \N \N \N {} -1223 391 2025-02-24 22:09:52.859803-08 2025-02-24 20:25:04.338196-08 test f L4 \N f \N \N \N {} -1224 391 2025-02-24 20:57:57.971901-08 2025-02-24 22:07:50.668858-08 test f T4 \N f \N \N \N {} -1225 391 2025-02-24 21:26:12.689911-08 2025-02-24 20:21:51.434441-08 test f H100 \N f \N \N \N {} -1226 392 2025-02-24 21:22:04.179078-08 2025-02-24 21:02:36.047543-08 test f A100 \N f \N \N \N {} -1227 393 2025-02-24 21:27:17.29266-08 2025-02-24 21:05:23.934691-08 test t A100 \N t \N \N \N {} -1228 393 2025-02-24 21:57:42.36025-08 2025-02-24 20:56:27.385816-08 benchmark t A100 \N t \N \N \N {} -1232 393 2025-02-24 21:19:55.551644-08 2025-02-24 20:43:59.005894-08 leaderboard f A100 0.0033388506666666667 t \N \N \N {} -1265 416 2025-02-24 21:37:21.368251-08 2025-02-24 21:44:53.806727-08 leaderboard t H100 0.0021056229500000002 t \N \N \N {} -1233 394 2025-02-24 20:48:53.683982-08 2025-02-24 21:10:47.005539-08 test f A100 \N f \N \N \N {} -1234 395 2025-02-24 21:42:25.587766-08 2025-02-24 21:13:54.206602-08 test f A100 \N f \N \N \N {} -1235 396 2025-02-24 21:31:47.507986-08 2025-02-24 21:41:14.621874-08 test f A100 \N f \N \N \N {} -1236 397 2025-02-24 20:53:51.866306-08 2025-02-24 21:41:52.555281-08 test f A100 \N f \N \N \N {} -1237 398 2025-02-24 22:13:49.271446-08 2025-02-24 21:54:00.876085-08 test f A100 \N f \N \N \N {} -1273 424 2025-02-24 23:15:56.010969-08 2025-02-24 22:22:18.360257-08 test f H100 \N f \N \N \N {} -1238 399 2025-02-24 21:20:05.240709-08 2025-02-24 22:10:17.303543-08 test f A100 \N f \N \N \N {} -1239 400 2025-02-24 21:02:33.153711-08 2025-02-24 22:14:52.737511-08 test f A100 \N f \N \N \N {} -1242 403 2025-02-24 21:44:20.600901-08 2025-02-24 22:19:00.048671-08 test f A100 \N t \N \N \N {} -1274 425 2025-02-24 22:32:28.342997-08 2025-02-24 22:46:18.153042-08 test f H100 \N f \N \N \N {} -1244 405 2025-02-24 21:15:42.934708-08 2025-02-24 22:00:36.897134-08 benchmark f A100 \N t \N \N \N {} -1245 406 2025-02-24 22:41:21.855098-08 2025-02-24 21:22:41.861309-08 test f A100 \N f \N \N \N {} -1246 407 2025-02-24 22:14:38.935217-08 2025-02-24 21:17:28.999889-08 test f A100 \N f \N \N \N {} -1247 408 2025-02-24 21:15:17.037056-08 2025-02-24 22:47:31.85541-08 test f A100 \N f \N \N \N {} -1255 413 2025-02-24 22:41:32.544432-08 2025-02-24 21:50:59.410312-08 test t A100 \N t \N \N \N {} -1275 426 2025-02-24 22:50:00.313014-08 2025-02-24 22:01:51.946596-08 test f H100 \N f \N \N \N {} -1256 413 2025-02-24 21:28:53.296618-08 2025-02-24 21:56:09.683656-08 benchmark t A100 \N t \N \N \N {} -1257 413 2025-02-24 22:52:41.725644-08 2025-02-24 21:02:44.177771-08 leaderboard t A100 0.00082829875 t \N \N \N {} -1258 414 2025-02-24 22:43:01.622934-08 2025-02-24 21:44:54.450946-08 test f H100 \N t \N \N \N {} -1259 415 2025-02-24 21:05:33.640351-08 2025-02-24 22:22:12.322858-08 benchmark f H100 \N t \N \N \N {} -1260 416 2025-02-24 22:50:18.115204-08 2025-02-24 22:10:37.88296-08 test f H100 \N t \N \N \N {} -1263 416 2025-02-24 22:41:58.320403-08 2025-02-24 22:46:15.121319-08 test t H100 \N t \N \N \N {} -1264 416 2025-02-24 21:33:11.644917-08 2025-02-24 22:43:20.018249-08 benchmark t H100 \N t \N \N \N {} -1266 417 2025-02-24 23:17:42.124344-08 2025-02-24 22:14:36.706679-08 test f L4 \N f \N \N \N {} -1271 422 2025-02-24 23:24:55.93824-08 2025-02-24 23:09:05.952447-08 test f H100 \N f \N \N \N {} -1272 423 2025-02-24 23:33:25.559896-08 2025-02-24 23:04:20.171899-08 test f H100 \N f \N \N \N {} -1277 427 2025-02-25 00:21:05.704041-08 2025-02-24 23:17:15.254253-08 benchmark t H100 \N t \N \N \N {} -1278 427 2025-02-24 23:05:44.473555-08 2025-02-24 22:36:18.623353-08 leaderboard t H100 0.00151173175 t \N \N \N {} -1279 427 2025-02-24 23:22:06.892269-08 2025-02-25 00:08:22.994675-08 test f H100 \N t \N \N \N {} -1280 427 2025-02-25 00:06:49.982745-08 2025-02-25 00:02:16.454215-08 benchmark f H100 \N t \N \N \N {} -1281 427 2025-02-24 23:30:10.509612-08 2025-02-24 22:24:37.733967-08 leaderboard f H100 0.0014917988095238094 t \N \N \N {} -1282 428 2025-02-25 00:29:11.621733-08 2025-02-25 00:25:31.895544-08 test f L4 \N t \N \N \N {} -1283 429 2025-02-25 00:23:40.437427-08 2025-02-25 00:28:38.668036-08 benchmark f L4 \N t \N \N \N {} -1420 472 2025-02-25 05:09:52.78126-08 2025-02-25 03:33:17.606456-08 test f A100 \N t \N \N \N {} -1284 430 2025-02-24 22:56:08.41611-08 2025-02-24 23:53:11.011713-08 benchmark f L4 \N t \N \N \N {} -1291 434 2025-02-25 00:38:27.016325-08 2025-02-25 01:33:09.270327-08 test t A100 \N f \N \N \N {} -1292 435 2025-02-25 01:13:37.578908-08 2025-02-25 02:04:12.790967-08 test t A100 \N f \N \N \N {} -1293 435 2025-02-25 00:34:17.674028-08 2025-02-25 01:20:00.978456-08 test f A100 \N f \N \N \N {} -1294 436 2025-02-25 00:52:16.868772-08 2025-02-25 01:46:34.105184-08 test t A100 \N t \N \N \N {} -1295 436 2025-02-25 00:34:18.664042-08 2025-02-25 01:29:38.828363-08 benchmark t A100 \N t \N \N \N {} -2768 965 2025-02-26 19:49:02.478379-08 2025-02-26 19:27:59.359226-08 benchmark f A100 \N t \N \N \N {} -2769 965 2025-02-26 20:58:22.764397-08 2025-02-26 20:38:26.615803-08 leaderboard f A100 0.014282038666666667 t \N \N \N {} -2770 966 2025-02-26 20:24:44.026742-08 2025-02-26 19:35:29.731923-08 test t A100 \N t \N \N \N {} -2771 966 2025-02-26 19:37:41.565742-08 2025-02-26 20:03:41.368376-08 benchmark t A100 \N t \N \N \N {} -2790 974 2025-02-26 20:10:51.878483-08 2025-02-26 21:28:32.546366-08 benchmark t A100 \N t \N \N \N {} -1296 436 2025-02-25 00:18:28.614962-08 2025-02-25 00:32:08.809058-08 leaderboard t A100 0.01866328503614458 t \N \N \N {} -1297 436 2025-02-25 00:51:06.440802-08 2025-02-25 00:45:31.265356-08 test f A100 \N t \N \N \N {} -1423 472 2025-02-25 04:26:56.092902-08 2025-02-25 04:03:45.687235-08 test t A100 \N t \N \N \N {} -1298 436 2025-02-25 00:31:34.02733-08 2025-02-25 01:09:08.022812-08 benchmark f A100 \N t \N \N \N {} -1299 436 2025-02-25 01:34:03.732205-08 2025-02-25 01:19:15.994949-08 leaderboard f A100 0.01933390226 t \N \N \N {} -1302 438 2025-02-25 02:13:44.700479-08 2025-02-25 01:53:56.844408-08 benchmark f A100 \N t \N \N \N {} -1303 438 2025-02-25 01:52:21.421101-08 2025-02-25 01:13:41.748765-08 leaderboard f A100 0.00318387075 t \N \N \N {} -1304 438 2025-02-25 00:58:34.99604-08 2025-02-25 00:26:07.558315-08 test t A100 \N t \N \N \N {} -1426 473 2025-02-25 03:33:23.323189-08 2025-02-25 04:45:59.064294-08 test t A100 \N t \N \N \N {} -1522 506 2025-02-25 06:29:30.73263-08 2025-02-25 06:29:47.306703-08 test f H100 \N t \N \N \N {} -1305 438 2025-02-25 01:45:16.792963-08 2025-02-25 00:41:59.854224-08 benchmark t A100 \N t \N \N \N {} -1308 439 2025-02-25 01:05:46.577398-08 2025-02-25 01:11:50.101033-08 benchmark t A100 \N t \N \N \N {} -1309 439 2025-02-25 00:31:25.37309-08 2025-02-25 01:33:59.391129-08 leaderboard t A100 0.01829094864864865 t \N \N \N {} -1429 473 2025-02-25 04:12:04.123883-08 2025-02-25 04:05:49.033486-08 test f A100 \N t \N \N \N {} -1310 439 2025-02-25 00:48:25.781713-08 2025-02-25 00:53:44.022446-08 test f A100 \N t \N \N \N {} -1311 439 2025-02-25 00:44:11.592056-08 2025-02-25 01:42:04.821849-08 benchmark f A100 \N t \N \N \N {} -3577 1224 2025-02-28 13:39:20.462449-08 2025-02-28 14:41:42.556499-08 benchmark f H100 \N t \N \N \N {} -2781 969 2025-02-26 19:55:03.2042-08 2025-02-26 20:10:38.143515-08 test f A100 \N f \N \N \N {} -2792 975 2025-02-26 20:50:18.58077-08 2025-02-26 21:14:29.36909-08 test t A100 \N t \N \N \N {} -2785 973 2025-02-26 19:58:45.213429-08 2025-02-26 20:46:29.95438-08 test f A100 \N t \N \N \N {} -2786 974 2025-02-26 21:50:24.917282-08 2025-02-26 21:26:01.992988-08 test f A100 \N t \N \N \N {} -1317 441 2025-02-25 01:27:39.741757-08 2025-02-25 01:07:56.270804-08 test f A100 \N t \N \N \N {} -1318 441 2025-02-25 00:45:28.035248-08 2025-02-25 01:01:14.020655-08 benchmark f A100 \N f \N \N \N {} -1319 442 2025-02-25 01:17:35.027985-08 2025-02-25 01:08:09.855491-08 test f T4 \N t \N \N \N {} -1320 442 2025-02-25 02:23:45.784619-08 2025-02-25 01:07:58.424072-08 benchmark f T4 \N t \N \N \N {} -1321 442 2025-02-25 02:07:32.64996-08 2025-02-25 01:07:33.995652-08 leaderboard f T4 0.017366781666666668 t \N \N \N {} -1323 442 2025-02-25 02:26:58.697335-08 2025-02-25 02:40:12.565833-08 benchmark t T4 \N t \N \N \N {} -1324 442 2025-02-25 02:12:07.116909-08 2025-02-25 00:49:02.771059-08 leaderboard t T4 0.017343184 t \N \N \N {} -1325 443 2025-02-25 02:37:32.638106-08 2025-02-25 02:42:15.65973-08 test t A100 \N t \N \N \N {} -1331 444 2025-02-25 01:34:34.727263-08 2025-02-25 01:39:09.005069-08 test f A100 \N f \N \N \N {} -1332 444 2025-02-25 02:59:13.676931-08 2025-02-25 02:28:54.180908-08 test t A100 \N f \N \N \N {} -1333 445 2025-02-25 01:51:47.936405-08 2025-02-25 03:05:33.196327-08 test f A100 \N t \N \N \N {} -1334 445 2025-02-25 02:55:01.288465-08 2025-02-25 02:42:58.024491-08 benchmark f A100 \N t \N \N \N {} -1337 445 2025-02-25 02:37:39.036581-08 2025-02-25 03:06:46.109288-08 benchmark t A100 \N t \N \N \N {} -1338 445 2025-02-25 02:27:46.062726-08 2025-02-25 02:38:14.514333-08 leaderboard t A100 0.0030933733333333335 t \N \N \N {} -2902 1013 2025-02-27 02:13:19.283137-08 2025-02-27 00:49:55.016565-08 benchmark f L4 \N t \N \N \N {} -2972 1054 2025-02-27 11:12:54.54238-08 2025-02-27 09:51:41.285788-08 benchmark f A100 \N t \N \N \N {} -3032 1085 2025-02-27 16:19:10.737007-08 2025-02-27 15:35:26.72855-08 benchmark t H100 \N t \N \N \N {} -3033 1085 2025-02-27 15:30:06.20078-08 2025-02-27 14:54:00.220725-08 leaderboard t H100 0.0025986981400000003 t \N \N \N {} -3190 1122 2025-02-28 05:22:46.19105-08 2025-02-28 05:19:12.334732-08 test f A100 \N t \N \N \N {} -2787 974 2025-02-26 21:02:26.857575-08 2025-02-26 20:41:35.440073-08 benchmark f A100 \N t \N \N \N {} -2788 974 2025-02-26 21:28:18.31903-08 2025-02-26 20:11:08.511332-08 leaderboard f A100 0.003155315 t \N \N \N {} -3512 1201 2025-02-28 12:09:29.633702-08 2025-02-28 11:30:22.549861-08 test t A100 \N t \N \N \N {} -2794 975 2025-02-26 21:18:52.089772-08 2025-02-26 20:24:31.866857-08 leaderboard t A100 0.003347336 t \N \N \N {} -2796 975 2025-02-26 21:50:59.541074-08 2025-02-26 21:19:54.451136-08 benchmark f A100 \N t \N \N \N {} -2797 975 2025-02-26 21:39:11.70514-08 2025-02-26 20:18:16.784568-08 leaderboard f A100 0.0033508456666666666 t \N \N \N {} -2798 976 2025-02-26 20:38:37.194925-08 2025-02-26 20:58:59.28349-08 test f A100 \N t \N \N \N {} -2799 976 2025-02-26 21:00:51.351138-08 2025-02-26 21:11:09.227246-08 benchmark f A100 \N t \N \N \N {} -2814 982 2025-02-26 23:11:35.068266-08 2025-02-26 22:05:12.788499-08 test t A100 \N t \N \N \N {} -2815 982 2025-02-26 22:41:38.849192-08 2025-02-26 22:55:52.272396-08 benchmark t A100 \N t \N \N \N {} -2816 982 2025-02-26 22:55:13.597102-08 2025-02-26 22:18:52.107493-08 leaderboard t A100 0.0031956527999999996 t \N \N \N {} -2817 982 2025-02-26 22:04:16.387504-08 2025-02-26 22:18:31.80599-08 test f A100 \N t \N \N \N {} -2818 982 2025-02-26 23:45:04.257267-08 2025-02-26 22:14:37.906328-08 benchmark f A100 \N t \N \N \N {} -2819 982 2025-02-26 22:55:44.283823-08 2025-02-26 23:06:19.863356-08 leaderboard f A100 0.00318407825 t \N \N \N {} -1362 459 2025-02-25 02:55:11.371716-08 2025-02-25 03:57:41.197189-08 benchmark t A100 \N t \N \N \N {} -1363 459 2025-02-25 04:23:27.373732-08 2025-02-25 03:20:58.157958-08 leaderboard t A100 0.0030870156666666666 t \N \N \N {} -2903 1014 2025-02-27 01:15:06.478798-08 2025-02-27 01:26:08.615896-08 benchmark f L4 \N t \N \N \N {} -3034 1086 2025-02-27 16:07:05.961453-08 2025-02-27 15:24:55.001093-08 test t A100 \N t \N \N \N {} -3035 1086 2025-02-27 14:56:09.755895-08 2025-02-27 14:44:03.736702-08 benchmark t A100 \N t \N \N \N {} -3036 1086 2025-02-27 15:11:18.717508-08 2025-02-27 16:25:37.497879-08 leaderboard t A100 0.00219666254 t \N \N \N {} -2822 983 2025-02-26 23:58:17.638886-08 2025-02-26 23:08:43.554386-08 leaderboard f T4 0.017258812666666668 t \N \N \N {} -2823 983 2025-02-26 23:08:02.482508-08 2025-02-26 22:46:14.565363-08 test t T4 \N t \N \N \N {} -2824 983 2025-02-26 22:41:21.612063-08 2025-02-26 23:37:03.076526-08 benchmark t T4 \N t \N \N \N {} -2825 983 2025-02-26 22:11:02.562982-08 2025-02-26 23:08:38.480756-08 leaderboard t T4 0.017232594333333334 t \N \N \N {} -2826 984 2025-02-26 23:55:23.356201-08 2025-02-26 22:34:06.41761-08 test t L4 \N t \N \N \N {} -2827 984 2025-02-26 23:39:16.573547-08 2025-02-26 23:45:06.294967-08 benchmark t L4 \N t \N \N \N {} -1365 459 2025-02-25 04:10:18.717828-08 2025-02-25 03:53:14.773432-08 benchmark f A100 \N t \N \N \N {} -1366 459 2025-02-25 02:36:43.695926-08 2025-02-25 03:37:20.520163-08 leaderboard f A100 0.003083136 t \N \N \N {} -2904 1015 2025-02-27 01:39:23.749307-08 2025-02-27 00:47:34.63079-08 benchmark f L4 \N t \N \N \N {} -3037 1086 2025-02-27 15:04:43.578785-08 2025-02-27 16:05:13.802056-08 test f A100 \N t \N \N \N {} -3038 1086 2025-02-27 15:52:40.863821-08 2025-02-27 15:55:36.239122-08 benchmark f A100 \N t \N \N \N {} -3039 1086 2025-02-27 14:57:57.258869-08 2025-02-27 16:22:35.716253-08 leaderboard f A100 0.006317708599999999 t \N \N \N {} -2828 984 2025-02-26 22:43:53.565232-08 2025-02-26 22:20:13.280092-08 leaderboard t L4 0.01789583566666667 t \N \N \N {} -2835 986 2025-02-26 23:41:34.087432-08 2025-02-26 22:22:07.716492-08 test f H100 \N f \N \N \N {} -2839 988 2025-02-26 23:14:11.180538-08 2025-02-26 22:55:38.097536-08 benchmark t H100 \N t \N \N \N {} -2840 988 2025-02-26 23:51:51.947336-08 2025-02-27 00:11:57.409637-08 leaderboard t H100 0.0014754095555555556 t \N \N \N {} -2841 988 2025-02-26 23:43:56.311146-08 2025-02-26 22:51:36.110909-08 test f H100 \N t \N \N \N {} -2842 988 2025-02-27 00:31:41.8135-08 2025-02-26 23:01:52.346478-08 benchmark f H100 \N t \N \N \N {} -1368 460 2025-02-25 03:56:45.320682-08 2025-02-25 04:12:23.36629-08 benchmark f H100 \N t \N \N \N {} -1369 460 2025-02-25 03:15:26.015945-08 2025-02-25 03:02:05.905441-08 leaderboard f H100 0.0014066026666666668 t \N \N \N {} -2905 1016 2025-02-27 01:18:12.382929-08 2025-02-27 01:17:06.286041-08 test t L4 \N t \N \N \N {} -2906 1016 2025-02-27 01:26:20.055734-08 2025-02-27 00:56:16.777841-08 benchmark t L4 \N t \N \N \N {} -2907 1016 2025-02-27 02:18:18.942716-08 2025-02-27 02:21:22.058687-08 leaderboard t L4 0.017129494 t \N \N \N {} -3040 1087 2025-02-27 16:44:40.535196-08 2025-02-27 15:03:57.635039-08 test f A100 \N t \N \N \N {} -3193 1122 2025-02-28 03:38:44.016859-08 2025-02-28 03:42:55.449712-08 test t A100 \N t \N \N \N {} -2847 989 2025-02-26 23:11:09.581049-08 2025-02-26 23:57:11.977062-08 test f H100 \N t \N \N \N {} -2848 989 2025-02-26 23:28:17.877389-08 2025-02-26 22:53:38.145262-08 benchmark f H100 \N t \N \N \N {} -1371 460 2025-02-25 03:58:04.611454-08 2025-02-25 02:31:49.105023-08 benchmark t H100 \N t \N \N \N {} -1372 460 2025-02-25 04:03:19.955243-08 2025-02-25 04:08:28.45914-08 leaderboard t H100 0.001423033 t \N \N \N {} -2908 1016 2025-02-27 01:21:48.281072-08 2025-02-27 00:41:29.117391-08 test f L4 \N t \N \N \N {} -1374 461 2025-02-25 04:09:34.446371-08 2025-02-25 02:35:46.24839-08 benchmark t H100 \N t \N \N \N {} -1375 461 2025-02-25 03:23:18.745022-08 2025-02-25 04:16:13.049744-08 leaderboard t H100 0.0014008253333333332 t \N \N \N {} -2911 1017 2025-02-27 03:00:45.796745-08 2025-02-27 01:43:35.233574-08 test f H100 \N f \N \N \N {} -3042 1089 2025-02-27 16:38:00.448626-08 2025-02-27 15:58:00.42158-08 benchmark f A100 \N t \N \N \N {} -3074 1102 2025-02-28 02:56:01.659272-08 2025-02-28 01:40:10.731732-08 benchmark t A100 \N t \N \N \N {} -3075 1102 2025-02-28 02:21:16.939778-08 2025-02-28 02:01:28.710259-08 leaderboard t A100 0.0030923946666666664 t \N \N \N {} -2849 989 2025-02-27 00:12:17.835592-08 2025-02-26 22:40:43.431694-08 leaderboard f H100 0.001638096 t \N \N \N {} -2850 990 2025-02-26 22:57:30.808633-08 2025-02-27 00:18:26.83744-08 test t H100 \N t \N \N \N {} -2851 990 2025-02-26 23:06:02.704806-08 2025-02-26 23:22:40.730823-08 benchmark t H100 \N t \N \N \N {} -2852 990 2025-02-26 23:16:39.034056-08 2025-02-27 00:31:18.681964-08 leaderboard t H100 0.001507587888888889 t \N \N \N {} -2855 990 2025-02-26 22:47:36.052869-08 2025-02-27 00:05:47.184279-08 leaderboard f H100 0.0015178355 t \N \N \N {} -2856 991 2025-02-26 23:22:25.154438-08 2025-02-27 00:25:57.062615-08 test f H100 \N f \N \N \N {} -1377 461 2025-02-25 03:11:23.466982-08 2025-02-25 03:14:58.304434-08 benchmark f H100 \N t \N \N \N {} -1378 461 2025-02-25 03:19:43.776884-08 2025-02-25 03:47:15.782517-08 leaderboard f H100 0.0014126543333333333 t \N \N \N {} -2912 1018 2025-02-27 02:15:28.857362-08 2025-02-27 03:05:57.617691-08 test f H100 \N t \N \N \N {} -3043 1090 2025-02-27 16:23:02.016071-08 2025-02-27 15:37:11.121549-08 benchmark f A100 \N t \N \N \N {} -3076 1103 2025-02-28 01:45:24.142839-08 2025-02-28 01:57:12.178482-08 test f A100 \N t \N \N \N {} -3119 1110 2025-02-28 03:01:10.825974-08 2025-02-28 02:12:58.251641-08 benchmark t A100 \N t \N \N \N {} -2857 991 2025-02-27 00:12:21.904324-08 2025-02-26 23:35:25.811072-08 test t H100 \N f \N \N \N {} -2858 992 2025-02-27 00:04:38.346043-08 2025-02-26 23:29:33.532762-08 test t H100 \N t \N \N \N {} -2859 992 2025-02-27 00:22:03.205134-08 2025-02-26 23:23:33.781116-08 benchmark t H100 \N t \N \N \N {} -2864 993 2025-02-26 23:55:03.486115-08 2025-02-26 23:18:13.205693-08 test f H100 \N t \N \N \N {} -2865 993 2025-02-27 00:45:02.104993-08 2025-02-26 23:57:45.704039-08 benchmark f H100 \N t \N \N \N {} -2866 993 2025-02-26 23:20:23.784092-08 2025-02-26 23:18:10.376928-08 leaderboard f H100 0.0015042338999999999 t \N \N \N {} -1382 464 2025-02-25 04:24:56.79314-08 2025-02-25 04:46:29.308114-08 benchmark f H100 \N t \N \N \N {} -1383 464 2025-02-25 03:22:15.683757-08 2025-02-25 03:47:50.3734-08 leaderboard f H100 0.0014120043333333333 t \N \N \N {} -2913 1019 2025-02-27 02:15:25.259011-08 2025-02-27 02:01:14.038287-08 test f H100 \N f \N \N \N {} -3044 1091 2025-02-27 16:34:45.373039-08 2025-02-27 17:04:56.053201-08 benchmark f A100 \N t \N \N \N {} -3077 1103 2025-02-28 02:26:24.054371-08 2025-02-28 02:57:27.049866-08 benchmark f A100 \N t \N \N \N {} -2867 993 2025-02-27 00:20:03.256964-08 2025-02-26 23:38:08.774778-08 test t H100 \N t \N \N \N {} -2868 993 2025-02-27 00:17:21.143055-08 2025-02-26 23:43:44.61358-08 benchmark t H100 \N t \N \N \N {} -2869 993 2025-02-27 00:41:17.765937-08 2025-02-27 00:38:24.740261-08 leaderboard t H100 0.0014829258999999998 t \N \N \N {} -2873 995 2025-02-26 23:14:08.542885-08 2025-02-27 00:24:07.258227-08 benchmark t H100 \N t \N \N \N {} -2874 995 2025-02-26 22:57:32.493875-08 2025-02-26 23:39:16.1763-08 leaderboard t H100 0.0014810648 t \N \N \N {} -2875 995 2025-02-26 23:55:02.996586-08 2025-02-27 00:01:45.681815-08 test f H100 \N t \N \N \N {} -2889 1000 2025-02-27 00:47:29.323693-08 2025-02-27 01:19:30.043071-08 benchmark f L4 \N t \N \N \N {} -2876 995 2025-02-27 00:48:27.410452-08 2025-02-26 23:02:52.737338-08 benchmark f H100 \N t \N \N \N {} -2877 995 2025-02-27 00:46:15.879218-08 2025-02-26 22:53:09.26022-08 leaderboard f H100 0.00151809525 t \N \N \N {} -2878 996 2025-02-26 23:32:33.325133-08 2025-02-27 00:20:52.668636-08 test t H100 \N f \N \N \N {} -2879 996 2025-02-26 23:32:09.509312-08 2025-02-27 00:16:13.600353-08 test f H100 \N f \N \N \N {} -2880 997 2025-02-26 23:10:26.947465-08 2025-02-27 00:59:43.704157-08 test f H100 \N f \N \N \N {} -2895 1006 2025-02-27 00:30:50.677826-08 2025-02-27 02:02:45.191772-08 benchmark f T4 \N t \N \N \N {} -2881 997 2025-02-26 23:16:25.148851-08 2025-02-26 23:15:22.787437-08 test t H100 \N f \N \N \N {} -1385 464 2025-02-25 03:54:35.416434-08 2025-02-25 03:14:13.277902-08 benchmark t H100 \N t \N \N \N {} -1386 464 2025-02-25 03:44:28.181743-08 2025-02-25 04:52:22.685863-08 leaderboard t H100 0.0013942456666666667 t \N \N \N {} -2914 1021 2025-02-27 04:12:21.245081-08 2025-02-27 02:35:09.897785-08 test f H100 \N t \N \N \N {} -3045 1092 2025-02-27 15:48:36.342402-08 2025-02-27 16:50:15.479403-08 benchmark f A100 \N t \N \N \N {} -2882 998 2025-02-26 23:31:45.732141-08 2025-02-26 23:28:03.798982-08 test f H100 \N t \N \N \N {} -2883 998 2025-02-27 00:46:38.657542-08 2025-02-27 00:13:36.156442-08 benchmark f H100 \N t \N \N \N {} -2884 998 2025-02-26 23:36:22.797455-08 2025-02-27 00:38:35.235535-08 leaderboard f H100 0.0014596549 t \N \N \N {} -2885 998 2025-02-27 00:25:48.975564-08 2025-02-27 00:17:37.217535-08 test t H100 \N t \N \N \N {} -2886 998 2025-02-27 01:13:36.817864-08 2025-02-27 00:29:21.912198-08 benchmark t H100 \N t \N \N \N {} -2887 998 2025-02-27 00:25:46.035637-08 2025-02-27 00:48:51.353021-08 leaderboard t H100 0.00146596 t \N \N \N {} -2888 999 2025-02-26 23:44:36.851934-08 2025-02-27 00:13:44.746988-08 test f H100 \N f \N \N \N {} -2891 1002 2025-02-27 00:08:35.698636-08 2025-02-27 00:37:38.529546-08 benchmark f L4 \N t \N \N \N {} -2892 1003 2025-02-27 01:04:08.050505-08 2025-02-27 01:56:08.100836-08 benchmark f T4 \N f \N \N \N {} -2893 1004 2025-02-27 01:20:48.626974-08 2025-02-27 02:00:18.32054-08 benchmark f T4 \N t \N \N \N {} -2894 1005 2025-02-27 00:48:26.399604-08 2025-02-27 01:32:13.188985-08 benchmark f T4 \N t \N \N \N {} -3515 1202 2025-02-28 10:44:20.111423-08 2025-02-28 10:41:52.613484-08 test f A100 \N t \N \N \N {} -3883 1296 2025-03-01 03:31:39.319577-08 2025-03-01 04:35:51.070787-08 test t A100 \N t \N \N \N {} -2896 1007 2025-02-27 00:30:01.989297-08 2025-02-27 01:26:11.483563-08 benchmark f L4 \N t \N \N \N {} -2897 1008 2025-02-27 00:19:27.820406-08 2025-02-27 01:13:08.554592-08 benchmark f L4 \N t \N \N \N {} -2898 1009 2025-02-27 00:14:37.058625-08 2025-02-27 00:33:25.198225-08 benchmark f T4 \N t \N \N \N {} -2899 1010 2025-02-27 01:32:50.152071-08 2025-02-27 01:26:35.416449-08 benchmark f L4 \N f \N \N \N {} -2900 1011 2025-02-27 00:40:17.675693-08 2025-02-27 00:34:54.012193-08 benchmark f L4 \N t \N \N \N {} -2901 1012 2025-02-27 00:57:52.617774-08 2025-02-27 01:00:23.764973-08 benchmark f L4 \N t \N \N \N {} -3031 1085 2025-02-27 15:24:29.601416-08 2025-02-27 14:51:34.912701-08 test t H100 \N t \N \N \N {} -6462 2006 2025-03-14 07:51:45.870673-07 2025-03-14 07:40:39.999265-07 benchmark f A100 \N t \N \N \N {} -3102 1107 2025-02-28 01:54:23.349256-08 2025-02-28 01:56:27.939879-08 leaderboard f A100 0.003092532 t \N \N \N {} -6142 1883 2025-03-11 11:28:02.967532-07 2025-03-11 11:35:29.392865-07 test t T4 \N f \N \N \N {} -6314 1954 2025-03-12 20:17:28.206705-07 2025-03-12 20:22:28.033335-07 test f A100 \N t \N \N \N {} -6315 1954 2025-03-12 19:44:04.442778-07 2025-03-12 20:05:34.313377-07 benchmark f A100 \N t \N \N \N {} -6894 2143 2025-03-16 12:07:38.644838-07 2025-03-16 12:03:19.501535-07 test f L4 \N f \N \N \N {} -1388 465 2025-02-25 03:44:13.489138-08 2025-02-25 04:58:52.178206-08 benchmark t H100 \N t \N \N \N {} -1389 465 2025-02-25 04:48:22.345502-08 2025-02-25 03:34:29.544054-08 leaderboard t H100 0.000999849 t \N \N \N {} -2915 1022 2025-02-27 03:08:40.368035-08 2025-02-27 03:55:02.355278-08 benchmark f A100 \N t \N \N \N {} -3046 1093 2025-02-27 16:57:16.382054-08 2025-02-27 16:07:28.917105-08 test t A100 \N t \N \N \N {} -6895 2144 2025-03-16 12:40:59.707177-07 2025-03-16 12:04:30.038571-07 benchmark f T4 \N f \N \N \N {} -3108 1108 2025-02-28 02:25:29.231129-08 2025-02-28 01:30:03.455293-08 leaderboard t A100 0.0030845013333333335 t \N \N \N {} -6144 1885 2025-03-11 11:27:23.29413-07 2025-03-11 12:06:00.249613-07 test f T4 \N f \N \N \N {} -6317 1954 2025-03-12 20:05:57.526603-07 2025-03-12 19:36:21.76166-07 test t A100 \N t \N \N \N {} -6318 1954 2025-03-12 20:14:29.660956-07 2025-03-12 20:07:14.51853-07 benchmark t A100 \N t \N \N \N {} -6319 1954 2025-03-12 20:17:32.676671-07 2025-03-12 19:52:30.024723-07 leaderboard t A100 0.0013705575900000001 t \N \N \N {} -6465 2008 2025-03-14 06:25:10.510564-07 2025-03-14 06:10:34.856104-07 benchmark f A100 \N t \N \N \N {} -6467 2008 2025-03-14 07:05:50.283776-07 2025-03-14 06:54:47.878347-07 test t A100 \N t \N \N \N {} -6468 2008 2025-03-14 07:57:23.655081-07 2025-03-14 07:57:46.860585-07 benchmark t A100 \N t \N \N \N {} -6321 1956 2025-03-13 09:39:59.949032-07 2025-03-13 10:36:07.030735-07 test t T4 \N f \N \N \N {} -6470 2009 2025-03-14 07:54:17.367584-07 2025-03-14 07:52:14.667761-07 test t A100 \N t \N \N \N {} -6471 2009 2025-03-14 08:25:39.938379-07 2025-03-14 06:36:09.564554-07 benchmark t A100 \N t \N \N \N {} -3122 1110 2025-02-28 03:18:32.981187-08 2025-02-28 02:42:29.642779-08 benchmark f A100 \N t \N \N \N {} -3123 1110 2025-02-28 02:50:51.08401-08 2025-02-28 01:46:09.197929-08 leaderboard f A100 0.003097124 t \N \N \N {} -6322 1956 2025-03-13 08:55:36.22372-07 2025-03-13 10:04:13.347907-07 test f T4 \N f \N \N \N {} -6473 2009 2025-03-14 07:46:08.399013-07 2025-03-14 08:25:12.694431-07 test f A100 \N t \N \N \N {} -3578 1225 2025-02-28 14:25:25.372721-08 2025-02-28 13:59:09.123935-08 benchmark f A100 \N t \N \N \N {} -3128 1111 2025-02-28 02:02:43.50836-08 2025-02-28 01:29:59.933956-08 benchmark t A100 \N t \N \N \N {} -3129 1111 2025-02-28 02:09:35.864061-08 2025-02-28 02:11:31.900287-08 leaderboard t A100 0.00320221 t \N \N \N {} -6150 1888 2025-03-11 12:00:16.100477-07 2025-03-11 12:59:35.590119-07 test f T4 \N f \N \N \N {} -6324 1957 2025-03-13 10:47:36.919965-07 2025-03-13 09:33:50.353862-07 test f T4 \N f \N \N \N {} -3132 1112 2025-02-28 02:00:15.175704-08 2025-02-28 01:42:45.822482-08 leaderboard f A100 0.0030739046666666664 t \N \N \N {} -6151 1888 2025-03-11 11:24:19.34183-07 2025-03-11 11:15:09.363246-07 test t T4 \N f \N \N \N {} -6325 1958 2025-03-13 10:54:39.858929-07 2025-03-13 10:57:53.21954-07 test t T4 \N f \N \N \N {} -6481 2010 2025-03-14 07:16:40.068566-07 2025-03-14 08:11:25.193743-07 leaderboard f A100 0.0026579623333333334 t \N \N \N {} -3134 1112 2025-02-28 02:21:04.795319-08 2025-02-28 03:19:20.667414-08 benchmark t A100 \N t \N \N \N {} -3135 1112 2025-02-28 02:49:56.527812-08 2025-02-28 02:10:59.95073-08 leaderboard t A100 0.0030922646666666667 t \N \N \N {} -6153 1889 2025-03-11 11:42:50.98288-07 2025-03-11 13:36:59.142943-07 benchmark t T4 \N f \N \N \N {} -1391 465 2025-02-25 04:55:41.868356-08 2025-02-25 03:36:33.891601-08 benchmark f H100 \N t \N \N \N {} -1392 465 2025-02-25 04:37:36.417233-08 2025-02-25 03:28:58.50135-08 leaderboard f H100 0.00140509 t \N \N \N {} -1393 466 2025-02-25 03:10:36.309883-08 2025-02-25 03:21:53.515918-08 test f H100 \N f \N \N \N {} -1394 466 2025-02-25 03:11:29.000771-08 2025-02-25 03:41:58.917775-08 test t H100 \N f \N \N \N {} -2916 1023 2025-02-27 02:46:36.620437-08 2025-02-27 02:58:33.660277-08 test f A100 \N t \N \N \N {} -3078 1103 2025-02-28 01:22:48.676241-08 2025-02-28 02:52:22.813395-08 leaderboard f A100 0.0031237426666666664 t \N \N \N {} -6155 1889 2025-03-11 12:10:23.846696-07 2025-03-11 12:13:59.236186-07 benchmark f T4 \N f \N \N \N {} -1397 468 2025-02-25 03:38:51.530876-08 2025-02-25 04:52:35.406014-08 benchmark f A100 \N t \N \N \N {} -1398 468 2025-02-25 04:38:38.385416-08 2025-02-25 03:58:06.257632-08 leaderboard f A100 0.0033990103333333336 t \N \N \N {} -2917 1023 2025-02-27 03:14:37.23347-08 2025-02-27 03:11:45.911539-08 benchmark f A100 \N t \N \N \N {} -2918 1023 2025-02-27 03:03:16.65023-08 2025-02-27 04:18:18.937857-08 leaderboard f A100 0.0001010261724137931 t \N \N \N {} -3079 1103 2025-02-28 01:31:17.940598-08 2025-02-28 02:41:55.766318-08 test t A100 \N t \N \N \N {} -6897 2146 2025-03-16 12:39:46.739273-07 2025-03-16 13:06:56.642031-07 benchmark f T4 \N f \N \N \N {} -3141 1113 2025-02-28 01:34:25.294505-08 2025-02-28 02:09:38.976633-08 leaderboard t A100 0.0030885226666666665 t \N \N \N {} -3579 1226 2025-02-28 13:23:19.822733-08 2025-02-28 13:38:01.202424-08 benchmark f H100 \N t \N \N \N {} -3144 1114 2025-02-28 03:32:16.197256-08 2025-02-28 02:47:43.563886-08 leaderboard t A100 0.003070266 t \N \N \N {} -6158 1890 2025-03-11 13:28:57.157083-07 2025-03-11 12:19:09.927667-07 test f T4 \N t \N \N \N {} -3147 1114 2025-02-28 03:28:26.003711-08 2025-02-28 01:44:07.837604-08 leaderboard f A100 0.0030685503333333333 t \N \N \N {} -6160 1891 2025-03-11 13:43:23.540375-07 2025-03-11 13:07:39.214045-07 test f T4 \N t \N \N \N {} -6484 2011 2025-03-14 07:02:10.29025-07 2025-03-14 08:11:39.923824-07 leaderboard f A100 0.0025160156666666667 t \N \N \N {} -3149 1115 2025-02-28 02:35:43.630546-08 2025-02-28 02:15:39.443589-08 benchmark f A100 \N t \N \N \N {} -3150 1115 2025-02-28 02:15:43.119849-08 2025-02-28 01:43:19.893985-08 leaderboard f A100 0.0030932896666666667 t \N \N \N {} -6161 1892 2025-03-11 11:57:49.455042-07 2025-03-11 12:21:21.948804-07 benchmark f T4 \N f \N \N \N {} -3580 1227 2025-02-28 13:22:39.155063-08 2025-02-28 13:51:24.355953-08 benchmark f H100 \N t \N \N \N {} -3152 1115 2025-02-28 01:56:27.157682-08 2025-02-28 01:41:45.62036-08 benchmark t A100 \N t \N \N \N {} -3153 1115 2025-02-28 03:24:29.539728-08 2025-02-28 03:03:11.575433-08 leaderboard t A100 0.0030942826666666666 t \N \N \N {} -6162 1893 2025-03-11 12:05:53.460089-07 2025-03-11 13:52:23.016257-07 benchmark f T4 \N f \N \N \N {} -3155 1116 2025-02-28 01:56:03.204295-08 2025-02-28 02:29:07.453242-08 benchmark f A100 \N t \N \N \N {} -3156 1116 2025-02-28 03:41:05.90631-08 2025-02-28 03:37:39.667047-08 leaderboard f A100 0.0030983496666666666 t \N \N \N {} -3581 1228 2025-02-28 14:07:30.971092-08 2025-02-28 14:44:25.55772-08 test f A100 \N t \N \N \N {} -6487 2011 2025-03-14 07:33:24.422282-07 2025-03-14 07:03:24.285193-07 leaderboard t A100 0.0024820293333333333 t \N \N \N {} -6674 2065 2025-03-14 15:48:12.867984-07 2025-03-14 15:10:31.440491-07 test t T4 \N f \N \N \N {} -1406 469 2025-02-25 04:38:40.125755-08 2025-02-25 04:37:21.18501-08 benchmark t A100 \N t \N \N \N {} -1407 469 2025-02-25 04:41:04.15737-08 2025-02-25 04:10:44.089578-08 leaderboard t A100 0.0030951176666666664 t \N \N \N {} -2925 1024 2025-02-27 04:23:16.250435-08 2025-02-27 03:24:58.288515-08 test t T4 \N t \N \N \N {} -1412 470 2025-02-25 03:40:12.003397-08 2025-02-25 04:12:17.525955-08 benchmark f A100 \N t \N \N \N {} -1413 470 2025-02-25 04:37:09.951863-08 2025-02-25 03:30:09.91909-08 leaderboard f A100 0.003099017 t \N \N \N {} -2931 1025 2025-02-27 03:01:05.861477-08 2025-02-27 04:00:58.846217-08 test t L4 \N t \N \N \N {} -2932 1025 2025-02-27 04:33:16.006317-08 2025-02-27 04:33:30.552743-08 benchmark t L4 \N t \N \N \N {} -2933 1025 2025-02-27 03:00:47.162532-08 2025-02-27 03:18:26.934968-08 leaderboard t L4 0.00011731689361702128 t \N \N \N {} -3127 1111 2025-02-28 03:03:45.476308-08 2025-02-28 01:34:30.667207-08 test t A100 \N t \N \N \N {} -1415 471 2025-02-25 05:07:03.031352-08 2025-02-25 04:44:07.11511-08 benchmark f A100 \N t \N \N \N {} -1416 471 2025-02-25 03:49:37.382368-08 2025-02-25 03:16:26.151128-08 leaderboard f A100 0.003083305 t \N \N \N {} -2934 1026 2025-02-27 03:29:31.212038-08 2025-02-27 03:36:48.365495-08 benchmark f A100 \N t \N \N \N {} -3049 1093 2025-02-27 15:47:45.331679-08 2025-02-27 16:54:04.404866-08 test f A100 \N t \N \N \N {} -3130 1112 2025-02-28 02:53:32.669829-08 2025-02-28 01:59:58.104291-08 test f A100 \N t \N \N \N {} -3158 1116 2025-02-28 03:00:27.935274-08 2025-02-28 03:06:58.848252-08 benchmark t A100 \N t \N \N \N {} -3582 1228 2025-02-28 14:47:56.576495-08 2025-02-28 13:38:26.115181-08 benchmark f A100 \N t \N \N \N {} -6334 1962 2025-03-13 12:17:44.907621-07 2025-03-13 10:47:39.798292-07 test f T4 \N f \N \N \N {} -6488 2012 2025-03-14 07:37:48.319936-07 2025-03-14 06:40:10.451158-07 test t A100 \N t \N \N \N {} -6905 2149 2025-03-16 13:54:55.728222-07 2025-03-16 12:29:31.998021-07 test f A100 \N f \N \N \N {} -3161 1117 2025-02-28 03:20:59.962421-08 2025-02-28 03:07:10.955256-08 benchmark t A100 \N t \N \N \N {} -6489 2012 2025-03-14 07:07:28.772602-07 2025-03-14 06:43:54.465366-07 benchmark t A100 \N t \N \N \N {} -6490 2012 2025-03-14 07:43:28.617109-07 2025-03-14 07:02:38.240011-07 leaderboard t A100 0.002517398 t \N \N \N {} -6912 2154 2025-03-16 16:29:02.680063-07 2025-03-16 16:07:12.630657-07 test t T4 \N f \N \N \N {} -3164 1117 2025-02-28 01:55:34.743977-08 2025-02-28 03:46:05.60909-08 benchmark f A100 \N t \N \N \N {} -3165 1117 2025-02-28 03:22:15.291884-08 2025-02-28 03:01:38.071188-08 leaderboard f A100 0.0031003043333333335 t \N \N \N {} -3166 1118 2025-02-28 02:36:20.544219-08 2025-02-28 02:03:38.914711-08 test t A100 \N t \N \N \N {} -3167 1118 2025-02-28 02:27:47.915164-08 2025-02-28 02:31:18.69465-08 benchmark t A100 \N t \N \N \N {} -3168 1118 2025-02-28 03:38:38.742518-08 2025-02-28 02:42:05.29698-08 leaderboard t A100 0.0031557713333333335 t \N \N \N {} -3467 1192 2025-02-28 11:34:31.331723-08 2025-02-28 10:29:39.69554-08 test f A100 \N f \N \N \N {} -1418 471 2025-02-25 04:36:59.718452-08 2025-02-25 03:23:10.279404-08 benchmark t A100 \N t \N \N \N {} -1419 471 2025-02-25 03:23:16.170077-08 2025-02-25 04:14:16.278504-08 leaderboard t A100 0.0030839833333333334 t \N \N \N {} -2935 1027 2025-02-27 04:13:09.20703-08 2025-02-27 03:53:36.446829-08 benchmark f A100 \N t \N \N \N {} -3133 1112 2025-02-28 01:44:54.216121-08 2025-02-28 02:38:03.906338-08 test t A100 \N t \N \N \N {} -3518 1202 2025-02-28 11:33:17.396025-08 2025-02-28 12:16:13.271104-08 test t A100 \N t \N \N \N {} -6913 2154 2025-03-16 16:38:39.962668-07 2025-03-16 14:53:33.732977-07 test f T4 \N f \N \N \N {} -3568 1211 2025-02-28 11:12:30.656088-08 2025-02-28 12:38:32.554868-08 test t H100 \N f \N \N \N {} -3171 1118 2025-02-28 03:57:16.999936-08 2025-02-28 02:52:16.718421-08 leaderboard f A100 0.003146471 t \N \N \N {} -3172 1119 2025-02-28 03:47:11.316659-08 2025-02-28 03:09:45.561459-08 test f A100 \N t \N \N \N {} -3173 1119 2025-02-28 02:39:13.039039-08 2025-02-28 02:10:32.463474-08 benchmark f A100 \N t \N \N \N {} -6914 2155 2025-03-16 15:06:19.793513-07 2025-03-16 16:22:51.137738-07 test t T4 \N f \N \N \N {} -3177 1119 2025-02-28 03:56:27.839518-08 2025-02-28 03:53:00.543375-08 leaderboard t A100 0.003148472 t \N \N \N {} -6166 1897 2025-03-11 12:32:48.919729-07 2025-03-11 13:04:35.751745-07 benchmark f T4 \N t \N \N \N {} -6336 1963 2025-03-13 11:44:12.666263-07 2025-03-13 11:35:54.4531-07 test t T4 \N f \N \N \N {} -6491 2012 2025-03-14 06:56:50.114221-07 2025-03-14 07:03:33.962589-07 test f A100 \N t \N \N \N {} -6915 2155 2025-03-16 16:05:45.981468-07 2025-03-16 16:53:11.056967-07 test f T4 \N f \N \N \N {} -3886 1296 2025-03-01 04:03:42.524707-08 2025-03-01 04:26:34.298074-08 test f A100 \N t \N \N \N {} -6167 1898 2025-03-11 14:02:34.088566-07 2025-03-11 13:21:01.318443-07 test f T4 \N t \N \N \N {} -6168 1898 2025-03-11 13:56:37.141151-07 2025-03-11 12:54:02.739765-07 benchmark f T4 \N t \N \N \N {} -6169 1898 2025-03-11 13:26:27.542536-07 2025-03-11 12:35:42.421543-07 leaderboard f T4 3.1035724833333336 t \N \N \N {} -6337 1964 2025-03-13 12:31:57.776048-07 2025-03-13 12:44:18.662819-07 test t T4 \N f \N \N \N {} -6675 2065 2025-03-14 15:10:20.43581-07 2025-03-14 15:25:35.47986-07 test f T4 \N f \N \N \N {} -6170 1898 2025-03-11 14:05:54.41326-07 2025-03-11 13:31:34.094196-07 test t T4 \N t \N \N \N {} -6171 1898 2025-03-11 13:56:20.919926-07 2025-03-11 12:56:21.007587-07 benchmark t T4 \N t \N \N \N {} -6172 1898 2025-03-11 13:32:59.303232-07 2025-03-11 14:04:58.378204-07 leaderboard t T4 3.1852416556666667 t \N \N \N {} -6338 1964 2025-03-13 11:48:09.106344-07 2025-03-13 11:48:18.838973-07 test f T4 \N f \N \N \N {} -6676 2066 2025-03-14 15:18:53.997082-07 2025-03-14 14:33:10.917221-07 test f T4 \N f \N \N \N {} -3185 1121 2025-02-28 04:01:47.89985-08 2025-02-28 04:25:50.220487-08 benchmark f A100 \N t \N \N \N {} -3186 1121 2025-02-28 04:48:14.978807-08 2025-02-28 04:05:51.546019-08 leaderboard f A100 0.0031212863333333336 t \N \N \N {} -6173 1899 2025-03-11 13:25:54.795056-07 2025-03-11 13:19:39.098623-07 test t T4 \N f \N \N \N {} -6339 1965 2025-03-13 11:39:17.316162-07 2025-03-13 11:28:19.690857-07 test t T4 \N f \N \N \N {} -3468 1193 2025-02-28 12:15:00.613441-08 2025-02-28 10:38:48.030816-08 test f A100 \N t \N \N \N {} -1421 472 2025-02-25 04:29:57.622938-08 2025-02-25 04:49:21.634274-08 benchmark f A100 \N t \N \N \N {} -1422 472 2025-02-25 03:37:41.571503-08 2025-02-25 03:49:33.02462-08 leaderboard f A100 0.0030871423333333333 t \N \N \N {} -2936 1028 2025-02-27 03:16:50.844905-08 2025-02-27 03:44:10.314673-08 benchmark f A100 \N t \N \N \N {} -3136 1113 2025-02-28 02:58:56.415099-08 2025-02-28 02:44:36.404391-08 test f A100 \N t \N \N \N {} -6492 2012 2025-03-14 08:00:25.963285-07 2025-03-14 06:46:07.163228-07 benchmark f A100 \N t \N \N \N {} -6493 2012 2025-03-14 07:09:08.72814-07 2025-03-14 07:18:27.447251-07 leaderboard f A100 0.002505465 t \N \N \N {} -6599 2045 2025-03-14 12:56:47.350056-07 2025-03-14 13:11:04.653905-07 benchmark f A100 \N t \N \N \N {} -6600 2045 2025-03-14 13:04:51.216706-07 2025-03-14 13:43:13.503645-07 leaderboard f A100 0.0025198196666666667 t \N \N \N {} -6645 2053 2025-03-14 13:47:46.801513-07 2025-03-14 12:15:48.666184-07 benchmark f A100 \N t \N \N \N {} -3192 1122 2025-02-28 03:46:09.723947-08 2025-02-28 04:28:57.570091-08 leaderboard f A100 0.0031000573333333334 t \N \N \N {} -6341 1966 2025-03-13 12:45:43.296749-07 2025-03-13 12:17:33.727157-07 test f T4 \N t \N \N \N {} -6342 1966 2025-03-13 12:13:23.993863-07 2025-03-13 12:35:05.634615-07 benchmark f T4 \N t \N \N \N {} -6343 1966 2025-03-13 13:00:24.511463-07 2025-03-13 12:19:23.080746-07 leaderboard f T4 3.5582660766666665 t \N \N \N {} -6495 2013 2025-03-14 12:06:59.03304-07 2025-03-14 11:21:26.358885-07 test f H100 \N f \N \N \N {} -3198 1123 2025-02-28 04:42:25.083061-08 2025-02-28 04:52:17.118702-08 leaderboard f A100 0.0031039456666666666 t \N \N \N {} -3521 1203 2025-02-28 12:13:18.73249-08 2025-02-28 11:42:59.789557-08 test t A100 \N f \N \N \N {} -3200 1123 2025-02-28 04:09:22.67407-08 2025-02-28 05:42:51.81569-08 benchmark t A100 \N t \N \N \N {} -3201 1123 2025-02-28 04:49:03.536471-08 2025-02-28 05:30:26.750128-08 leaderboard t A100 0.003107339 t \N \N \N {} -6176 1900 2025-03-11 14:27:12.915155-07 2025-03-11 13:27:30.861214-07 test f T4 \N f \N \N \N {} -6344 1966 2025-03-13 12:39:39.896955-07 2025-03-13 11:42:10.53502-07 test t T4 \N t \N \N \N {} -6345 1966 2025-03-13 11:46:33.159161-07 2025-03-13 11:42:35.832436-07 benchmark t T4 \N t \N \N \N {} -6346 1966 2025-03-13 12:49:02.202475-07 2025-03-13 11:43:42.365767-07 leaderboard t T4 3.6324577356666663 t \N \N \N {} -3202 1124 2025-02-28 05:47:04.982464-08 2025-02-28 04:47:56.713443-08 test f A100 \N t \N \N \N {} -3203 1124 2025-02-28 04:08:24.397878-08 2025-02-28 04:24:14.653298-08 benchmark f A100 \N t \N \N \N {} -3204 1124 2025-02-28 05:40:56.722412-08 2025-02-28 05:44:46.320859-08 leaderboard f A100 0.0030972656666666664 t \N \N \N {} -3205 1124 2025-02-28 04:40:01.037489-08 2025-02-28 03:58:41.704134-08 test t A100 \N t \N \N \N {} -3206 1124 2025-02-28 05:42:55.343085-08 2025-02-28 04:23:54.198083-08 benchmark t A100 \N t \N \N \N {} -3207 1124 2025-02-28 05:15:33.617644-08 2025-02-28 05:36:54.360407-08 leaderboard t A100 0.0031034153333333336 t \N \N \N {} -3212 1125 2025-02-28 05:41:25.119279-08 2025-02-28 06:47:46.688795-08 benchmark f A100 \N t \N \N \N {} -3218 1126 2025-02-28 06:15:42.624802-08 2025-02-28 05:18:36.139802-08 benchmark f A100 \N t \N \N \N {} -3224 1127 2025-02-28 05:23:45.306771-08 2025-02-28 06:39:04.576528-08 benchmark f A100 \N t \N \N \N {} -3230 1130 2025-02-28 06:11:34.161879-08 2025-02-28 07:19:05.529092-08 benchmark t A100 \N t \N \N \N {} -3522 1203 2025-02-28 11:17:41.164832-08 2025-02-28 10:45:35.314206-08 test f A100 \N f \N \N \N {} -3238 1133 2025-02-28 05:58:19.469647-08 2025-02-28 05:27:59.168429-08 leaderboard t A100 0.0030885113333333335 t \N \N \N {} -3471 1193 2025-02-28 12:08:38.072989-08 2025-02-28 11:36:29.661671-08 test t A100 \N t \N \N \N {} -1424 472 2025-02-25 05:07:18.96443-08 2025-02-25 04:27:31.619653-08 benchmark t A100 \N t \N \N \N {} -1425 472 2025-02-25 04:26:35.663772-08 2025-02-25 03:52:24.217898-08 leaderboard t A100 0.0031005363333333337 t \N \N \N {} -2937 1029 2025-02-27 04:45:23.397168-08 2025-02-27 04:26:08.472553-08 benchmark f A100 \N t \N \N \N {} -3050 1093 2025-02-27 15:36:27.175994-08 2025-02-27 15:34:55.376904-08 benchmark f A100 \N t \N \N \N {} -3239 1134 2025-02-28 06:50:37.72243-08 2025-02-28 06:25:11.379874-08 benchmark f A100 \N t \N \N \N {} -3240 1133 2025-02-28 05:32:42.89057-08 2025-02-28 06:35:04.012708-08 test f A100 \N t \N \N \N {} -3241 1133 2025-02-28 05:55:48.275123-08 2025-02-28 06:28:43.235753-08 benchmark f A100 \N t \N \N \N {} -3242 1133 2025-02-28 06:28:41.176944-08 2025-02-28 06:11:12.832836-08 leaderboard f A100 0.003086809 t \N \N \N {} -3243 1136 2025-02-28 05:47:39.958836-08 2025-02-28 05:44:16.957515-08 benchmark f A100 \N t \N \N \N {} -3244 1135 2025-02-28 06:38:46.857624-08 2025-02-28 07:16:05.450915-08 test f A100 \N t \N \N \N {} -3245 1135 2025-02-28 06:04:38.048901-08 2025-02-28 07:07:00.823655-08 benchmark f A100 \N t \N \N \N {} -3246 1135 2025-02-28 05:37:48.064623-08 2025-02-28 05:38:45.603311-08 leaderboard f A100 0.0030916216666666664 t \N \N \N {} -3247 1135 2025-02-28 05:52:38.194905-08 2025-02-28 05:43:19.652095-08 test t A100 \N t \N \N \N {} -3523 1204 2025-02-28 11:24:15.172488-08 2025-02-28 11:35:08.725248-08 test f A100 \N t \N \N \N {} -3252 1137 2025-02-28 07:21:26.0063-08 2025-02-28 07:16:56.022377-08 leaderboard f A100 0.0030857416666666667 t \N \N \N {} -3253 1137 2025-02-28 06:00:28.572485-08 2025-02-28 05:34:19.879496-08 test t A100 \N t \N \N \N {} -3254 1137 2025-02-28 06:10:53.662809-08 2025-02-28 06:32:30.162306-08 benchmark t A100 \N t \N \N \N {} -3255 1137 2025-02-28 07:12:35.431057-08 2025-02-28 06:05:27.261706-08 leaderboard t A100 0.0030894023333333334 t \N \N \N {} -3256 1138 2025-02-28 05:44:23.296315-08 2025-02-28 05:54:30.649398-08 benchmark f A100 \N t \N \N \N {} -3258 1139 2025-02-28 06:04:00.85979-08 2025-02-28 06:27:21.740985-08 benchmark f A100 \N f \N \N \N {} -3139 1113 2025-02-28 02:22:31.319401-08 2025-02-28 02:55:45.560083-08 test t A100 \N t \N \N \N {} -1427 473 2025-02-25 04:47:05.317584-08 2025-02-25 03:32:09.61539-08 benchmark t A100 \N t \N \N \N {} -1428 473 2025-02-25 04:44:32.069859-08 2025-02-25 03:55:20.86169-08 leaderboard t A100 0.003175767 t \N \N \N {} -2938 1030 2025-02-27 04:38:20.668328-08 2025-02-27 04:24:06.448431-08 test f A100 \N t \N \N \N {} -2939 1030 2025-02-27 03:05:03.419869-08 2025-02-27 04:22:37.87157-08 benchmark f A100 \N t \N \N \N {} -2940 1030 2025-02-27 04:07:07.030129-08 2025-02-27 04:03:57.310379-08 leaderboard f A100 0.00007918067999999999 t \N \N \N {} -3142 1114 2025-02-28 01:49:44.822785-08 2025-02-28 02:25:24.680714-08 test t A100 \N t \N \N \N {} -3259 1139 2025-02-28 06:24:17.521761-08 2025-02-28 07:13:37.812929-08 test t A100 \N t \N \N \N {} -3562 1210 2025-02-28 11:57:02.979824-08 2025-02-28 10:51:28.47295-08 test t A100 \N t \N \N \N {} -1430 473 2025-02-25 04:40:01.662393-08 2025-02-25 04:30:55.139642-08 benchmark f A100 \N t \N \N \N {} -1431 473 2025-02-25 03:31:35.707171-08 2025-02-25 03:31:53.832049-08 leaderboard f A100 0.00317240225 t \N \N \N {} -1432 474 2025-02-25 04:06:52.039054-08 2025-02-25 04:58:28.878029-08 test f A100 \N f \N \N \N {} -1438 479 2025-02-25 05:28:40.021595-08 2025-02-25 04:21:12.668713-08 test f A100 \N f \N \N \N {} -1445 484 2025-02-25 04:56:56.648978-08 2025-02-25 04:48:16.147785-08 benchmark t A100 \N t \N \N \N {} -1446 484 2025-02-25 05:51:53.636258-08 2025-02-25 04:20:30.28663-08 leaderboard t A100 0.0030884286666666666 t \N \N \N {} -2942 1030 2025-02-27 04:11:40.863984-08 2025-02-27 03:12:21.326746-08 benchmark t A100 \N t \N \N \N {} -2943 1030 2025-02-27 04:47:50.715647-08 2025-02-27 04:01:18.005466-08 leaderboard t A100 0.0000793426 t \N \N \N {} -3260 1139 2025-02-28 07:19:43.310162-08 2025-02-28 06:04:41.287437-08 benchmark t A100 \N f \N \N \N {} -3526 1204 2025-02-28 12:03:21.457321-08 2025-02-28 12:30:39.578159-08 test t A100 \N t \N \N \N {} -6647 2054 2025-03-14 13:43:53.190099-07 2025-03-14 12:39:40.431314-07 test f A100 \N t \N \N \N {} -6500 2015 2025-03-14 12:17:09.225085-07 2025-03-14 10:53:14.727282-07 leaderboard f H100 0.006131182333333333 t \N \N \N {} -6607 2046 2025-03-14 13:24:07.723525-07 2025-03-14 12:31:15.468896-07 test f A100 \N t \N \N \N {} -6504 2016 2025-03-14 12:12:39.778966-07 2025-03-14 12:30:56.454439-07 benchmark f A100 \N t \N \N \N {} -3274 1143 2025-02-28 07:21:32.682632-08 2025-02-28 07:30:37.256411-08 leaderboard f A100 0.003102605 t \N \N \N {} -6184 1903 2025-03-11 17:13:03.62363-07 2025-03-11 16:22:00.434413-07 test f T4 \N t \N \N \N {} -6185 1903 2025-03-11 16:06:37.941692-07 2025-03-11 16:41:23.331167-07 benchmark f T4 \N t \N \N \N {} -6186 1903 2025-03-11 16:13:32.336442-07 2025-03-11 16:38:58.569653-07 leaderboard f T4 3.2504637173333335 t \N \N \N {} -6352 1969 2025-03-13 12:36:05.935244-07 2025-03-13 13:06:47.416038-07 test f T4 \N f \N \N \N {} -6505 2017 2025-03-14 12:15:29.300377-07 2025-03-14 12:34:01.640423-07 test t A100 \N t \N \N \N {} -6506 2017 2025-03-14 11:18:20.450795-07 2025-03-14 12:30:54.661761-07 benchmark t A100 \N t \N \N \N {} -6608 2046 2025-03-14 13:48:29.521438-07 2025-03-14 13:48:51.889234-07 benchmark f A100 \N t \N \N \N {} -6609 2046 2025-03-14 12:28:36.914817-07 2025-03-14 12:50:31.439118-07 leaderboard f A100 0.002515373 t \N \N \N {} -3279 1144 2025-02-28 06:01:10.470211-08 2025-02-28 07:19:57.064988-08 benchmark t A100 \N t \N \N \N {} -3280 1144 2025-02-28 05:49:45.171368-08 2025-02-28 05:55:10.670943-08 leaderboard t A100 0.0031060543333333336 t \N \N \N {} -3624 1239 2025-02-28 16:58:22.066529-08 2025-02-28 16:33:30.877062-08 benchmark t A100 \N t \N \N \N {} -3281 1145 2025-02-28 05:37:52.569256-08 2025-02-28 07:15:46.585683-08 benchmark f A100 \N t \N \N \N {} -3282 1146 2025-02-28 05:57:19.501056-08 2025-02-28 05:49:46.099644-08 benchmark f A100 \N t \N \N \N {} -3287 1148 2025-02-28 07:03:20.664723-08 2025-02-28 07:36:21.224833-08 test f A100 \N t \N \N \N {} -3288 1148 2025-02-28 06:40:03.960027-08 2025-02-28 07:14:31.066145-08 benchmark f A100 \N t \N \N \N {} -3289 1148 2025-02-28 06:38:52.008227-08 2025-02-28 06:21:28.630464-08 leaderboard f A100 0.0037143906666666665 t \N \N \N {} -3290 1148 2025-02-28 07:14:12.948729-08 2025-02-28 06:08:46.103883-08 test t A100 \N t \N \N \N {} -3291 1148 2025-02-28 07:54:28.192996-08 2025-02-28 06:24:00.652714-08 benchmark t A100 \N t \N \N \N {} -3292 1148 2025-02-28 06:29:06.723096-08 2025-02-28 06:39:10.572344-08 leaderboard t A100 0.0036469966666666663 t \N \N \N {} -3293 1148 2025-02-28 06:40:23.470841-08 2025-02-28 07:24:25.091246-08 test f H100 \N t \N \N \N {} -3294 1148 2025-02-28 07:40:06.006249-08 2025-02-28 07:18:53.588443-08 benchmark f H100 \N t \N \N \N {} -3148 1115 2025-02-28 02:02:27.848298-08 2025-02-28 02:01:06.813683-08 test f A100 \N t \N \N \N {} -1448 484 2025-02-25 05:29:00.477152-08 2025-02-25 05:41:06.371795-08 benchmark f A100 \N t \N \N \N {} -1449 484 2025-02-25 04:52:55.95894-08 2025-02-25 04:18:57.804532-08 leaderboard f A100 0.003093351 t \N \N \N {} -2944 1031 2025-02-27 07:43:30.027296-08 2025-02-27 09:06:35.166274-08 benchmark f A100 \N t \N \N \N {} -3295 1148 2025-02-28 07:32:20.445776-08 2025-02-28 07:54:04.00939-08 leaderboard f H100 0.0018497506666666668 t \N \N \N {} -3296 1148 2025-02-28 07:09:18.873569-08 2025-02-28 07:00:06.853282-08 test t T4 \N t \N \N \N {} -3298 1148 2025-02-28 06:42:57.127187-08 2025-02-28 07:48:36.011787-08 leaderboard t T4 0.010518033333333334 t \N \N \N {} -3299 1148 2025-02-28 07:34:33.292144-08 2025-02-28 07:45:13.263944-08 test t H100 \N t \N \N \N {} -3305 1148 2025-02-28 06:56:08.877192-08 2025-02-28 07:30:09.470963-08 test f L4 \N t \N \N \N {} -3306 1148 2025-02-28 06:49:29.091858-08 2025-02-28 06:59:56.481035-08 benchmark f L4 \N t \N \N \N {} -3316 1151 2025-02-28 07:32:43.276426-08 2025-02-28 06:52:43.01807-08 test t H100 \N f \N \N \N {} -3317 1151 2025-02-28 06:04:40.63296-08 2025-02-28 06:57:06.096217-08 test t T4 \N f \N \N \N {} -3318 1151 2025-02-28 06:13:37.060218-08 2025-02-28 06:40:07.898882-08 test f L4 \N f \N \N \N {} -3529 1205 2025-02-28 11:00:20.479694-08 2025-02-28 11:27:21.065676-08 test f A100 \N t \N \N \N {} -3319 1151 2025-02-28 07:44:25.505195-08 2025-02-28 06:17:10.426155-08 test t L4 \N f \N \N \N {} -3569 1212 2025-02-28 12:31:15.223201-08 2025-02-28 12:02:50.307398-08 benchmark f H100 \N t \N \N \N {} -3327 1153 2025-02-28 07:48:24.592846-08 2025-02-28 06:08:43.919391-08 test t A100 \N f \N \N \N {} -3328 1153 2025-02-28 06:13:02.122552-08 2025-02-28 07:59:20.324753-08 test f A100 \N f \N \N \N {} -3535 1206 2025-02-28 11:07:39.749164-08 2025-02-28 11:05:51.393804-08 test f A100 \N t \N \N \N {} -3330 1153 2025-02-28 06:48:38.54272-08 2025-02-28 06:12:06.160611-08 test t H100 \N f \N \N \N {} -3331 1153 2025-02-28 06:44:57.334042-08 2025-02-28 06:27:45.550938-08 test f H100 \N f \N \N \N {} -3332 1153 2025-02-28 06:51:06.071549-08 2025-02-28 07:43:30.61761-08 test f T4 \N f \N \N \N {} -3333 1153 2025-02-28 07:22:29.016321-08 2025-02-28 07:25:37.384757-08 test t T4 \N f \N \N \N {} -3538 1206 2025-02-28 11:19:04.318559-08 2025-02-28 11:59:22.984501-08 test t A100 \N t \N \N \N {} -3337 1154 2025-02-28 06:12:20.751759-08 2025-02-28 07:24:13.732841-08 leaderboard t A100 0.003106905 t \N \N \N {} -3338 1154 2025-02-28 06:28:19.088439-08 2025-02-28 07:42:01.461767-08 test f A100 \N t \N \N \N {} -3339 1154 2025-02-28 07:24:35.655311-08 2025-02-28 07:21:32.784212-08 benchmark f A100 \N t \N \N \N {} -3340 1154 2025-02-28 06:30:11.131902-08 2025-02-28 07:23:31.696175-08 leaderboard f A100 0.0030856966666666665 t \N \N \N {} -3341 1155 2025-02-28 06:29:19.511228-08 2025-02-28 07:29:02.42522-08 test t A100 \N t \N \N \N {} -3342 1155 2025-02-28 07:51:44.284677-08 2025-02-28 07:48:29.018028-08 benchmark t A100 \N t \N \N \N {} -3343 1155 2025-02-28 06:47:28.573079-08 2025-02-28 06:44:05.949627-08 leaderboard t A100 0.0030834483333333335 t \N \N \N {} -3363 1161 2025-02-28 09:17:07.230642-08 2025-02-28 08:40:19.665408-08 test t T4 \N f \N \N \N {} -3348 1156 2025-02-28 07:16:58.973512-08 2025-02-28 07:33:23.078046-08 benchmark f A100 \N t \N \N \N {} -6188 1904 2025-03-11 18:10:38.388791-07 2025-03-11 18:14:58.210124-07 test t T4 \N f \N \N \N {} -6354 1970 2025-03-13 12:17:14.735768-07 2025-03-13 13:49:45.117322-07 test f T4 \N f \N \N \N {} -6508 2017 2025-03-14 11:42:27.682678-07 2025-03-14 12:36:52.887607-07 test f A100 \N t \N \N \N {} -6610 2047 2025-03-14 13:00:31.603918-07 2025-03-14 13:30:16.003681-07 test f A100 \N t \N \N \N {} -3570 1213 2025-02-28 11:12:09.580297-08 2025-02-28 11:15:38.840673-08 benchmark f H100 \N t \N \N \N {} -3541 1207 2025-02-28 11:14:12.554775-08 2025-02-28 11:14:24.949948-08 test t A100 \N t \N \N \N {} -3364 1161 2025-02-28 08:18:06.851362-08 2025-02-28 09:42:01.27446-08 test f T4 \N f \N \N \N {} -3365 1162 2025-02-28 09:22:47.122104-08 2025-02-28 08:23:26.975718-08 test f L4 \N f \N \N \N {} -3051 1093 2025-02-27 15:52:12.940276-08 2025-02-27 17:09:05.153707-08 leaderboard f A100 0.0031512003333333334 t \N \N \N {} -1451 485 2025-02-25 04:52:22.63147-08 2025-02-25 05:18:27.658437-08 benchmark t A100 \N t \N \N \N {} -1452 485 2025-02-25 05:30:56.8532-08 2025-02-25 05:31:55.77683-08 leaderboard t A100 0.0030837706666666663 t \N \N \N {} -2945 1032 2025-02-27 08:03:40.62355-08 2025-02-27 07:23:04.933128-08 test f A100 \N f \N \N \N {} -3151 1115 2025-02-28 03:28:35.730379-08 2025-02-28 02:37:28.944792-08 test t A100 \N t \N \N \N {} -3178 1120 2025-02-28 04:33:49.102329-08 2025-02-28 04:08:21.310974-08 test f A100 \N t \N \N \N {} -1454 485 2025-02-25 04:56:31.907725-08 2025-02-25 05:13:12.842041-08 benchmark f A100 \N t \N \N \N {} -1455 485 2025-02-25 04:05:06.520603-08 2025-02-25 04:56:19.51449-08 leaderboard f A100 0.003085375 t \N \N \N {} -2946 1033 2025-02-27 08:49:30.211741-08 2025-02-27 09:05:35.900691-08 test f T4 \N f \N \N \N {} -3003 1070 2025-02-27 13:00:06.212495-08 2025-02-27 11:57:37.768801-08 test f A100 \N f \N \N \N {} -3053 1094 2025-02-27 16:12:39.715401-08 2025-02-27 16:16:10.311482-08 benchmark f A100 \N t \N \N \N {} -3366 1162 2025-02-28 09:33:20.151769-08 2025-02-28 08:19:11.959347-08 test t L4 \N f \N \N \N {} -3571 1214 2025-02-28 12:01:55.895534-08 2025-02-28 12:13:51.232283-08 benchmark f H100 \N f \N \N \N {} -3373 1164 2025-02-28 09:54:50.944709-08 2025-02-28 09:48:36.826131-08 leaderboard f L4 0.01711390133333333 t \N \N \N {} -3374 1164 2025-02-28 10:03:17.913215-08 2025-02-28 08:48:38.575349-08 test t L4 \N t \N \N \N {} -3375 1164 2025-02-28 10:12:12.736365-08 2025-02-28 09:31:26.847036-08 benchmark t L4 \N t \N \N \N {} -3376 1164 2025-02-28 10:13:36.426125-08 2025-02-28 10:11:53.721613-08 leaderboard t L4 0.01711174266666667 t \N \N \N {} -3054 1094 2025-02-27 16:06:27.122167-08 2025-02-27 16:37:13.76371-08 leaderboard f A100 0.003111287 t \N \N \N {} -3154 1116 2025-02-28 02:29:06.631341-08 2025-02-28 01:58:59.085604-08 test f A100 \N t \N \N \N {} -1457 486 2025-02-25 04:36:15.216458-08 2025-02-25 05:15:34.163173-08 benchmark f A100 \N t \N \N \N {} -1458 486 2025-02-25 04:14:50.938805-08 2025-02-25 05:01:24.288432-08 leaderboard f A100 0.0030878703333333335 t \N \N \N {} -2947 1034 2025-02-27 09:13:44.098036-08 2025-02-27 09:11:18.488648-08 test f A100 \N f \N \N \N {} -3056 1094 2025-02-27 16:41:39.934588-08 2025-02-27 16:25:19.394991-08 benchmark t A100 \N t \N \N \N {} -3213 1125 2025-02-28 06:48:12.252064-08 2025-02-28 06:03:15.742155-08 leaderboard f A100 0.0030832093333333335 t \N \N \N {} -1460 486 2025-02-25 04:41:59.887933-08 2025-02-25 04:23:57.454866-08 benchmark t A100 \N t \N \N \N {} -1461 486 2025-02-25 05:49:45.663044-08 2025-02-25 04:12:57.109398-08 leaderboard t A100 0.003092806 t \N \N \N {} -2948 1035 2025-02-27 08:17:18.304303-08 2025-02-27 07:43:31.506965-08 test f T4 \N f \N \N \N {} -3005 1072 2025-02-27 13:09:50.152666-08 2025-02-27 12:57:15.8831-08 test f A100 \N t \N \N \N {} -3057 1094 2025-02-27 17:34:54.982672-08 2025-02-27 16:26:07.35543-08 leaderboard t A100 0.0031275906666666667 t \N \N \N {} -3080 1103 2025-02-28 02:02:56.286012-08 2025-02-28 02:38:01.79013-08 benchmark t A100 \N t \N \N \N {} -3081 1103 2025-02-28 01:56:35.783923-08 2025-02-28 01:56:41.958069-08 leaderboard t A100 0.0031360743333333336 t \N \N \N {} -3219 1126 2025-02-28 05:38:06.586102-08 2025-02-28 05:05:43.074581-08 leaderboard f A100 0.003108399 t \N \N \N {} -1463 487 2025-02-25 04:52:33.166187-08 2025-02-25 04:41:41.65243-08 benchmark t H100 \N t \N \N \N {} -1464 487 2025-02-25 05:44:02.421962-08 2025-02-25 04:30:44.563192-08 leaderboard t H100 0.0013963186666666667 t \N \N \N {} -2949 1036 2025-02-27 09:05:30.430291-08 2025-02-27 08:16:44.717534-08 test f A100 \N f \N \N \N {} -3006 1073 2025-02-27 14:03:17.549065-08 2025-02-27 12:33:09.482347-08 benchmark f A100 \N t \N \N \N {} -3583 1228 2025-02-28 13:36:44.96965-08 2025-02-28 13:58:50.097299-08 leaderboard f A100 0.0021488616666666665 t \N \N \N {} -3082 1104 2025-02-28 01:20:46.117646-08 2025-02-28 01:48:56.099424-08 test t A100 \N t \N \N \N {} -3083 1104 2025-02-28 02:40:27.969759-08 2025-02-28 03:00:37.086929-08 benchmark t A100 \N t \N \N \N {} -1466 487 2025-02-25 06:00:31.318157-08 2025-02-25 05:21:10.22327-08 benchmark f H100 \N t \N \N \N {} -1467 487 2025-02-25 05:08:04.83803-08 2025-02-25 05:35:28.456713-08 leaderboard f H100 0.0013990763333333333 t \N \N \N {} -1468 488 2025-02-25 04:11:00.167833-08 2025-02-25 05:53:43.66447-08 test f A100 \N f \N \N \N {} -3386 1166 2025-02-28 08:58:24.708767-08 2025-02-28 08:59:35.546096-08 test f L4 \N t \N \N \N {} -3572 1216 2025-02-28 13:25:32.928609-08 2025-02-28 13:06:59.918087-08 test f H100 \N f \N \N \N {} -3402 1173 2025-02-28 08:50:27.553378-08 2025-02-28 09:25:30.916359-08 leaderboard f L4 0.017073246333333333 t \N \N \N {} -3403 1173 2025-02-28 09:24:43.249263-08 2025-02-28 10:19:27.328955-08 test t L4 \N f \N \N \N {} -3404 1174 2025-02-28 09:36:18.604104-08 2025-02-28 10:01:25.201171-08 test t L4 \N t \N \N \N {} -3407 1174 2025-02-28 10:04:35.458188-08 2025-02-28 09:06:27.208576-08 test f L4 \N f \N \N \N {} -3409 1175 2025-02-28 10:42:07.749021-08 2025-02-28 10:23:25.763853-08 test t L4 \N f \N \N \N {} -3410 1176 2025-02-28 09:35:38.594297-08 2025-02-28 10:42:52.909956-08 test f L4 \N t \N \N \N {} -3411 1176 2025-02-28 10:40:17.639715-08 2025-02-28 09:51:35.220213-08 benchmark f L4 \N t \N \N \N {} -3412 1176 2025-02-28 10:11:43.370341-08 2025-02-28 10:27:44.06876-08 leaderboard f L4 0.017120271333333333 t \N \N \N {} -3547 1208 2025-02-28 12:27:27.702985-08 2025-02-28 11:17:47.788966-08 test f A100 \N t \N \N \N {} -3413 1176 2025-02-28 11:01:01.943548-08 2025-02-28 09:36:24.833158-08 test t L4 \N t \N \N \N {} -3414 1176 2025-02-28 09:58:29.179458-08 2025-02-28 09:26:37.706314-08 benchmark t L4 \N t \N \N \N {} -3415 1176 2025-02-28 10:55:29.876048-08 2025-02-28 09:14:25.427957-08 leaderboard t L4 0.017113951333333332 t \N \N \N {} -3416 1177 2025-02-28 10:07:18.386046-08 2025-02-28 10:14:01.64194-08 test f L4 \N f \N \N \N {} -3417 1177 2025-02-28 09:37:22.22405-08 2025-02-28 10:32:18.953094-08 test t L4 \N f \N \N \N {} -3550 1208 2025-02-28 11:50:19.101588-08 2025-02-28 12:45:50.950117-08 test t A100 \N t \N \N \N {} -3422 1180 2025-02-28 10:56:47.404757-08 2025-02-28 10:56:22.71621-08 test f L4 \N f \N \N \N {} -1470 490 2025-02-25 04:41:11.741201-08 2025-02-25 06:11:53.932837-08 benchmark f A100 \N t \N \N \N {} -2950 1037 2025-02-27 07:35:16.658999-08 2025-02-27 09:21:21.845387-08 test f A100 \N t \N \N \N {} -3007 1074 2025-02-27 13:18:44.138568-08 2025-02-27 13:54:19.684997-08 test t A100 \N t \N \N \N {} -3008 1074 2025-02-27 12:10:25.097092-08 2025-02-27 12:22:43.495573-08 benchmark t A100 \N t \N \N \N {} -1472 491 2025-02-25 04:16:51.40767-08 2025-02-25 04:44:20.032912-08 benchmark t A100 \N t \N \N \N {} -1473 491 2025-02-25 04:39:56.631178-08 2025-02-25 04:50:34.949043-08 leaderboard t A100 0.02900779833333333 t \N \N \N {} -3084 1104 2025-02-28 01:25:02.828823-08 2025-02-28 01:40:54.029102-08 leaderboard t A100 0.003145336 t \N \N \N {} -3092 1105 2025-02-28 03:07:23.548326-08 2025-02-28 02:21:49.2888-08 benchmark f A100 \N t \N \N \N {} -3093 1105 2025-02-28 03:14:38.771483-08 2025-02-28 02:41:25.038891-08 leaderboard f A100 0.003120996 t \N \N \N {} -3792 1279 2025-03-01 02:43:50.248573-08 2025-03-01 02:16:59.092747-08 test t A100 \N t \N \N \N {} -1475 491 2025-02-25 05:21:21.516015-08 2025-02-25 05:04:36.503808-08 benchmark f A100 \N t \N \N \N {} -1476 491 2025-02-25 04:41:54.691799-08 2025-02-25 05:44:48.514036-08 leaderboard f A100 0.02915152266666667 t \N \N \N {} -3423 1180 2025-02-28 09:29:33.8094-08 2025-02-28 10:27:43.704792-08 test t L4 \N f \N \N \N {} -3424 1181 2025-02-28 09:43:15.513902-08 2025-02-28 09:32:49.900783-08 test f L4 \N f \N \N \N {} -3425 1181 2025-02-28 09:43:45.066653-08 2025-02-28 10:25:58.378242-08 test t L4 \N f \N \N \N {} -3426 1182 2025-02-28 10:47:18.289906-08 2025-02-28 10:12:47.391263-08 test f L4 \N f \N \N \N {} -3427 1182 2025-02-28 09:47:39.824926-08 2025-02-28 11:31:24.156151-08 test t L4 \N f \N \N \N {} -3430 1184 2025-02-28 10:56:20.592407-08 2025-02-28 09:53:03.031846-08 test t L4 \N t \N \N \N {} -3431 1184 2025-02-28 11:16:58.29005-08 2025-02-28 10:48:40.540813-08 benchmark t L4 \N t \N \N \N {} -3432 1184 2025-02-28 09:40:03.831648-08 2025-02-28 11:23:08.875773-08 leaderboard t L4 0.017144596666666668 t \N \N \N {} -3433 1184 2025-02-28 10:51:16.362382-08 2025-02-28 11:01:47.958995-08 test f L4 \N f \N \N \N {} -3553 1209 2025-02-28 12:15:04.393062-08 2025-02-28 12:20:51.635003-08 test t A100 \N t \N \N \N {} -3436 1185 2025-02-28 10:58:38.735282-08 2025-02-28 09:55:56.205615-08 leaderboard t L4 0.017078157666666666 t \N \N \N {} -3437 1185 2025-02-28 09:43:25.915772-08 2025-02-28 11:05:27.656365-08 test f L4 \N f \N \N \N {} -3438 1186 2025-02-28 10:35:22.023921-08 2025-02-28 10:01:02.327904-08 test f L4 \N t \N \N \N {} -3443 1186 2025-02-28 10:41:33.074483-08 2025-02-28 10:48:09.890989-08 leaderboard t L4 0.017077551 t \N \N \N {} -3455 1190 2025-02-28 11:31:02.465158-08 2025-02-28 11:33:20.889525-08 benchmark f A100 \N t \N \N \N {} -3444 1187 2025-02-28 11:17:11.801344-08 2025-02-28 10:45:20.9261-08 test f L4 \N f \N \N \N {} -3445 1187 2025-02-28 11:46:40.120384-08 2025-02-28 11:16:52.048543-08 test t L4 \N f \N \N \N {} -3446 1188 2025-02-28 12:11:52.882164-08 2025-02-28 11:38:47.364032-08 test t A100 \N t \N \N \N {} -3447 1188 2025-02-28 11:53:16.182886-08 2025-02-28 11:57:38.888939-08 benchmark t A100 \N t \N \N \N {} -3448 1188 2025-02-28 11:31:39.142552-08 2025-02-28 10:34:32.539419-08 leaderboard t A100 0.003089445 t \N \N \N {} -3460 1191 2025-02-28 10:50:29.600852-08 2025-02-28 10:55:28.066601-08 test f A100 \N t \N \N \N {} -3451 1188 2025-02-28 11:25:05.48421-08 2025-02-28 11:26:09.847138-08 leaderboard f A100 0.003099992 t \N \N \N {} -3452 1189 2025-02-28 11:27:07.929922-08 2025-02-28 10:27:25.882031-08 test f A100 \N f \N \N \N {} -2956 1040 2025-02-27 07:53:09.243149-08 2025-02-27 08:42:54.201109-08 test t A100 \N t \N \N \N {} -3014 1076 2025-02-27 13:56:41.106043-08 2025-02-27 13:46:16.794281-08 benchmark f A100 \N t \N \N \N {} -1487 496 2025-02-25 06:02:20.071956-08 2025-02-25 06:20:28.361733-08 test t A100 \N f \N \N \N {} -1488 496 2025-02-25 06:37:53.255009-08 2025-02-25 07:06:16.467792-08 test f A100 \N f \N \N \N {} -3453 1189 2025-02-28 11:50:14.581562-08 2025-02-28 10:31:03.166396-08 test t A100 \N f \N \N \N {} -3463 1191 2025-02-28 10:39:12.659548-08 2025-02-28 11:48:27.810322-08 test t A100 \N t \N \N \N {} -3556 1209 2025-02-28 12:30:43.675467-08 2025-02-28 12:41:17.817806-08 test f A100 \N t \N \N \N {} -6514 2018 2025-03-14 12:18:15.299016-07 2025-03-14 11:04:40.892319-07 test f A100 \N t \N \N \N {} -6515 2018 2025-03-14 11:49:32.366043-07 2025-03-14 11:56:59.199313-07 benchmark f A100 \N t \N \N \N {} -6360 1972 2025-03-13 12:35:05.799956-07 2025-03-13 13:55:16.538902-07 benchmark f T4 \N f \N \N \N {} -6517 2019 2025-03-14 11:49:17.362182-07 2025-03-14 10:59:40.482475-07 benchmark f A100 \N t \N \N \N {} -6363 1973 2025-03-13 15:12:13.132853-07 2025-03-13 13:39:11.582645-07 test t T4 \N t \N \N \N {} -6364 1973 2025-03-13 13:54:16.988636-07 2025-03-13 14:15:30.672524-07 benchmark t T4 \N f \N \N \N {} -6521 2020 2025-03-14 11:11:59.364683-07 2025-03-14 12:39:27.185178-07 test t A100 \N t \N \N \N {} -3479 1196 2025-02-28 10:53:45.214965-08 2025-02-28 11:40:18.287407-08 benchmark f A100 \N t \N \N \N {} -3480 1196 2025-02-28 12:21:44.871556-08 2025-02-28 12:18:32.615765-08 leaderboard f A100 0.0030916253333333333 t \N \N \N {} -3481 1195 2025-02-28 10:38:20.149926-08 2025-02-28 11:37:11.075833-08 test f A100 \N t \N \N \N {} -3482 1195 2025-02-28 11:40:32.30581-08 2025-02-28 11:07:23.741189-08 benchmark f A100 \N f \N \N \N {} -3483 1195 2025-02-28 11:06:17.163408-08 2025-02-28 11:09:41.125501-08 test t A100 \N t \N \N \N {} -6196 1908 2025-03-11 17:42:13.201789-07 2025-03-11 16:36:53.748973-07 test f T4 \N f \N \N \N {} -6394 1987 2025-03-13 16:17:38.428534-07 2025-03-13 17:49:29.688605-07 test t T4 \N f \N \N \N {} -6367 1974 2025-03-13 15:18:15.442709-07 2025-03-13 14:52:02.574384-07 test t T4 \N t \N \N \N {} -6611 2047 2025-03-14 12:25:13.352133-07 2025-03-14 12:54:20.14153-07 benchmark f A100 \N t \N \N \N {} -3492 1198 2025-02-28 10:31:42.778359-08 2025-02-28 12:29:56.888072-08 benchmark f A100 \N t \N \N \N {} -3493 1198 2025-02-28 10:35:47.467941-08 2025-02-28 11:50:52.415689-08 leaderboard f A100 0.0030801673333333336 t \N \N \N {} -6198 1909 2025-03-11 17:57:18.149458-07 2025-03-11 17:27:06.275858-07 test f T4 \N f \N \N \N {} -6369 1975 2025-03-13 16:40:00.881683-07 2025-03-13 16:06:45.585849-07 test f T4 \N f \N \N \N {} -6916 2156 2025-03-16 15:11:39.340389-07 2025-03-16 16:54:21.426913-07 test f T4 \N t \N \N \N {} -3495 1198 2025-02-28 12:22:48.855653-08 2025-02-28 11:22:36.934387-08 benchmark t A100 \N t \N \N \N {} -3496 1198 2025-02-28 11:02:01.041551-08 2025-02-28 10:55:18.662378-08 leaderboard t A100 0.0030712036666666665 t \N \N \N {} -6199 1910 2025-03-11 17:51:06.602448-07 2025-03-11 17:45:19.999575-07 test t T4 \N f \N \N \N {} -6370 1975 2025-03-13 17:09:02.372261-07 2025-03-13 15:49:54.86776-07 test t T4 \N f \N \N \N {} -6528 2021 2025-03-14 11:20:09.013884-07 2025-03-14 11:51:31.5343-07 benchmark f A100 \N t \N \N \N {} -6529 2021 2025-03-14 12:17:59.33897-07 2025-03-14 11:57:07.878145-07 leaderboard f A100 0.0024379966666666663 t \N \N \N {} -6613 2047 2025-03-14 13:28:02.649127-07 2025-03-14 13:08:42.984819-07 test t A100 \N t \N \N \N {} -6677 2066 2025-03-14 14:28:21.53377-07 2025-03-14 15:18:12.092115-07 test t T4 \N f \N \N \N {} -3498 1199 2025-02-28 10:55:47.187252-08 2025-02-28 11:02:06.984812-08 benchmark f A100 \N t \N \N \N {} -3499 1199 2025-02-28 11:57:15.471579-08 2025-02-28 11:00:10.750543-08 leaderboard f A100 0.0030867046666666663 t \N \N \N {} -6200 1910 2025-03-11 18:13:57.459745-07 2025-03-11 17:39:14.073278-07 test f T4 \N f \N \N \N {} -6371 1976 2025-03-13 16:10:15.976652-07 2025-03-13 16:59:38.423812-07 test t T4 \N f \N \N \N {} -6530 2022 2025-03-14 12:42:51.830687-07 2025-03-14 12:44:20.985583-07 test f A100 \N t \N \N \N {} -6531 2022 2025-03-14 12:43:29.503929-07 2025-03-14 11:48:37.299314-07 benchmark f A100 \N t \N \N \N {} -6532 2022 2025-03-14 12:33:03.009856-07 2025-03-14 11:10:10.079119-07 leaderboard f A100 0.0031564046666666666 t \N \N \N {} -6614 2047 2025-03-14 12:41:54.04883-07 2025-03-14 12:42:12.07854-07 benchmark t A100 \N t \N \N \N {} -6615 2047 2025-03-14 12:28:03.957753-07 2025-03-14 12:16:08.077601-07 leaderboard t A100 0.0025148893333333333 t \N \N \N {} -3501 1199 2025-02-28 10:44:00.815458-08 2025-02-28 12:25:29.005606-08 benchmark t A100 \N t \N \N \N {} -1489 497 2025-02-25 06:09:45.421735-08 2025-02-25 06:06:56.895044-08 test t A100 \N f \N \N \N {} -1490 497 2025-02-25 07:30:16.061013-08 2025-02-25 05:58:05.353035-08 test f A100 \N f \N \N \N {} -1493 499 2025-02-25 07:29:41.937766-08 2025-02-25 07:35:35.73218-08 test t A100 \N f \N \N \N {} -1494 499 2025-02-25 06:55:16.544585-08 2025-02-25 06:42:04.831851-08 test f A100 \N f \N \N \N {} -1495 500 2025-02-25 06:11:38.525649-08 2025-02-25 06:06:09.247788-08 test f A100 \N f \N \N \N {} -1496 500 2025-02-25 06:28:17.374206-08 2025-02-25 07:36:57.6453-08 test t A100 \N f \N \N \N {} -1497 501 2025-02-25 06:22:45.354033-08 2025-02-25 06:04:40.972205-08 test t A100 \N f \N \N \N {} -1498 501 2025-02-25 07:26:32.437486-08 2025-02-25 06:26:05.660958-08 test f A100 \N f \N \N \N {} -2957 1040 2025-02-27 08:15:29.867269-08 2025-02-27 08:02:02.503639-08 benchmark t A100 \N t \N \N \N {} -2958 1040 2025-02-27 08:46:34.959091-08 2025-02-27 08:49:51.207345-08 leaderboard t A100 0.0031735606666666665 t \N \N \N {} -3106 1108 2025-02-28 02:29:24.19387-08 2025-02-28 02:25:44.234748-08 test t A100 \N t \N \N \N {} -2772 966 2025-02-26 19:49:15.757238-08 2025-02-26 20:10:22.740071-08 leaderboard t A100 0.014294890199999999 t \N \N \N {} -3502 1199 2025-02-28 11:15:50.983592-08 2025-02-28 11:56:22.474618-08 leaderboard t A100 0.00308841 t \N \N \N {} -1500 502 2025-02-25 05:50:41.376697-08 2025-02-25 06:42:15.925754-08 benchmark f T4 \N t \N \N \N {} -1501 502 2025-02-25 06:35:11.719711-08 2025-02-25 06:16:59.633058-08 leaderboard f T4 0.016468595 t \N \N \N {} -3015 1077 2025-02-27 14:04:51.482078-08 2025-02-27 14:44:42.488782-08 benchmark f H100 \N t \N \N \N {} -3109 1108 2025-02-28 02:01:50.848448-08 2025-02-28 01:49:08.698992-08 test f A100 \N t \N \N \N {} -3157 1116 2025-02-28 02:17:56.372163-08 2025-02-28 02:21:38.342465-08 test t A100 \N t \N \N \N {} -6201 1911 2025-03-11 18:01:48.518324-07 2025-03-11 18:32:00.643951-07 test t T4 \N f \N \N \N {} -6372 1976 2025-03-13 15:35:23.586028-07 2025-03-13 16:38:44.74512-07 test f T4 \N f \N \N \N {} -6533 2022 2025-03-14 12:55:57.39393-07 2025-03-14 12:29:44.638225-07 test t A100 \N t \N \N \N {} -6534 2022 2025-03-14 11:24:57.714064-07 2025-03-14 12:27:54.539497-07 benchmark t A100 \N t \N \N \N {} -6535 2022 2025-03-14 11:34:47.019969-07 2025-03-14 11:00:15.310359-07 leaderboard t A100 0.0039016193333333334 t \N \N \N {} -6616 2048 2025-03-14 12:53:00.225497-07 2025-03-14 13:45:31.447067-07 test t A100 \N t \N \N \N {} -6617 2048 2025-03-14 12:44:51.583069-07 2025-03-14 13:05:24.372941-07 benchmark t A100 \N t \N \N \N {} -3504 1200 2025-02-28 10:37:28.152845-08 2025-02-28 12:03:13.176444-08 benchmark f A100 \N t \N \N \N {} -3505 1200 2025-02-28 11:14:32.252149-08 2025-02-28 11:47:51.650604-08 leaderboard f A100 0.003092401 t \N \N \N {} -6202 1911 2025-03-11 17:19:23.061602-07 2025-03-11 18:30:37.372737-07 test f T4 \N f \N \N \N {} -6373 1977 2025-03-13 16:45:46.79345-07 2025-03-13 17:04:17.571101-07 test t T4 \N t \N \N \N {} -6375 1977 2025-03-13 15:59:28.148453-07 2025-03-13 15:45:43.825301-07 test f T4 \N t \N \N \N {} -6376 1977 2025-03-13 15:47:35.023166-07 2025-03-13 15:51:13.628825-07 benchmark f T4 \N f \N \N \N {} -6539 2023 2025-03-14 12:34:47.676474-07 2025-03-14 11:26:38.083854-07 test f A100 \N t \N \N \N {} -6540 2023 2025-03-14 12:11:02.732324-07 2025-03-14 12:35:39.482955-07 benchmark f A100 \N t \N \N \N {} -3510 1201 2025-02-28 12:09:30.867012-08 2025-02-28 11:14:15.654068-08 benchmark f A100 \N t \N \N \N {} -3511 1201 2025-02-28 12:22:28.128389-08 2025-02-28 10:55:21.592984-08 leaderboard f A100 0.0030901086666666666 t \N \N \N {} -6204 1912 2025-03-11 18:30:54.347773-07 2025-03-11 18:09:47.823091-07 test t T4 \N f \N \N \N {} -6205 1913 2025-03-11 17:40:25.001398-07 2025-03-11 16:51:30.586544-07 test f T4 \N f \N \N \N {} -6542 2024 2025-03-14 11:14:42.747731-07 2025-03-14 12:11:19.545486-07 benchmark f A100 \N t \N \N \N {} -6619 2048 2025-03-14 12:04:08.070953-07 2025-03-14 12:48:51.357583-07 test f A100 \N t \N \N \N {} -6650 2054 2025-03-14 13:53:44.337385-07 2025-03-14 13:48:46.599335-07 test t A100 \N t \N \N \N {} -6656 2058 2025-03-14 13:54:10.740579-07 2025-03-14 12:34:46.676855-07 test t A100 \N t \N \N \N {} -3516 1202 2025-02-28 12:33:09.097002-08 2025-02-28 10:58:45.829133-08 benchmark f A100 \N t \N \N \N {} -3517 1202 2025-02-28 11:58:25.410318-08 2025-02-28 11:30:47.992124-08 leaderboard f A100 0.003084266 t \N \N \N {} -6206 1913 2025-03-11 18:05:22.9157-07 2025-03-11 17:36:14.408717-07 test t T4 \N f \N \N \N {} -6379 1980 2025-03-13 16:36:27.760868-07 2025-03-13 16:20:53.675757-07 test t T4 \N f \N \N \N {} -6548 2025 2025-03-14 11:20:57.236222-07 2025-03-14 12:24:25.970334-07 leaderboard f A100 0.002452236 t \N \N \N {} -3524 1204 2025-02-28 12:20:39.907459-08 2025-02-28 11:12:35.546414-08 benchmark f A100 \N t \N \N \N {} -1503 502 2025-02-25 06:45:45.886953-08 2025-02-25 06:50:12.165567-08 benchmark t T4 \N t \N \N \N {} -1504 502 2025-02-25 07:36:18.732222-08 2025-02-25 06:56:09.60856-08 leaderboard t T4 0.016514245333333333 t \N \N \N {} -1505 503 2025-02-25 07:24:08.994752-08 2025-02-25 07:42:21.014151-08 test t A100 \N f \N \N \N {} -1506 503 2025-02-25 07:27:58.277977-08 2025-02-25 06:58:20.391073-08 test f A100 \N f \N \N \N {} -3112 1109 2025-02-28 02:49:13.950386-08 2025-02-28 03:20:33.833093-08 test t A100 \N t \N \N \N {} -3525 1204 2025-02-28 10:57:17.218778-08 2025-02-28 12:11:28.522376-08 leaderboard f A100 0.0030873746666666667 t \N \N \N {} -6549 2026 2025-03-14 11:43:25.469312-07 2025-03-14 11:20:32.899243-07 test t A100 \N t \N \N \N {} -6550 2026 2025-03-14 12:20:36.010926-07 2025-03-14 12:14:30.57356-07 benchmark t A100 \N t \N \N \N {} -3527 1204 2025-02-28 11:04:54.217825-08 2025-02-28 11:23:20.827974-08 benchmark t A100 \N t \N \N \N {} -3528 1204 2025-02-28 12:01:09.198033-08 2025-02-28 12:15:21.496381-08 leaderboard t A100 0.003084299 t \N \N \N {} -6382 1981 2025-03-13 16:02:38.157698-07 2025-03-13 16:43:08.39172-07 test f T4 \N f \N \N \N {} -6551 2026 2025-03-14 11:33:10.201136-07 2025-03-14 12:55:26.704593-07 leaderboard t A100 0.0025385736666666664 t \N \N \N {} -6623 2049 2025-03-14 12:51:48.541281-07 2025-03-14 13:37:41.702339-07 benchmark f A100 \N t \N \N \N {} -3530 1205 2025-02-28 11:05:21.637922-08 2025-02-28 10:59:24.063245-08 benchmark f A100 \N t \N \N \N {} -3531 1205 2025-02-28 11:51:46.708741-08 2025-02-28 10:54:12.608529-08 leaderboard f A100 0.003087388 t \N \N \N {} -6383 1982 2025-03-13 16:30:23.243012-07 2025-03-13 17:19:59.354647-07 test f T4 \N f \N \N \N {} -6552 2026 2025-03-14 12:22:01.49041-07 2025-03-14 13:00:33.669546-07 test f A100 \N t \N \N \N {} -6553 2026 2025-03-14 11:20:49.862991-07 2025-03-14 11:11:51.945168-07 benchmark f A100 \N t \N \N \N {} -3533 1205 2025-02-28 10:59:21.335705-08 2025-02-28 11:32:43.758531-08 benchmark t A100 \N t \N \N \N {} -3534 1205 2025-02-28 11:42:18.774429-08 2025-02-28 11:30:52.036125-08 leaderboard t A100 0.0030747123333333334 t \N \N \N {} -6211 1916 2025-03-11 18:51:26.328567-07 2025-03-11 17:34:02.97454-07 test f T4 \N f \N \N \N {} -6384 1982 2025-03-13 16:29:22.478385-07 2025-03-13 16:29:50.49526-07 test t T4 \N f \N \N \N {} -6554 2026 2025-03-14 11:32:39.712087-07 2025-03-14 13:01:51.543072-07 leaderboard f A100 0.0024447563333333333 t \N \N \N {} -6624 2049 2025-03-14 13:31:46.298149-07 2025-03-14 13:02:46.422741-07 leaderboard f A100 0.003148123 t \N \N \N {} -6659 2058 2025-03-14 12:36:09.483207-07 2025-03-14 12:28:00.634473-07 test f A100 \N t \N \N \N {} -6971 2169 2025-03-16 19:08:50.226241-07 2025-03-16 19:52:33.539167-07 leaderboard t T4 0.00026675565000000004 t \N \N \N {} -3536 1206 2025-02-28 12:01:21.310531-08 2025-02-28 10:59:24.032043-08 benchmark f A100 \N t \N \N \N {} -1508 504 2025-02-25 07:36:12.320858-08 2025-02-25 06:45:09.929248-08 benchmark f A100 \N t \N \N \N {} -1509 504 2025-02-25 06:24:05.118745-08 2025-02-25 07:05:31.47031-08 leaderboard f A100 0.003201205285714286 t \N \N \N {} -3016 1078 2025-02-27 15:09:58.383782-08 2025-02-27 15:09:34.428187-08 benchmark f A100 \N t \N \N \N {} -3086 1104 2025-02-28 02:37:38.797917-08 2025-02-28 02:36:42.886532-08 benchmark f A100 \N t \N \N \N {} -3537 1206 2025-02-28 11:44:02.859148-08 2025-02-28 12:17:13.765459-08 leaderboard f A100 0.0030771996666666664 t \N \N \N {} -6555 2027 2025-03-14 11:55:38.194672-07 2025-03-14 13:12:55.354266-07 benchmark f A100 \N t \N \N \N {} -6625 2049 2025-03-14 12:03:46.633551-07 2025-03-14 13:14:01.521077-07 test t A100 \N t \N \N \N {} -3539 1206 2025-02-28 12:05:54.440089-08 2025-02-28 11:48:11.736334-08 benchmark t A100 \N t \N \N \N {} -3540 1206 2025-02-28 11:50:30.166906-08 2025-02-28 11:34:39.086558-08 leaderboard t A100 0.003082872 t \N \N \N {} -6626 2049 2025-03-14 12:41:30.656497-07 2025-03-14 12:04:51.089204-07 benchmark t A100 \N t \N \N \N {} -3542 1207 2025-02-28 10:49:11.612247-08 2025-02-28 12:30:07.673052-08 benchmark t A100 \N t \N \N \N {} -3543 1207 2025-02-28 11:35:24.228637-08 2025-02-28 11:37:10.231465-08 leaderboard t A100 0.0031054823333333333 t \N \N \N {} -3546 1207 2025-02-28 11:42:05.635418-08 2025-02-28 12:05:11.256023-08 leaderboard f A100 0.0030748303333333333 t \N \N \N {} -3593 1230 2025-02-28 13:36:11.392976-08 2025-02-28 15:07:41.559929-08 benchmark f A100 \N t \N \N \N {} -3594 1231 2025-02-28 13:56:55.748686-08 2025-02-28 14:50:55.966775-08 test f H100 \N t \N \N \N {} -3595 1231 2025-02-28 14:21:28.207811-08 2025-02-28 13:22:24.302597-08 benchmark f H100 \N t \N \N \N {} -3596 1231 2025-02-28 13:34:32.178088-08 2025-02-28 14:18:02.106124-08 leaderboard f H100 0.0012008532 t \N \N \N {} -3597 1231 2025-02-28 14:33:14.730553-08 2025-02-28 13:51:57.351344-08 test t H100 \N t \N \N \N {} -3598 1231 2025-02-28 13:27:10.531166-08 2025-02-28 14:13:56.712585-08 benchmark t H100 \N t \N \N \N {} -3650 1248 2025-03-01 01:03:42.861509-08 2025-03-01 01:06:00.433924-08 leaderboard t A100 0.0031414876666666664 t \N \N \N {} -3652 1248 2025-03-01 02:29:49.148831-08 2025-03-01 01:52:31.634785-08 benchmark f A100 \N t \N \N \N {} -3658 1249 2025-03-01 01:37:20.868883-08 2025-03-01 01:41:30.386305-08 benchmark t A100 \N t \N \N \N {} -3659 1249 2025-03-01 02:08:57.983273-08 2025-03-01 02:33:34.854721-08 leaderboard t A100 0.0031066326666666665 t \N \N \N {} -3660 1250 2025-03-01 00:47:36.371435-08 2025-03-01 02:29:41.827776-08 test t A100 \N t \N \N \N {} -1511 504 2025-02-25 07:09:54.962145-08 2025-02-25 06:23:56.896069-08 benchmark t A100 \N t \N \N \N {} -1512 504 2025-02-25 06:32:05.54038-08 2025-02-25 07:46:23.871842-08 leaderboard t A100 0.0032573723333333333 t \N \N \N {} -3017 1079 2025-02-27 15:33:40.79789-08 2025-02-27 15:03:19.237768-08 benchmark f H100 \N t \N \N \N {} -3087 1104 2025-02-28 01:35:18.867193-08 2025-02-28 03:11:29.495526-08 leaderboard f A100 0.0031234573333333336 t \N \N \N {} -3095 1106 2025-02-28 02:29:26.843816-08 2025-02-28 03:12:54.70341-08 benchmark t A100 \N t \N \N \N {} -3661 1250 2025-03-01 02:05:16.755613-08 2025-03-01 01:23:08.151871-08 benchmark t A100 \N t \N \N \N {} -3662 1250 2025-03-01 01:38:13.707281-08 2025-03-01 01:47:51.283498-08 leaderboard t A100 0.0031076263333333336 t \N \N \N {} -3663 1250 2025-03-01 01:59:32.222492-08 2025-03-01 01:21:35.81262-08 test f A100 \N t \N \N \N {} -3664 1250 2025-03-01 00:54:01.041112-08 2025-03-01 01:19:58.345142-08 benchmark f A100 \N t \N \N \N {} -3892 1297 2025-03-01 03:24:01.588752-08 2025-03-01 03:18:14.954764-08 test f A100 \N t \N \N \N {} -3665 1250 2025-03-01 01:14:04.759133-08 2025-03-01 02:40:17.88009-08 leaderboard f A100 0.0031095133333333335 t \N \N \N {} -3666 1251 2025-03-01 00:49:00.146116-08 2025-03-01 01:10:29.020161-08 test t A100 \N t \N \N \N {} -3667 1251 2025-03-01 01:22:57.30705-08 2025-03-01 01:48:27.82583-08 benchmark t A100 \N t \N \N \N {} -3668 1251 2025-03-01 00:53:18.345319-08 2025-03-01 01:17:18.281427-08 leaderboard t A100 0.0031184623333333334 t \N \N \N {} -3675 1252 2025-03-01 00:51:13.234543-08 2025-03-01 01:19:47.389964-08 test t A100 \N t \N \N \N {} -3684 1254 2025-03-01 01:05:05.887393-08 2025-03-01 02:05:56.520759-08 benchmark f A100 \N t \N \N \N {} -3685 1255 2025-03-01 01:33:40.444936-08 2025-03-01 02:40:57.193542-08 benchmark f A100 \N t \N \N \N {} -3686 1256 2025-03-01 02:04:57.939143-08 2025-03-01 01:28:58.395269-08 benchmark f A100 \N t \N \N \N {} -3687 1257 2025-03-01 02:11:44.828858-08 2025-03-01 02:14:45.504175-08 benchmark f A100 \N t \N \N \N {} -3703 1263 2025-03-01 01:53:07.320683-08 2025-03-01 01:53:17.511001-08 test f A100 \N t \N \N \N {} -3688 1259 2025-03-01 02:02:24.550405-08 2025-03-01 01:42:30.841662-08 benchmark f H100 \N t \N \N \N {} -3689 1259 2025-03-01 02:51:13.657136-08 2025-03-01 01:36:15.328544-08 benchmark f A100 \N t \N \N \N {} -3695 1261 2025-03-01 02:37:45.341146-08 2025-03-01 02:45:50.374217-08 benchmark f A100 \N t \N \N \N {} -3696 1261 2025-03-01 02:43:06.297215-08 2025-03-01 02:38:52.020845-08 leaderboard f A100 0.0030843926666666664 t \N \N \N {} -1514 505 2025-02-25 06:11:31.116371-08 2025-02-25 07:53:55.571435-08 benchmark f A100 \N t \N \N \N {} -1515 505 2025-02-25 06:32:19.052576-08 2025-02-25 07:10:25.937469-08 leaderboard f A100 0.0032101916 t \N \N \N {} -3018 1080 2025-02-27 14:22:23.426466-08 2025-02-27 14:44:08.206039-08 benchmark f H100 \N f \N \N \N {} -3088 1105 2025-02-28 02:12:41.458282-08 2025-02-28 02:42:49.673656-08 test t A100 \N t \N \N \N {} -3096 1106 2025-02-28 03:02:13.970567-08 2025-02-28 02:27:13.461777-08 leaderboard t A100 0.0030811183333333335 t \N \N \N {} -3115 1109 2025-02-28 02:52:03.948052-08 2025-02-28 02:24:21.911819-08 test f A100 \N t \N \N \N {} -3705 1263 2025-03-01 02:09:53.923033-08 2025-03-01 02:59:38.816636-08 leaderboard f A100 0.0031043356666666665 t \N \N \N {} -3706 1263 2025-03-01 02:45:35.826522-08 2025-03-01 01:31:39.66555-08 test t A100 \N t \N \N \N {} -3707 1263 2025-03-01 02:23:56.866303-08 2025-03-01 02:48:25.991039-08 benchmark t A100 \N t \N \N \N {} -3708 1263 2025-03-01 02:48:53.22977-08 2025-03-01 01:38:57.844846-08 leaderboard t A100 0.0030862586666666665 t \N \N \N {} -3709 1264 2025-03-01 02:07:56.190346-08 2025-03-01 01:40:21.086921-08 test f A100 \N t \N \N \N {} -3710 1264 2025-03-01 03:21:38.843116-08 2025-03-01 01:42:57.620283-08 benchmark f A100 \N t \N \N \N {} -3711 1264 2025-03-01 01:50:57.95035-08 2025-03-01 02:14:10.580881-08 leaderboard f A100 0.003111637 t \N \N \N {} -3922 1304 2025-03-01 04:37:37.399916-08 2025-03-01 05:09:08.597247-08 test t A100 \N t \N \N \N {} -3713 1264 2025-03-01 02:34:54.769991-08 2025-03-01 02:40:30.444044-08 benchmark t A100 \N t \N \N \N {} -3714 1264 2025-03-01 03:26:13.41665-08 2025-03-01 01:31:26.999441-08 leaderboard t A100 0.003092787 t \N \N \N {} -3715 1265 2025-03-01 02:52:42.719228-08 2025-03-01 03:08:21.753062-08 test f A100 \N t \N \N \N {} -3716 1265 2025-03-01 01:34:00.130632-08 2025-03-01 01:35:33.719501-08 benchmark f A100 \N t \N \N \N {} -3717 1265 2025-03-01 03:18:58.998043-08 2025-03-01 01:56:17.857259-08 leaderboard f A100 0.00310427 t \N \N \N {} -3718 1265 2025-03-01 02:49:09.783091-08 2025-03-01 01:54:04.181859-08 test t A100 \N t \N \N \N {} -3719 1265 2025-03-01 01:35:45.872919-08 2025-03-01 01:33:30.016652-08 benchmark t A100 \N t \N \N \N {} -3720 1265 2025-03-01 03:24:17.497602-08 2025-03-01 01:42:24.245018-08 leaderboard t A100 0.0030956306666666666 t \N \N \N {} -3925 1305 2025-03-01 04:56:09.680619-08 2025-03-01 04:07:20.707758-08 test t A100 \N t \N \N \N {} -5248 1613 2025-03-06 08:34:35.577688-08 2025-03-06 08:01:22.528601-08 benchmark f H100 \N t \N \N \N {} -3726 1267 2025-03-01 02:36:57.461321-08 2025-03-01 02:35:02.941022-08 benchmark f A100 \N t \N \N \N {} -3727 1267 2025-03-01 01:38:43.401017-08 2025-03-01 02:32:47.651112-08 leaderboard f A100 0.0030772653333333336 t \N \N \N {} -3728 1268 2025-03-01 02:49:19.195623-08 2025-03-01 02:53:55.254272-08 test t A100 \N t \N \N \N {} -1517 505 2025-02-25 07:05:29.686612-08 2025-02-25 06:37:54.120803-08 benchmark t A100 \N t \N \N \N {} -1518 505 2025-02-25 07:41:30.63433-08 2025-02-25 06:35:14.486014-08 leaderboard t A100 0.0031655055 t \N \N \N {} -3019 1081 2025-02-27 14:48:37.142158-08 2025-02-27 15:06:31.119054-08 benchmark f H100 \N t \N \N \N {} -3097 1106 2025-02-28 03:19:00.154781-08 2025-02-28 02:16:02.356601-08 test f A100 \N t \N \N \N {} -3640 1244 2025-03-01 00:18:40.054434-08 2025-02-28 23:54:18.664151-08 test t A100 \N t \N \N \N {} -3740 1270 2025-03-01 02:37:04.861998-08 2025-03-01 03:28:09.585563-08 benchmark f A100 \N t \N \N \N {} -3741 1271 2025-03-01 02:44:07.033471-08 2025-03-01 03:41:23.057192-08 test f A100 \N t \N \N \N {} -3742 1271 2025-03-01 02:44:35.686645-08 2025-03-01 02:00:04.66024-08 benchmark f A100 \N t \N \N \N {} -3743 1271 2025-03-01 02:38:32.681534-08 2025-03-01 02:37:50.930025-08 leaderboard f A100 0.0030790513333333333 t \N \N \N {} -3744 1271 2025-03-01 03:21:20.979999-08 2025-03-01 02:59:00.012827-08 test t A100 \N t \N \N \N {} -3961 1321 2025-03-01 05:32:54.083384-08 2025-03-01 05:23:18.742132-08 test f A100 \N t \N \N \N {} -3745 1271 2025-03-01 03:15:49.381338-08 2025-03-01 02:53:14.040077-08 benchmark t A100 \N t \N \N \N {} -3746 1271 2025-03-01 02:06:31.769676-08 2025-03-01 01:46:51.112379-08 leaderboard t A100 0.0031014993333333333 t \N \N \N {} -3747 1272 2025-03-01 02:53:41.383476-08 2025-03-01 01:51:32.778962-08 test f A100 \N t \N \N \N {} -3748 1272 2025-03-01 02:45:32.603439-08 2025-03-01 03:10:17.870749-08 benchmark f A100 \N t \N \N \N {} -3749 1272 2025-03-01 02:08:50.626266-08 2025-03-01 02:33:18.807788-08 leaderboard f A100 0.0030842223333333333 t \N \N \N {} -3750 1272 2025-03-01 02:05:35.450725-08 2025-03-01 01:53:09.147022-08 test t A100 \N t \N \N \N {} -3751 1272 2025-03-01 02:35:41.645448-08 2025-03-01 02:31:48.954685-08 benchmark t A100 \N t \N \N \N {} -3756 1273 2025-03-01 03:40:36.388523-08 2025-03-01 02:50:54.185135-08 test t A100 \N t \N \N \N {} -3757 1273 2025-03-01 03:17:02.112839-08 2025-03-01 03:24:26.296631-08 benchmark t A100 \N t \N \N \N {} -3758 1273 2025-03-01 02:23:04.796296-08 2025-03-01 03:14:47.333595-08 leaderboard t A100 0.0031108526666666666 t \N \N \N {} -3759 1274 2025-03-01 03:30:42.935942-08 2025-03-01 02:39:37.720415-08 test f A100 \N t \N \N \N {} -3760 1274 2025-03-01 01:53:05.004026-08 2025-03-01 03:03:30.821872-08 benchmark f A100 \N t \N \N \N {} -3967 1322 2025-03-01 06:17:47.572392-08 2025-03-01 05:09:34.491694-08 test t A100 \N t \N \N \N {} -3761 1274 2025-03-01 02:33:50.346797-08 2025-03-01 02:26:36.964124-08 leaderboard f A100 0.0030782356666666667 t \N \N \N {} -3762 1274 2025-03-01 01:58:07.815646-08 2025-03-01 01:59:41.282385-08 test t A100 \N t \N \N \N {} -3763 1274 2025-03-01 03:17:32.966317-08 2025-03-01 01:57:52.417038-08 benchmark t A100 \N t \N \N \N {} -3764 1274 2025-03-01 02:01:00.831839-08 2025-03-01 02:45:44.696937-08 leaderboard t A100 0.0030974313333333335 t \N \N \N {} -3765 1275 2025-03-01 01:54:45.759236-08 2025-03-01 02:30:33.498327-08 test t A100 \N t \N \N \N {} -3766 1275 2025-03-01 01:51:25.781893-08 2025-03-01 01:50:19.627497-08 benchmark t A100 \N t \N \N \N {} -3767 1275 2025-03-01 02:55:04.68876-08 2025-03-01 03:34:18.634501-08 leaderboard t A100 0.003100908 t \N \N \N {} -3769 1275 2025-03-01 02:04:44.900517-08 2025-03-01 01:53:44.454925-08 benchmark f A100 \N t \N \N \N {} -3770 1275 2025-03-01 03:44:33.933845-08 2025-03-01 03:07:56.729501-08 leaderboard f A100 0.003100629 t \N \N \N {} -3771 1276 2025-03-01 03:45:41.230682-08 2025-03-01 03:07:19.802554-08 test f A100 \N t \N \N \N {} -3772 1276 2025-03-01 02:49:49.866518-08 2025-03-01 02:09:15.199733-08 benchmark f A100 \N t \N \N \N {} -3773 1276 2025-03-01 01:49:21.076445-08 2025-03-01 03:19:58.2239-08 leaderboard f A100 0.0030967063333333334 t \N \N \N {} -1520 506 2025-02-25 07:09:15.225758-08 2025-02-25 07:45:41.962123-08 benchmark t H100 \N t \N \N \N {} -1521 506 2025-02-25 07:34:19.048083-08 2025-02-25 07:59:41.638877-08 leaderboard t H100 0.00147709 t \N \N \N {} -3020 1082 2025-02-27 16:12:37.20378-08 2025-02-27 16:11:58.805622-08 benchmark f H100 \N t \N \N \N {} -3118 1110 2025-02-28 01:59:42.263954-08 2025-02-28 02:02:39.491672-08 test t A100 \N t \N \N \N {} -3774 1276 2025-03-01 02:29:59.337188-08 2025-03-01 03:02:44.666583-08 test t A100 \N t \N \N \N {} -5249 1613 2025-03-06 08:14:23.750809-08 2025-03-06 08:08:33.670096-08 leaderboard f H100 0.0014276973333333333 t \N \N \N {} -3781 1277 2025-03-01 03:22:11.445902-08 2025-03-01 02:55:41.41361-08 benchmark f A100 \N t \N \N \N {} -3782 1277 2025-03-01 03:39:09.245513-08 2025-03-01 03:08:32.889614-08 leaderboard f A100 0.0030874316666666666 t \N \N \N {} -3783 1278 2025-03-01 03:31:43.822781-08 2025-03-01 02:37:43.947702-08 test t A100 \N t \N \N \N {} -6787 2100 2025-03-15 05:14:40.803878-07 2025-03-15 04:12:35.487565-07 test t A100 \N t \N \N \N {} -6239 1924 2025-03-12 01:52:17.600422-07 2025-03-12 01:58:47.314377-07 test f T4 \N f \N \N \N {} -6241 1925 2025-03-12 01:51:25.617795-07 2025-03-12 00:25:45.175368-07 benchmark f T4 \N t \N \N \N {} -6242 1925 2025-03-12 00:18:20.920266-07 2025-03-12 00:37:09.552561-07 leaderboard f T4 3.623034557 t \N \N \N {} -6400 1990 2025-03-13 16:37:41.150108-07 2025-03-13 17:03:46.340373-07 test f T4 \N f \N \N \N {} -6684 2070 2025-03-14 16:12:52.115243-07 2025-03-14 15:40:16.793308-07 test t T4 \N f \N \N \N {} -3796 1280 2025-03-01 03:59:00.997836-08 2025-03-01 02:29:44.953873-08 benchmark f A100 \N t \N \N \N {} -3797 1280 2025-03-01 03:51:51.435647-08 2025-03-01 02:43:07.654041-08 leaderboard f A100 0.0030720253333333335 t \N \N \N {} -6243 1925 2025-03-12 01:00:01.616489-07 2025-03-12 00:47:35.44717-07 test t T4 \N t \N \N \N {} -6402 1991 2025-03-13 17:32:53.433867-07 2025-03-13 18:39:52.759282-07 benchmark f T4 \N t \N \N \N {} -3799 1280 2025-03-01 02:05:15.848809-08 2025-03-01 03:19:03.034027-08 benchmark t A100 \N t \N \N \N {} -3800 1280 2025-03-01 03:51:27.480737-08 2025-03-01 03:58:54.299511-08 leaderboard t A100 0.0031044756666666665 t \N \N \N {} -6246 1926 2025-03-12 01:05:44.944448-07 2025-03-12 01:36:04.272554-07 test t T4 \N t \N \N \N {} -6247 1926 2025-03-12 02:16:02.972538-07 2025-03-12 00:53:47.157218-07 benchmark t T4 \N t \N \N \N {} -6248 1926 2025-03-12 02:04:35.195202-07 2025-03-12 00:39:34.3763-07 leaderboard t T4 3.6946682543333336 t \N \N \N {} -6403 1991 2025-03-13 17:33:19.394407-07 2025-03-13 18:00:50.499601-07 leaderboard f T4 0.012992361650000001 t \N \N \N {} -6564 2035 2025-03-14 13:11:15.918389-07 2025-03-14 13:28:42.918799-07 benchmark f A100 \N t \N \N \N {} -3802 1281 2025-03-01 02:49:11.571703-08 2025-03-01 03:03:34.155129-08 benchmark f A100 \N t \N \N \N {} -3803 1281 2025-03-01 03:11:47.348485-08 2025-03-01 02:42:24.967997-08 leaderboard f A100 0.0030754246666666665 t \N \N \N {} -6249 1926 2025-03-12 02:18:23.699919-07 2025-03-12 01:38:21.807805-07 test f T4 \N t \N \N \N {} -6641 2053 2025-03-14 13:13:01.717161-07 2025-03-14 12:56:42.018712-07 test t A100 \N t \N \N \N {} -3827 1286 2025-03-01 03:21:43.151398-08 2025-03-01 04:03:44.372245-08 benchmark f A100 \N t \N \N \N {} -3828 1286 2025-03-01 02:16:32.267354-08 2025-03-01 02:27:13.261291-08 leaderboard f A100 0.0032163636666666666 t \N \N \N {} -6258 1934 2025-03-12 07:02:26.8957-07 2025-03-12 06:48:21.923788-07 benchmark f A100 \N t \N \N \N {} -6412 1994 2025-03-13 17:40:48.083573-07 2025-03-13 17:33:28.539709-07 test f T4 \N f \N \N \N {} -6573 2039 2025-03-14 11:47:52.741123-07 2025-03-14 13:13:09.01997-07 test t A100 \N t \N \N \N {} -6574 2039 2025-03-14 13:30:31.134825-07 2025-03-14 12:56:58.461643-07 benchmark t A100 \N t \N \N \N {} -6575 2039 2025-03-14 13:14:01.573829-07 2025-03-14 13:06:52.578339-07 leaderboard t A100 0.0025201923333333337 t \N \N \N {} -1523 506 2025-02-25 07:26:02.548226-08 2025-02-25 07:25:46.716931-08 benchmark f H100 \N t \N \N \N {} -1529 510 2025-02-25 06:46:53.396184-08 2025-02-25 07:55:23.310917-08 benchmark f A100 \N t \N \N \N {} -1530 511 2025-02-25 08:01:36.384337-08 2025-02-25 07:34:22.650715-08 benchmark f A100 \N t \N \N \N {} -3021 1083 2025-02-27 15:20:59.403393-08 2025-02-27 15:45:28.074308-08 test t H100 \N t \N \N \N {} -3022 1083 2025-02-27 15:38:29.43689-08 2025-02-27 15:22:38.825121-08 benchmark t H100 \N t \N \N \N {} -3023 1083 2025-02-27 16:15:56.367001-08 2025-02-27 16:18:18.215051-08 leaderboard t H100 0.0025371098599999997 t \N \N \N {} -3070 1102 2025-02-28 02:33:54.637798-08 2025-02-28 01:22:56.978086-08 test f A100 \N t \N \N \N {} -1547 512 2025-02-25 06:52:50.899054-08 2025-02-25 06:49:27.130424-08 benchmark t T4 \N t \N \N \N {} -1548 512 2025-02-25 06:32:04.498659-08 2025-02-25 07:33:16.893589-08 leaderboard t T4 0.021058454833333334 t \N \N \N {} -1549 512 2025-02-25 07:27:14.309213-08 2025-02-25 07:47:52.099855-08 test f L4 \N t \N \N \N {} -1550 512 2025-02-25 06:57:32.191599-08 2025-02-25 07:03:02.042374-08 benchmark f L4 \N t \N \N \N {} -1552 512 2025-02-25 06:58:24.333124-08 2025-02-25 06:33:18.568674-08 test t L4 \N t \N \N \N {} -1556 513 2025-02-25 06:14:45.491004-08 2025-02-25 07:16:46.376307-08 benchmark t A100 \N t \N \N \N {} -1557 513 2025-02-25 07:34:02.46493-08 2025-02-25 08:06:10.020194-08 leaderboard t A100 0.007567747333333333 t \N \N \N {} -3024 1083 2025-02-27 14:39:16.285794-08 2025-02-27 16:33:31.980742-08 test f H100 \N t \N \N \N {} -3025 1083 2025-02-27 16:31:07.646609-08 2025-02-27 14:51:20.50896-08 benchmark f H100 \N t \N \N \N {} -3026 1083 2025-02-27 14:40:27.566259-08 2025-02-27 16:02:44.891034-08 leaderboard f H100 0.00193133143 t \N \N \N {} -3071 1102 2025-02-28 01:56:21.879786-08 2025-02-28 02:17:53.253173-08 benchmark f A100 \N t \N \N \N {} -1559 513 2025-02-25 07:00:51.226524-08 2025-02-25 06:53:52.941813-08 benchmark f A100 \N t \N \N \N {} -1560 513 2025-02-25 07:40:54.286852-08 2025-02-25 07:20:37.31234-08 leaderboard f A100 0.007643499333333333 t \N \N \N {} -3027 1084 2025-02-27 14:58:06.567407-08 2025-02-27 14:38:42.214639-08 benchmark f A100 \N t \N \N \N {} -3072 1102 2025-02-28 02:51:58.714497-08 2025-02-28 02:17:05.194701-08 leaderboard f A100 0.0031002953333333335 t \N \N \N {} -3089 1105 2025-02-28 03:09:52.010667-08 2025-02-28 01:20:13.790445-08 benchmark t A100 \N t \N \N \N {} -3090 1105 2025-02-28 02:51:45.323434-08 2025-02-28 02:33:52.770692-08 leaderboard t A100 0.0030956803333333336 t \N \N \N {} -1562 514 2025-02-25 07:35:28.616691-08 2025-02-25 06:29:07.554479-08 benchmark f A100 \N t \N \N \N {} -1563 514 2025-02-25 07:01:33.808022-08 2025-02-25 06:30:57.182348-08 leaderboard f A100 0.00319349475 t \N \N \N {} -3028 1085 2025-02-27 15:14:39.199509-08 2025-02-27 15:48:14.221616-08 test f H100 \N t \N \N \N {} -3029 1085 2025-02-27 15:48:18.376186-08 2025-02-27 15:01:22.031548-08 benchmark f H100 \N t \N \N \N {} -3030 1085 2025-02-27 15:34:36.352873-08 2025-02-27 15:03:55.584707-08 leaderboard f H100 0.0018411454 t \N \N \N {} -3073 1102 2025-02-28 02:05:44.512427-08 2025-02-28 02:46:15.64099-08 test t A100 \N t \N \N \N {} -1565 514 2025-02-25 06:29:07.165341-08 2025-02-25 07:51:19.989787-08 benchmark t A100 \N t \N \N \N {} -1566 514 2025-02-25 06:29:37.966737-08 2025-02-25 06:28:42.906056-08 leaderboard t A100 0.0032033456 t \N \N \N {} -1573 516 2025-02-25 06:33:48.857537-08 2025-02-25 07:43:18.501261-08 test t A100 \N t \N \N \N {} -1574 516 2025-02-25 08:15:17.851507-08 2025-02-25 08:32:05.853279-08 benchmark t A100 \N t \N \N \N {} -1579 517 2025-02-25 06:55:30.527319-08 2025-02-25 07:43:47.516317-08 test f T4 \N t \N \N \N {} -1581 519 2025-02-25 07:22:27.620876-08 2025-02-25 07:28:11.702548-08 test t H100 \N t \N \N \N {} -1589 522 2025-02-25 08:42:40.422194-08 2025-02-25 08:02:12.607584-08 benchmark f A100 \N t \N \N \N {} -1593 526 2025-02-25 07:56:16.683261-08 2025-02-25 08:23:55.488514-08 test f A100 \N t \N \N \N {} -1594 527 2025-02-25 07:07:18.475511-08 2025-02-25 08:55:21.597339-08 benchmark f A100 \N t \N \N \N {} -1609 533 2025-02-25 08:14:37.090411-08 2025-02-25 07:43:04.159111-08 test f H100 \N t \N \N \N {} -1625 539 2025-02-25 08:06:46.284758-08 2025-02-25 09:01:35.105248-08 test t H100 \N t \N \N \N {} -6919 2156 2025-03-16 16:25:14.914949-07 2025-03-16 16:52:24.968404-07 test t T4 \N t \N \N \N {} -3830 1287 2025-03-01 02:52:44.123034-08 2025-03-01 03:04:16.432487-08 benchmark f A100 \N t \N \N \N {} -3831 1287 2025-03-01 03:18:09.205414-08 2025-03-01 02:51:41.151421-08 leaderboard f A100 0.0030734603333333337 t \N \N \N {} -6414 1995 2025-03-13 18:08:53.756809-07 2025-03-13 17:29:29.189385-07 test f T4 \N f \N \N \N {} -6579 2040 2025-03-14 12:48:20.53274-07 2025-03-14 13:09:22.939558-07 test t A100 \N t \N \N \N {} -6580 2040 2025-03-14 12:55:54.163861-07 2025-03-14 12:30:14.281856-07 benchmark t A100 \N t \N \N \N {} -6581 2040 2025-03-14 12:55:49.972531-07 2025-03-14 13:31:53.117136-07 leaderboard t A100 0.0025155676666666666 t \N \N \N {} -6664 2060 2025-03-14 13:51:59.840678-07 2025-03-14 12:26:11.365457-07 test f T4 \N f \N \N \N {} -3836 1288 2025-03-01 04:15:45.238418-08 2025-03-01 03:47:16.55003-08 benchmark f A100 \N t \N \N \N {} -3837 1288 2025-03-01 03:57:06.451424-08 2025-03-01 02:21:13.360645-08 leaderboard f A100 0.0030780183333333337 t \N \N \N {} -3840 1288 2025-03-01 02:23:11.113845-08 2025-03-01 03:57:50.503081-08 leaderboard t A100 0.0030832926666666664 t \N \N \N {} -6263 1939 2025-03-12 07:51:31.81151-07 2025-03-12 07:26:02.800143-07 benchmark f A100 \N t \N \N \N {} -6264 1939 2025-03-12 08:16:12.289193-07 2025-03-12 08:05:41.403712-07 leaderboard f A100 0.00006743858 t \N \N \N {} -6416 1996 2025-03-13 18:45:10.175348-07 2025-03-13 18:56:40.818899-07 test t T4 \N f \N \N \N {} -3842 1289 2025-03-01 04:13:05.572388-08 2025-03-01 02:54:57.38981-08 benchmark f A100 \N t \N \N \N {} -3843 1289 2025-03-01 02:45:06.634242-08 2025-03-01 03:39:15.781501-08 leaderboard f A100 0.0030902456666666665 t \N \N \N {} -6265 1939 2025-03-12 08:51:35.314623-07 2025-03-12 08:24:58.103706-07 test t A100 \N t \N \N \N {} -6266 1939 2025-03-12 07:29:33.603048-07 2025-03-12 08:44:42.162501-07 benchmark t A100 \N t \N \N \N {} -6267 1939 2025-03-12 07:09:09.345344-07 2025-03-12 07:39:08.752326-07 leaderboard t A100 0.00006758480769230768 t \N \N \N {} -3845 1289 2025-03-01 04:09:16.526703-08 2025-03-01 02:35:11.693428-08 benchmark t A100 \N t \N \N \N {} -3846 1289 2025-03-01 03:33:27.94603-08 2025-03-01 02:24:48.469479-08 leaderboard t A100 0.0030774643333333334 t \N \N \N {} -6269 1940 2025-03-12 08:08:47.902846-07 2025-03-12 07:11:33.996302-07 benchmark f H100 \N t \N \N \N {} -6270 1940 2025-03-12 07:11:39.417039-07 2025-03-12 08:15:41.878762-07 leaderboard f H100 0.00004438749 t \N \N \N {} -6418 1997 2025-03-13 18:59:25.941543-07 2025-03-13 19:24:02.914954-07 test f T4 \N f \N \N \N {} -3848 1290 2025-03-01 03:36:59.507399-08 2025-03-01 03:50:52.826622-08 benchmark f A100 \N t \N \N \N {} -3849 1290 2025-03-01 03:20:17.52254-08 2025-03-01 04:10:31.201707-08 leaderboard f A100 0.0030958916666666667 t \N \N \N {} -6587 2041 2025-03-14 12:56:09.267865-07 2025-03-14 12:56:01.714341-07 leaderboard t A100 0.0025133303333333334 t \N \N \N {} -3857 1291 2025-03-01 03:54:20.136882-08 2025-03-01 02:57:32.144781-08 benchmark f A100 \N t \N \N \N {} -1595 528 2025-02-25 07:57:34.081957-08 2025-02-25 08:46:00.770848-08 benchmark f A100 \N t \N \N \N {} -1596 529 2025-02-25 08:42:32.027812-08 2025-02-25 08:28:47.379598-08 benchmark f H100 \N t \N \N \N {} -1597 530 2025-02-25 08:13:24.437551-08 2025-02-25 08:25:42.046848-08 benchmark f H100 \N t \N \N \N {} -1598 531 2025-02-25 07:18:37.587178-08 2025-02-25 07:26:28.050318-08 test t A100 \N f \N \N \N {} -1599 531 2025-02-25 07:10:05.242952-08 2025-02-25 07:51:39.460156-08 test f A100 \N f \N \N \N {} -1600 532 2025-02-25 07:30:03.467759-08 2025-02-25 07:48:58.024839-08 test f A100 \N t \N \N \N {} -1676 550 2025-02-25 08:20:23.376176-08 2025-02-25 09:14:51.808165-08 leaderboard f H100 0.0015442018125 t \N \N \N {} -1601 532 2025-02-25 09:03:02.38871-08 2025-02-25 09:05:51.634571-08 benchmark f A100 \N t \N \N \N {} -1602 532 2025-02-25 08:03:27.143172-08 2025-02-25 08:12:43.058027-08 leaderboard f A100 0.003081489 t \N \N \N {} -1610 533 2025-02-25 08:22:53.82146-08 2025-02-25 08:19:04.111413-08 benchmark f H100 \N t \N \N \N {} -1611 533 2025-02-25 08:59:03.973218-08 2025-02-25 08:01:47.560678-08 leaderboard f H100 0.0010464732307692308 t \N \N \N {} -1612 534 2025-02-25 08:13:49.798132-08 2025-02-25 07:38:47.976643-08 benchmark f A100 \N t \N \N \N {} -1613 535 2025-02-25 07:35:30.962867-08 2025-02-25 08:49:27.767886-08 benchmark f H100 \N f \N \N \N {} -1623 539 2025-02-25 09:08:19.427448-08 2025-02-25 07:54:22.18938-08 benchmark f H100 \N t \N \N \N {} -1624 539 2025-02-25 09:12:30.335139-08 2025-02-25 09:19:28.18725-08 leaderboard f H100 0.001406501 t \N \N \N {} -1632 540 2025-02-25 08:41:53.357227-08 2025-02-25 07:46:39.377359-08 benchmark t H100 \N t \N \N \N {} -1633 540 2025-02-25 07:58:37.599753-08 2025-02-25 08:48:17.13888-08 leaderboard t H100 0.0014054696666666667 t \N \N \N {} -1635 542 2025-02-25 07:55:32.669761-08 2025-02-25 08:25:28.836254-08 test f A100 \N t \N \N \N {} -1636 542 2025-02-25 07:43:32.658836-08 2025-02-25 08:54:50.899175-08 benchmark f A100 \N t \N \N \N {} -1637 542 2025-02-25 09:16:00.130562-08 2025-02-25 08:20:39.142553-08 leaderboard f A100 0.003090287 t \N \N \N {} -1638 542 2025-02-25 07:35:49.713063-08 2025-02-25 08:24:45.44148-08 test t A100 \N t \N \N \N {} -2773 966 2025-02-26 20:06:46.265649-08 2025-02-26 20:37:56.859709-08 test f A100 \N t \N \N \N {} -6281 1943 2025-03-12 08:27:24.754837-07 2025-03-12 09:00:16.045266-07 test t L4 \N t \N \N \N {} -6282 1943 2025-03-12 09:15:13.475839-07 2025-03-12 08:12:28.990502-07 benchmark t L4 \N t \N \N \N {} -6283 1943 2025-03-12 07:28:57.790932-07 2025-03-12 07:49:47.805831-07 leaderboard t L4 0.00009934762 t \N \N \N {} -6665 2060 2025-03-14 14:00:15.697848-07 2025-03-14 13:40:02.788256-07 test t T4 \N f \N \N \N {} -3863 1292 2025-03-01 04:08:38.155179-08 2025-03-01 03:06:47.8337-08 benchmark t A100 \N t \N \N \N {} -6287 1944 2025-03-12 10:01:22.337882-07 2025-03-12 10:02:16.243525-07 test f T4 \N t \N \N \N {} -6288 1944 2025-03-12 10:35:51.44506-07 2025-03-12 10:53:31.762253-07 benchmark f T4 \N t \N \N \N {} -6289 1944 2025-03-12 11:15:49.75933-07 2025-03-12 10:30:20.890743-07 leaderboard f T4 3.5460847016666666 t \N \N \N {} -6421 1999 2025-03-14 05:54:56.594885-07 2025-03-14 05:07:15.151352-07 benchmark f A100 \N t \N \N \N {} -6422 1999 2025-03-14 04:48:17.257143-07 2025-03-14 04:38:30.49237-07 leaderboard f A100 0.002516429 t \N \N \N {} -6666 2061 2025-03-14 15:01:59.053891-07 2025-03-14 14:18:38.868311-07 test t T4 \N f \N \N \N {} -6291 1944 2025-03-12 09:57:38.536736-07 2025-03-12 10:33:16.849388-07 benchmark t T4 \N t \N \N \N {} -1639 542 2025-02-25 09:00:22.32772-08 2025-02-25 07:59:32.226862-08 benchmark t A100 \N t \N \N \N {} -1640 542 2025-02-25 09:11:58.980108-08 2025-02-25 08:19:49.179574-08 leaderboard t A100 0.0030916486666666665 t \N \N \N {} -1641 543 2025-02-25 07:54:29.239831-08 2025-02-25 09:35:38.991324-08 test t A100 \N t \N \N \N {} -1646 543 2025-02-25 08:14:16.552698-08 2025-02-25 08:35:18.8746-08 leaderboard f A100 0.0030903186666666666 t \N \N \N {} -1656 545 2025-02-25 08:18:12.922307-08 2025-02-25 09:29:21.123215-08 leaderboard t A100 0.0031894605 t \N \N \N {} -1657 546 2025-02-25 08:26:28.000804-08 2025-02-25 09:50:00.709982-08 test t A100 \N t \N \N \N {} -1658 546 2025-02-25 09:48:18.886362-08 2025-02-25 09:10:41.999421-08 benchmark t A100 \N t \N \N \N {} -1659 546 2025-02-25 07:55:49.954375-08 2025-02-25 09:05:11.341596-08 leaderboard t A100 0.00063344525 t \N \N \N {} -1660 546 2025-02-25 08:01:12.970128-08 2025-02-25 08:24:15.501382-08 test f A100 \N t \N \N \N {} -1661 546 2025-02-25 09:18:23.952092-08 2025-02-25 08:15:13.029454-08 benchmark f A100 \N t \N \N \N {} -6292 1944 2025-03-12 10:58:18.817212-07 2025-03-12 10:46:59.456595-07 leaderboard t T4 3.53122528 t \N \N \N {} -1662 546 2025-02-25 08:35:51.067428-08 2025-02-25 08:31:39.935762-08 leaderboard f A100 0.0005320072 t \N \N \N {} -1663 547 2025-02-25 09:32:55.917437-08 2025-02-25 08:44:26.311971-08 benchmark f H100 \N f \N \N \N {} -1684 557 2025-02-25 09:44:40.003266-08 2025-02-25 08:13:24.412498-08 benchmark f A100 \N f \N \N \N {} -1664 548 2025-02-25 08:06:02.985208-08 2025-02-25 08:56:36.303961-08 benchmark f H100 \N t \N \N \N {} -1666 549 2025-02-25 09:38:40.901485-08 2025-02-25 09:32:01.788835-08 benchmark t H100 \N t \N \N \N {} -1667 549 2025-02-25 08:26:34.238735-08 2025-02-25 08:25:38.843118-08 leaderboard t H100 0.000413247 t \N \N \N {} -1668 549 2025-02-25 09:52:24.604979-08 2025-02-25 08:02:29.9005-08 test f H100 \N t \N \N \N {} -1685 557 2025-02-25 09:19:53.854754-08 2025-02-25 08:46:52.219004-08 test t A100 \N t \N \N \N {} -1669 549 2025-02-25 08:53:05.362645-08 2025-02-25 08:47:47.898667-08 benchmark f H100 \N t \N \N \N {} -1670 549 2025-02-25 08:59:06.217181-08 2025-02-25 09:09:07.897493-08 leaderboard f H100 0.0004161602 t \N \N \N {} -6423 1999 2025-03-14 04:42:51.926225-07 2025-03-14 04:23:11.348669-07 test t A100 \N t \N \N \N {} -1678 553 2025-02-25 10:02:18.136454-08 2025-02-25 08:09:06.800521-08 test f H100 \N f \N \N \N {} -1682 556 2025-02-25 08:53:34.265116-08 2025-02-25 08:39:01.371239-08 benchmark f A100 \N t \N \N \N {} -1686 557 2025-02-25 08:35:22.195064-08 2025-02-25 09:08:11.056354-08 benchmark t A100 \N f \N \N \N {} -6424 1999 2025-03-14 05:09:18.158024-07 2025-03-14 04:42:27.024761-07 benchmark t A100 \N t \N \N \N {} -6667 2061 2025-03-14 15:25:37.443228-07 2025-03-14 15:06:32.582085-07 test f T4 \N f \N \N \N {} -6643 2053 2025-03-14 13:12:40.43597-07 2025-03-14 13:44:06.267317-07 leaderboard t A100 0.0024720393333333337 t \N \N \N {} -6668 2062 2025-03-14 14:17:49.224385-07 2025-03-14 14:26:32.460019-07 test t T4 \N f \N \N \N {} -3876 1294 2025-03-01 04:14:10.442655-08 2025-03-01 02:49:35.269118-08 leaderboard t A100 0.0030766496666666666 t \N \N \N {} -6294 1945 2025-03-12 10:57:16.296692-07 2025-03-12 11:33:24.443736-07 test f T4 \N f \N \N \N {} -6426 2000 2025-03-14 06:30:53.7156-07 2025-03-14 06:12:43.209048-07 test f H100 \N t \N \N \N {} -6427 2000 2025-03-14 06:57:59.87542-07 2025-03-14 06:15:38.776251-07 benchmark f H100 \N t \N \N \N {} -6428 2000 2025-03-14 07:35:27.590998-07 2025-03-14 06:32:04.615938-07 leaderboard f H100 0.0014073136666666668 t \N \N \N {} -6591 2042 2025-03-14 11:50:38.099796-07 2025-03-14 12:40:25.372521-07 test f A100 \N t \N \N \N {} -6644 2053 2025-03-14 13:49:44.146284-07 2025-03-14 12:26:28.495965-07 test f A100 \N t \N \N \N {} -6669 2062 2025-03-14 16:02:16.728023-07 2025-03-14 14:51:42.161638-07 test f T4 \N f \N \N \N {} -6436 2001 2025-03-14 06:50:57.915641-07 2025-03-14 07:00:20.008226-07 benchmark t A100 \N t \N \N \N {} -6437 2001 2025-03-14 07:44:58.201024-07 2025-03-14 07:24:40.594633-07 leaderboard t A100 0.002543368 t \N \N \N {} -3887 1296 2025-03-01 04:21:55.785115-08 2025-03-01 04:09:44.553825-08 benchmark f A100 \N t \N \N \N {} -3888 1296 2025-03-01 03:39:58.6244-08 2025-03-01 03:09:40.552669-08 leaderboard f A100 0.00309473 t \N \N \N {} -6299 1948 2025-03-12 10:32:56.767467-07 2025-03-12 11:22:11.851694-07 test t T4 \N t \N \N \N {} -6300 1948 2025-03-12 10:31:11.093009-07 2025-03-12 11:06:33.740742-07 benchmark t T4 \N f \N \N \N {} -6438 2002 2025-03-14 07:39:39.918952-07 2025-03-14 07:42:19.454653-07 test t A100 \N t \N \N \N {} -3891 1297 2025-03-01 04:29:07.565859-08 2025-03-01 03:39:00.355245-08 leaderboard t A100 0.0030872086666666665 t \N \N \N {} -6301 1949 2025-03-12 11:11:45.381428-07 2025-03-12 11:41:44.803347-07 test f T4 \N t \N \N \N {} -6302 1949 2025-03-12 11:19:11.369574-07 2025-03-12 11:47:00.702215-07 benchmark f T4 \N t \N \N \N {} -6303 1949 2025-03-12 11:08:31.94301-07 2025-03-12 11:22:31.160474-07 leaderboard f T4 5.264521755 t \N \N \N {} -3894 1297 2025-03-01 03:08:30.539157-08 2025-03-01 04:20:39.317282-08 leaderboard f A100 0.003077314 t \N \N \N {} -3895 1298 2025-03-01 03:26:56.396066-08 2025-03-01 04:02:10.736561-08 benchmark f A100 \N t \N \N \N {} -3896 1298 2025-03-01 04:30:06.792705-08 2025-03-01 05:08:52.916106-08 benchmark f H100 \N t \N \N \N {} -3897 1299 2025-03-01 03:32:41.876039-08 2025-03-01 03:28:04.603739-08 benchmark f H100 \N t \N \N \N {} -3902 1301 2025-03-01 03:45:08.15341-08 2025-03-01 03:30:46.472923-08 benchmark t A100 \N t \N \N \N {} -3903 1301 2025-03-01 05:18:11.482755-08 2025-03-01 04:15:04.738792-08 leaderboard t A100 0.0017082903333333333 t \N \N \N {} -3904 1301 2025-03-01 05:20:05.351706-08 2025-03-01 04:57:15.691666-08 test f H100 \N t \N \N \N {} -3905 1301 2025-03-01 04:11:22.209627-08 2025-03-01 03:29:30.979359-08 benchmark f H100 \N t \N \N \N {} -3909 1301 2025-03-01 05:05:05.368527-08 2025-03-01 05:21:07.368377-08 leaderboard t H100 0.0009410016 t \N \N \N {} -3911 1302 2025-03-01 04:15:21.468517-08 2025-03-01 04:16:28.01241-08 benchmark f A100 \N t \N \N \N {} -3912 1302 2025-03-01 05:15:06.388507-08 2025-03-01 05:04:17.52923-08 benchmark f H100 \N f \N \N \N {} -3914 1303 2025-03-01 03:58:49.506515-08 2025-03-01 05:03:02.215649-08 benchmark t A100 \N t \N \N \N {} -3915 1303 2025-03-01 05:02:46.480424-08 2025-03-01 05:10:24.739066-08 leaderboard t A100 0.0017033548333333334 t \N \N \N {} -3916 1303 2025-03-01 03:49:31.466637-08 2025-03-01 04:14:50.882192-08 test f A100 \N t \N \N \N {} -3917 1303 2025-03-01 04:37:55.798922-08 2025-03-01 05:09:32.972724-08 benchmark f A100 \N t \N \N \N {} -3918 1303 2025-03-01 04:24:18.36739-08 2025-03-01 04:58:17.307429-08 leaderboard f A100 0.0017008582 t \N \N \N {} -6304 1949 2025-03-12 10:47:37.368806-07 2025-03-12 12:09:25.453957-07 test t T4 \N t \N \N \N {} -1687 558 2025-02-25 09:27:10.084906-08 2025-02-25 09:48:43.848525-08 test f A100 \N f \N \N \N {} -1688 558 2025-02-25 08:29:44.125579-08 2025-02-25 08:24:09.285219-08 test t A100 \N f \N \N \N {} -1689 559 2025-02-25 09:17:25.101881-08 2025-02-25 09:24:03.785066-08 test f A100 \N t \N \N \N {} -1690 559 2025-02-25 08:36:17.748573-08 2025-02-25 09:33:30.838585-08 benchmark f A100 \N f \N \N \N {} -1691 559 2025-02-25 08:55:41.256091-08 2025-02-25 08:48:26.806972-08 test t A100 \N t \N \N \N {} -1692 559 2025-02-25 10:04:14.443146-08 2025-02-25 08:43:41.839368-08 benchmark t A100 \N f \N \N \N {} -1694 561 2025-02-25 10:05:36.595463-08 2025-02-25 10:09:40.331096-08 test f A100 \N t \N \N \N {} -3567 1211 2025-02-28 11:38:42.437274-08 2025-02-28 11:48:25.289361-08 leaderboard f H100 0.0011942561428571429 t \N \N \N {} -1696 562 2025-02-25 09:44:07.680498-08 2025-02-25 09:28:05.067515-08 test f A100 \N t \N \N \N {} -1697 562 2025-02-25 09:28:26.771379-08 2025-02-25 09:08:24.955188-08 benchmark f A100 \N t \N \N \N {} -1698 562 2025-02-25 08:47:34.033962-08 2025-02-25 08:35:38.541602-08 leaderboard f A100 0.023603107 t \N \N \N {} -1699 562 2025-02-25 09:50:30.387686-08 2025-02-25 10:01:59.310649-08 test t A100 \N t \N \N \N {} -1700 562 2025-02-25 08:34:58.738598-08 2025-02-25 08:38:55.902288-08 benchmark t A100 \N t \N \N \N {} -1701 562 2025-02-25 09:37:56.413765-08 2025-02-25 10:02:25.234169-08 leaderboard t A100 0.02387513042857143 t \N \N \N {} -6305 1949 2025-03-12 11:11:14.460487-07 2025-03-12 11:31:45.219851-07 benchmark t T4 \N t \N \N \N {} -6306 1949 2025-03-12 10:28:32.79482-07 2025-03-12 10:15:38.766761-07 leaderboard t T4 5.288479138666667 t \N \N \N {} -6443 2002 2025-03-14 07:34:37.078718-07 2025-03-14 07:14:50.86738-07 leaderboard f A100 0.0025309973333333337 t \N \N \N {} -6595 2043 2025-03-14 11:56:33.434461-07 2025-03-14 12:42:20.017678-07 test t A100 \N f \N \N \N {} -6685 2070 2025-03-14 15:59:15.307918-07 2025-03-14 15:02:13.119994-07 test f T4 \N f \N \N \N {} -3962 1321 2025-03-01 05:24:43.049186-08 2025-03-01 05:35:56.69-08 benchmark f A100 \N t \N \N \N {} -3963 1321 2025-03-01 06:08:58.229359-08 2025-03-01 05:22:31.386839-08 leaderboard f A100 0.0030768063333333337 t \N \N \N {} -6310 1951 2025-03-12 12:07:14.331628-07 2025-03-12 12:08:49.086672-07 test f T4 \N f \N \N \N {} -6453 2004 2025-03-14 06:42:37.075587-07 2025-03-14 07:33:35.882343-07 test t A100 \N t \N \N \N {} -6454 2004 2025-03-14 06:29:13.454127-07 2025-03-14 07:27:34.849727-07 benchmark t A100 \N t \N \N \N {} -6455 2004 2025-03-14 07:29:30.832561-07 2025-03-14 06:24:40.619546-07 leaderboard t A100 0.0025143146666666664 t \N \N \N {} -3965 1321 2025-03-01 05:11:28.825751-08 2025-03-01 04:35:18.89017-08 benchmark t A100 \N t \N \N \N {} -3966 1321 2025-03-01 06:16:12.443097-08 2025-03-01 04:37:00.745753-08 leaderboard t A100 0.0030776773333333336 t \N \N \N {} -6456 2005 2025-03-14 07:23:16.64199-07 2025-03-14 07:01:50.750143-07 test f A100 \N t \N \N \N {} -6457 2005 2025-03-14 06:22:29.76973-07 2025-03-14 06:09:06.773695-07 benchmark f A100 \N t \N \N \N {} -1703 565 2025-02-25 08:37:05.35978-08 2025-02-25 08:21:52.880091-08 test t A100 \N f \N \N \N {} -1713 572 2025-02-25 10:08:35.53301-08 2025-02-25 09:47:59.416619-08 benchmark f A100 \N t \N \N \N {} -1714 574 2025-02-25 08:57:21.205944-08 2025-02-25 08:58:46.708203-08 test f T4 \N f \N \N \N {} -1715 574 2025-02-25 08:22:30.782939-08 2025-02-25 09:22:33.716306-08 test t T4 \N f \N \N \N {} -1716 577 2025-02-25 09:25:17.463083-08 2025-02-25 09:32:17.15906-08 test t T4 \N f \N \N \N {} -1717 577 2025-02-25 08:30:00.171838-08 2025-02-25 09:40:29.944237-08 test f T4 \N f \N \N \N {} -1719 576 2025-02-25 10:16:42.240136-08 2025-02-25 09:11:50.029271-08 test f A100 \N t \N \N \N {} -3983 1326 2025-03-01 05:25:23.898128-08 2025-03-01 04:57:04.509129-08 test t A100 \N t \N \N \N {} -3992 1327 2025-03-01 05:54:28.752918-08 2025-03-01 05:57:31.677252-08 test t A100 \N t \N \N \N {} -3984 1326 2025-03-01 06:08:48.324057-08 2025-03-01 05:49:30.098232-08 benchmark t A100 \N t \N \N \N {} -3985 1326 2025-03-01 05:42:14.249969-08 2025-03-01 04:34:37.024307-08 leaderboard t A100 0.0030766156666666663 t \N \N \N {} -3986 1326 2025-03-01 04:55:11.733421-08 2025-03-01 05:15:01.56334-08 test f A100 \N t \N \N \N {} -3987 1326 2025-03-01 04:56:41.549319-08 2025-03-01 06:23:31.903277-08 benchmark f A100 \N t \N \N \N {} -3988 1326 2025-03-01 06:05:23.530527-08 2025-03-01 05:11:56.630856-08 leaderboard f A100 0.003088671 t \N \N \N {} -3991 1327 2025-03-01 05:48:54.261383-08 2025-03-01 06:04:01.141671-08 leaderboard f A100 0.003079475 t \N \N \N {} -3993 1327 2025-03-01 06:15:07.619494-08 2025-03-01 05:26:34.114027-08 benchmark t A100 \N t \N \N \N {} -3994 1327 2025-03-01 05:16:38.809307-08 2025-03-01 05:49:08.846786-08 leaderboard t A100 0.0031005003333333335 t \N \N \N {} -3995 1328 2025-03-01 04:33:03.029344-08 2025-03-01 04:45:00.030881-08 test f A100 \N t \N \N \N {} -3996 1328 2025-03-01 05:09:28.825437-08 2025-03-01 06:19:26.3327-08 benchmark f A100 \N t \N \N \N {} -1720 576 2025-02-25 08:55:00.326542-08 2025-02-25 10:00:29.575335-08 benchmark f A100 \N t \N \N \N {} -1721 576 2025-02-25 08:21:46.705866-08 2025-02-25 10:00:43.93478-08 leaderboard f A100 0.00010041933333333332 t \N \N \N {} -1722 576 2025-02-25 10:17:49.927789-08 2025-02-25 08:37:55.11806-08 test t A100 \N t \N \N \N {} -1723 576 2025-02-25 09:33:22.088092-08 2025-02-25 09:24:57.641368-08 benchmark t A100 \N t \N \N \N {} -1724 576 2025-02-25 08:39:15.730368-08 2025-02-25 08:39:06.879053-08 leaderboard t A100 0.00009493443478260869 t \N \N \N {} -3997 1328 2025-03-01 05:05:03.87852-08 2025-03-01 05:17:53.273059-08 leaderboard f A100 0.0030810756666666667 t \N \N \N {} -4001 1329 2025-03-01 05:01:28.939311-08 2025-03-01 05:15:07.131981-08 test t A100 \N t \N \N \N {} -4002 1329 2025-03-01 06:12:19.11185-08 2025-03-01 06:29:57.519732-08 benchmark t A100 \N t \N \N \N {} -4003 1329 2025-03-01 05:41:46.640503-08 2025-03-01 05:30:00.149015-08 leaderboard t A100 0.003084419 t \N \N \N {} -4004 1329 2025-03-01 04:37:14.969684-08 2025-03-01 05:59:19.610966-08 test f A100 \N t \N \N \N {} -4005 1329 2025-03-01 05:09:39.146484-08 2025-03-01 05:15:15.340274-08 benchmark f A100 \N t \N \N \N {} -4022 1332 2025-03-01 06:29:41.552466-08 2025-03-01 06:00:54.805207-08 test t A100 \N t \N \N \N {} -2084 702 2025-02-25 14:51:18.373776-08 2025-02-25 14:07:09.597234-08 test t T4 \N t \N \N \N {} -1728 582 2025-02-25 09:02:10.715863-08 2025-02-25 08:32:41.855511-08 test f T4 \N t \N \N \N {} -1729 582 2025-02-25 10:08:04.120077-08 2025-02-25 09:31:28.613967-08 benchmark f T4 \N f \N \N \N {} -1730 582 2025-02-25 09:12:24.120001-08 2025-02-25 08:35:23.070755-08 test t T4 \N t \N \N \N {} -1731 582 2025-02-25 08:45:58.419751-08 2025-02-25 09:50:28.503841-08 benchmark t T4 \N f \N \N \N {} -1732 581 2025-02-25 08:57:10.342617-08 2025-02-25 09:35:43.172418-08 test f T4 \N f \N \N \N {} -4023 1332 2025-03-01 05:59:07.048515-08 2025-03-01 05:19:06.578904-08 benchmark t A100 \N t \N \N \N {} -4024 1332 2025-03-01 06:30:59.985589-08 2025-03-01 05:19:38.789542-08 leaderboard t A100 0.0032329573333333334 t \N \N \N {} -4026 1333 2025-03-01 04:39:47.738838-08 2025-03-01 06:18:45.96386-08 benchmark f A100 \N t \N \N \N {} -4027 1333 2025-03-01 06:14:49.837768-08 2025-03-01 04:40:08.352975-08 leaderboard f A100 0.003070329 t \N \N \N {} -4028 1333 2025-03-01 05:10:32.553371-08 2025-03-01 04:42:50.943725-08 test t A100 \N t \N \N \N {} -4038 1335 2025-03-01 04:42:30.486974-08 2025-03-01 06:10:26.032842-08 test f A100 \N f \N \N \N {} -4039 1336 2025-03-01 06:21:06.572554-08 2025-03-01 06:39:44.644728-08 test t A100 \N t \N \N \N {} -4040 1336 2025-03-01 05:35:17.533391-08 2025-03-01 05:00:53.904121-08 benchmark t A100 \N t \N \N \N {} -4041 1336 2025-03-01 04:42:43.164597-08 2025-03-01 05:32:09.887961-08 leaderboard t A100 0.003087946 t \N \N \N {} -4043 1336 2025-03-01 05:22:32.085041-08 2025-03-01 05:29:36.766261-08 benchmark f A100 \N t \N \N \N {} -4044 1336 2025-03-01 04:46:43.10424-08 2025-03-01 05:19:36.970936-08 leaderboard f A100 0.0030770363333333336 t \N \N \N {} -4047 1337 2025-03-01 06:26:58.612027-08 2025-03-01 05:02:55.827739-08 leaderboard t A100 0.0030799923333333333 t \N \N \N {} -4048 1337 2025-03-01 04:51:49.552059-08 2025-03-01 05:32:32.010537-08 test f A100 \N t \N \N \N {} -4049 1337 2025-03-01 06:28:22.821883-08 2025-03-01 05:36:52.041982-08 benchmark f A100 \N t \N \N \N {} -4050 1337 2025-03-01 06:44:28.006933-08 2025-03-01 06:34:44.60449-08 leaderboard f A100 0.0030728746666666665 t \N \N \N {} -1733 581 2025-02-25 09:41:55.102032-08 2025-02-25 09:45:47.156909-08 test t T4 \N f \N \N \N {} -1739 583 2025-02-25 08:29:33.716534-08 2025-02-25 09:20:58.544378-08 benchmark f H100 \N t \N \N \N {} -1740 583 2025-02-25 09:22:37.035257-08 2025-02-25 08:36:17.972523-08 leaderboard f H100 0.00007956952 t \N \N \N {} -1747 587 2025-02-25 10:20:06.796411-08 2025-02-25 09:35:43.20184-08 test f T4 \N f \N \N \N {} -1750 589 2025-02-25 09:17:20.905239-08 2025-02-25 08:39:09.677788-08 test f A100 \N f \N \N \N {} -1754 593 2025-02-25 10:35:01.237155-08 2025-02-25 09:42:17.876251-08 benchmark f H100 \N f \N \N \N {} -2232 731 2025-02-25 20:55:56.356497-08 2025-02-25 21:54:01.459326-08 test f H100 \N f \N \N \N {} -1752 590 2025-02-25 09:26:44.50262-08 2025-02-25 09:02:57.304272-08 test f T4 \N t \N \N \N {} -1753 592 2025-02-25 10:16:57.848423-08 2025-02-25 09:02:46.758153-08 benchmark f H100 \N f \N \N \N {} -1755 594 2025-02-25 09:52:50.948573-08 2025-02-25 09:17:35.552434-08 test t T4 \N t \N \N \N {} -4051 1338 2025-03-01 04:53:46.779577-08 2025-03-01 05:51:05.617144-08 test t A100 \N t \N \N \N {} -4052 1338 2025-03-01 05:14:44.787303-08 2025-03-01 05:10:58.544283-08 benchmark t A100 \N t \N \N \N {} -4056 1338 2025-03-01 06:06:59.390985-08 2025-03-01 05:45:01.431744-08 leaderboard f A100 0.003124134 t \N \N \N {} -4057 1339 2025-03-01 05:45:53.840411-08 2025-03-01 04:57:01.712759-08 test f A100 \N t \N \N \N {} -4058 1339 2025-03-01 05:12:08.845443-08 2025-03-01 06:00:32.90691-08 benchmark f A100 \N t \N \N \N {} -4059 1339 2025-03-01 06:17:04.287969-08 2025-03-01 06:40:13.98261-08 leaderboard f A100 0.0030740783333333336 t \N \N \N {} -4060 1339 2025-03-01 05:30:41.282216-08 2025-03-01 06:19:05.26954-08 test t A100 \N t \N \N \N {} -4826 1490 2025-03-02 13:31:40.085228-08 2025-03-02 14:56:41.325187-08 test t A100 \N t \N \N \N {} -4069 1341 2025-03-01 06:46:40.043845-08 2025-03-01 05:08:54.482442-08 test t A100 \N t \N \N \N {} -4070 1341 2025-03-01 06:53:39.351776-08 2025-03-01 05:56:37.814647-08 benchmark t A100 \N t \N \N \N {} -4071 1341 2025-03-01 05:41:03.872073-08 2025-03-01 04:57:16.263647-08 leaderboard t A100 0.003135153 t \N \N \N {} -4072 1341 2025-03-01 06:36:12.592251-08 2025-03-01 05:33:09.049244-08 test f A100 \N t \N \N \N {} -4073 1341 2025-03-01 04:57:36.121576-08 2025-03-01 06:04:24.508424-08 benchmark f A100 \N t \N \N \N {} -4074 1341 2025-03-01 06:20:16.915193-08 2025-03-01 05:10:41.943093-08 leaderboard f A100 0.0031289636666666665 t \N \N \N {} -4079 1342 2025-03-01 06:28:17.155594-08 2025-03-01 05:18:07.512479-08 benchmark t A100 \N t \N \N \N {} -4080 1342 2025-03-01 05:17:17.167143-08 2025-03-01 06:19:18.451357-08 leaderboard t A100 0.003087757 t \N \N \N {} -1762 596 2025-02-25 09:48:18.872842-08 2025-02-25 09:08:03.874583-08 benchmark f H100 \N f \N \N \N {} -1763 596 2025-02-25 09:26:41.784533-08 2025-02-25 08:49:26.718913-08 benchmark f A100 \N f \N \N \N {} -1765 598 2025-02-25 10:31:00.584025-08 2025-02-25 09:31:07.814689-08 test f T4 \N f \N \N \N {} -4081 1343 2025-03-01 05:02:29.799238-08 2025-03-01 05:20:01.319572-08 test t A100 \N t \N \N \N {} -4082 1343 2025-03-01 06:56:56.021121-08 2025-03-01 05:37:48.321713-08 benchmark t A100 \N t \N \N \N {} -4083 1343 2025-03-01 05:51:20.985976-08 2025-03-01 06:10:38.11579-08 leaderboard t A100 0.003137047 t \N \N \N {} -4087 1344 2025-03-01 06:50:56.204942-08 2025-03-01 06:57:42.428539-08 test f A100 \N t \N \N \N {} -4088 1344 2025-03-01 05:59:31.101205-08 2025-03-01 05:10:00.245441-08 benchmark f A100 \N t \N \N \N {} -4089 1344 2025-03-01 06:31:19.255093-08 2025-03-01 05:37:19.624461-08 leaderboard f A100 0.0031286543333333334 t \N \N \N {} -4090 1344 2025-03-01 05:39:26.723837-08 2025-03-01 05:18:13.835391-08 test t A100 \N t \N \N \N {} -4091 1344 2025-03-01 05:16:22.102322-08 2025-03-01 05:57:13.821456-08 benchmark t A100 \N t \N \N \N {} -4092 1344 2025-03-01 06:48:00.846042-08 2025-03-01 06:36:07.876646-08 leaderboard t A100 0.0031320843333333334 t \N \N \N {} -4095 1346 2025-03-01 06:54:50.741501-08 2025-03-01 06:37:05.660285-08 test f A100 \N t \N \N \N {} -4096 1346 2025-03-01 06:43:56.667076-08 2025-03-01 05:03:01.487169-08 benchmark f A100 \N t \N \N \N {} -4097 1346 2025-03-01 06:20:12.926762-08 2025-03-01 05:15:59.317062-08 leaderboard f A100 0.0030682336666666664 t \N \N \N {} -4098 1346 2025-03-01 06:31:50.959915-08 2025-03-01 06:24:17.817323-08 test t A100 \N t \N \N \N {} -4099 1346 2025-03-01 05:34:30.160891-08 2025-03-01 05:36:09.807594-08 benchmark t A100 \N t \N \N \N {} -4100 1346 2025-03-01 06:40:10.997004-08 2025-03-01 06:45:08.049407-08 leaderboard t A100 0.003080214 t \N \N \N {} -4103 1348 2025-03-01 06:49:47.231825-08 2025-03-01 05:57:18.775954-08 test f A100 \N t \N \N \N {} -4104 1348 2025-03-01 06:30:42.302161-08 2025-03-01 06:25:51.821249-08 benchmark f A100 \N t \N \N \N {} -4105 1348 2025-03-01 05:19:35.876581-08 2025-03-01 05:20:21.682221-08 leaderboard f A100 0.0030814403333333336 t \N \N \N {} -4106 1348 2025-03-01 06:43:59.230765-08 2025-03-01 06:48:14.067513-08 test t A100 \N t \N \N \N {} -4107 1348 2025-03-01 06:33:05.642007-08 2025-03-01 05:34:35.401784-08 benchmark t A100 \N t \N \N \N {} -4108 1348 2025-03-01 06:33:53.834077-08 2025-03-01 06:05:52.452967-08 leaderboard t A100 0.0030692503333333335 t \N \N \N {} -4112 1349 2025-03-01 05:20:23.470116-08 2025-03-01 06:42:43.607444-08 test f A100 \N t \N \N \N {} -4113 1349 2025-03-01 06:18:41.622091-08 2025-03-01 06:59:47.322159-08 benchmark f A100 \N t \N \N \N {} -4114 1349 2025-03-01 06:55:36.386367-08 2025-03-01 06:13:36.564538-08 leaderboard f A100 0.0030793546666666666 t \N \N \N {} -4115 1350 2025-03-01 06:47:22.861306-08 2025-03-01 06:36:04.525113-08 test f A100 \N t \N \N \N {} -4116 1350 2025-03-01 05:51:42.091206-08 2025-03-01 06:44:09.234411-08 benchmark f A100 \N t \N \N \N {} -4117 1350 2025-03-01 07:00:25.183656-08 2025-03-01 06:56:10.192153-08 leaderboard f A100 0.003075028 t \N \N \N {} -4121 1351 2025-03-01 07:03:16.315538-08 2025-03-01 05:35:39.265352-08 test f A100 \N t \N \N \N {} -4122 1351 2025-03-01 06:26:56.34005-08 2025-03-01 06:15:23.856925-08 benchmark f A100 \N t \N \N \N {} -4123 1351 2025-03-01 06:27:06.070522-08 2025-03-01 05:20:19.58787-08 leaderboard f A100 0.003077125 t \N \N \N {} -4124 1351 2025-03-01 06:56:18.609617-08 2025-03-01 06:45:35.932387-08 test t A100 \N t \N \N \N {} -4125 1351 2025-03-01 05:59:31.787912-08 2025-03-01 05:12:15.522556-08 benchmark t A100 \N t \N \N \N {} -4127 1352 2025-03-01 06:15:58.415656-08 2025-03-01 05:15:59.770051-08 test t A100 \N t \N \N \N {} -4128 1352 2025-03-01 05:38:07.3835-08 2025-03-01 05:42:40.878001-08 benchmark t A100 \N t \N \N \N {} -4129 1352 2025-03-01 06:36:57.941443-08 2025-03-01 06:23:59.790542-08 leaderboard t A100 0.0030807613333333336 t \N \N \N {} -4130 1352 2025-03-01 06:43:58.284545-08 2025-03-01 05:17:08.992478-08 test f A100 \N t \N \N \N {} -4131 1352 2025-03-01 05:18:15.942457-08 2025-03-01 06:36:47.992588-08 benchmark f A100 \N t \N \N \N {} -4132 1352 2025-03-01 05:45:57.090043-08 2025-03-01 05:13:09.294944-08 leaderboard f A100 0.003084765 t \N \N \N {} -4133 1353 2025-03-01 05:45:21.57909-08 2025-03-01 05:50:08.24199-08 test t A100 \N t \N \N \N {} -4142 1354 2025-03-01 05:19:50.397498-08 2025-03-01 06:24:38.013703-08 test t A100 \N t \N \N \N {} -1767 600 2025-02-25 09:57:39.280293-08 2025-02-25 09:01:18.92218-08 benchmark f H100 \N t \N \N \N {} -1769 602 2025-02-25 09:26:39.998077-08 2025-02-25 10:45:34.040607-08 benchmark f H100 \N t \N \N \N {} -1770 603 2025-02-25 09:14:10.5262-08 2025-02-25 10:34:36.545027-08 benchmark f H100 \N t \N \N \N {} -1771 604 2025-02-25 09:18:20.318954-08 2025-02-25 09:47:55.570928-08 test t H100 \N t \N \N \N {} -4141 1354 2025-03-01 05:54:11.96859-08 2025-03-01 06:24:29.905592-08 leaderboard f A100 0.0030812046666666665 t \N \N \N {} -4145 1355 2025-03-01 05:52:29.99551-08 2025-03-01 05:50:58.136374-08 test f A100 \N t \N \N \N {} -4146 1355 2025-03-01 06:50:16.294841-08 2025-03-01 06:10:42.413759-08 benchmark f A100 \N t \N \N \N {} -4147 1355 2025-03-01 06:54:23.821235-08 2025-03-01 07:02:38.84073-08 leaderboard f A100 0.00309179 t \N \N \N {} -4148 1355 2025-03-01 05:36:48.581155-08 2025-03-01 06:55:13.189694-08 test t A100 \N t \N \N \N {} -4149 1355 2025-03-01 06:20:18.889242-08 2025-03-01 06:29:20.71451-08 benchmark t A100 \N t \N \N \N {} -4150 1355 2025-03-01 05:46:07.870361-08 2025-03-01 06:38:31.9395-08 leaderboard t A100 0.0030817543333333335 t \N \N \N {} -4151 1356 2025-03-01 07:33:56.778034-08 2025-03-01 06:51:39.921874-08 test f A100 \N f \N \N \N {} -4154 1359 2025-03-01 09:55:27.574174-08 2025-03-01 08:42:50.314131-08 benchmark f A100 \N t \N \N \N {} -4155 1359 2025-03-01 09:47:12.768835-08 2025-03-01 09:52:39.842141-08 benchmark f H100 \N t \N \N \N {} -4156 1360 2025-03-01 09:34:23.502047-08 2025-03-01 09:57:57.454449-08 benchmark f A100 \N t \N \N \N {} -4157 1360 2025-03-01 08:24:34.434752-08 2025-03-01 08:43:59.423015-08 benchmark f H100 \N t \N \N \N {} -4158 1361 2025-03-01 09:17:52.106156-08 2025-03-01 10:06:57.89908-08 test t H100 \N t \N \N \N {} -4159 1361 2025-03-01 09:16:48.1668-08 2025-03-01 08:15:25.825071-08 benchmark t H100 \N t \N \N \N {} -4160 1361 2025-03-01 09:01:03.843546-08 2025-03-01 09:36:32.754952-08 leaderboard t H100 0.0014479743333333333 t \N \N \N {} -4161 1361 2025-03-01 08:25:39.569213-08 2025-03-01 08:40:52.057697-08 test f H100 \N t \N \N \N {} -4162 1361 2025-03-01 08:47:33.253096-08 2025-03-01 09:19:00.121841-08 benchmark f H100 \N t \N \N \N {} -4163 1361 2025-03-01 09:21:19.831956-08 2025-03-01 09:37:55.855839-08 leaderboard f H100 0.001452041 t \N \N \N {} -4164 1363 2025-03-01 08:46:09.799691-08 2025-03-01 10:24:44.599745-08 benchmark f H100 \N t \N \N \N {} -4211 1390 2025-03-01 15:10:09.501721-08 2025-03-01 15:41:51.166433-08 benchmark f A100 \N t \N \N \N {} -4168 1364 2025-03-01 09:03:53.682811-08 2025-03-01 10:14:06.09417-08 leaderboard t A100 0.0031122263333333336 t \N \N \N {} -4169 1364 2025-03-01 09:49:16.863307-08 2025-03-01 10:08:01.584412-08 test f A100 \N t \N \N \N {} -1772 604 2025-02-25 10:42:51.901096-08 2025-02-25 10:19:33.430206-08 benchmark t H100 \N t \N \N \N {} -1773 604 2025-02-25 09:32:09.301008-08 2025-02-25 10:21:06.499152-08 leaderboard t H100 0.006142186 t \N \N \N {} -1774 604 2025-02-25 10:43:27.145802-08 2025-02-25 09:56:58.387594-08 test f H100 \N t \N \N \N {} -1775 604 2025-02-25 10:39:50.506911-08 2025-02-25 09:47:48.72776-08 benchmark f H100 \N t \N \N \N {} -1776 604 2025-02-25 09:10:42.599596-08 2025-02-25 09:30:41.654648-08 leaderboard f H100 0.006143060666666667 t \N \N \N {} -2414 816 2025-02-26 12:08:46.674357-08 2025-02-26 11:49:40.827855-08 benchmark f H100 \N f \N \N \N {} -1777 605 2025-02-25 10:52:46.64778-08 2025-02-25 09:29:41.278017-08 benchmark f H100 \N t \N \N \N {} -4213 1391 2025-03-01 15:19:09.143259-08 2025-03-01 14:01:35.420716-08 benchmark f H100 \N f \N \N \N {} -1778 605 2025-02-25 10:28:01.040475-08 2025-02-25 09:43:43.248513-08 benchmark f A100 \N t \N \N \N {} -1779 608 2025-02-25 10:43:12.211893-08 2025-02-25 10:07:05.017448-08 benchmark f H100 \N t \N \N \N {} -1780 609 2025-02-25 10:20:07.015553-08 2025-02-25 09:42:49.0735-08 benchmark f H100 \N t \N \N \N {} -2233 732 2025-02-25 22:08:54.161836-08 2025-02-25 22:20:50.391923-08 test f H100 \N t \N \N \N {} -1781 610 2025-02-25 10:34:24.601084-08 2025-02-25 10:19:19.681462-08 benchmark f H100 \N f \N \N \N {} -4677 1467 2025-03-02 05:46:48.283452-08 2025-03-02 06:33:48.786791-08 test f A100 \N t \N \N \N {} -1782 611 2025-02-25 10:20:28.964054-08 2025-02-25 09:52:02.934599-08 benchmark f H100 \N t \N \N \N {} -1791 616 2025-02-25 10:57:56.194437-08 2025-02-25 11:14:23.934399-08 benchmark t A100 \N t \N \N \N {} -1797 616 2025-02-25 10:18:29.893893-08 2025-02-25 10:05:32.012644-08 benchmark t T4 \N t \N \N \N {} -1802 616 2025-02-25 10:35:10.93135-08 2025-02-25 10:50:54.582852-08 test t H100 \N t \N \N \N {} -1807 616 2025-02-25 10:38:00.594029-08 2025-02-25 09:44:40.215164-08 leaderboard f L4 0.0002189721 t \N \N \N {} -1808 616 2025-02-25 10:30:31.534914-08 2025-02-25 10:05:10.928409-08 test t L4 \N t \N \N \N {} -1809 616 2025-02-25 10:42:40.135466-08 2025-02-25 10:28:37.438567-08 benchmark t L4 \N t \N \N \N {} -1813 619 2025-02-25 10:40:45.957535-08 2025-02-25 10:54:37.204319-08 benchmark f H100 \N t \N \N \N {} -1814 620 2025-02-25 10:47:50.841428-08 2025-02-25 09:37:03.436577-08 test t A100 \N t \N \N \N {} -1815 620 2025-02-25 11:04:50.249753-08 2025-02-25 09:57:25.703303-08 benchmark t A100 \N t \N \N \N {} -1820 621 2025-02-25 10:25:46.173072-08 2025-02-25 11:00:06.872432-08 test f A100 \N t \N \N \N {} -1825 621 2025-02-25 10:28:18.242991-08 2025-02-25 10:32:06.378285-08 leaderboard f H100 0.00006850659090909092 t \N \N \N {} -1826 621 2025-02-25 09:41:56.614729-08 2025-02-25 10:07:25.694167-08 test t A100 \N t \N \N \N {} -1827 621 2025-02-25 09:37:04.460998-08 2025-02-25 09:48:35.57183-08 benchmark t A100 \N t \N \N \N {} -1828 621 2025-02-25 10:08:41.300361-08 2025-02-25 11:30:23.969988-08 leaderboard t A100 0.00012062903571428571 t \N \N \N {} -1830 621 2025-02-25 09:45:11.698381-08 2025-02-25 10:09:44.153963-08 benchmark t H100 \N t \N \N \N {} -1838 621 2025-02-25 10:17:56.497108-08 2025-02-25 10:28:25.597743-08 test f L4 \N t \N \N \N {} -1841 621 2025-02-25 10:04:54.287311-08 2025-02-25 10:49:27.389838-08 test t L4 \N t \N \N \N {} -1842 621 2025-02-25 10:15:18.832896-08 2025-02-25 09:43:06.885167-08 benchmark t L4 \N t \N \N \N {} -1843 621 2025-02-25 11:14:37.289396-08 2025-02-25 09:37:11.924231-08 leaderboard t L4 0.00015086871428571428 t \N \N \N {} -1846 624 2025-02-25 10:33:21.190201-08 2025-02-25 11:11:47.766913-08 benchmark f A100 \N t \N \N \N {} -1847 625 2025-02-25 11:32:53.839235-08 2025-02-25 11:35:34.878827-08 benchmark f H100 \N t \N \N \N {} -1848 626 2025-02-25 10:29:31.639414-08 2025-02-25 09:50:52.046112-08 test f H100 \N t \N \N \N {} -1849 626 2025-02-25 10:56:50.659567-08 2025-02-25 11:09:07.456975-08 benchmark f H100 \N t \N \N \N {} -1850 626 2025-02-25 11:01:56.450365-08 2025-02-25 11:13:04.781975-08 leaderboard f H100 0.0014884195 t \N \N \N {} -3801 1281 2025-03-01 02:21:25.649213-08 2025-03-01 03:35:34.124629-08 test f A100 \N t \N \N \N {} -1852 626 2025-02-25 09:52:11.744323-08 2025-02-25 10:17:21.865184-08 benchmark t H100 \N t \N \N \N {} -1853 626 2025-02-25 11:11:09.345105-08 2025-02-25 09:56:58.318051-08 leaderboard t H100 0.0015167958125 t \N \N \N {} -1864 629 2025-02-25 10:03:17.408394-08 2025-02-25 10:31:15.377248-08 test t T4 \N f \N \N \N {} -1865 630 2025-02-25 10:33:02.54222-08 2025-02-25 09:57:27.268701-08 benchmark f A100 \N t \N \N \N {} -1866 631 2025-02-25 11:40:59.404486-08 2025-02-25 11:06:13.593002-08 benchmark f A100 \N t \N \N \N {} -1867 632 2025-02-25 10:30:21.975844-08 2025-02-25 10:10:22.159184-08 benchmark f A100 \N t \N \N \N {} -4171 1364 2025-03-01 09:02:25.076208-08 2025-03-01 09:47:26.827613-08 leaderboard f A100 0.003101713 t \N \N \N {} -4172 1365 2025-03-01 10:16:31.355759-08 2025-03-01 09:11:20.987034-08 benchmark f A100 \N t \N \N \N {} -4173 1366 2025-03-01 10:41:44.391735-08 2025-03-01 10:08:59.397316-08 test t A100 \N t \N \N \N {} -4174 1366 2025-03-01 10:41:36.980935-08 2025-03-01 10:58:56.581662-08 benchmark t A100 \N t \N \N \N {} -4686 1469 2025-03-02 07:07:48.25556-08 2025-03-02 07:20:29.156987-08 test f A100 \N t \N \N \N {} -4177 1366 2025-03-01 10:12:47.989298-08 2025-03-01 09:39:27.09578-08 benchmark f A100 \N t \N \N \N {} -1868 633 2025-02-25 11:43:07.576973-08 2025-02-25 10:22:50.962065-08 benchmark f A100 \N t \N \N \N {} -2454 860 2025-02-26 14:41:46.094612-08 2025-02-26 14:44:15.460594-08 test f A100 \N t \N \N \N {} -1869 634 2025-02-25 10:28:18.751593-08 2025-02-25 11:15:24.986518-08 benchmark f A100 \N t \N \N \N {} -1870 635 2025-02-25 11:53:39.07753-08 2025-02-25 11:27:22.45986-08 test f A100 \N t \N \N \N {} -1871 635 2025-02-25 11:53:25.443777-08 2025-02-25 10:35:30.145788-08 benchmark f A100 \N t \N \N \N {} -1874 635 2025-02-25 11:16:09.682168-08 2025-02-25 11:49:38.387512-08 benchmark t A100 \N t \N \N \N {} -1876 636 2025-02-25 11:49:31.647654-08 2025-02-25 11:20:21.285756-08 test f H100 \N t \N \N \N {} -1882 637 2025-02-25 11:02:50.678205-08 2025-02-25 11:53:07.5339-08 test f A100 \N t \N \N \N {} -1883 637 2025-02-25 12:15:08.69237-08 2025-02-25 12:05:12.143404-08 benchmark f A100 \N t \N \N \N {} -1884 637 2025-02-25 12:15:07.966893-08 2025-02-25 12:16:54.89578-08 leaderboard f A100 0.003200573 t \N \N \N {} -2419 822 2025-02-26 13:19:13.535475-08 2025-02-26 13:18:00.548275-08 test f T4 \N f \N \N \N {} -1889 639 2025-02-25 13:15:11.381768-08 2025-02-25 11:52:40.943412-08 benchmark f A100 \N t \N \N \N {} -1890 640 2025-02-25 13:03:11.780508-08 2025-02-25 11:39:48.194084-08 benchmark f A100 \N t \N \N \N {} -1892 641 2025-02-25 12:41:04.706804-08 2025-02-25 12:31:14.054076-08 benchmark t A100 \N t \N \N \N {} -1893 641 2025-02-25 12:48:58.704728-08 2025-02-25 12:33:09.050832-08 leaderboard t A100 0.003076926 t \N \N \N {} -1894 641 2025-02-25 13:10:49.145744-08 2025-02-25 11:53:53.607428-08 test f A100 \N t \N \N \N {} -1899 644 2025-02-25 12:19:53.732245-08 2025-02-25 13:40:01.838553-08 benchmark f A100 \N t \N \N \N {} -1900 646 2025-02-25 12:52:16.315942-08 2025-02-25 12:55:52.154965-08 test f T4 \N f \N \N \N {} -1901 647 2025-02-25 12:46:38.219456-08 2025-02-25 13:22:45.626265-08 benchmark f A100 \N t \N \N \N {} -1913 658 2025-02-25 13:14:34.483591-08 2025-02-25 12:48:18.641973-08 leaderboard f L4 0.017395948 t \N \N \N {} -1902 648 2025-02-25 13:22:19.470394-08 2025-02-25 13:20:10.149185-08 benchmark f A100 \N t \N \N \N {} -1903 649 2025-02-25 13:39:40.813885-08 2025-02-25 13:36:01.166239-08 benchmark f A100 \N t \N \N \N {} -1917 659 2025-02-25 12:18:43.146652-08 2025-02-25 13:44:37.824554-08 test t H100 \N t \N \N \N {} -1918 659 2025-02-25 12:38:33.511656-08 2025-02-25 12:09:10.447116-08 benchmark t H100 \N t \N \N \N {} -1919 659 2025-02-25 12:30:56.836878-08 2025-02-25 12:57:56.451178-08 leaderboard t H100 0.0014114212 t \N \N \N {} -1920 659 2025-02-25 13:27:03.426084-08 2025-02-25 14:05:30.708212-08 test f H100 \N t \N \N \N {} -1921 659 2025-02-25 13:32:15.353997-08 2025-02-25 13:54:42.055867-08 benchmark f H100 \N t \N \N \N {} -1922 659 2025-02-25 13:10:35.530509-08 2025-02-25 13:20:46.182913-08 leaderboard f H100 0.00142643575 t \N \N \N {} -3807 1283 2025-03-01 03:58:57.932103-08 2025-03-01 02:04:21.585952-08 test t A100 \N t \N \N \N {} -4178 1366 2025-03-01 09:15:06.396332-08 2025-03-01 09:50:59.628509-08 leaderboard f A100 0.0031052183333333335 t \N \N \N {} -4179 1367 2025-03-01 10:00:51.737363-08 2025-03-01 10:00:20.686025-08 benchmark f A100 \N t \N \N \N {} -4245 1401 2025-03-01 17:40:55.269982-08 2025-03-01 17:02:11.828026-08 benchmark f H100 \N t \N \N \N {} -4180 1368 2025-03-01 09:53:14.622803-08 2025-03-01 10:45:13.461477-08 benchmark f A100 \N t \N \N \N {} -4181 1369 2025-03-01 10:19:49.557836-08 2025-03-01 10:31:18.303541-08 benchmark f A100 \N t \N \N \N {} -4182 1370 2025-03-01 10:05:46.891703-08 2025-03-01 10:47:36.638039-08 benchmark f A100 \N t \N \N \N {} -4183 1371 2025-03-01 10:09:50.741844-08 2025-03-01 09:32:36.999656-08 benchmark f A100 \N t \N \N \N {} -4246 1401 2025-03-01 16:27:23.159511-08 2025-03-01 16:38:26.273265-08 benchmark f A100 \N t \N \N \N {} -4186 1374 2025-03-01 10:29:07.179112-08 2025-03-01 10:42:10.711955-08 benchmark f A100 \N f \N \N \N {} -4187 1375 2025-03-01 10:45:16.587627-08 2025-03-01 10:28:33.902324-08 benchmark f A100 \N f \N \N \N {} -4188 1376 2025-03-01 12:01:30.707689-08 2025-03-01 11:24:17.478908-08 test f A100 \N t \N \N \N {} -4189 1377 2025-03-01 12:12:57.585435-08 2025-03-01 11:53:33.240089-08 benchmark f A100 \N t \N \N \N {} -4190 1379 2025-03-01 12:16:36.769477-08 2025-03-01 13:18:08.829651-08 test f A100 \N t \N \N \N {} -4199 1388 2025-03-01 12:52:38.473441-08 2025-03-01 13:32:16.90025-08 test f A100 \N t \N \N \N {} -4200 1388 2025-03-01 13:20:31.349585-08 2025-03-01 12:39:30.194675-08 benchmark f A100 \N t \N \N \N {} -4201 1388 2025-03-01 13:30:38.204623-08 2025-03-01 12:55:51.306591-08 leaderboard f A100 0.4955258506666667 t \N \N \N {} -4805 1487 2025-03-02 12:59:51.989286-08 2025-03-02 12:35:17.947042-08 benchmark t T4 \N f \N \N \N {} -4202 1388 2025-03-01 12:42:31.113413-08 2025-03-01 13:17:21.163086-08 test t A100 \N t \N \N \N {} -4207 1389 2025-03-01 13:01:25.628609-08 2025-03-01 14:02:58.147439-08 leaderboard f H100 0.3205130943333333 t \N \N \N {} -4208 1389 2025-03-01 12:35:43.992201-08 2025-03-01 13:54:37.702884-08 test t H100 \N t \N \N \N {} -4209 1389 2025-03-01 13:46:31.426364-08 2025-03-01 14:02:32.562267-08 benchmark t H100 \N t \N \N \N {} -4210 1389 2025-03-01 12:35:26.710521-08 2025-03-01 13:30:46.516965-08 leaderboard t H100 0.31866500166666667 t \N \N \N {} -4212 1390 2025-03-01 14:35:10.227405-08 2025-03-01 15:03:07.466266-08 benchmark f H100 \N f \N \N \N {} -4214 1392 2025-03-01 15:54:46.023585-08 2025-03-01 15:50:44.87514-08 benchmark f A100 \N t \N \N \N {} -4215 1393 2025-03-01 15:05:15.318883-08 2025-03-01 15:06:05.433626-08 benchmark f A100 \N t \N \N \N {} -4216 1394 2025-03-01 16:24:31.141388-08 2025-03-01 16:30:42.940699-08 test f A100 \N t \N \N \N {} -4217 1394 2025-03-01 15:20:57.053534-08 2025-03-01 15:01:30.702462-08 benchmark f A100 \N t \N \N \N {} -2651 906 2025-02-26 14:32:29.620169-08 2025-02-26 14:03:45.689019-08 benchmark f A100 \N f \N \N \N {} -4773 1485 2025-03-02 12:07:24.955555-08 2025-03-02 12:12:01.099174-08 test t T4 \N t \N \N \N {} -4774 1485 2025-03-02 12:20:07.259834-08 2025-03-02 12:27:22.223302-08 benchmark t T4 \N t \N \N \N {} -4775 1485 2025-03-02 13:25:29.463588-08 2025-03-02 12:38:52.204635-08 leaderboard t T4 0.013066945 t \N \N \N {} -4776 1486 2025-03-02 13:33:31.030105-08 2025-03-02 12:15:35.189097-08 test t A100 \N t \N \N \N {} -4777 1486 2025-03-02 12:13:19.346928-08 2025-03-02 12:46:44.113151-08 benchmark t A100 \N f \N \N \N {} -4778 1486 2025-03-02 13:36:11.751592-08 2025-03-02 12:27:50.077759-08 test f A100 \N t \N \N \N {} -4779 1486 2025-03-02 13:14:12.733535-08 2025-03-02 13:18:37.639912-08 benchmark f A100 \N f \N \N \N {} -6738 2086 2025-03-15 03:23:36.712806-07 2025-03-15 04:23:06.297428-07 test f A100 \N t \N \N \N {} -4781 1486 2025-03-02 12:38:20.279542-08 2025-03-02 13:10:45.650515-08 benchmark t L4 \N t \N \N \N {} -1947 661 2025-02-25 13:28:58.74373-08 2025-02-25 13:47:27.943218-08 benchmark f H100 \N t \N \N \N {} -1949 663 2025-02-25 14:01:25.359523-08 2025-02-25 14:10:06.975147-08 benchmark f H100 \N t \N \N \N {} -1951 665 2025-02-25 12:52:25.628404-08 2025-02-25 13:09:46.048653-08 test f T4 \N t \N \N \N {} -1952 666 2025-02-25 13:17:01.172423-08 2025-02-25 13:04:42.018743-08 test f T4 \N t \N \N \N {} -5081 1553 2025-03-04 16:37:31.290979-08 2025-03-04 17:11:58.415963-08 leaderboard f H100 0.0014990966666666666 t \N \N \N {} -5082 1554 2025-03-04 17:14:21.826358-08 2025-03-04 18:23:09.521817-08 test f A100 \N f \N \N \N {} -5106 1565 2025-03-04 17:28:02.642565-08 2025-03-04 17:19:33.683442-08 leaderboard t A100 0.006331331333333333 t \N \N \N {} -5083 1555 2025-03-04 16:57:56.662828-08 2025-03-04 17:02:59.352083-08 test f A100 \N f \N \N \N {} -5258 1613 2025-03-06 07:52:29.561261-08 2025-03-06 07:25:55.418568-08 leaderboard t T4 0.016500428666666667 t \N \N \N {} -5120 1569 2025-03-04 23:12:31.354705-08 2025-03-04 21:49:58.854631-08 test t A100 \N t \N \N \N {} -5121 1569 2025-03-04 23:09:51.663868-08 2025-03-04 21:37:24.887769-08 benchmark t A100 \N t \N \N \N {} -5122 1569 2025-03-04 22:02:16.559653-08 2025-03-04 22:32:25.782751-08 leaderboard t A100 0.001608786 t \N \N \N {} -5123 1569 2025-03-04 22:05:47.583362-08 2025-03-04 21:33:49.87689-08 test t L4 \N t \N \N \N {} -5124 1569 2025-03-04 21:35:17.104808-08 2025-03-04 22:09:20.389921-08 benchmark t L4 \N t \N \N \N {} -5125 1569 2025-03-04 22:38:25.160967-08 2025-03-04 21:26:50.027334-08 leaderboard t L4 0.009029581666666666 t \N \N \N {} -5126 1569 2025-03-04 22:29:13.641854-08 2025-03-04 23:15:17.197089-08 test f H100 \N t \N \N \N {} -5127 1569 2025-03-04 22:01:12.379517-08 2025-03-04 23:10:54.429836-08 benchmark f H100 \N t \N \N \N {} -5128 1569 2025-03-04 23:11:31.285632-08 2025-03-04 21:42:40.185619-08 leaderboard f H100 0.0010705946666666668 t \N \N \N {} -5129 1569 2025-03-04 21:57:38.963122-08 2025-03-04 21:31:36.054672-08 test t H100 \N t \N \N \N {} -5130 1569 2025-03-04 21:27:09.873699-08 2025-03-04 22:20:40.268062-08 benchmark t H100 \N t \N \N \N {} -6778 2099 2025-03-15 03:30:57.66264-07 2025-03-15 04:43:08.964646-07 test f A100 \N t \N \N \N {} -5134 1569 2025-03-04 22:04:14.888682-08 2025-03-04 22:46:38.905995-08 leaderboard f T4 0.014848202 t \N \N \N {} -5135 1569 2025-03-04 22:03:01.49793-08 2025-03-04 22:36:42.090475-08 test t T4 \N t \N \N \N {} -5136 1569 2025-03-04 22:08:23.328887-08 2025-03-04 22:16:18.514342-08 benchmark t T4 \N t \N \N \N {} -5137 1569 2025-03-04 23:14:20.184391-08 2025-03-04 22:39:50.12814-08 leaderboard t T4 0.014794430666666665 t \N \N \N {} -5141 1570 2025-03-04 23:13:42.914895-08 2025-03-04 21:59:06.843057-08 test f A100 \N t \N \N \N {} -5142 1570 2025-03-04 21:36:21.746898-08 2025-03-04 22:16:20.82538-08 benchmark f A100 \N t \N \N \N {} -5143 1570 2025-03-04 22:33:56.277934-08 2025-03-04 22:10:31.797238-08 leaderboard f A100 0.0017140163333333332 t \N \N \N {} -5144 1570 2025-03-04 23:21:19.677221-08 2025-03-04 22:48:43.348393-08 test t A100 \N t \N \N \N {} -5145 1570 2025-03-04 23:16:49.330286-08 2025-03-04 22:14:55.550056-08 benchmark t A100 \N t \N \N \N {} -5146 1570 2025-03-04 22:23:09.667416-08 2025-03-04 21:43:24.622016-08 leaderboard t A100 0.0017186463333333333 t \N \N \N {} -5147 1570 2025-03-04 23:10:37.127588-08 2025-03-04 22:50:15.83577-08 test t H100 \N t \N \N \N {} -5148 1570 2025-03-04 22:15:05.089919-08 2025-03-04 21:59:44.284611-08 benchmark t H100 \N t \N \N \N {} -5149 1570 2025-03-04 22:40:39.695868-08 2025-03-04 22:51:12.192064-08 leaderboard t H100 0.0010933593333333333 t \N \N \N {} -5150 1570 2025-03-04 22:06:34.697175-08 2025-03-04 22:22:03.050409-08 test f H100 \N t \N \N \N {} -5151 1570 2025-03-04 23:14:32.375663-08 2025-03-04 23:16:06.022274-08 benchmark f H100 \N t \N \N \N {} -6781 2099 2025-03-15 04:17:18.910592-07 2025-03-15 04:27:25.440486-07 test t A100 \N t \N \N \N {} -5154 1571 2025-03-04 21:29:49.913428-08 2025-03-04 22:56:49.726281-08 benchmark t A100 \N t \N \N \N {} -5155 1571 2025-03-04 22:09:17.380828-08 2025-03-04 23:17:01.528387-08 leaderboard t A100 0.00149756 t \N \N \N {} -5156 1571 2025-03-04 22:42:35.691391-08 2025-03-04 22:30:28.371632-08 test f A100 \N t \N \N \N {} -1953 666 2025-02-25 13:11:27.663789-08 2025-02-25 12:56:49.679792-08 benchmark f T4 \N t \N \N \N {} -1954 666 2025-02-25 14:01:28.591074-08 2025-02-25 12:36:59.206687-08 leaderboard f T4 0.03533333425 t \N \N \N {} -1955 666 2025-02-25 13:24:34.96066-08 2025-02-25 12:23:48.496925-08 test t T4 \N t \N \N \N {} -1956 666 2025-02-25 13:11:21.334723-08 2025-02-25 13:18:01.451228-08 benchmark t T4 \N t \N \N \N {} -1957 666 2025-02-25 14:11:36.256788-08 2025-02-25 12:59:40.696196-08 leaderboard t T4 0.035418843285714284 t \N \N \N {} -1958 667 2025-02-25 13:33:38.816072-08 2025-02-25 13:27:18.187004-08 benchmark f A100 \N t \N \N \N {} -1975 679 2025-02-25 13:39:40.730805-08 2025-02-25 12:47:36.780104-08 leaderboard f T4 0.053832773 t \N \N \N {} -5170 1574 2025-03-04 22:47:12.679139-08 2025-03-04 22:51:50.789451-08 test t H100 \N f \N \N \N {} -5177 1575 2025-03-04 23:31:30.176049-08 2025-03-04 23:39:24.402655-08 benchmark t H100 \N t \N \N \N {} -5175 1575 2025-03-04 23:09:05.999145-08 2025-03-04 23:03:45.299121-08 leaderboard f H100 0.0010994093333333334 t \N \N \N {} -5176 1575 2025-03-04 21:58:21.866372-08 2025-03-04 23:01:50.540448-08 test t H100 \N t \N \N \N {} -5197 1589 2025-03-05 04:48:37.908985-08 2025-03-05 05:42:21.123456-08 test f H100 \N f \N \N \N {} -5178 1575 2025-03-04 23:49:34.473234-08 2025-03-04 22:47:13.576304-08 leaderboard t H100 0.001105298 t \N \N \N {} -5179 1576 2025-03-05 05:05:54.983075-08 2025-03-05 04:15:53.893439-08 benchmark f H100 \N t \N \N \N {} -1969 674 2025-02-25 14:18:09.782068-08 2025-02-25 12:29:36.512538-08 benchmark f A100 \N t \N \N \N {} -1971 675 2025-02-25 13:57:33.707833-08 2025-02-25 14:11:11.249992-08 benchmark f A100 \N t \N \N \N {} -1973 679 2025-02-25 13:04:53.521829-08 2025-02-25 14:18:59.793772-08 test f T4 \N t \N \N \N {} -1974 679 2025-02-25 14:13:13.965846-08 2025-02-25 13:57:26.833503-08 benchmark f T4 \N t \N \N \N {} -5180 1577 2025-03-05 03:52:35.002755-08 2025-03-05 04:21:45.713683-08 benchmark f H100 \N t \N \N \N {} -5181 1578 2025-03-05 05:17:31.803633-08 2025-03-05 05:50:42.297338-08 test t H100 \N t \N \N \N {} -5182 1578 2025-03-05 04:56:30.788431-08 2025-03-05 05:45:31.103522-08 benchmark t H100 \N t \N \N \N {} -5183 1578 2025-03-05 05:01:35.150294-08 2025-03-05 05:14:55.594706-08 leaderboard t H100 0.0014070166666666668 t \N \N \N {} -5184 1578 2025-03-05 05:28:07.773821-08 2025-03-05 05:16:37.68584-08 test f H100 \N t \N \N \N {} -5233 1611 2025-03-06 07:25:53.030783-08 2025-03-06 07:55:02.790842-08 test f T4 \N f \N \N \N {} -5190 1582 2025-03-05 04:44:16.876495-08 2025-03-05 04:31:50.278622-08 test f H100 \N f \N \N \N {} -5199 1591 2025-03-05 05:58:19.154295-08 2025-03-05 05:51:19.562708-08 test f H100 \N f \N \N \N {} -5200 1592 2025-03-05 06:51:32.869767-08 2025-03-05 07:03:41.586486-08 test f A100 \N f \N \N \N {} -5201 1593 2025-03-05 11:53:54.672796-08 2025-03-05 10:22:24.681764-08 test f T4 \N f \N \N \N {} -5202 1593 2025-03-05 11:34:29.199094-08 2025-03-05 11:32:56.415861-08 test f H100 \N f \N \N \N {} -5203 1594 2025-03-05 10:15:31.539292-08 2025-03-05 11:41:36.518701-08 test f T4 \N f \N \N \N {} -5204 1595 2025-03-05 11:50:17.097474-08 2025-03-05 10:50:25.088498-08 test f T4 \N f \N \N \N {} -5205 1596 2025-03-05 10:17:15.840178-08 2025-03-05 11:21:12.907749-08 test f T4 \N t \N \N \N {} -5206 1597 2025-03-05 10:38:59.781202-08 2025-03-05 11:53:31.710828-08 benchmark f T4 \N t \N \N \N {} -5207 1598 2025-03-05 15:43:10.18498-08 2025-03-05 15:44:26.256063-08 test f T4 \N f \N \N \N {} -5208 1599 2025-03-05 16:08:47.877222-08 2025-03-05 16:17:45.165244-08 test f T4 \N f \N \N \N {} -5215 1605 2025-03-05 22:28:57.064698-08 2025-03-05 22:38:29.672952-08 test t A100 \N t \N \N \N {} -5216 1605 2025-03-06 00:10:40.145439-08 2025-03-05 23:04:41.009576-08 benchmark t A100 \N t \N \N \N {} -5217 1605 2025-03-06 00:13:35.847747-08 2025-03-05 23:40:14.544266-08 leaderboard t A100 0.0022575486666666666 t \N \N \N {} -5244 1613 2025-03-06 07:19:13.628319-08 2025-03-06 06:35:37.599294-08 test f A100 \N t \N \N \N {} -5245 1613 2025-03-06 06:54:50.780479-08 2025-03-06 06:45:07.226442-08 benchmark f A100 \N t \N \N \N {} -5246 1613 2025-03-06 07:28:04.913981-08 2025-03-06 08:10:28.487649-08 leaderboard f A100 0.0025635133333333335 t \N \N \N {} -5276 1614 2025-03-06 07:19:59.675744-08 2025-03-06 07:43:31.134657-08 leaderboard f H100 0.00140798875 t \N \N \N {} -5277 1614 2025-03-06 08:41:38.090423-08 2025-03-06 08:37:03.818704-08 test t H100 \N t \N \N \N {} -5278 1614 2025-03-06 08:18:49.218286-08 2025-03-06 08:16:42.816157-08 benchmark t H100 \N t \N \N \N {} -5279 1614 2025-03-06 07:03:16.465007-08 2025-03-06 06:46:29.690376-08 leaderboard t H100 0.0014296095 t \N \N \N {} -5280 1615 2025-03-06 08:22:03.432718-08 2025-03-06 08:24:28.297651-08 test f A100 \N f \N \N \N {} -5281 1617 2025-03-06 12:18:02.879373-08 2025-03-06 11:00:55.592702-08 test f L4 \N f \N \N \N {} -5286 1616 2025-03-06 12:36:10.700755-08 2025-03-06 11:50:29.347443-08 test t T4 \N t \N \N \N {} -5287 1616 2025-03-06 12:25:41.61897-08 2025-03-06 11:16:54.998394-08 benchmark t T4 \N t \N \N \N {} -5312 1620 2025-03-06 12:33:59.587669-08 2025-03-06 11:40:59.729965-08 test f L4 \N t \N \N \N {} -5313 1620 2025-03-06 10:43:35.438642-08 2025-03-06 12:38:29.763377-08 benchmark f L4 \N t \N \N \N {} -5314 1620 2025-03-06 12:06:29.829934-08 2025-03-06 12:34:56.969836-08 leaderboard f L4 0.017135117666666668 t \N \N \N {} -1977 679 2025-02-25 12:51:05.905455-08 2025-02-25 12:53:16.176109-08 benchmark t T4 \N t \N \N \N {} -1978 679 2025-02-25 13:37:32.257654-08 2025-02-25 13:03:09.630302-08 leaderboard t T4 0.05386631333333334 t \N \N \N {} -3812 1283 2025-03-01 02:43:07.132486-08 2025-03-01 03:36:19.063234-08 test f A100 \N t \N \N \N {} -5584 1707 2025-03-09 09:22:46.945115-07 2025-03-09 08:37:58.159049-07 test t T4 \N t \N \N \N {} -5550 1693 2025-03-08 16:00:57.23753-08 2025-03-08 17:10:45.887607-08 test f H100 \N f \N \N \N {} -5551 1694 2025-03-08 17:58:29.601266-08 2025-03-08 17:28:23.434527-08 test t H100 \N f \N \N \N {} -5552 1694 2025-03-08 16:17:00.585648-08 2025-03-08 17:26:10.548997-08 test f H100 \N f \N \N \N {} -5553 1695 2025-03-08 17:31:35.366001-08 2025-03-08 16:43:40.087454-08 test t H100 \N f \N \N \N {} -5568 1702 2025-03-08 17:12:40.242062-08 2025-03-08 18:24:17.952735-08 test f H100 \N f \N \N \N {} -5582 1707 2025-03-09 08:41:44.387525-07 2025-03-09 09:18:33.589319-07 benchmark f T4 \N t \N \N \N {} -5557 1697 2025-03-08 18:09:07.446602-08 2025-03-08 16:49:50.71993-08 test f H100 \N f \N \N \N {} -5558 1697 2025-03-08 16:45:59.890505-08 2025-03-08 17:49:20.795913-08 test t H100 \N f \N \N \N {} -5559 1698 2025-03-08 16:44:33.058529-08 2025-03-08 16:55:51.404262-08 test t H100 \N f \N \N \N {} -5560 1698 2025-03-08 17:45:21.683334-08 2025-03-08 17:40:25.662216-08 test f H100 \N f \N \N \N {} -5561 1699 2025-03-08 17:23:07.204592-08 2025-03-08 16:29:40.8521-08 test f H100 \N f \N \N \N {} -5562 1699 2025-03-08 17:14:04.744552-08 2025-03-08 16:45:33.633138-08 test t H100 \N f \N \N \N {} -5565 1701 2025-03-08 17:19:00.706175-08 2025-03-08 16:50:25.370782-08 test t H100 \N f \N \N \N {} -5566 1701 2025-03-08 18:00:39.905743-08 2025-03-08 18:04:31.681329-08 test f H100 \N f \N \N \N {} -5583 1707 2025-03-09 10:07:02.952329-07 2025-03-09 08:33:06.895971-07 leaderboard f T4 0.016582775333333334 t \N \N \N {} -5569 1703 2025-03-08 17:21:28.286711-08 2025-03-08 17:59:25.372338-08 test f H100 \N f \N \N \N {} -5570 1703 2025-03-08 18:05:15.430458-08 2025-03-08 17:19:42.190919-08 test t H100 \N f \N \N \N {} -5571 1704 2025-03-09 09:07:05.945234-07 2025-03-09 09:40:55.738939-07 test f T4 \N f \N \N \N {} -5572 1704 2025-03-09 08:38:56.049572-07 2025-03-09 09:19:08.106745-07 test t T4 \N f \N \N \N {} -5573 1705 2025-03-09 09:08:47.834128-07 2025-03-09 08:44:55.26167-07 test t T4 \N f \N \N \N {} -6852 2125 2025-03-15 10:54:23.765567-07 2025-03-15 10:56:50.760245-07 test t A100 \N t \N \N \N {} -5574 1705 2025-03-09 08:19:09.822926-07 2025-03-09 08:17:05.627722-07 test f T4 \N f \N \N \N {} -5575 1706 2025-03-09 08:15:37.716285-07 2025-03-09 09:07:48.253727-07 test t T4 \N t \N \N \N {} -5576 1706 2025-03-09 09:03:21.820355-07 2025-03-09 09:23:13.212892-07 benchmark t T4 \N t \N \N \N {} -5577 1706 2025-03-09 09:24:37.224862-07 2025-03-09 09:06:00.619832-07 leaderboard t T4 0.017249751 t \N \N \N {} -5578 1706 2025-03-09 09:20:25.180918-07 2025-03-09 08:18:24.424228-07 test f T4 \N t \N \N \N {} -5579 1706 2025-03-09 08:21:53.936645-07 2025-03-09 08:29:28.573712-07 benchmark f T4 \N t \N \N \N {} -5580 1706 2025-03-09 08:30:06.61725-07 2025-03-09 09:22:13.972549-07 leaderboard f T4 0.017213831 t \N \N \N {} -6855 2126 2025-03-15 10:01:43.438198-07 2025-03-15 09:26:54.425537-07 test f A100 \N t \N \N \N {} -5586 1707 2025-03-09 08:33:05.304746-07 2025-03-09 08:45:18.547459-07 leaderboard t T4 0.016620549 t \N \N \N {} -2800 976 2025-02-26 21:36:15.417887-08 2025-02-26 21:39:28.329229-08 leaderboard f A100 0.0031626593333333335 t \N \N \N {} -5634 1718 2025-03-09 09:13:24.672764-07 2025-03-09 10:18:48.792588-07 test f T4 \N t \N \N \N {} -5635 1718 2025-03-09 10:16:44.041935-07 2025-03-09 09:08:48.252549-07 benchmark f T4 \N t \N \N \N {} -5641 1719 2025-03-09 10:29:41.062663-07 2025-03-09 10:42:23.185635-07 benchmark t T4 \N t \N \N \N {} -5642 1719 2025-03-09 10:03:38.725028-07 2025-03-09 10:55:41.903081-07 leaderboard t T4 0.01683973533333333 t \N \N \N {} -5643 1720 2025-03-09 09:57:20.289686-07 2025-03-09 10:34:13.152652-07 test f T4 \N t \N \N \N {} -5644 1720 2025-03-09 09:16:46.353693-07 2025-03-09 10:33:41.020996-07 benchmark f T4 \N t \N \N \N {} -5650 1721 2025-03-09 10:43:52.72403-07 2025-03-09 09:09:04.6489-07 benchmark t T4 \N t \N \N \N {} -5651 1721 2025-03-09 10:26:40.021059-07 2025-03-09 10:38:45.997727-07 leaderboard t T4 0.016620553333333333 t \N \N \N {} -5653 1722 2025-03-09 09:41:53.232041-07 2025-03-09 10:03:19.288174-07 benchmark t T4 \N t \N \N \N {} -5659 1723 2025-03-09 10:23:53.251832-07 2025-03-09 10:13:49.143494-07 benchmark f T4 \N t \N \N \N {} -2801 976 2025-02-26 21:49:50.078075-08 2025-02-26 21:02:25.183977-08 test t A100 \N t \N \N \N {} -6096 1844 2025-03-10 22:34:55.225767-07 2025-03-10 23:16:53.927941-07 leaderboard t A100 0.02345483125 t \N \N \N {} -6097 1845 2025-03-10 22:27:27.260452-07 2025-03-10 23:10:29.127667-07 test f H100 \N t \N \N \N {} -6098 1845 2025-03-10 22:33:31.470385-07 2025-03-10 23:33:08.346801-07 benchmark f H100 \N t \N \N \N {} -6099 1845 2025-03-10 23:19:50.390365-07 2025-03-10 23:55:52.423957-07 leaderboard f H100 0.007574126333333333 t \N \N \N {} -6100 1845 2025-03-10 23:23:59.756697-07 2025-03-10 22:36:01.810723-07 test t H100 \N t \N \N \N {} -6103 1846 2025-03-10 23:34:33.401447-07 2025-03-10 22:38:46.785595-07 test t L4 \N t \N \N \N {} -6104 1846 2025-03-10 22:40:13.180982-07 2025-03-10 22:03:25.587196-07 benchmark t L4 \N t \N \N \N {} -6105 1846 2025-03-10 22:07:31.471617-07 2025-03-10 22:36:23.340517-07 leaderboard t L4 0.289734965 t \N \N \N {} -6106 1846 2025-03-10 22:19:05.931425-07 2025-03-10 22:31:11.627074-07 test f L4 \N t \N \N \N {} -225 37 2025-02-23 10:59:25.499888-08 2025-02-23 11:56:22.371923-08 test f T4 \N t \N \N \N {} -332 80 2025-02-23 15:08:05.190515-08 2025-02-23 14:44:23.958644-08 test f H100 \N t \N \N \N {} -203 36 2025-02-23 11:46:05.335042-08 2025-02-23 12:11:22.163693-08 benchmark f H100 \N t \N \N \N {} -358 94 2025-02-23 18:49:28.760988-08 2025-02-23 19:12:31.385313-08 leaderboard f H100 0.012098538666666665 t \N \N \N {} -359 95 2025-02-23 17:48:36.820226-08 2025-02-23 19:17:34.067307-08 test f H100 \N t \N \N \N {} -1206 374 2025-02-24 20:28:11.986011-08 2025-02-24 19:36:30.92237-08 benchmark f A100 \N f \N \N \N {} -1207 375 2025-02-24 20:12:22.417421-08 2025-02-24 20:46:01.292845-08 benchmark f A100 \N f \N \N \N {} -1414 471 2025-02-25 03:42:52.544828-08 2025-02-25 04:20:26.74565-08 test f A100 \N t \N \N \N {} -1208 376 2025-02-24 19:48:12.808077-08 2025-02-24 20:45:57.879274-08 benchmark f A100 \N t \N \N \N {} -1209 377 2025-02-24 21:24:44.702336-08 2025-02-24 20:39:40.644905-08 benchmark f A100 \N f \N \N \N {} -1210 378 2025-02-24 21:34:59.47251-08 2025-02-24 21:22:02.439541-08 benchmark f A100 \N t \N \N \N {} -1211 379 2025-02-24 21:22:12.927474-08 2025-02-24 19:58:49.367481-08 benchmark f A100 \N t \N \N \N {} -1243 404 2025-02-24 22:06:51.766358-08 2025-02-24 22:05:43.599472-08 benchmark f A100 \N t \N \N \N {} -1216 384 2025-02-24 21:02:40.893555-08 2025-02-24 20:57:25.702865-08 benchmark f A100 \N f \N \N \N {} -1229 393 2025-02-24 20:52:22.785486-08 2025-02-24 21:45:46.13494-08 leaderboard t A100 0.0033359293333333337 t \N \N \N {} -1230 393 2025-02-24 21:16:21.658637-08 2025-02-24 20:25:01.054235-08 test f A100 \N t \N \N \N {} -1231 393 2025-02-24 22:03:36.537687-08 2025-02-24 21:08:30.465614-08 benchmark f A100 \N t \N \N \N {} -1240 401 2025-02-24 21:15:55.733324-08 2025-02-24 21:57:58.972958-08 test f A100 \N f \N \N \N {} -1241 402 2025-02-24 20:44:20.914092-08 2025-02-24 21:56:00.068563-08 test f A100 \N f \N \N \N {} -1248 409 2025-02-24 22:06:04.453015-08 2025-02-24 22:17:03.583941-08 test f A100 \N f \N \N \N {} -1417 471 2025-02-25 03:51:19.78766-08 2025-02-25 03:47:58.900665-08 test t A100 \N t \N \N \N {} -1249 410 2025-02-24 21:39:05.838922-08 2025-02-24 20:58:53.013953-08 test f A100 \N f \N \N \N {} -1250 411 2025-02-24 22:40:19.562086-08 2025-02-24 21:27:25.950857-08 test f A100 \N t \N \N \N {} -1404 469 2025-02-25 03:53:45.5055-08 2025-02-25 04:07:42.563768-08 leaderboard f A100 0.003393497 t \N \N \N {} -2922 1024 2025-02-27 04:31:40.23618-08 2025-02-27 02:56:46.161479-08 test f T4 \N t \N \N \N {} -2923 1024 2025-02-27 02:49:24.009049-08 2025-02-27 03:11:16.56453-08 benchmark f T4 \N t \N \N \N {} -2924 1024 2025-02-27 04:11:37.18876-08 2025-02-27 04:34:12.87404-08 leaderboard f T4 0.00039354559999999996 t \N \N \N {} -3121 1110 2025-02-28 03:11:43.352425-08 2025-02-28 01:42:01.408142-08 test f A100 \N t \N \N \N {} -3466 1192 2025-02-28 10:34:17.327676-08 2025-02-28 12:02:48.511506-08 test t A100 \N f \N \N \N {} -2926 1024 2025-02-27 03:07:44.576399-08 2025-02-27 04:23:59.379675-08 benchmark t T4 \N t \N \N \N {} -2927 1024 2025-02-27 03:35:18.806103-08 2025-02-27 04:01:32.334755-08 leaderboard t T4 0.000564142375 t \N \N \N {} -3124 1111 2025-02-28 03:22:39.920088-08 2025-02-28 03:21:57.983095-08 test f A100 \N t \N \N \N {} -3174 1119 2025-02-28 03:45:25.024721-08 2025-02-28 03:11:06.974641-08 leaderboard f A100 0.0031492663333333335 t \N \N \N {} -1409 470 2025-02-25 03:16:55.220474-08 2025-02-25 04:31:56.202607-08 benchmark t A100 \N t \N \N \N {} -1410 470 2025-02-25 04:11:58.013793-08 2025-02-25 03:21:32.360753-08 leaderboard t A100 0.0030943103333333334 t \N \N \N {} -2928 1025 2025-02-27 04:12:51.460546-08 2025-02-27 02:49:12.29781-08 test f L4 \N t \N \N \N {} -2929 1025 2025-02-27 03:26:34.775483-08 2025-02-27 04:12:58.357761-08 benchmark f L4 \N t \N \N \N {} -2930 1025 2025-02-27 03:19:52.147083-08 2025-02-27 03:27:21.649998-08 leaderboard f L4 0.00011601083783783784 t \N \N \N {} -3047 1093 2025-02-27 15:53:15.961217-08 2025-02-27 15:41:20.80553-08 benchmark t A100 \N t \N \N \N {} -3048 1093 2025-02-27 15:45:02.22711-08 2025-02-27 15:31:35.36741-08 leaderboard t A100 0.0031404533333333337 t \N \N \N {} -1433 475 2025-02-25 04:29:39.664763-08 2025-02-25 05:08:55.732423-08 test f A100 \N f \N \N \N {} -1436 477 2025-02-25 05:11:23.154044-08 2025-02-25 03:41:03.100879-08 test f A100 \N f \N \N \N {} -1437 478 2025-02-25 05:37:15.695274-08 2025-02-25 03:55:07.870743-08 test f A100 \N f \N \N \N {} -1441 481 2025-02-25 04:15:28.477993-08 2025-02-25 05:06:41.311478-08 test f A100 \N f \N \N \N {} -1442 481 2025-02-25 05:43:19.547838-08 2025-02-25 05:02:59.725963-08 test t A100 \N f \N \N \N {} -2941 1030 2025-02-27 04:33:34.388412-08 2025-02-27 03:16:04.017304-08 test t A100 \N t \N \N \N {} -3145 1114 2025-02-28 02:20:48.546337-08 2025-02-28 03:25:08.187009-08 test f A100 \N t \N \N \N {} -3002 1069 2025-02-27 11:57:26.315579-08 2025-02-27 12:26:49.008997-08 test f A100 \N f \N \N \N {} -3052 1094 2025-02-27 17:23:36.313978-08 2025-02-27 17:02:03.79401-08 test f A100 \N t \N \N \N {} -3004 1071 2025-02-27 12:13:32.872228-08 2025-02-27 13:48:22.621332-08 test f A100 \N f \N \N \N {} -3055 1094 2025-02-27 15:46:34.78066-08 2025-02-27 17:32:46.992293-08 test t A100 \N t \N \N \N {} -2951 1038 2025-02-27 08:08:02.949594-08 2025-02-27 07:45:52.672741-08 test f A100 \N t \N \N \N {} -1539 512 2025-02-25 07:42:24.70614-08 2025-02-25 07:55:31.991399-08 leaderboard t H100 0.0014827313333333333 t \N \N \N {} -1540 512 2025-02-25 06:33:27.810792-08 2025-02-25 07:40:47.978218-08 test f H100 \N t \N \N \N {} -1541 512 2025-02-25 07:57:36.75449-08 2025-02-25 07:43:37.029383-08 benchmark f H100 \N t \N \N \N {} -1542 512 2025-02-25 06:29:41.231954-08 2025-02-25 06:14:39.960451-08 leaderboard f H100 0.0014742606666666668 t \N \N \N {} -1543 512 2025-02-25 07:38:34.722488-08 2025-02-25 07:23:26.785114-08 test f T4 \N t \N \N \N {} -1544 512 2025-02-25 06:25:49.430545-08 2025-02-25 07:43:59.473865-08 benchmark f T4 \N t \N \N \N {} -1545 512 2025-02-25 07:44:49.156876-08 2025-02-25 07:09:53.612336-08 leaderboard f T4 0.021663530142857143 t \N \N \N {} -1546 512 2025-02-25 06:35:54.036675-08 2025-02-25 07:10:22.696453-08 test t T4 \N t \N \N \N {} -1561 514 2025-02-25 08:21:20.423985-08 2025-02-25 07:53:25.914837-08 test f A100 \N t \N \N \N {} -1564 514 2025-02-25 06:26:23.245478-08 2025-02-25 07:52:14.438492-08 test t A100 \N t \N \N \N {} -1551 512 2025-02-25 07:08:46.613751-08 2025-02-25 06:42:56.111896-08 leaderboard f L4 0.017496731666666668 t \N \N \N {} -1553 512 2025-02-25 06:58:42.387657-08 2025-02-25 08:00:31.070279-08 benchmark t L4 \N t \N \N \N {} -1554 512 2025-02-25 07:24:12.688624-08 2025-02-25 06:31:02.770404-08 leaderboard t L4 0.017451008666666667 t \N \N \N {} -3231 1130 2025-02-28 05:20:47.157439-08 2025-02-28 06:11:32.587664-08 leaderboard t A100 0.0031105466666666664 t \N \N \N {} -3091 1105 2025-02-28 02:11:54.38507-08 2025-02-28 02:14:17.819311-08 test f A100 \N t \N \N \N {} -3250 1137 2025-02-28 07:15:32.403653-08 2025-02-28 05:44:54.894859-08 test f A100 \N t \N \N \N {} -1567 515 2025-02-25 06:40:37.056239-08 2025-02-25 08:18:23.071234-08 test f A100 \N t \N \N \N {} -1568 515 2025-02-25 07:51:44.723769-08 2025-02-25 06:52:35.487372-08 benchmark f A100 \N t \N \N \N {} -1569 515 2025-02-25 08:12:57.053326-08 2025-02-25 07:23:24.825804-08 leaderboard f A100 0.00322647325 t \N \N \N {} -1570 515 2025-02-25 07:59:41.537278-08 2025-02-25 08:18:25.884866-08 test t A100 \N t \N \N \N {} -1571 515 2025-02-25 07:33:12.913858-08 2025-02-25 07:10:10.306586-08 benchmark t A100 \N t \N \N \N {} -1572 515 2025-02-25 07:41:19.245397-08 2025-02-25 07:29:16.962881-08 leaderboard t A100 0.003220757 t \N \N \N {} -1575 516 2025-02-25 07:41:18.473174-08 2025-02-25 07:29:29.868748-08 leaderboard t A100 0.0032304103333333336 t \N \N \N {} -1738 583 2025-02-25 09:31:47.936241-08 2025-02-25 09:08:32.636598-08 test f H100 \N t \N \N \N {} -1748 587 2025-02-25 10:24:59.666061-08 2025-02-25 09:27:46.691352-08 test t T4 \N f \N \N \N {} -1749 588 2025-02-25 08:59:07.160327-08 2025-02-25 08:38:22.92218-08 benchmark f H100 \N f \N \N \N {} -4803 1487 2025-03-02 12:55:09.203143-08 2025-03-02 13:27:27.333426-08 benchmark t A100 \N f \N \N \N {} -1756 594 2025-02-25 09:11:38.300189-08 2025-02-25 10:28:52.150779-08 benchmark t T4 \N t \N \N \N {} -1757 594 2025-02-25 08:46:53.338218-08 2025-02-25 09:57:23.906953-08 leaderboard t T4 0.017254103 t \N \N \N {} -1758 594 2025-02-25 08:57:48.338091-08 2025-02-25 09:10:25.874419-08 test f T4 \N t \N \N \N {} -1759 594 2025-02-25 09:38:33.456671-08 2025-02-25 09:43:17.759144-08 benchmark f T4 \N t \N \N \N {} -1760 594 2025-02-25 09:40:53.716064-08 2025-02-25 10:08:25.184778-08 leaderboard f T4 0.017332067125 t \N \N \N {} -4804 1487 2025-03-02 12:53:07.905973-08 2025-03-02 13:20:31.048316-08 test t T4 \N t \N \N \N {} -1783 612 2025-02-25 10:30:16.608473-08 2025-02-25 10:08:59.631399-08 benchmark f A100 \N t \N \N \N {} -2280 767 2025-02-26 03:31:52.024635-08 2025-02-26 03:22:12.199287-08 leaderboard f A100 0.0008225438000000001 t \N \N \N {} -2281 767 2025-02-26 03:40:30.007645-08 2025-02-26 04:29:52.334337-08 test t A100 \N t \N \N \N {} -2300 774 2025-02-26 04:14:40.857031-08 2025-02-26 04:33:29.483727-08 test f A100 \N t \N \N \N {} -3588 1229 2025-02-28 14:32:08.489693-08 2025-02-28 14:05:23.563906-08 benchmark f H100 \N t \N \N \N {} -2282 767 2025-02-26 03:24:59.166944-08 2025-02-26 04:45:17.998433-08 benchmark t A100 \N t \N \N \N {} -2284 768 2025-02-26 04:18:53.859554-08 2025-02-26 03:24:39.46805-08 benchmark f A100 \N t \N \N \N {} -3283 1147 2025-02-28 06:11:40.284397-08 2025-02-28 07:19:05.74238-08 benchmark f H100 \N t \N \N \N {} -2483 870 2025-02-26 13:16:44.041088-08 2025-02-26 13:47:23.050449-08 benchmark f A100 \N t \N \N \N {} -2484 870 2025-02-26 13:10:26.731301-08 2025-02-26 14:42:14.746386-08 leaderboard f A100 0.0035295413333333333 t \N \N \N {} -2485 870 2025-02-26 13:33:50.524318-08 2025-02-26 14:10:40.249698-08 test f T4 \N t \N \N \N {} -2486 870 2025-02-26 13:05:54.760424-08 2025-02-26 13:38:28.475426-08 benchmark f T4 \N t \N \N \N {} -2499 869 2025-02-26 13:54:53.311947-08 2025-02-26 13:58:02.187272-08 leaderboard t H100 0.002322158 t \N \N \N {} -3392 1168 2025-02-28 09:12:51.277351-08 2025-02-28 09:23:36.312793-08 test f L4 \N f \N \N \N {} -2510 872 2025-02-26 13:10:41.998428-08 2025-02-26 13:52:20.643803-08 test f H100 \N t \N \N \N {} -2505 872 2025-02-26 14:18:21.923745-08 2025-02-26 14:06:00.875303-08 benchmark t H100 \N t \N \N \N {} -2506 872 2025-02-26 13:16:25.959445-08 2025-02-26 14:06:50.761927-08 leaderboard t H100 0.0014855651428571429 t \N \N \N {} -2507 872 2025-02-26 14:54:25.186275-08 2025-02-26 13:13:47.344332-08 test f A100 \N t \N \N \N {} -2593 884 2025-02-26 14:14:00.181524-08 2025-02-26 13:46:27.570181-08 benchmark f L4 \N t \N \N \N {} -2594 884 2025-02-26 13:32:06.001069-08 2025-02-26 15:05:08.151616-08 leaderboard f L4 0.017391052333333334 t \N \N \N {} -2595 884 2025-02-26 15:04:28.988097-08 2025-02-26 15:18:03.931934-08 test t L4 \N t \N \N \N {} -3420 1179 2025-02-28 10:40:09.83029-08 2025-02-28 10:08:07.480948-08 test f L4 \N f \N \N \N {} -2596 884 2025-02-26 14:56:24.039776-08 2025-02-26 14:30:30.751724-08 benchmark t L4 \N t \N \N \N {} -2597 884 2025-02-26 14:35:42.960818-08 2025-02-26 15:14:14.400532-08 leaderboard t L4 0.017414738333333332 t \N \N \N {} -4203 1388 2025-03-01 13:04:56.163242-08 2025-03-01 14:12:53.003168-08 benchmark t A100 \N t \N \N \N {} -4136 1353 2025-03-01 05:57:54.388305-08 2025-03-01 05:50:17.87171-08 test f A100 \N t \N \N \N {} -4137 1353 2025-03-01 05:16:52.584167-08 2025-03-01 06:12:22.385227-08 benchmark f A100 \N t \N \N \N {} -4138 1353 2025-03-01 05:33:09.219491-08 2025-03-01 06:16:54.189813-08 leaderboard f A100 0.003070895 t \N \N \N {} -4139 1354 2025-03-01 06:20:09.60044-08 2025-03-01 05:18:22.96192-08 test f A100 \N t \N \N \N {} -4140 1354 2025-03-01 05:58:29.731003-08 2025-03-01 05:41:56.193045-08 benchmark f A100 \N t \N \N \N {} -4218 1394 2025-03-01 15:09:41.391316-08 2025-03-01 15:57:05.307002-08 leaderboard f A100 0.003101874 t \N \N \N {} -4695 1470 2025-03-02 07:38:44.249494-08 2025-03-02 07:11:45.382141-08 test f A100 \N t \N \N \N {} -4219 1394 2025-03-01 14:45:22.969713-08 2025-03-01 14:48:00.593278-08 test f H100 \N t \N \N \N {} -4220 1394 2025-03-01 15:38:36.009293-08 2025-03-01 16:26:03.890826-08 benchmark f H100 \N t \N \N \N {} -4221 1394 2025-03-01 15:14:09.768318-08 2025-03-01 14:35:45.346475-08 leaderboard f H100 0.00139569175 t \N \N \N {} -4222 1394 2025-03-01 14:47:10.848925-08 2025-03-01 16:25:02.786375-08 test t A100 \N t \N \N \N {} -4430 1443 2025-03-01 23:16:02.466485-08 2025-03-01 23:58:09.823321-08 benchmark f T4 \N t \N \N \N {} -4431 1443 2025-03-01 23:17:08.006949-08 2025-03-02 00:41:05.844634-08 leaderboard f T4 0.009837714666666665 t \N \N \N {} -4432 1443 2025-03-02 00:29:59.441844-08 2025-03-02 00:59:33.051229-08 test t T4 \N t \N \N \N {} -4433 1443 2025-03-02 00:28:05.820758-08 2025-03-01 23:33:08.733358-08 benchmark t T4 \N t \N \N \N {} -4434 1443 2025-03-01 23:22:06.634593-08 2025-03-01 23:36:43.202641-08 leaderboard t T4 0.009814015333333334 t \N \N \N {} -4435 1444 2025-03-02 00:23:07.374461-08 2025-03-02 00:27:41.123548-08 test f A100 \N t \N \N \N {} -4436 1444 2025-03-02 00:26:01.958483-08 2025-03-01 23:49:21.361713-08 benchmark f A100 \N t \N \N \N {} -6459 2005 2025-03-14 06:48:41.779094-07 2025-03-14 06:41:15.666539-07 test t A100 \N t \N \N \N {} -4439 1444 2025-03-01 23:56:43.720976-08 2025-03-02 00:29:41.591684-08 benchmark f T4 \N t \N \N \N {} -4440 1444 2025-03-02 01:00:37.296922-08 2025-03-01 23:59:24.879229-08 leaderboard f T4 0.009901969333333333 t \N \N \N {} -4441 1444 2025-03-02 00:32:35.468671-08 2025-03-02 00:07:20.667198-08 test f H100 \N t \N \N \N {} -4442 1444 2025-03-02 00:23:25.716537-08 2025-03-01 23:07:19.512954-08 benchmark f H100 \N t \N \N \N {} -4443 1444 2025-03-01 23:26:32.233267-08 2025-03-02 00:51:40.562071-08 leaderboard f H100 0.0011824463333333333 t \N \N \N {} -4447 1444 2025-03-01 23:33:37.582909-08 2025-03-02 00:50:04.426217-08 test t A100 \N t \N \N \N {} -4448 1444 2025-03-01 23:21:44.542236-08 2025-03-02 00:08:03.502567-08 benchmark t A100 \N t \N \N \N {} -4449 1444 2025-03-01 23:37:38.569687-08 2025-03-02 00:55:50.035923-08 leaderboard t A100 0.002102921 t \N \N \N {} -4450 1444 2025-03-02 00:52:28.079251-08 2025-03-01 23:41:04.354527-08 test t H100 \N t \N \N \N {} -4451 1444 2025-03-01 23:53:41.734266-08 2025-03-02 00:14:25.295609-08 benchmark t H100 \N t \N \N \N {} -4452 1444 2025-03-01 23:41:52.983649-08 2025-03-02 00:12:57.932353-08 leaderboard t H100 0.001193107 t \N \N \N {} -4453 1444 2025-03-02 00:27:47.992493-08 2025-03-01 23:08:52.737482-08 test f L4 \N t \N \N \N {} -4454 1444 2025-03-01 23:32:46.550951-08 2025-03-01 23:59:44.887779-08 benchmark f L4 \N t \N \N \N {} -4455 1444 2025-03-02 01:06:16.006685-08 2025-03-02 01:03:15.272313-08 leaderboard f L4 0.009334652333333334 t \N \N \N {} -4456 1444 2025-03-02 00:34:05.739298-08 2025-03-01 23:57:09.516919-08 test t L4 \N t \N \N \N {} -4457 1444 2025-03-01 23:54:57.096397-08 2025-03-02 00:38:20.034987-08 benchmark t L4 \N t \N \N \N {} -6704 2080 2025-03-15 03:44:28.237409-07 2025-03-15 02:59:07.329339-07 test t A100 \N t \N \N \N {} -4460 1445 2025-03-02 00:36:15.053645-08 2025-03-02 00:04:32.394667-08 benchmark f A100 \N t \N \N \N {} -4461 1445 2025-03-02 00:39:57.735714-08 2025-03-01 23:20:00.105801-08 leaderboard f A100 0.0018449429166666666 t \N \N \N {} -4462 1445 2025-03-02 00:55:37.500529-08 2025-03-02 00:06:09.985896-08 test t H100 \N t \N \N \N {} -4463 1445 2025-03-02 01:06:57.541572-08 2025-03-02 00:21:30.815554-08 benchmark t H100 \N t \N \N \N {} -4464 1445 2025-03-02 00:13:41.720834-08 2025-03-01 23:43:07.361692-08 leaderboard t H100 0.0010767913333333333 t \N \N \N {} -4468 1445 2025-03-02 01:09:32.900149-08 2025-03-01 23:58:50.176924-08 test f T4 \N t \N \N \N {} -4469 1445 2025-03-02 00:07:37.426171-08 2025-03-02 01:02:05.35442-08 benchmark f T4 \N t \N \N \N {} -4470 1445 2025-03-02 00:31:13.200358-08 2025-03-02 00:29:49.702127-08 leaderboard f T4 0.009909986333333334 t \N \N \N {} -4471 1445 2025-03-02 00:16:25.62727-08 2025-03-02 00:16:09.130264-08 test t T4 \N t \N \N \N {} -4472 1445 2025-03-02 00:04:53.640853-08 2025-03-01 23:24:24.363564-08 benchmark t T4 \N t \N \N \N {} -4473 1445 2025-03-02 01:02:57.604673-08 2025-03-01 23:55:02.547237-08 leaderboard t T4 0.009850056666666666 t \N \N \N {} -4474 1445 2025-03-01 23:14:41.583535-08 2025-03-02 01:03:01.853819-08 test t A100 \N t \N \N \N {} -4475 1445 2025-03-01 23:54:31.749676-08 2025-03-02 00:19:06.090943-08 benchmark t A100 \N t \N \N \N {} -4476 1445 2025-03-01 23:28:11.845822-08 2025-03-02 00:35:45.681919-08 leaderboard t A100 0.001977769 t \N \N \N {} -4477 1445 2025-03-01 23:10:57.421619-08 2025-03-01 23:46:37.516111-08 test f L4 \N t \N \N \N {} -4478 1445 2025-03-02 00:00:13.580017-08 2025-03-01 23:29:52.639891-08 benchmark f L4 \N t \N \N \N {} -6707 2080 2025-03-15 03:00:01.742093-07 2025-03-15 02:58:02.613361-07 test f A100 \N t \N \N \N {} -4488 1447 2025-03-02 00:17:43.55991-08 2025-03-02 01:03:56.914497-08 test t H100 \N t \N \N \N {} -4489 1447 2025-03-02 01:08:54.903269-08 2025-03-01 23:34:35.298743-08 benchmark t H100 \N f \N \N \N {} -4490 1447 2025-03-01 23:20:16.331584-08 2025-03-02 01:02:40.427181-08 test f A100 \N t \N \N \N {} -4491 1447 2025-03-01 23:58:12.688525-08 2025-03-02 00:00:00.160252-08 benchmark f A100 \N f \N \N \N {} -4492 1447 2025-03-02 00:06:10.369337-08 2025-03-02 00:53:30.708303-08 test f T4 \N t \N \N \N {} -6710 2081 2025-03-15 03:34:31.806339-07 2025-03-15 04:12:10.063053-07 test f A100 \N t \N \N \N {} -4493 1447 2025-03-02 01:03:35.98745-08 2025-03-02 00:54:11.188019-08 benchmark f T4 \N f \N \N \N {} -4494 1447 2025-03-02 00:30:24.438417-08 2025-03-01 23:49:43.972048-08 test t T4 \N t \N \N \N {} -4495 1447 2025-03-02 00:06:52.894921-08 2025-03-02 00:53:07.74224-08 benchmark t T4 \N f \N \N \N {} -4496 1447 2025-03-01 23:56:15.387541-08 2025-03-02 01:11:05.860239-08 test f L4 \N t \N \N \N {} -4497 1447 2025-03-01 23:59:26.042618-08 2025-03-02 00:05:19.797422-08 benchmark f L4 \N f \N \N \N {} -4676 1467 2025-03-02 06:38:57.431816-08 2025-03-02 05:44:56.24281-08 leaderboard t A100 0.0030988743333333333 t \N \N \N {} -4679 1467 2025-03-02 06:07:05.077247-08 2025-03-02 06:27:34.618874-08 leaderboard f A100 0.0030814753333333333 t \N \N \N {} -4681 1468 2025-03-02 05:51:53.90541-08 2025-03-02 05:57:04.320667-08 benchmark f A100 \N t \N \N \N {} -4682 1468 2025-03-02 06:59:21.935782-08 2025-03-02 07:08:58.21826-08 leaderboard f A100 0.0030820743333333333 t \N \N \N {} -4683 1468 2025-03-02 07:33:48.301938-08 2025-03-02 05:51:18.662826-08 test t A100 \N t \N \N \N {} -4684 1468 2025-03-02 06:04:29.029074-08 2025-03-02 06:40:01.015883-08 benchmark t A100 \N t \N \N \N {} -4685 1468 2025-03-02 05:46:36.618897-08 2025-03-02 07:31:14.275548-08 leaderboard t A100 0.0030780586666666666 t \N \N \N {} -4689 1469 2025-03-02 05:56:50.321497-08 2025-03-02 05:54:49.230629-08 test t A100 \N t \N \N \N {} -4690 1469 2025-03-02 06:10:42.998637-08 2025-03-02 06:36:41.015153-08 benchmark t A100 \N t \N \N \N {} -4691 1469 2025-03-02 06:43:44.220952-08 2025-03-02 07:42:47.37538-08 leaderboard t A100 0.00307954 t \N \N \N {} -4692 1470 2025-03-02 07:27:34.955858-08 2025-03-02 07:19:49.777082-08 test t A100 \N t \N \N \N {} -4797 1487 2025-03-02 12:47:39.303587-08 2025-03-02 14:24:58.396542-08 benchmark t H100 \N f \N \N \N {} -4798 1487 2025-03-02 14:03:11.635121-08 2025-03-02 12:41:11.568145-08 test f A100 \N t \N \N \N {} -4799 1487 2025-03-02 14:16:43.466988-08 2025-03-02 13:43:02.452058-08 benchmark f A100 \N f \N \N \N {} -4800 1487 2025-03-02 12:52:53.590829-08 2025-03-02 13:24:26.405768-08 test f H100 \N t \N \N \N {} -4801 1487 2025-03-02 12:48:49.45642-08 2025-03-02 14:27:42.149075-08 benchmark f H100 \N f \N \N \N {} -6752 2093 2025-03-15 03:33:18.596707-07 2025-03-15 05:00:28.91036-07 test f A100 \N t \N \N \N {} -4846 1493 2025-03-02 14:07:28.816942-08 2025-03-02 13:42:53.598883-08 leaderboard f A100 0.001957988 t \N \N \N {} -4847 1493 2025-03-02 13:42:25.008987-08 2025-03-02 15:06:27.494787-08 test t A100 \N t \N \N \N {} -4848 1493 2025-03-02 15:03:52.371645-08 2025-03-02 15:22:56.10921-08 benchmark t A100 \N t \N \N \N {} -4849 1493 2025-03-02 13:36:12.4802-08 2025-03-02 13:52:29.977751-08 leaderboard t A100 0.0019754496666666665 t \N \N \N {} -4850 1494 2025-03-02 14:37:31.400329-08 2025-03-02 14:01:28.604796-08 test f L4 \N t \N \N \N {} -4851 1494 2025-03-02 14:43:42.095203-08 2025-03-02 13:36:47.15097-08 benchmark f L4 \N t \N \N \N {} -4974 1533 2025-03-03 22:57:29.38809-08 2025-03-03 21:55:26.67234-08 test f H100 \N t \N \N \N {} -4865 1495 2025-03-02 14:13:05.528764-08 2025-03-02 14:22:55.062967-08 benchmark t A100 \N t \N \N \N {} -4866 1495 2025-03-02 15:19:34.20592-08 2025-03-02 15:11:10.458129-08 leaderboard t A100 0.0017338363333333333 t \N \N \N {} -4867 1495 2025-03-02 13:51:41.741994-08 2025-03-02 14:01:52.182359-08 test f A100 \N t \N \N \N {} -4880 1501 2025-03-03 08:43:05.642759-08 2025-03-03 09:27:05.185793-08 benchmark f A100 \N t \N \N \N {} -4881 1502 2025-03-03 09:35:30.629754-08 2025-03-03 09:05:15.805475-08 benchmark f A100 \N t \N \N \N {} -4894 1507 2025-03-03 13:08:55.752365-08 2025-03-03 13:41:41.116144-08 test f A100 \N t \N \N \N {} -4895 1507 2025-03-03 14:18:00.216385-08 2025-03-03 14:01:18.093034-08 benchmark f A100 \N t \N \N \N {} -4896 1507 2025-03-03 12:36:28.706864-08 2025-03-03 13:53:27.430536-08 leaderboard f A100 0.023420895 t \N \N \N {} -4902 1508 2025-03-03 13:21:12.73487-08 2025-03-03 13:02:32.458846-08 leaderboard f H100 0.007582660333333333 t \N \N \N {} -4904 1510 2025-03-03 14:37:41.941114-08 2025-03-03 13:46:22.666922-08 test f T4 \N f \N \N \N {} -5251 1613 2025-03-06 07:16:47.168832-08 2025-03-06 08:00:37.45677-08 benchmark t L4 \N t \N \N \N {} -4942 1522 2025-03-03 14:30:23.866503-08 2025-03-03 15:41:40.516523-08 benchmark f H100 \N t \N \N \N {} -4943 1522 2025-03-03 14:48:33.754294-08 2025-03-03 15:31:43.342939-08 leaderboard f H100 0.0009028792280701754 t \N \N \N {} -5163 1571 2025-03-04 22:04:28.946672-08 2025-03-04 22:40:14.074477-08 benchmark t H100 \N t \N \N \N {} -5164 1571 2025-03-04 22:14:23.494998-08 2025-03-04 21:56:46.443828-08 leaderboard t H100 0.0010517243333333333 t \N \N \N {} -5165 1572 2025-03-04 22:44:09.245212-08 2025-03-04 22:55:47.144621-08 test f H100 \N f \N \N \N {} -5166 1572 2025-03-04 22:51:29.817905-08 2025-03-04 22:29:55.447229-08 test t H100 \N f \N \N \N {} -5167 1573 2025-03-04 23:41:43.36022-08 2025-03-04 22:29:50.136285-08 test f H100 \N f \N \N \N {} -5168 1573 2025-03-04 23:23:11.4983-08 2025-03-04 22:11:11.957865-08 test t H100 \N f \N \N \N {} -5169 1574 2025-03-04 22:13:23.349969-08 2025-03-04 22:18:11.101194-08 test f H100 \N f \N \N \N {} -5588 1708 2025-03-09 10:19:34.720879-07 2025-03-09 08:50:34.606837-07 test t T4 \N f \N \N \N {} -5589 1709 2025-03-09 08:35:57.715637-07 2025-03-09 08:57:16.363193-07 test f T4 \N t \N \N \N {} -5590 1709 2025-03-09 08:48:05.388801-07 2025-03-09 08:51:52.822296-07 benchmark f T4 \N t \N \N \N {} -5598 1710 2025-03-09 09:29:22.059881-07 2025-03-09 09:55:48.650399-07 test f T4 \N t \N \N \N {} -5599 1710 2025-03-09 08:55:39.716901-07 2025-03-09 09:35:45.035771-07 benchmark f T4 \N t \N \N \N {} -5600 1710 2025-03-09 08:57:39.621985-07 2025-03-09 09:06:05.154876-07 leaderboard f T4 0.016820244 t \N \N \N {} -5603 1712 2025-03-09 08:59:13.701641-07 2025-03-09 10:05:42.666778-07 test f T4 \N t \N \N \N {} -5604 1712 2025-03-09 08:36:53.940611-07 2025-03-09 09:15:28.314509-07 benchmark f T4 \N t \N \N \N {} -5605 1712 2025-03-09 09:42:44.562696-07 2025-03-09 08:51:10.667131-07 leaderboard f T4 0.017130335 t \N \N \N {} -5606 1712 2025-03-09 09:25:40.097472-07 2025-03-09 09:24:28.034725-07 test t T4 \N t \N \N \N {} -5609 1713 2025-03-09 09:05:10.861954-07 2025-03-09 09:44:03.851528-07 test f T4 \N f \N \N \N {} -5610 1713 2025-03-09 09:44:19.160845-07 2025-03-09 09:34:04.669254-07 test t T4 \N f \N \N \N {} -5611 1714 2025-03-09 08:42:31.949236-07 2025-03-09 08:41:53.417604-07 test f T4 \N t \N \N \N {} -5612 1714 2025-03-09 09:57:39.262941-07 2025-03-09 09:05:14.002676-07 benchmark f T4 \N f \N \N \N {} -5615 1715 2025-03-09 09:17:39.516615-07 2025-03-09 09:12:41.592177-07 test t T4 \N t \N \N \N {} -5616 1715 2025-03-09 10:38:09.131269-07 2025-03-09 10:32:40.256801-07 benchmark t T4 \N t \N \N \N {} -5617 1715 2025-03-09 10:30:21.604304-07 2025-03-09 09:39:45.046006-07 leaderboard t T4 0.020089393 t \N \N \N {} -5618 1715 2025-03-09 09:22:37.846053-07 2025-03-09 09:51:45.425382-07 test f T4 \N t \N \N \N {} -5619 1715 2025-03-09 10:06:35.483321-07 2025-03-09 08:49:05.978541-07 benchmark f T4 \N t \N \N \N {} -5620 1715 2025-03-09 09:57:04.884393-07 2025-03-09 09:09:40.630581-07 leaderboard f T4 0.020089969 t \N \N \N {} -5621 1716 2025-03-09 09:09:36.619819-07 2025-03-09 09:58:56.525307-07 test t T4 \N t \N \N \N {} -5922 1799 2025-03-10 12:08:29.726347-07 2025-03-10 12:16:05.005078-07 test t T4 \N f \N \N \N {} -5927 1802 2025-03-10 12:32:14.926187-07 2025-03-10 12:54:34.768223-07 test t T4 \N f \N \N \N {} -5928 1802 2025-03-10 12:50:16.888351-07 2025-03-10 13:21:42.853839-07 test f T4 \N f \N \N \N {} -5929 1803 2025-03-10 13:37:22.444553-07 2025-03-10 13:05:55.49148-07 test t T4 \N f \N \N \N {} -5930 1803 2025-03-10 12:23:55.172369-07 2025-03-10 12:26:21.880798-07 test f T4 \N f \N \N \N {} -5935 1806 2025-03-10 13:48:38.054827-07 2025-03-10 13:23:13.229669-07 test t T4 \N f \N \N \N {} -5978 1820 2025-03-10 13:29:06.016869-07 2025-03-10 14:22:13.274644-07 leaderboard f H100 0.0002589177142857143 t \N \N \N {} -5959 1814 2025-03-10 12:44:05.167824-07 2025-03-10 14:16:11.26654-07 test f T4 \N f \N \N \N {} -5979 1820 2025-03-10 14:17:15.853549-07 2025-03-10 13:56:33.556629-07 test t H100 \N t \N \N \N {} -5964 1816 2025-03-10 13:24:28.295909-07 2025-03-10 12:59:46.71286-07 test f T4 \N f \N \N \N {} -5987 1819 2025-03-10 12:59:46.080421-07 2025-03-10 13:46:59.872067-07 leaderboard t A100 0.0007810198461538461 t \N \N \N {} -5968 1818 2025-03-10 12:49:46.935079-07 2025-03-10 14:29:30.320975-07 benchmark f T4 \N t \N \N \N {} -5970 1818 2025-03-10 12:50:38.37402-07 2025-03-10 12:59:21.234188-07 test t T4 \N t \N \N \N {} -2078 702 2025-02-25 14:41:18.894451-08 2025-02-25 14:59:05.107708-08 test t H100 \N t \N \N \N {} -2079 702 2025-02-25 13:46:28.574004-08 2025-02-25 14:33:01.405183-08 benchmark t H100 \N t \N \N \N {} -2080 702 2025-02-25 14:30:42.768365-08 2025-02-25 13:11:14.979116-08 leaderboard t H100 0.0019116912857142856 t \N \N \N {} -2081 702 2025-02-25 14:40:48.443884-08 2025-02-25 13:54:57.498846-08 test f T4 \N t \N \N \N {} -2082 702 2025-02-25 14:30:54.980313-08 2025-02-25 14:44:49.165667-08 benchmark f T4 \N t \N \N \N {} -2085 702 2025-02-25 14:18:01.193831-08 2025-02-25 14:59:07.45264-08 benchmark t T4 \N t \N \N \N {} -2086 702 2025-02-25 14:20:00.930365-08 2025-02-25 13:58:38.993991-08 leaderboard t T4 0.02001272633333333 t \N \N \N {} -6950 2162 2025-03-16 16:48:51.39716-07 2025-03-16 15:12:01.465889-07 leaderboard f T4 0.00032559321276595747 t \N \N \N {} -6974 2169 2025-03-16 19:35:21.550002-07 2025-03-16 20:31:39.018156-07 leaderboard f T4 0.00024278 t \N \N \N {} -6976 2170 2025-03-16 20:19:56.143081-07 2025-03-16 19:14:22.00651-07 benchmark f T4 \N t \N \N \N {} -6977 2170 2025-03-16 20:16:18.708649-07 2025-03-16 20:36:29.899891-07 leaderboard f T4 0.00021283630303030305 t \N \N \N {} -6978 2170 2025-03-16 20:24:22.625394-07 2025-03-16 20:12:10.318919-07 test t T4 \N t \N \N \N {} -6979 2170 2025-03-16 20:24:19.649393-07 2025-03-16 19:07:58.941721-07 benchmark t T4 \N t \N \N \N {} -6980 2170 2025-03-16 19:29:57.914998-07 2025-03-16 20:10:27.993196-07 leaderboard t T4 0.00021353688461538462 t \N \N \N {} -6981 2171 2025-03-16 20:30:12.137849-07 2025-03-16 20:46:29.948675-07 test t T4 \N t \N \N \N {} -6983 2171 2025-03-16 20:50:17.763964-07 2025-03-16 19:14:31.186365-07 leaderboard t T4 0.0002254876842105263 t \N \N \N {} -6984 2171 2025-03-16 21:04:10.03094-07 2025-03-16 19:42:37.771415-07 test f T4 \N t \N \N \N {} -7060 2185 2025-03-16 21:00:49.129817-07 2025-03-16 20:45:01.000696-07 test t L4 \N t \N \N \N {} -6985 2171 2025-03-16 20:30:05.903023-07 2025-03-16 20:53:27.393446-07 benchmark f T4 \N t \N \N \N {} -6986 2171 2025-03-16 20:39:51.238598-07 2025-03-16 21:10:32.960644-07 leaderboard f T4 0.00024411110344827586 t \N \N \N {} -6987 2172 2025-03-16 20:16:03.916165-07 2025-03-16 20:44:26.852206-07 test t T4 \N t \N \N \N {} -6995 2173 2025-03-16 19:35:01.975689-07 2025-03-16 20:14:16.885975-07 leaderboard t T4 0.00023485917647058822 t \N \N \N {} -6996 2173 2025-03-16 20:21:16.631389-07 2025-03-16 20:29:29.668201-07 test f T4 \N t \N \N \N {} -6997 2173 2025-03-16 20:27:58.230216-07 2025-03-16 20:09:23.910606-07 benchmark f T4 \N t \N \N \N {} -6998 2173 2025-03-16 20:58:20.871815-07 2025-03-16 20:25:33.751661-07 leaderboard f T4 0.000249013375 t \N \N \N {} -6999 2174 2025-03-16 19:59:32.772964-07 2025-03-16 20:00:12.299702-07 test t T4 \N f \N \N \N {} -7000 2174 2025-03-16 20:57:34.509848-07 2025-03-16 20:51:30.601681-07 test f T4 \N f \N \N \N {} -7001 2175 2025-03-16 21:17:45.195814-07 2025-03-16 20:18:47.813213-07 test t T4 \N t \N \N \N {} -7002 2175 2025-03-16 21:20:26.692848-07 2025-03-16 19:32:57.604151-07 benchmark t T4 \N t \N \N \N {} -7003 2175 2025-03-16 20:44:57.359938-07 2025-03-16 19:58:30.404808-07 leaderboard t T4 0.0002332306 t \N \N \N {} -7004 2175 2025-03-16 21:10:00.970602-07 2025-03-16 21:02:06.246562-07 test f T4 \N t \N \N \N {} -7005 2175 2025-03-16 20:07:09.492932-07 2025-03-16 20:42:42.346737-07 benchmark f T4 \N t \N \N \N {} -7008 2176 2025-03-16 20:09:25.465826-07 2025-03-16 20:12:12.087915-07 benchmark t T4 \N t \N \N \N {} -7009 2176 2025-03-16 20:39:02.786771-07 2025-03-16 20:32:18.347951-07 leaderboard t T4 0.0003024322631578947 t \N \N \N {} -7010 2176 2025-03-16 20:09:27.479373-07 2025-03-16 19:53:27.585268-07 test f T4 \N t \N \N \N {} -7011 2176 2025-03-16 20:40:20.373215-07 2025-03-16 20:35:48.949342-07 benchmark f T4 \N t \N \N \N {} -7012 2176 2025-03-16 20:27:53.725911-07 2025-03-16 21:32:11.632172-07 leaderboard f T4 0.0003064759545454545 t \N \N \N {} -7014 2177 2025-03-16 20:47:48.960713-07 2025-03-16 21:32:50.346065-07 benchmark t T4 \N t \N \N \N {} -7015 2177 2025-03-16 20:22:27.227663-07 2025-03-16 21:27:05.019116-07 leaderboard t T4 0.00023873836363636365 t \N \N \N {} -7016 2177 2025-03-16 20:52:07.570894-07 2025-03-16 21:28:14.032602-07 test f T4 \N t \N \N \N {} -7017 2177 2025-03-16 21:04:02.088955-07 2025-03-16 20:13:59.587543-07 benchmark f T4 \N t \N \N \N {} -7018 2177 2025-03-16 20:52:17.778254-07 2025-03-16 21:07:11.801446-07 leaderboard f T4 0.00021685985 t \N \N \N {} -7019 2178 2025-03-16 20:51:58.999601-07 2025-03-16 20:03:37.893564-07 test f T4 \N t \N \N \N {} -7043 2182 2025-03-16 21:03:58.995548-07 2025-03-16 20:05:41.20667-07 test f H100 \N t \N \N \N {} -7049 2183 2025-03-16 21:44:08.747705-07 2025-03-16 20:09:06.554136-07 test f L4 \N t \N \N \N {} -7050 2183 2025-03-16 21:20:04.412484-07 2025-03-16 21:04:19.112094-07 benchmark f L4 \N t \N \N \N {} -7051 2183 2025-03-16 20:20:24.080976-07 2025-03-16 20:12:16.039498-07 leaderboard f L4 0.00013962746913580247 t \N \N \N {} -7052 2183 2025-03-16 21:06:27.300343-07 2025-03-16 21:35:48.036723-07 test t L4 \N t \N \N \N {} -7053 2183 2025-03-16 21:08:28.281165-07 2025-03-16 21:05:19.303361-07 benchmark t L4 \N t \N \N \N {} -7054 2183 2025-03-16 21:03:17.39211-07 2025-03-16 21:12:38.71214-07 leaderboard t L4 0.00013929977922077922 t \N \N \N {} -7057 2185 2025-03-16 20:57:18.459356-07 2025-03-16 20:58:40.616284-07 test f L4 \N t \N \N \N {} -7058 2185 2025-03-16 21:09:41.620917-07 2025-03-16 20:26:34.270924-07 benchmark f L4 \N t \N \N \N {} -7072 2187 2025-03-16 21:18:46.105748-07 2025-03-16 21:30:58.658604-07 test t A100 \N t \N \N \N {} -3823 1286 2025-03-01 02:51:18.738565-08 2025-03-01 02:35:56.135696-08 test t A100 \N t \N \N \N {} -2423 828 2025-02-26 14:04:34.908137-08 2025-02-26 12:48:44.157236-08 benchmark f H100 \N t \N \N \N {} -2844 989 2025-02-26 23:05:37.389856-08 2025-02-26 23:57:49.949237-08 test t H100 \N t \N \N \N {} -3826 1286 2025-03-01 03:10:29.776175-08 2025-03-01 02:15:26.657539-08 test f A100 \N t \N \N \N {} -2853 990 2025-02-26 23:48:13.530611-08 2025-02-27 00:27:37.952577-08 test f H100 \N t \N \N \N {} -3829 1287 2025-03-01 03:52:39.094858-08 2025-03-01 03:40:13.588698-08 test f A100 \N t \N \N \N {} -2860 992 2025-02-26 23:57:24.598857-08 2025-02-26 22:46:51.241065-08 leaderboard t H100 0.001483009888888889 t \N \N \N {} -3832 1287 2025-03-01 02:40:18.855128-08 2025-03-01 02:20:51.580001-08 test t A100 \N t \N \N \N {} -3575 1222 2025-02-28 14:25:08.626031-08 2025-02-28 13:57:58.851842-08 test f H100 \N t \N \N \N {} -2861 992 2025-02-27 00:10:48.414508-08 2025-02-27 00:15:26.826737-08 test f H100 \N t \N \N \N {} -3835 1288 2025-03-01 03:56:27.936941-08 2025-03-01 03:22:45.323479-08 test f A100 \N t \N \N \N {} -3838 1288 2025-03-01 02:49:58.666605-08 2025-03-01 03:28:40.936194-08 test t A100 \N t \N \N \N {} -3586 1228 2025-02-28 13:04:01.825429-08 2025-02-28 13:19:18.114759-08 leaderboard t A100 0.0018734915714285713 t \N \N \N {} -2208 723 2025-02-25 15:12:24.768432-08 2025-02-25 14:15:55.618693-08 benchmark f A100 \N t \N \N \N {} -3841 1289 2025-03-01 02:47:05.119415-08 2025-03-01 04:04:07.592057-08 test f A100 \N t \N \N \N {} -4808 1487 2025-03-02 13:15:32.005735-08 2025-03-02 14:08:45.412831-08 test t L4 \N t \N \N \N {} -2209 723 2025-02-25 15:18:48.649607-08 2025-02-25 15:21:36.817173-08 benchmark f H100 \N t \N \N \N {} -6755 2093 2025-03-15 04:31:09.91887-07 2025-03-15 04:32:31.426367-07 test t A100 \N t \N \N \N {} -2217 724 2025-02-25 14:40:36.314858-08 2025-02-25 15:59:23.436752-08 benchmark t H100 \N t \N \N \N {} -2227 726 2025-02-25 21:11:10.262768-08 2025-02-25 21:16:28.616268-08 test f H100 \N f \N \N \N {} -2971 1053 2025-02-27 10:22:46.566956-08 2025-02-27 11:14:00.450534-08 test f H100 \N f \N \N \N {} -2229 728 2025-02-25 21:49:59.544732-08 2025-02-25 21:13:13.071273-08 test f H100 \N f \N \N \N {} -2249 748 2025-02-26 01:20:54.630857-08 2025-02-26 01:48:02.906843-08 test f H100 \N f \N \N \N {} -2251 750 2025-02-26 01:02:46.596404-08 2025-02-26 01:40:29.396697-08 benchmark f H100 \N f \N \N \N {} -2203 721 2025-02-25 14:21:58.314111-08 2025-02-25 15:40:17.725093-08 test f A100 \N f \N \N \N {} -2204 721 2025-02-25 15:30:04.482461-08 2025-02-25 15:34:55.709429-08 test f H100 \N f \N \N \N {} -2205 721 2025-02-25 15:37:24.227133-08 2025-02-25 14:52:16.301155-08 test f T4 \N f \N \N \N {} -2206 721 2025-02-25 14:14:52.248428-08 2025-02-25 14:35:57.115198-08 test f L4 \N f \N \N \N {} -2207 722 2025-02-25 14:23:22.656547-08 2025-02-25 14:28:52.286564-08 test f A100 \N t \N \N \N {} -2231 730 2025-02-25 22:25:36.727385-08 2025-02-25 22:07:18.246829-08 test f H100 \N f \N \N \N {} -2252 751 2025-02-26 01:22:15.758845-08 2025-02-26 00:47:07.586743-08 benchmark f H100 \N f \N \N \N {} -2210 724 2025-02-25 15:01:55.831184-08 2025-02-25 15:27:25.721588-08 test f H100 \N t \N \N \N {} -2211 724 2025-02-25 16:00:18.379653-08 2025-02-25 15:14:19.774224-08 benchmark f H100 \N t \N \N \N {} -2212 724 2025-02-25 15:21:00.820815-08 2025-02-25 14:21:32.48071-08 leaderboard f H100 0.00347018762 t \N \N \N {} -2213 724 2025-02-25 15:55:13.720814-08 2025-02-25 14:39:28.319218-08 test t A100 \N t \N \N \N {} -2214 724 2025-02-25 15:15:10.350587-08 2025-02-25 15:20:05.920535-08 benchmark t A100 \N t \N \N \N {} -2222 724 2025-02-25 15:51:08.090008-08 2025-02-25 14:52:07.078563-08 test t L4 \N f \N \N \N {} -2223 724 2025-02-25 15:59:35.150135-08 2025-02-25 15:41:40.617138-08 test t T4 \N f \N \N \N {} -2224 724 2025-02-25 15:59:58.549919-08 2025-02-25 16:16:59.086077-08 test f T4 \N f \N \N \N {} -2225 724 2025-02-25 15:31:29.288566-08 2025-02-25 14:20:08.177759-08 test f L4 \N f \N \N \N {} -2236 735 2025-02-25 22:08:20.946921-08 2025-02-25 22:28:19.364454-08 test f H100 \N f \N \N \N {} -2241 740 2025-02-25 23:14:33.485835-08 2025-02-25 23:51:46.740346-08 benchmark f A100 \N t \N \N \N {} -2242 741 2025-02-25 23:35:50.055644-08 2025-02-25 22:49:24.455708-08 benchmark f A100 \N t \N \N \N {} -2243 742 2025-02-26 00:25:26.264646-08 2025-02-25 23:17:09.093175-08 benchmark f A100 \N f \N \N \N {} -2245 744 2025-02-26 00:42:02.958881-08 2025-02-26 00:45:16.278457-08 test f H100 \N f \N \N \N {} -2254 753 2025-02-26 02:08:05.477951-08 2025-02-26 01:13:20.051352-08 benchmark f H100 \N f \N \N \N {} -2255 754 2025-02-26 00:59:44.093208-08 2025-02-26 02:58:08.265275-08 benchmark f H100 \N f \N \N \N {} -2259 758 2025-02-26 01:17:58.081473-08 2025-02-26 01:34:49.549302-08 benchmark f H100 \N t \N \N \N {} -2260 759 2025-02-26 01:37:29.394797-08 2025-02-26 02:47:14.747208-08 benchmark f H100 \N t \N \N \N {} -2261 760 2025-02-26 01:39:24.386253-08 2025-02-26 02:18:01.358833-08 benchmark f H100 \N t \N \N \N {} -2262 761 2025-02-26 03:15:59.591174-08 2025-02-26 01:36:18.508508-08 benchmark f H100 \N t \N \N \N {} -2265 762 2025-02-26 02:01:34.231469-08 2025-02-26 03:54:41.609703-08 leaderboard t H100 0.0014416166666666667 t \N \N \N {} -2274 766 2025-02-26 04:05:07.529508-08 2025-02-26 04:58:07.255773-08 leaderboard f A100 0.0008179115 t \N \N \N {} -2275 766 2025-02-26 03:48:00.408098-08 2025-02-26 04:08:59.825239-08 test t A100 \N t \N \N \N {} -2301 774 2025-02-26 04:32:05.77669-08 2025-02-26 04:38:11.895003-08 benchmark f A100 \N t \N \N \N {} -2302 774 2025-02-26 04:13:31.801702-08 2025-02-26 04:01:09.656019-08 leaderboard f A100 0.0031047866666666663 t \N \N \N {} -2303 774 2025-02-26 04:24:37.740211-08 2025-02-26 03:39:00.774209-08 test t A100 \N t \N \N \N {} -2308 777 2025-02-26 04:25:35.388029-08 2025-02-26 03:30:27.492815-08 benchmark f A100 \N t \N \N \N {} -2309 778 2025-02-26 03:49:41.847565-08 2025-02-26 05:07:38.629246-08 test t A100 \N t \N \N \N {} -2310 778 2025-02-26 04:40:13.137449-08 2025-02-26 03:29:03.613607-08 benchmark t A100 \N t \N \N \N {} -2316 781 2025-02-26 08:04:59.695461-08 2025-02-26 08:20:17.559059-08 benchmark t A100 \N t \N \N \N {} -2317 781 2025-02-26 07:31:58.361931-08 2025-02-26 07:59:50.437989-08 leaderboard t A100 0.0031859682 t \N \N \N {} -2324 783 2025-02-26 08:10:22.678801-08 2025-02-26 07:07:39.819759-08 benchmark t A100 \N t \N \N \N {} -2325 783 2025-02-26 08:24:49.179778-08 2025-02-26 08:36:36.413259-08 leaderboard t A100 0.00325122275 t \N \N \N {} -2335 785 2025-02-26 08:25:39.641455-08 2025-02-26 06:58:31.005281-08 benchmark t A100 \N t \N \N \N {} -2336 785 2025-02-26 08:09:56.776695-08 2025-02-26 08:13:07.269228-08 leaderboard t A100 0.004335441666666667 t \N \N \N {} -2340 787 2025-02-26 07:39:15.682026-08 2025-02-26 08:18:25.113435-08 test f A100 \N f \N \N \N {} -2341 788 2025-02-26 07:31:19.708086-08 2025-02-26 08:57:53.961399-08 test t A100 \N f \N \N \N {} -2342 788 2025-02-26 08:25:02.656473-08 2025-02-26 08:25:08.005045-08 test f A100 \N f \N \N \N {} -2343 789 2025-02-26 08:43:37.93693-08 2025-02-26 08:07:41.07638-08 test t A100 \N t \N \N \N {} -2347 789 2025-02-26 09:06:31.119554-08 2025-02-26 07:11:28.595374-08 benchmark f A100 \N t \N \N \N {} -2348 789 2025-02-26 07:29:52.547362-08 2025-02-26 08:50:51.272086-08 leaderboard f A100 0.003266832777777778 t \N \N \N {} -2359 793 2025-02-26 09:11:48.758042-08 2025-02-26 08:04:27.206933-08 test t H100 \N f \N \N \N {} -2360 793 2025-02-26 09:01:14.507106-08 2025-02-26 08:36:00.01203-08 test f H100 \N f \N \N \N {} -2361 794 2025-02-26 10:31:01.311335-08 2025-02-26 10:24:32.132628-08 test f T4 \N t \N \N \N {} -2365 795 2025-02-26 10:42:54.506552-08 2025-02-26 10:33:48.974343-08 benchmark f T4 \N f \N \N \N {} -2366 796 2025-02-26 10:39:57.933478-08 2025-02-26 09:13:35.87411-08 test f T4 \N t \N \N \N {} -2367 796 2025-02-26 08:55:18.203941-08 2025-02-26 10:26:58.100193-08 test f A100 \N f \N \N \N {} -2368 796 2025-02-26 09:46:15.118953-08 2025-02-26 10:28:06.814641-08 test f L4 \N f \N \N \N {} -2373 800 2025-02-26 11:48:00.696003-08 2025-02-26 11:05:20.167368-08 test f L4 \N t \N \N \N {} -2374 800 2025-02-26 11:07:59.074012-08 2025-02-26 12:43:47.36544-08 benchmark f L4 \N t \N \N \N {} -2375 800 2025-02-26 12:41:54.306115-08 2025-02-26 11:06:41.948227-08 leaderboard f L4 0.019241309 t \N \N \N {} -2376 800 2025-02-26 11:45:20.290145-08 2025-02-26 12:31:25.711521-08 test t L4 \N t \N \N \N {} -2377 800 2025-02-26 12:39:01.06552-08 2025-02-26 12:57:56.856584-08 benchmark t L4 \N t \N \N \N {} -2378 800 2025-02-26 11:19:43.410096-08 2025-02-26 12:49:08.158954-08 leaderboard t L4 0.01928871 t \N \N \N {} -2379 801 2025-02-26 11:38:56.285241-08 2025-02-26 12:19:54.264843-08 test f A100 \N t \N \N \N {} -2382 804 2025-02-26 12:57:43.215115-08 2025-02-26 13:30:55.355298-08 benchmark f H100 \N t \N \N \N {} -2383 805 2025-02-26 13:12:00.965515-08 2025-02-26 12:47:43.718436-08 test t H100 \N t \N \N \N {} -2384 805 2025-02-26 12:02:09.112152-08 2025-02-26 13:33:00.538551-08 benchmark t H100 \N t \N \N \N {} -2385 805 2025-02-26 12:07:29.57397-08 2025-02-26 11:40:32.117689-08 leaderboard t H100 0.006281822 t \N \N \N {} -2386 805 2025-02-26 13:23:11.459374-08 2025-02-26 12:01:38.625408-08 test f H100 \N t \N \N \N {} -2387 805 2025-02-26 12:40:46.998681-08 2025-02-26 13:19:43.340082-08 benchmark f H100 \N t \N \N \N {} -2388 805 2025-02-26 12:28:02.085984-08 2025-02-26 12:00:56.151234-08 leaderboard f H100 0.006283131666666667 t \N \N \N {} -2400 812 2025-02-26 12:35:43.081119-08 2025-02-26 11:46:25.709461-08 leaderboard f A100 0.0073987305599999996 t \N \N \N {} -2399 812 2025-02-26 13:00:44.684656-08 2025-02-26 12:40:03.546738-08 benchmark f A100 \N t \N \N \N {} -2412 814 2025-02-26 11:55:21.072277-08 2025-02-26 13:21:18.388476-08 benchmark f H100 \N f \N \N \N {} -2406 812 2025-02-26 11:58:36.640565-08 2025-02-26 12:25:12.325022-08 benchmark t H100 \N t \N \N \N {} -2413 815 2025-02-26 12:34:40.3804-08 2025-02-26 12:01:23.839657-08 benchmark f H100 \N f \N \N \N {} -2407 812 2025-02-26 13:09:11.965272-08 2025-02-26 13:07:20.626098-08 leaderboard t H100 0.0034966516099999997 t \N \N \N {} -2408 813 2025-02-26 12:32:37.101507-08 2025-02-26 13:16:23.500546-08 benchmark f H100 \N t \N \N \N {} -2409 812 2025-02-26 13:29:54.187186-08 2025-02-26 13:23:28.632317-08 test t L4 \N f \N \N \N {} -2424 829 2025-02-26 13:07:19.553-08 2025-02-26 14:01:46.739578-08 benchmark f H100 \N f \N \N \N {} -2425 830 2025-02-26 14:02:43.362147-08 2025-02-26 12:33:07.345304-08 benchmark f H100 \N t \N \N \N {} -2426 831 2025-02-26 12:31:38.224085-08 2025-02-26 13:19:43.236285-08 benchmark f H100 \N t \N \N \N {} -3850 1290 2025-03-01 04:03:10.861329-08 2025-03-01 03:39:38.665791-08 test t A100 \N t \N \N \N {} -2428 834 2025-02-26 13:15:44.571141-08 2025-02-26 13:44:55.710615-08 benchmark f H100 \N t \N \N \N {} -2429 835 2025-02-26 13:42:00.820827-08 2025-02-26 13:22:37.497485-08 benchmark f H100 \N t \N \N \N {} -2430 836 2025-02-26 13:48:13.271344-08 2025-02-26 13:35:52.895605-08 benchmark f H100 \N t \N \N \N {} -2431 839 2025-02-26 12:27:35.852032-08 2025-02-26 13:54:55.127064-08 test f H100 \N t \N \N \N {} -2432 839 2025-02-26 13:13:16.24415-08 2025-02-26 12:31:35.696649-08 benchmark f H100 \N t \N \N \N {} -2433 839 2025-02-26 13:25:16.844207-08 2025-02-26 12:41:49.998945-08 leaderboard f H100 0.0014103766666666668 t \N \N \N {} -2434 839 2025-02-26 13:02:21.500262-08 2025-02-26 13:19:32.907466-08 test t H100 \N t \N \N \N {} -3275 1144 2025-02-28 05:52:25.859901-08 2025-02-28 06:09:38.762195-08 test f A100 \N t \N \N \N {} -2437 843 2025-02-26 14:26:52.109887-08 2025-02-26 14:21:54.916482-08 benchmark f A100 \N t \N \N \N {} -2438 845 2025-02-26 12:52:37.260995-08 2025-02-26 12:43:22.265476-08 test f L4 \N t \N \N \N {} -2439 845 2025-02-26 13:15:51.000933-08 2025-02-26 13:07:58.39425-08 benchmark f L4 \N t \N \N \N {} -2440 845 2025-02-26 13:28:47.225016-08 2025-02-26 13:45:32.97832-08 leaderboard f L4 0.043612971333333334 t \N \N \N {} -2443 845 2025-02-26 14:12:08.997237-08 2025-02-26 12:35:40.416269-08 leaderboard t L4 0.04366572966666667 t \N \N \N {} -2444 850 2025-02-26 14:13:58.990169-08 2025-02-26 12:46:54.480342-08 benchmark f A100 \N t \N \N \N {} -2445 851 2025-02-26 14:31:38.156136-08 2025-02-26 14:03:21.128088-08 test f A100 \N f \N \N \N {} -2446 851 2025-02-26 14:23:33.344316-08 2025-02-26 12:39:36.843848-08 test t A100 \N f \N \N \N {} -3853 1291 2025-03-01 03:13:24.343849-08 2025-03-01 02:57:41.913482-08 test t A100 \N t \N \N \N {} -2448 854 2025-02-26 14:40:16.012015-08 2025-02-26 13:30:53.984566-08 benchmark f A100 \N t \N \N \N {} -2449 855 2025-02-26 13:12:11.591988-08 2025-02-26 14:39:17.48303-08 benchmark f A100 \N t \N \N \N {} -2450 857 2025-02-26 13:02:50.277788-08 2025-02-26 13:15:55.340603-08 benchmark f A100 \N t \N \N \N {} -2456 860 2025-02-26 12:50:27.406788-08 2025-02-26 14:27:11.783431-08 leaderboard f A100 0.0030875686666666665 t \N \N \N {} -2457 860 2025-02-26 13:17:23.373759-08 2025-02-26 14:15:06.821417-08 test t A100 \N t \N \N \N {} -2458 860 2025-02-26 13:49:59.089223-08 2025-02-26 14:34:48.510454-08 benchmark t A100 \N t \N \N \N {} -2459 860 2025-02-26 12:54:53.87546-08 2025-02-26 13:27:24.625628-08 leaderboard t A100 0.003084496 t \N \N \N {} -2460 862 2025-02-26 14:25:24.115849-08 2025-02-26 14:24:38.388715-08 benchmark f A100 \N t \N \N \N {} -2461 863 2025-02-26 12:52:35.025536-08 2025-02-26 13:59:55.535093-08 benchmark f A100 \N t \N \N \N {} -3278 1144 2025-02-28 06:16:01.039147-08 2025-02-28 06:22:29.16157-08 test t A100 \N t \N \N \N {} -2467 865 2025-02-26 14:29:40.579994-08 2025-02-26 13:01:17.531306-08 benchmark t A100 \N t \N \N \N {} -2468 865 2025-02-26 14:27:27.068975-08 2025-02-26 13:15:03.336598-08 leaderboard t A100 0.0068730191799999995 t \N \N \N {} -2469 867 2025-02-26 14:38:45.220709-08 2025-02-26 13:20:49.410663-08 benchmark f H100 \N t \N \N \N {} -2470 870 2025-02-26 14:35:14.099811-08 2025-02-26 13:16:45.84219-08 test t L4 \N t \N \N \N {} -2471 870 2025-02-26 14:54:17.97184-08 2025-02-26 14:53:55.242909-08 benchmark t L4 \N t \N \N \N {} -3163 1117 2025-02-28 02:58:49.256531-08 2025-02-28 02:51:45.758919-08 test f A100 \N t \N \N \N {} -2472 870 2025-02-26 14:49:05.199571-08 2025-02-26 13:43:51.365921-08 leaderboard t L4 0.01752669466666667 t \N \N \N {} -2473 870 2025-02-26 14:29:38.067462-08 2025-02-26 13:42:20.183691-08 test t H100 \N t \N \N \N {} -2474 870 2025-02-26 14:28:13.427627-08 2025-02-26 14:43:39.03146-08 benchmark t H100 \N t \N \N \N {} -2475 870 2025-02-26 14:44:05.364863-08 2025-02-26 14:18:20.873024-08 leaderboard t H100 0.0014439003333333333 t \N \N \N {} -2476 870 2025-02-26 14:09:52.898678-08 2025-02-26 14:05:33.382355-08 test t T4 \N t \N \N \N {} -2482 870 2025-02-26 14:26:54.587486-08 2025-02-26 13:04:15.11736-08 test f A100 \N t \N \N \N {} -3391 1168 2025-02-28 09:57:12.308014-08 2025-02-28 09:07:40.869103-08 test t L4 \N f \N \N \N {} -2477 870 2025-02-26 14:11:49.901716-08 2025-02-26 13:57:28.933272-08 benchmark t T4 \N t \N \N \N {} -2478 870 2025-02-26 14:52:49.914318-08 2025-02-26 14:30:14.957771-08 leaderboard t T4 0.018135662333333333 t \N \N \N {} -2479 870 2025-02-26 13:04:33.014453-08 2025-02-26 13:28:38.801176-08 test f H100 \N t \N \N \N {} -2480 870 2025-02-26 13:05:56.396107-08 2025-02-26 13:11:26.670759-08 benchmark f H100 \N t \N \N \N {} -2487 870 2025-02-26 13:53:37.8607-08 2025-02-26 13:42:40.91502-08 leaderboard f T4 0.018169958333333333 t \N \N \N {} -3284 1147 2025-02-28 06:34:56.869895-08 2025-02-28 06:59:54.488727-08 benchmark f A100 \N t \N \N \N {} -2488 870 2025-02-26 13:55:29.990434-08 2025-02-26 13:42:48.566834-08 test t A100 \N t \N \N \N {} -2489 870 2025-02-26 13:33:21.862277-08 2025-02-26 13:18:14.133316-08 benchmark t A100 \N t \N \N \N {} -2490 870 2025-02-26 14:25:53.367799-08 2025-02-26 14:32:23.689127-08 leaderboard t A100 0.0035314313333333334 t \N \N \N {} -2491 870 2025-02-26 13:48:35.450822-08 2025-02-26 14:06:57.276021-08 test f L4 \N t \N \N \N {} -2492 870 2025-02-26 13:52:29.744988-08 2025-02-26 13:53:25.403361-08 benchmark f L4 \N t \N \N \N {} -3285 1147 2025-02-28 06:24:43.203152-08 2025-02-28 06:13:29.756699-08 benchmark f T4 \N t \N \N \N {} -2493 870 2025-02-26 13:59:31.991194-08 2025-02-26 13:59:26.579581-08 leaderboard f L4 0.017607324 t \N \N \N {} -2494 869 2025-02-26 13:53:41.469056-08 2025-02-26 14:38:54.569715-08 test f H100 \N t \N \N \N {} -2495 869 2025-02-26 13:23:26.566841-08 2025-02-26 14:00:54.453804-08 benchmark f H100 \N t \N \N \N {} -2496 869 2025-02-26 14:13:25.627702-08 2025-02-26 13:16:17.10637-08 leaderboard f H100 0.0029435355699999996 t \N \N \N {} -2497 869 2025-02-26 13:45:34.876263-08 2025-02-26 13:53:58.749847-08 test t H100 \N t \N \N \N {} -2498 869 2025-02-26 13:46:26.837661-08 2025-02-26 13:24:26.415607-08 benchmark t H100 \N t \N \N \N {} -2503 872 2025-02-26 14:19:49.973867-08 2025-02-26 13:58:04.924559-08 leaderboard t A100 0.0031960086666666665 t \N \N \N {} -2524 872 2025-02-26 14:59:20.978098-08 2025-02-26 14:04:05.964393-08 leaderboard t L4 0.01734968933333333 t \N \N \N {} -2525 873 2025-02-26 14:17:24.445616-08 2025-02-26 13:21:44.500572-08 benchmark f T4 \N t \N \N \N {} -3311 1149 2025-02-28 07:38:30.746822-08 2025-02-28 06:13:50.041675-08 benchmark f H100 \N t \N \N \N {} -2526 874 2025-02-26 14:02:11.631299-08 2025-02-26 14:54:22.504542-08 benchmark f H100 \N f \N \N \N {} -2528 875 2025-02-26 13:53:59.206204-08 2025-02-26 13:59:51.952233-08 benchmark t L4 \N t \N \N \N {} -2529 875 2025-02-26 13:45:06.978432-08 2025-02-26 14:27:23.069181-08 leaderboard t L4 0.017381977333333333 t \N \N \N {} -2530 875 2025-02-26 13:14:04.120622-08 2025-02-26 13:37:23.311234-08 test f H100 \N t \N \N \N {} -2536 875 2025-02-26 14:17:45.203727-08 2025-02-26 13:52:51.20816-08 test t A100 \N t \N \N \N {} -3398 1172 2025-02-28 10:30:25.502848-08 2025-02-28 09:32:32.438948-08 test t L4 \N f \N \N \N {} -2531 875 2025-02-26 13:27:29.331193-08 2025-02-26 13:29:35.204364-08 benchmark f H100 \N t \N \N \N {} -2538 875 2025-02-26 13:53:08.870239-08 2025-02-26 14:00:49.668667-08 leaderboard t A100 0.0032747976666666666 t \N \N \N {} -2539 875 2025-02-26 13:14:52.010055-08 2025-02-26 14:18:34.81441-08 test f T4 \N t \N \N \N {} -2540 875 2025-02-26 13:20:18.964191-08 2025-02-26 13:13:55.628835-08 benchmark f T4 \N t \N \N \N {} -2541 875 2025-02-26 14:54:26.113811-08 2025-02-26 13:43:25.579169-08 leaderboard f T4 0.018149821333333333 t \N \N \N {} -3349 1156 2025-02-28 06:29:55.568265-08 2025-02-28 08:02:02.535704-08 leaderboard f A100 0.0031082066666666664 t \N \N \N {} -2542 875 2025-02-26 14:51:18.161936-08 2025-02-26 14:37:36.137121-08 test t H100 \N t \N \N \N {} -2543 875 2025-02-26 14:35:54.200978-08 2025-02-26 13:13:31.055337-08 benchmark t H100 \N t \N \N \N {} -2544 875 2025-02-26 14:33:45.385769-08 2025-02-26 13:26:20.622935-08 leaderboard t H100 0.0014337893333333334 t \N \N \N {} -2545 875 2025-02-26 13:53:24.267369-08 2025-02-26 13:18:55.978128-08 test t T4 \N t \N \N \N {} -2546 875 2025-02-26 13:38:11.861473-08 2025-02-26 14:59:51.393224-08 benchmark t T4 \N t \N \N \N {} -2567 877 2025-02-26 15:03:59.772268-08 2025-02-26 14:40:45.912645-08 test t A100 \N t \N \N \N {} -2547 875 2025-02-26 14:32:26.84369-08 2025-02-26 13:13:58.565371-08 leaderboard t T4 0.01822390733333333 t \N \N \N {} -2552 877 2025-02-26 13:20:01.898564-08 2025-02-26 13:58:51.317431-08 benchmark f L4 \N t \N \N \N {} -2557 876 2025-02-26 15:02:00.99544-08 2025-02-26 13:29:31.499121-08 benchmark f T4 \N t \N \N \N {} -2558 877 2025-02-26 13:46:19.16509-08 2025-02-26 14:56:55.471863-08 test f A100 \N t \N \N \N {} -2562 877 2025-02-26 14:11:45.890202-08 2025-02-26 14:42:19.158457-08 benchmark f H100 \N t \N \N \N {} -2574 877 2025-02-26 14:53:25.554208-08 2025-02-26 14:16:55.579672-08 benchmark t T4 \N t \N \N \N {} -2580 880 2025-02-26 15:07:50.100053-08 2025-02-26 13:18:56.845732-08 benchmark f H100 \N t \N \N \N {} -2585 883 2025-02-26 15:03:39.651289-08 2025-02-26 14:42:53.129821-08 benchmark f T4 \N t \N \N \N {} -2586 884 2025-02-26 14:50:00.889114-08 2025-02-26 15:10:04.434058-08 test f A100 \N t \N \N \N {} -2590 885 2025-02-26 15:19:01.393865-08 2025-02-26 13:28:15.590615-08 benchmark f H100 \N t \N \N \N {} -2591 885 2025-02-26 13:26:24.818556-08 2025-02-26 13:50:12.168949-08 leaderboard f H100 0.0014732981818181819 t \N \N \N {} -2602 884 2025-02-26 14:57:40.130347-08 2025-02-26 14:36:36.467514-08 benchmark t H100 \N t \N \N \N {} -2603 884 2025-02-26 14:47:56.199118-08 2025-02-26 14:47:39.751177-08 leaderboard t H100 0.001451446 t \N \N \N {} -2608 884 2025-02-26 14:52:54.969875-08 2025-02-26 14:45:17.829971-08 benchmark t T4 \N t \N \N \N {} -2609 884 2025-02-26 14:00:42.000568-08 2025-02-26 14:07:23.322658-08 leaderboard t T4 0.01839805933333333 t \N \N \N {} -2623 885 2025-02-26 13:24:51.303162-08 2025-02-26 14:59:25.978603-08 benchmark f L4 \N t \N \N \N {} -2624 885 2025-02-26 14:46:25.13041-08 2025-02-26 14:01:16.028106-08 leaderboard f L4 0.018322243399999997 t \N \N \N {} -2632 885 2025-02-26 13:42:28.590895-08 2025-02-26 15:11:24.84455-08 benchmark f T4 \N t \N \N \N {} -2633 885 2025-02-26 14:17:07.144881-08 2025-02-26 13:42:07.341899-08 leaderboard f T4 0.017316619 t \N \N \N {} -2638 890 2025-02-26 14:38:01.800384-08 2025-02-26 14:21:29.502344-08 benchmark f H100 \N t \N \N \N {} -2639 891 2025-02-26 14:00:22.235154-08 2025-02-26 13:49:58.414404-08 benchmark f H100 \N t \N \N \N {} -2640 893 2025-02-26 14:44:28.627123-08 2025-02-26 14:56:02.319874-08 benchmark f H100 \N t \N \N \N {} -2641 892 2025-02-26 14:06:16.166538-08 2025-02-26 14:45:31.778383-08 benchmark f H100 \N t \N \N \N {} -2646 898 2025-02-26 15:10:17.627593-08 2025-02-26 14:28:58.359942-08 benchmark f H100 \N t \N \N \N {} -2647 899 2025-02-26 13:42:07.536404-08 2025-02-26 14:35:17.861549-08 benchmark f H100 \N t \N \N \N {} -2652 909 2025-02-26 15:08:14.043409-08 2025-02-26 14:36:11.735601-08 test f T4 \N f \N \N \N {} -2674 917 2025-02-26 14:23:43.794578-08 2025-02-26 14:36:33.755414-08 benchmark t T4 \N t \N \N \N {} -2686 922 2025-02-26 15:57:13.427621-08 2025-02-26 15:26:20.611904-08 test f T4 \N t \N \N \N {} -2696 925 2025-02-26 14:17:38.083972-08 2025-02-26 16:03:31.423476-08 leaderboard f A100 0.0032370253333333333 t \N \N \N {} -2713 927 2025-02-26 14:37:41.802897-08 2025-02-26 14:31:27.897832-08 test f H100 \N t \N \N \N {} -2714 927 2025-02-26 15:15:35.837289-08 2025-02-26 14:47:25.94265-08 benchmark f H100 \N t \N \N \N {} -2718 927 2025-02-26 15:59:35.075538-08 2025-02-26 15:41:14.515832-08 leaderboard t H100 0.0008215225714285714 t \N \N \N {} -2724 937 2025-02-26 16:47:17.193058-08 2025-02-26 16:23:22.453844-08 test f H100 \N t \N \N \N {} -2725 937 2025-02-26 15:50:22.227979-08 2025-02-26 16:45:26.76204-08 benchmark f H100 \N t \N \N \N {} -2726 937 2025-02-26 16:51:40.672188-08 2025-02-26 16:03:32.138312-08 leaderboard f H100 0.00079554 t \N \N \N {} -2727 937 2025-02-26 15:40:40.022004-08 2025-02-26 17:18:13.128244-08 test t H100 \N t \N \N \N {} -2728 937 2025-02-26 17:19:47.937296-08 2025-02-26 17:33:18.727563-08 benchmark t H100 \N t \N \N \N {} -2789 974 2025-02-26 20:55:25.229033-08 2025-02-26 20:10:10.409265-08 test t A100 \N t \N \N \N {} -3494 1198 2025-02-28 11:44:26.606686-08 2025-02-28 12:27:34.310314-08 test t A100 \N t \N \N \N {} -3877 1295 2025-03-01 04:30:13.818352-08 2025-03-01 03:53:15.973054-08 test f A100 \N t \N \N \N {} -2734 939 2025-02-26 16:52:57.1295-08 2025-02-26 16:57:05.775532-08 test t H100 \N t \N \N \N {} -2735 939 2025-02-26 16:51:34.603971-08 2025-02-26 16:14:14.968064-08 benchmark t H100 \N t \N \N \N {} -2736 939 2025-02-26 17:22:29.074864-08 2025-02-26 17:19:20.267253-08 leaderboard t H100 0.0004968643 t \N \N \N {} -2737 940 2025-02-26 17:36:57.572482-08 2025-02-26 17:38:12.608294-08 benchmark f H100 \N t \N \N \N {} -2738 941 2025-02-26 16:31:57.595239-08 2025-02-26 17:53:18.674166-08 benchmark f H100 \N t \N \N \N {} -2740 943 2025-02-26 18:29:20.73428-08 2025-02-26 18:00:14.467783-08 test f H100 \N f \N \N \N {} -2742 945 2025-02-26 19:02:41.462274-08 2025-02-26 17:30:22.202193-08 test f H100 \N f \N \N \N {} -2743 946 2025-02-26 18:48:22.350038-08 2025-02-26 17:37:45.056255-08 test f H100 \N t \N \N \N {} -2744 947 2025-02-26 17:40:41.628733-08 2025-02-26 18:01:07.85212-08 benchmark f H100 \N t \N \N \N {} -3497 1199 2025-02-28 10:52:33.356367-08 2025-02-28 11:45:48.622925-08 test f A100 \N t \N \N \N {} -2746 949 2025-02-26 19:17:31.915891-08 2025-02-26 17:48:54.299006-08 test f H100 \N t \N \N \N {} -3590 1229 2025-02-28 14:30:29.574017-08 2025-02-28 13:08:20.706884-08 test t H100 \N t \N \N \N {} -2748 951 2025-02-26 17:49:30.605776-08 2025-02-26 19:07:43.438503-08 benchmark f H100 \N t \N \N \N {} -2749 952 2025-02-26 18:03:40.668218-08 2025-02-26 17:46:00.26634-08 test t H100 \N t \N \N \N {} -3500 1199 2025-02-28 11:02:55.892242-08 2025-02-28 11:47:33.229851-08 test t A100 \N t \N \N \N {} -2750 952 2025-02-26 17:51:37.477609-08 2025-02-26 18:26:25.871776-08 benchmark t H100 \N t \N \N \N {} -2753 952 2025-02-26 18:36:33.794858-08 2025-02-26 18:27:26.243733-08 benchmark f H100 \N t \N \N \N {} -2754 952 2025-02-26 18:28:16.765992-08 2025-02-26 19:25:37.400542-08 leaderboard f H100 0.00005700789 t \N \N \N {} -3503 1200 2025-02-28 12:12:33.080144-08 2025-02-28 10:58:43.275181-08 test f A100 \N t \N \N \N {} -3880 1295 2025-03-01 02:41:14.083823-08 2025-03-01 02:48:54.508486-08 test t A100 \N t \N \N \N {} -2758 956 2025-02-26 19:12:28.514931-08 2025-02-26 19:22:57.495178-08 test f A100 \N f \N \N \N {} -2764 962 2025-02-26 19:58:47.609776-08 2025-02-26 20:12:54.160596-08 test f A100 \N f \N \N \N {} -2777 965 2025-02-26 19:44:07.999345-08 2025-02-26 19:22:11.183958-08 benchmark t A100 \N t \N \N \N {} -2778 965 2025-02-26 20:27:24.433649-08 2025-02-26 21:00:25.129429-08 leaderboard t A100 0.014223114 t \N \N \N {} -2779 967 2025-02-26 21:12:06.665368-08 2025-02-26 19:59:07.508513-08 test f A100 \N t \N \N \N {} -2783 971 2025-02-26 21:16:26.422875-08 2025-02-26 20:09:38.194507-08 test f A100 \N f \N \N \N {} -2784 972 2025-02-26 19:47:01.998273-08 2025-02-26 20:04:32.122274-08 test f A100 \N f \N \N \N {} -2793 975 2025-02-26 21:52:56.123062-08 2025-02-26 20:49:58.093155-08 benchmark t A100 \N t \N \N \N {} -2795 975 2025-02-26 21:40:13.75942-08 2025-02-26 21:41:56.411339-08 test f A100 \N t \N \N \N {} -2802 976 2025-02-26 20:52:48.46039-08 2025-02-26 21:31:20.714827-08 benchmark t A100 \N t \N \N \N {} -2803 976 2025-02-26 20:35:27.685284-08 2025-02-26 20:31:29.866823-08 leaderboard t A100 0.0031533973333333336 t \N \N \N {} -2808 981 2025-02-26 23:31:14.250849-08 2025-02-26 22:50:58.663532-08 test f H100 \N t \N \N \N {} -2809 981 2025-02-26 22:06:44.871153-08 2025-02-26 23:16:53.896715-08 benchmark f H100 \N t \N \N \N {} -2812 981 2025-02-26 23:27:33.898802-08 2025-02-26 22:27:35.757556-08 benchmark t H100 \N t \N \N \N {} -2813 981 2025-02-26 22:50:45.630362-08 2025-02-26 22:38:26.024532-08 leaderboard t H100 0.0014750091999999999 t \N \N \N {} -2821 983 2025-02-26 23:54:19.617224-08 2025-02-26 22:54:43.658755-08 benchmark f T4 \N t \N \N \N {} -2830 984 2025-02-26 22:48:33.644224-08 2025-02-26 23:28:42.682233-08 benchmark f L4 \N t \N \N \N {} -2831 984 2025-02-26 22:41:43.757041-08 2025-02-26 22:20:51.450588-08 leaderboard f L4 0.01786917866666667 t \N \N \N {} -2832 985 2025-02-26 22:21:51.307477-08 2025-02-26 23:57:27.92374-08 test t H100 \N f \N \N \N {} -2833 985 2025-02-26 23:24:09.942894-08 2025-02-26 23:56:01.814252-08 test f H100 \N f \N \N \N {} -2834 986 2025-02-27 00:15:23.058422-08 2025-02-26 22:20:39.444729-08 test t H100 \N f \N \N \N {} -2836 987 2025-02-26 23:41:37.112761-08 2025-02-26 23:11:03.419055-08 test t H100 \N f \N \N \N {} -2837 987 2025-02-26 23:42:42.038768-08 2025-02-26 22:42:24.323417-08 test f H100 \N f \N \N \N {} -2838 988 2025-02-27 00:16:37.194207-08 2025-02-26 23:55:57.063136-08 test t H100 \N t \N \N \N {} -2845 989 2025-02-26 23:26:00.442015-08 2025-02-27 00:38:35.106507-08 benchmark t H100 \N t \N \N \N {} -2846 989 2025-02-27 00:27:01.196646-08 2025-02-26 23:54:58.682026-08 leaderboard t H100 0.0016006286666666667 t \N \N \N {} -2854 990 2025-02-26 23:50:17.183107-08 2025-02-27 00:18:03.383314-08 benchmark f H100 \N t \N \N \N {} -2862 992 2025-02-27 00:36:55.131454-08 2025-02-27 00:15:38.507114-08 benchmark f H100 \N t \N \N \N {} -2863 992 2025-02-27 00:40:16.481455-08 2025-02-26 22:52:39.64442-08 leaderboard f H100 0.0014829375555555555 t \N \N \N {} -2870 994 2025-02-26 23:16:51.538974-08 2025-02-26 23:17:43.660758-08 test t H100 \N f \N \N \N {} -2871 994 2025-02-26 23:04:28.177098-08 2025-02-26 23:47:57.196289-08 test f H100 \N f \N \N \N {} -2872 995 2025-02-26 23:07:49.94189-08 2025-02-26 23:01:43.419125-08 test t H100 \N t \N \N \N {} -2890 1001 2025-02-27 00:11:31.886016-08 2025-02-27 00:30:39.383216-08 benchmark f T4 \N f \N \N \N {} -3098 1106 2025-02-28 01:45:37.330184-08 2025-02-28 01:23:52.672209-08 benchmark f A100 \N t \N \N \N {} -3099 1106 2025-02-28 02:14:12.999333-08 2025-02-28 01:30:27.761931-08 leaderboard f A100 0.003088477 t \N \N \N {} -6141 1883 2025-03-11 12:00:12.478656-07 2025-03-11 10:31:10.503903-07 test f T4 \N f \N \N \N {} -6313 1953 2025-03-12 19:49:16.980515-07 2025-03-12 19:36:25.891323-07 benchmark f A100 \N t \N \N \N {} -3101 1107 2025-02-28 03:01:32.467064-08 2025-02-28 01:43:34.556856-08 benchmark f A100 \N t \N \N \N {} -3104 1107 2025-02-28 02:08:46.087586-08 2025-02-28 03:09:33.504864-08 benchmark t A100 \N t \N \N \N {} -3105 1107 2025-02-28 02:13:06.321782-08 2025-02-28 02:29:03.356769-08 leaderboard t A100 0.003087981 t \N \N \N {} -6143 1884 2025-03-11 10:32:50.120634-07 2025-03-11 10:37:50.909508-07 benchmark f A100 \N t \N \N \N {} -6316 1954 2025-03-12 19:55:01.537607-07 2025-03-12 19:24:32.57323-07 leaderboard f A100 0.0013369068899999998 t \N \N \N {} -6463 2007 2025-03-14 06:32:12.249801-07 2025-03-14 06:46:07.006143-07 benchmark f A100 \N t \N \N \N {} -3107 1108 2025-02-28 02:23:54.191397-08 2025-02-28 03:14:14.624324-08 benchmark t A100 \N t \N \N \N {} -6464 2008 2025-03-14 06:36:56.11805-07 2025-03-14 07:46:19.266759-07 test f A100 \N t \N \N \N {} -3110 1108 2025-02-28 01:35:00.983298-08 2025-02-28 01:52:32.794497-08 benchmark f A100 \N t \N \N \N {} -3111 1108 2025-02-28 02:00:47.78852-08 2025-02-28 02:24:47.205564-08 leaderboard f A100 0.0030859636666666665 t \N \N \N {} -6145 1885 2025-03-11 11:00:03.267504-07 2025-03-11 12:18:13.420225-07 test t T4 \N f \N \N \N {} -6466 2008 2025-03-14 07:55:41.158642-07 2025-03-14 06:50:35.791318-07 leaderboard f A100 0.003151319 t \N \N \N {} -3113 1109 2025-02-28 03:14:09.084685-08 2025-02-28 03:13:52.452047-08 benchmark t A100 \N t \N \N \N {} -3114 1109 2025-02-28 02:43:35.282513-08 2025-02-28 02:18:55.469658-08 leaderboard t A100 0.0030930446666666664 t \N \N \N {} -6146 1886 2025-03-11 11:52:02.632791-07 2025-03-11 12:03:46.235678-07 test t T4 \N f \N \N \N {} -6320 1955 2025-03-12 20:34:48.835946-07 2025-03-12 20:33:18.170617-07 benchmark f A100 \N t \N \N \N {} -6469 2008 2025-03-14 06:40:05.438504-07 2025-03-14 06:15:33.296357-07 leaderboard t A100 0.0031540403333333335 t \N \N \N {} -3116 1109 2025-02-28 02:52:12.076786-08 2025-02-28 02:54:24.827665-08 benchmark f A100 \N t \N \N \N {} -3117 1109 2025-02-28 01:28:21.790912-08 2025-02-28 02:23:58.811177-08 leaderboard f A100 0.003085927 t \N \N \N {} -6147 1886 2025-03-11 12:54:26.920466-07 2025-03-11 11:44:08.259058-07 test f T4 \N f \N \N \N {} -6472 2009 2025-03-14 07:41:35.13284-07 2025-03-14 07:39:10.080386-07 leaderboard t A100 0.0024421096666666663 t \N \N \N {} -6148 1887 2025-03-11 12:01:43.87785-07 2025-03-11 12:26:23.003639-07 test f T4 \N f \N \N \N {} -6474 2009 2025-03-14 06:55:16.190554-07 2025-03-14 07:53:24.794099-07 benchmark f A100 \N t \N \N \N {} -3125 1111 2025-02-28 03:11:12.541581-08 2025-02-28 01:41:13.937321-08 benchmark f A100 \N t \N \N \N {} -3126 1111 2025-02-28 02:42:24.500879-08 2025-02-28 03:15:29.89987-08 leaderboard f A100 0.00316644475 t \N \N \N {} -6149 1887 2025-03-11 11:38:18.005859-07 2025-03-11 12:03:46.05906-07 test t T4 \N f \N \N \N {} -6323 1957 2025-03-13 09:54:02.414286-07 2025-03-13 09:52:30.795121-07 test t T4 \N f \N \N \N {} -6475 2009 2025-03-14 06:45:29.792426-07 2025-03-14 07:50:32.215984-07 leaderboard f A100 0.0024576166666666665 t \N \N \N {} -6597 2044 2025-03-14 13:34:41.655918-07 2025-03-14 12:08:56.842687-07 test t T4 \N f \N \N \N {} -6896 2145 2025-03-16 13:27:48.549328-07 2025-03-16 13:17:35.997934-07 benchmark f T4 \N f \N \N \N {} -6476 2010 2025-03-14 08:15:20.795494-07 2025-03-14 08:16:59.451399-07 test t A100 \N t \N \N \N {} -6477 2010 2025-03-14 08:08:47.27748-07 2025-03-14 06:59:19.727174-07 benchmark t A100 \N t \N \N \N {} -6478 2010 2025-03-14 06:48:33.525468-07 2025-03-14 08:02:14.948812-07 leaderboard t A100 0.002638912 t \N \N \N {} -3131 1112 2025-02-28 02:57:25.964429-08 2025-02-28 01:48:05.5249-08 benchmark f A100 \N t \N \N \N {} -6479 2010 2025-03-14 06:44:08.522027-07 2025-03-14 07:44:23.847039-07 test f A100 \N t \N \N \N {} -6480 2010 2025-03-14 07:56:38.205116-07 2025-03-14 07:54:00.333047-07 benchmark f A100 \N t \N \N \N {} -6152 1889 2025-03-11 13:30:29.989092-07 2025-03-11 12:48:46.455566-07 test t T4 \N t \N \N \N {} -6326 1958 2025-03-13 10:24:57.188611-07 2025-03-13 10:35:01.884206-07 test f T4 \N f \N \N \N {} -6482 2011 2025-03-14 08:02:31.143266-07 2025-03-14 06:41:16.991591-07 test f A100 \N t \N \N \N {} -6598 2045 2025-03-14 12:19:29.381029-07 2025-03-14 12:25:30.310096-07 test f A100 \N t \N \N \N {} -3137 1113 2025-02-28 03:08:43.17474-08 2025-02-28 02:45:51.821714-08 benchmark f A100 \N t \N \N \N {} -3138 1113 2025-02-28 02:20:25.9795-08 2025-02-28 02:34:44.310115-08 leaderboard f A100 0.0030921646666666664 t \N \N \N {} -6154 1889 2025-03-11 13:37:01.73686-07 2025-03-11 12:13:13.284253-07 test f T4 \N t \N \N \N {} -6327 1959 2025-03-13 10:26:24.667252-07 2025-03-13 11:45:14.442974-07 test t T4 \N f \N \N \N {} -3140 1113 2025-02-28 01:57:21.325728-08 2025-02-28 03:23:21.141542-08 benchmark t A100 \N t \N \N \N {} -6156 1890 2025-03-11 12:21:39.727111-07 2025-03-11 12:34:12.250351-07 test t T4 \N t \N \N \N {} -6157 1890 2025-03-11 13:12:21.4034-07 2025-03-11 12:39:13.895393-07 benchmark t T4 \N f \N \N \N {} -6328 1959 2025-03-13 12:08:12.60756-07 2025-03-13 12:05:32.463165-07 test f T4 \N f \N \N \N {} -6899 2148 2025-03-16 12:45:15.052291-07 2025-03-16 12:31:02.61805-07 test t T4 \N t \N \N \N {} -3143 1114 2025-02-28 01:39:46.938722-08 2025-02-28 02:11:45.255844-08 benchmark t A100 \N t \N \N \N {} -6159 1890 2025-03-11 13:25:06.856726-07 2025-03-11 12:59:34.598467-07 benchmark f T4 \N f \N \N \N {} -6329 1960 2025-03-13 11:46:43.371-07 2025-03-13 10:55:57.695267-07 test f T4 \N f \N \N \N {} -6902 2148 2025-03-16 12:34:32.187225-07 2025-03-16 12:33:04.36163-07 test f T4 \N t \N \N \N {} -3146 1114 2025-02-28 03:06:33.724337-08 2025-02-28 03:00:58.980906-08 benchmark f A100 \N t \N \N \N {} -6330 1960 2025-03-13 12:01:53.271866-07 2025-03-13 10:58:04.509016-07 test t T4 \N f \N \N \N {} -6483 2011 2025-03-14 07:37:56.702294-07 2025-03-14 07:18:30.959485-07 benchmark f A100 \N t \N \N \N {} -6331 1961 2025-03-13 12:07:41.574197-07 2025-03-13 11:12:52.328699-07 test f T4 \N f \N \N \N {} -6485 2011 2025-03-14 08:12:16.79755-07 2025-03-14 07:27:22.899891-07 test t A100 \N t \N \N \N {} -6671 2063 2025-03-14 14:46:30.836769-07 2025-03-14 14:56:13.217033-07 test t T4 \N f \N \N \N {} -6332 1961 2025-03-13 11:55:09.823065-07 2025-03-13 11:32:59.676251-07 test t T4 \N f \N \N \N {} -6486 2011 2025-03-14 07:56:31.390698-07 2025-03-14 07:47:31.714989-07 benchmark t A100 \N t \N \N \N {} -6163 1894 2025-03-11 12:16:32.845989-07 2025-03-11 13:15:29.765532-07 benchmark f T4 \N f \N \N \N {} -6333 1962 2025-03-13 12:24:14.859057-07 2025-03-13 12:20:08.73536-07 test t T4 \N f \N \N \N {} -3159 1116 2025-02-28 02:38:21.177305-08 2025-02-28 02:21:09.980476-08 leaderboard t A100 0.0030984103333333334 t \N \N \N {} -6164 1895 2025-03-11 12:39:00.764376-07 2025-03-11 12:37:33.970367-07 benchmark f T4 \N f \N \N \N {} -3162 1117 2025-02-28 03:41:30.098596-08 2025-02-28 03:20:42.331939-08 leaderboard t A100 0.0031041603333333335 t \N \N \N {} -6165 1896 2025-03-11 14:01:29.688942-07 2025-03-11 14:06:15.280779-07 benchmark f T4 \N t \N \N \N {} -6335 1963 2025-03-13 12:06:34.502766-07 2025-03-13 12:03:06.093899-07 test f T4 \N f \N \N \N {} -3169 1118 2025-02-28 03:31:50.129542-08 2025-02-28 03:36:06.764464-08 test f A100 \N t \N \N \N {} -3170 1118 2025-02-28 03:57:44.683588-08 2025-02-28 02:28:12.168907-08 benchmark f A100 \N t \N \N \N {} -3175 1119 2025-02-28 03:54:43.890351-08 2025-02-28 02:21:17.673941-08 test t A100 \N t \N \N \N {} -3176 1119 2025-02-28 03:25:21.208695-08 2025-02-28 02:53:54.097447-08 benchmark t A100 \N t \N \N \N {} -3179 1120 2025-02-28 03:19:52.64551-08 2025-02-28 04:03:22.512601-08 benchmark f A100 \N t \N \N \N {} -3180 1120 2025-02-28 02:37:44.833383-08 2025-02-28 03:49:05.485113-08 leaderboard f A100 0.0030965363333333336 t \N \N \N {} -3182 1120 2025-02-28 03:37:30.50022-08 2025-02-28 02:42:14.289029-08 benchmark t A100 \N t \N \N \N {} -3183 1120 2025-02-28 04:27:43.205983-08 2025-02-28 04:09:58.805071-08 leaderboard t A100 0.0030974113333333336 t \N \N \N {} -3188 1121 2025-02-28 05:16:45.018621-08 2025-02-28 04:16:05.328123-08 benchmark t A100 \N t \N \N \N {} -3189 1121 2025-02-28 03:32:46.838908-08 2025-02-28 04:24:02.880315-08 leaderboard t A100 0.0031272676666666663 t \N \N \N {} -6174 1899 2025-03-11 13:02:25.812706-07 2025-03-11 14:15:36.592099-07 test f T4 \N f \N \N \N {} -6340 1965 2025-03-13 12:42:09.960264-07 2025-03-13 12:51:36.764465-07 test f T4 \N f \N \N \N {} -6494 2013 2025-03-14 11:17:10.359439-07 2025-03-14 12:08:51.813768-07 test t H100 \N f \N \N \N {} -3191 1122 2025-02-28 04:31:59.29779-08 2025-02-28 04:47:11.70033-08 benchmark f A100 \N t \N \N \N {} -6175 1900 2025-03-11 14:09:24.209402-07 2025-03-11 14:27:29.662604-07 test t T4 \N f \N \N \N {} -3194 1122 2025-02-28 04:47:15.662542-08 2025-02-28 03:39:02.874427-08 benchmark t A100 \N t \N \N \N {} -3195 1122 2025-02-28 05:10:42.206283-08 2025-02-28 04:42:15.656228-08 leaderboard t A100 0.0031017193333333333 t \N \N \N {} -3196 1123 2025-02-28 05:13:41.323903-08 2025-02-28 04:55:50.819985-08 test f A100 \N t \N \N \N {} -3197 1123 2025-02-28 04:32:26.171448-08 2025-02-28 05:16:39.991067-08 benchmark f A100 \N t \N \N \N {} -3199 1123 2025-02-28 03:54:47.179034-08 2025-02-28 04:59:40.706458-08 test t A100 \N t \N \N \N {} -3208 1125 2025-02-28 05:52:39.581361-08 2025-02-28 05:55:19.83514-08 test t A100 \N t \N \N \N {} -3209 1125 2025-02-28 06:17:26.794554-08 2025-02-28 05:23:32.742402-08 benchmark t A100 \N t \N \N \N {} -3210 1125 2025-02-28 06:05:54.312077-08 2025-02-28 06:38:52.236143-08 leaderboard t A100 0.0030843956666666665 t \N \N \N {} -3211 1125 2025-02-28 06:16:05.768963-08 2025-02-28 06:25:41.001152-08 test f A100 \N t \N \N \N {} -3214 1126 2025-02-28 05:05:57.182176-08 2025-02-28 05:32:39.383884-08 test t A100 \N t \N \N \N {} -3215 1126 2025-02-28 05:52:39.479649-08 2025-02-28 06:47:24.562236-08 benchmark t A100 \N t \N \N \N {} -3216 1126 2025-02-28 05:10:50.786526-08 2025-02-28 04:55:01.414872-08 leaderboard t A100 0.0030879633333333336 t \N \N \N {} -3217 1126 2025-02-28 05:53:55.969095-08 2025-02-28 05:27:21.527994-08 test f A100 \N t \N \N \N {} -3220 1127 2025-02-28 06:54:22.094293-08 2025-02-28 06:25:04.727769-08 test t A100 \N t \N \N \N {} -3221 1127 2025-02-28 05:59:30.794599-08 2025-02-28 06:35:17.919421-08 benchmark t A100 \N t \N \N \N {} -3222 1127 2025-02-28 06:46:45.538801-08 2025-02-28 06:24:24.183739-08 leaderboard t A100 0.0031056823333333334 t \N \N \N {} -3223 1127 2025-02-28 05:37:12.475011-08 2025-02-28 05:15:06.135555-08 test f A100 \N t \N \N \N {} -3226 1128 2025-02-28 06:50:10.03441-08 2025-02-28 07:04:42.596407-08 benchmark f A100 \N t \N \N \N {} -3227 1129 2025-02-28 05:42:24.496681-08 2025-02-28 05:46:06.806343-08 benchmark f A100 \N t \N \N \N {} -3228 1131 2025-02-28 06:46:23.079469-08 2025-02-28 07:01:32.420227-08 benchmark f A100 \N t \N \N \N {} -3229 1130 2025-02-28 05:31:01.081051-08 2025-02-28 05:35:16.365184-08 test t A100 \N t \N \N \N {} -3232 1130 2025-02-28 05:56:38.654734-08 2025-02-28 06:03:33.120382-08 test f A100 \N t \N \N \N {} -3233 1130 2025-02-28 06:46:27.388033-08 2025-02-28 07:12:24.723128-08 benchmark f A100 \N t \N \N \N {} -3234 1130 2025-02-28 05:46:53.817871-08 2025-02-28 06:42:04.198827-08 leaderboard f A100 0.00310292 t \N \N \N {} -3235 1132 2025-02-28 06:21:32.053551-08 2025-02-28 07:14:16.857496-08 benchmark f A100 \N t \N \N \N {} -3236 1133 2025-02-28 07:07:04.755708-08 2025-02-28 06:46:05.801511-08 test t A100 \N t \N \N \N {} -3237 1133 2025-02-28 07:17:33.624616-08 2025-02-28 07:02:03.837416-08 benchmark t A100 \N t \N \N \N {} -3248 1135 2025-02-28 05:37:54.093967-08 2025-02-28 05:45:04.283128-08 benchmark t A100 \N t \N \N \N {} -3249 1135 2025-02-28 06:33:33.396017-08 2025-02-28 06:15:49.659969-08 leaderboard t A100 0.0031071463333333335 t \N \N \N {} -6177 1901 2025-03-11 14:17:16.056405-07 2025-03-11 12:39:24.253923-07 test t T4 \N f \N \N \N {} -6347 1967 2025-03-13 13:09:40.841714-07 2025-03-13 12:52:38.371098-07 test f T4 \N f \N \N \N {} -6496 2014 2025-03-14 11:32:31.174534-07 2025-03-14 10:45:59.582663-07 test f H100 \N f \N \N \N {} -6601 2045 2025-03-14 13:39:24.594274-07 2025-03-14 12:37:33.021169-07 test t A100 \N t \N \N \N {} -6602 2045 2025-03-14 13:09:25.371778-07 2025-03-14 12:02:22.248161-07 benchmark t A100 \N t \N \N \N {} -6603 2045 2025-03-14 12:21:14.812855-07 2025-03-14 13:54:36.938359-07 leaderboard t A100 0.0025770586666666664 t \N \N \N {} -6646 2053 2025-03-14 12:10:23.321882-07 2025-03-14 13:10:56.484171-07 leaderboard f A100 0.0024435633333333333 t \N \N \N {} -3251 1137 2025-02-28 05:50:43.617739-08 2025-02-28 06:49:39.360729-08 benchmark f A100 \N t \N \N \N {} -3257 1139 2025-02-28 06:31:44.654978-08 2025-02-28 05:38:26.805158-08 test f A100 \N t \N \N \N {} -3261 1140 2025-02-28 06:07:13.230828-08 2025-02-28 05:47:24.282991-08 benchmark f A100 \N t \N \N \N {} -3262 1141 2025-02-28 05:55:39.227255-08 2025-02-28 05:48:38.619947-08 benchmark f A100 \N t \N \N \N {} -6178 1901 2025-03-11 13:27:16.149666-07 2025-03-11 13:38:34.371907-07 test f T4 \N f \N \N \N {} -6348 1967 2025-03-13 13:02:53.563358-07 2025-03-13 13:01:27.915249-07 test t T4 \N f \N \N \N {} -6497 2014 2025-03-14 12:15:59.319003-07 2025-03-14 11:30:37.118914-07 test t H100 \N f \N \N \N {} -6604 2046 2025-03-14 13:43:57.061271-07 2025-03-14 13:07:50.209665-07 test t A100 \N t \N \N \N {} -6605 2046 2025-03-14 12:17:52.139426-07 2025-03-14 12:29:22.656383-07 benchmark t A100 \N t \N \N \N {} -6606 2046 2025-03-14 12:53:29.636257-07 2025-03-14 12:33:45.393318-07 leaderboard t A100 0.00256954 t \N \N \N {} -3264 1142 2025-02-28 07:20:46.488258-08 2025-02-28 06:11:17.956226-08 benchmark f A100 \N t \N \N \N {} -3265 1142 2025-02-28 07:11:08.133535-08 2025-02-28 06:09:41.048274-08 leaderboard f A100 0.0030866826666666666 t \N \N \N {} -6179 1902 2025-03-11 17:31:19.851556-07 2025-03-11 16:59:27.86489-07 test t T4 \N f \N \N \N {} -6349 1968 2025-03-13 12:16:15.487592-07 2025-03-13 13:01:52.748945-07 test t T4 \N f \N \N \N {} -6498 2015 2025-03-14 10:52:26.469597-07 2025-03-14 10:53:06.720164-07 test f H100 \N t \N \N \N {} -6499 2015 2025-03-14 11:34:32.855847-07 2025-03-14 10:47:26.771183-07 benchmark f H100 \N t \N \N \N {} -3267 1142 2025-02-28 07:07:31.550653-08 2025-02-28 06:39:21.840614-08 benchmark t A100 \N t \N \N \N {} -3268 1142 2025-02-28 06:19:53.446652-08 2025-02-28 05:31:10.708334-08 leaderboard t A100 0.0030884806666666665 t \N \N \N {} -6180 1902 2025-03-11 16:42:29.121174-07 2025-03-11 17:29:34.053097-07 test f T4 \N f \N \N \N {} -6350 1968 2025-03-13 11:36:52.447175-07 2025-03-13 11:56:08.746664-07 test f T4 \N f \N \N \N {} -6501 2015 2025-03-14 11:04:29.106551-07 2025-03-14 11:04:06.594233-07 test t H100 \N t \N \N \N {} -6502 2015 2025-03-14 10:46:29.195352-07 2025-03-14 11:29:56.856874-07 benchmark t H100 \N t \N \N \N {} -6503 2015 2025-03-14 11:02:59.180189-07 2025-03-14 11:37:31.767013-07 leaderboard t H100 0.006124215666666667 t \N \N \N {} -3270 1143 2025-02-28 07:20:25.006918-08 2025-02-28 06:39:34.217517-08 benchmark t A100 \N t \N \N \N {} -3271 1143 2025-02-28 06:14:53.755216-08 2025-02-28 05:57:39.774657-08 leaderboard t A100 0.0031000976666666663 t \N \N \N {} -6181 1903 2025-03-11 16:58:37.492878-07 2025-03-11 15:58:30.703546-07 test t T4 \N t \N \N \N {} -6182 1903 2025-03-11 17:18:44.559625-07 2025-03-11 15:57:14.031513-07 benchmark t T4 \N t \N \N \N {} -6183 1903 2025-03-11 16:37:25.986127-07 2025-03-11 16:28:32.33402-07 leaderboard t T4 3.1959500263333336 t \N \N \N {} -6351 1969 2025-03-13 11:55:17.679659-07 2025-03-13 12:30:30.977919-07 test t T4 \N f \N \N \N {} -3273 1143 2025-02-28 07:13:08.532252-08 2025-02-28 06:24:09.587417-08 benchmark f A100 \N t \N \N \N {} -6353 1970 2025-03-13 13:09:01.840724-07 2025-03-13 13:09:06.040381-07 test t T4 \N f \N \N \N {} -6507 2017 2025-03-14 12:15:09.410382-07 2025-03-14 12:08:16.887223-07 leaderboard t A100 0.0024410596666666665 t \N \N \N {} -3300 1148 2025-02-28 07:07:41.615297-08 2025-02-28 07:39:21.848265-08 benchmark t H100 \N t \N \N \N {} -3301 1148 2025-02-28 06:10:52.271101-08 2025-02-28 06:58:11.397489-08 leaderboard t H100 0.001857518 t \N \N \N {} -3302 1148 2025-02-28 06:00:16.905751-08 2025-02-28 06:14:31.098179-08 test t L4 \N t \N \N \N {} -3303 1148 2025-02-28 07:52:17.086381-08 2025-02-28 06:16:18.985978-08 benchmark t L4 \N t \N \N \N {} -3304 1148 2025-02-28 07:32:12.045626-08 2025-02-28 06:24:56.573657-08 leaderboard t L4 0.008826167333333334 t \N \N \N {} -3307 1148 2025-02-28 07:29:43.892232-08 2025-02-28 06:09:46.083173-08 leaderboard f L4 0.008876066 t \N \N \N {} -3308 1148 2025-02-28 06:13:12.466775-08 2025-02-28 07:21:14.017377-08 test f T4 \N t \N \N \N {} -3309 1148 2025-02-28 06:14:05.077253-08 2025-02-28 07:11:50.628783-08 benchmark f T4 \N t \N \N \N {} -3310 1148 2025-02-28 06:45:02.322758-08 2025-02-28 06:31:27.319188-08 leaderboard f T4 0.010512813666666666 t \N \N \N {} -3313 1151 2025-02-28 06:13:28.870279-08 2025-02-28 06:59:04.57498-08 test f H100 \N f \N \N \N {} -3314 1151 2025-02-28 06:53:49.334214-08 2025-02-28 07:39:29.099662-08 test f A100 \N f \N \N \N {} -3315 1151 2025-02-28 06:23:38.170481-08 2025-02-28 06:10:53.877168-08 test t A100 \N f \N \N \N {} -3320 1151 2025-02-28 07:07:06.317012-08 2025-02-28 07:47:22.348612-08 test f T4 \N f \N \N \N {} -3321 1152 2025-02-28 06:41:39.144469-08 2025-02-28 07:15:44.323297-08 test f A100 \N t \N \N \N {} -3322 1152 2025-02-28 06:33:30.747147-08 2025-02-28 07:19:24.710801-08 benchmark f A100 \N t \N \N \N {} -3323 1152 2025-02-28 06:13:17.846448-08 2025-02-28 07:16:53.101236-08 leaderboard f A100 0.003092475 t \N \N \N {} -3532 1205 2025-02-28 11:36:17.404133-08 2025-02-28 11:04:59.294896-08 test t A100 \N t \N \N \N {} -3324 1152 2025-02-28 07:18:58.437402-08 2025-02-28 06:19:11.553801-08 test t A100 \N t \N \N \N {} -3325 1152 2025-02-28 07:55:29.183414-08 2025-02-28 07:03:55.443509-08 benchmark t A100 \N t \N \N \N {} -3326 1152 2025-02-28 07:19:25.423303-08 2025-02-28 07:50:35.236223-08 leaderboard t A100 0.0031075596666666665 t \N \N \N {} -3329 1153 2025-02-28 06:25:00.374655-08 2025-02-28 07:30:25.54331-08 test t L4 \N f \N \N \N {} -3334 1153 2025-02-28 07:49:11.602135-08 2025-02-28 07:52:08.469201-08 test f L4 \N f \N \N \N {} -3335 1154 2025-02-28 07:00:59.730078-08 2025-02-28 06:49:51.977949-08 test t A100 \N t \N \N \N {} -3336 1154 2025-02-28 07:54:33.813629-08 2025-02-28 06:56:00.534875-08 benchmark t A100 \N t \N \N \N {} -3344 1155 2025-02-28 07:18:52.719651-08 2025-02-28 07:58:30.473969-08 test f A100 \N t \N \N \N {} -3345 1155 2025-02-28 06:18:08.185243-08 2025-02-28 06:59:20.358203-08 benchmark f A100 \N t \N \N \N {} -3346 1155 2025-02-28 07:02:45.365698-08 2025-02-28 06:33:19.989005-08 leaderboard f A100 0.00308959 t \N \N \N {} -3347 1156 2025-02-28 07:59:44.021356-08 2025-02-28 06:48:49.388888-08 test f A100 \N t \N \N \N {} -3350 1156 2025-02-28 06:29:45.868814-08 2025-02-28 06:37:20.655964-08 test t A100 \N t \N \N \N {} -3351 1156 2025-02-28 07:13:45.617041-08 2025-02-28 07:15:15.742271-08 benchmark t A100 \N t \N \N \N {} -3352 1156 2025-02-28 07:46:00.514913-08 2025-02-28 06:43:16.480409-08 leaderboard t A100 0.0031107886666666665 t \N \N \N {} -3354 1157 2025-02-28 06:33:28.648011-08 2025-02-28 06:43:24.152901-08 benchmark t A100 \N t \N \N \N {} -3355 1157 2025-02-28 07:45:51.654693-08 2025-02-28 07:44:31.010515-08 leaderboard t A100 0.0030852116666666663 t \N \N \N {} -6189 1905 2025-03-11 17:31:04.799276-07 2025-03-11 17:23:51.876169-07 test t T4 \N f \N \N \N {} -6355 1971 2025-03-13 13:24:12.401709-07 2025-03-13 13:32:14.413028-07 test f T4 \N f \N \N \N {} -6509 2017 2025-03-14 11:29:46.886636-07 2025-03-14 12:19:10.774689-07 benchmark f A100 \N t \N \N \N {} -3357 1157 2025-02-28 08:03:49.481801-08 2025-02-28 08:00:06.310818-08 benchmark f A100 \N t \N \N \N {} -3358 1157 2025-02-28 07:39:56.271986-08 2025-02-28 07:00:59.378014-08 leaderboard f A100 0.0030816896666666666 t \N \N \N {} -3360 1159 2025-02-28 07:39:08.622355-08 2025-02-28 08:38:10.734027-08 test f T4 \N f \N \N \N {} -3361 1160 2025-02-28 09:26:30.209671-08 2025-02-28 09:51:28.232565-08 test f L4 \N f \N \N \N {} -3362 1160 2025-02-28 08:13:42.830297-08 2025-02-28 08:36:53.136944-08 test t L4 \N f \N \N \N {} -3367 1163 2025-02-28 09:08:01.615266-08 2025-02-28 09:04:38.110604-08 test t T4 \N t \N \N \N {} -3368 1163 2025-02-28 08:47:34.73436-08 2025-02-28 09:40:07.599281-08 benchmark t T4 \N t \N \N \N {} -3370 1163 2025-02-28 10:03:37.527045-08 2025-02-28 10:11:34.393096-08 test f T4 \N f \N \N \N {} -3371 1164 2025-02-28 10:01:44.770216-08 2025-02-28 09:27:21.603234-08 test f L4 \N t \N \N \N {} -3372 1164 2025-02-28 08:47:05.293177-08 2025-02-28 10:23:25.384173-08 benchmark f L4 \N t \N \N \N {} -3377 1165 2025-02-28 09:21:40.284625-08 2025-02-28 10:30:06.106699-08 test f L4 \N t \N \N \N {} -3378 1165 2025-02-28 08:56:55.571472-08 2025-02-28 09:49:21.797242-08 benchmark f L4 \N t \N \N \N {} -3379 1165 2025-02-28 10:05:42.150638-08 2025-02-28 09:36:46.848902-08 leaderboard f L4 0.017076701333333333 t \N \N \N {} -3454 1190 2025-02-28 12:13:49.181929-08 2025-02-28 11:09:14.008399-08 test f A100 \N t \N \N \N {} -3380 1165 2025-02-28 09:57:10.477902-08 2025-02-28 08:59:43.970761-08 test t L4 \N t \N \N \N {} -3381 1165 2025-02-28 09:34:07.882518-08 2025-02-28 10:28:42.342832-08 benchmark t L4 \N t \N \N \N {} -3382 1165 2025-02-28 10:25:49.905778-08 2025-02-28 08:45:12.787062-08 leaderboard t L4 0.017087161666666666 t \N \N \N {} -3383 1166 2025-02-28 10:09:06.401999-08 2025-02-28 09:14:44.292073-08 test t L4 \N t \N \N \N {} -3384 1166 2025-02-28 09:27:54.175727-08 2025-02-28 08:41:58.508616-08 benchmark t L4 \N t \N \N \N {} -3387 1166 2025-02-28 09:50:29.259635-08 2025-02-28 10:07:29.279896-08 benchmark f L4 \N t \N \N \N {} -3388 1166 2025-02-28 09:42:07.63904-08 2025-02-28 10:09:52.762874-08 leaderboard f L4 0.01713320033333333 t \N \N \N {} -3389 1167 2025-02-28 10:08:48.950368-08 2025-02-28 09:00:40.626022-08 test t L4 \N f \N \N \N {} -3390 1167 2025-02-28 09:23:41.075458-08 2025-02-28 08:55:11.552134-08 test f L4 \N f \N \N \N {} -3544 1207 2025-02-28 12:21:19.174948-08 2025-02-28 11:46:49.287104-08 test f A100 \N t \N \N \N {} -3394 1169 2025-02-28 09:19:45.607358-08 2025-02-28 10:30:06.679512-08 test t L4 \N f \N \N \N {} -3395 1169 2025-02-28 10:06:46.387305-08 2025-02-28 09:43:30.292106-08 test f L4 \N f \N \N \N {} -3397 1171 2025-02-28 09:03:44.961516-08 2025-02-28 10:20:27.853337-08 test f L4 \N f \N \N \N {} -3405 1174 2025-02-28 10:39:36.975544-08 2025-02-28 09:52:14.8899-08 benchmark t L4 \N t \N \N \N {} -3400 1173 2025-02-28 10:29:49.463718-08 2025-02-28 10:21:22.102387-08 test f L4 \N t \N \N \N {} -3401 1173 2025-02-28 09:21:57.128462-08 2025-02-28 10:30:59.993678-08 benchmark f L4 \N t \N \N \N {} -3434 1185 2025-02-28 11:34:12.254431-08 2025-02-28 10:21:43.385984-08 test t L4 \N t \N \N \N {} -3435 1185 2025-02-28 11:18:57.057952-08 2025-02-28 11:28:37.311547-08 benchmark t L4 \N t \N \N \N {} -3439 1186 2025-02-28 10:55:26.766127-08 2025-02-28 11:07:39.44103-08 benchmark f L4 \N t \N \N \N {} -3440 1186 2025-02-28 10:10:12.112939-08 2025-02-28 11:10:32.049899-08 leaderboard f L4 0.01707861033333333 t \N \N \N {} -3441 1186 2025-02-28 09:42:05.101791-08 2025-02-28 10:27:34.733276-08 test t L4 \N t \N \N \N {} -3442 1186 2025-02-28 10:06:18.981477-08 2025-02-28 10:02:38.94075-08 benchmark t L4 \N t \N \N \N {} -3449 1188 2025-02-28 11:22:41.568508-08 2025-02-28 10:32:23.811511-08 test f A100 \N t \N \N \N {} -3450 1188 2025-02-28 10:37:43.385883-08 2025-02-28 11:17:39.7834-08 benchmark f A100 \N t \N \N \N {} -3456 1190 2025-02-28 10:41:28.207826-08 2025-02-28 12:14:51.122436-08 leaderboard f A100 0.002069882 t \N \N \N {} -3457 1190 2025-02-28 11:29:53.359486-08 2025-02-28 11:55:18.355744-08 test t A100 \N t \N \N \N {} -3458 1190 2025-02-28 10:37:33.37378-08 2025-02-28 11:30:33.125044-08 benchmark t A100 \N t \N \N \N {} -3459 1190 2025-02-28 11:02:25.492442-08 2025-02-28 11:05:52.473542-08 leaderboard t A100 0.002045183 t \N \N \N {} -6190 1905 2025-03-11 16:26:41.241921-07 2025-03-11 16:38:33.020666-07 test f T4 \N f \N \N \N {} -6510 2017 2025-03-14 12:01:53.782121-07 2025-03-14 10:58:53.397351-07 leaderboard f A100 0.0025043896666666664 t \N \N \N {} -3461 1191 2025-02-28 10:52:09.093285-08 2025-02-28 11:24:50.914766-08 benchmark f A100 \N t \N \N \N {} -3462 1191 2025-02-28 12:13:09.43073-08 2025-02-28 11:11:59.107864-08 leaderboard f A100 0.003088071 t \N \N \N {} -6191 1906 2025-03-11 16:40:31.677341-07 2025-03-11 17:30:01.999781-07 test t T4 \N f \N \N \N {} -6356 1971 2025-03-13 12:18:33.704663-07 2025-03-13 12:52:16.173084-07 test t T4 \N f \N \N \N {} -6511 2018 2025-03-14 11:30:51.790617-07 2025-03-14 12:46:33.976449-07 test t A100 \N t \N \N \N {} -6512 2018 2025-03-14 11:50:37.040812-07 2025-03-14 12:00:32.698091-07 benchmark t A100 \N t \N \N \N {} -6513 2018 2025-03-14 11:58:09.461167-07 2025-03-14 10:57:34.439949-07 leaderboard t A100 0.0024541096666666666 t \N \N \N {} -3464 1191 2025-02-28 11:06:30.414003-08 2025-02-28 10:53:55.756396-08 benchmark t A100 \N t \N \N \N {} -3465 1191 2025-02-28 10:54:02.19782-08 2025-02-28 11:07:04.11224-08 leaderboard t A100 0.0030866083333333334 t \N \N \N {} -6192 1906 2025-03-11 18:04:00.474946-07 2025-03-11 18:14:14.534699-07 test f T4 \N f \N \N \N {} -6357 1972 2025-03-13 13:34:07.292711-07 2025-03-13 14:11:37.509333-07 test t T4 \N t \N \N \N {} -6358 1972 2025-03-13 13:17:51.104355-07 2025-03-13 14:01:28.265004-07 benchmark t T4 \N f \N \N \N {} -6516 2018 2025-03-14 10:52:38.328-07 2025-03-14 12:27:11.650928-07 leaderboard f A100 0.0024775536666666667 t \N \N \N {} -3469 1193 2025-02-28 12:13:49.642175-08 2025-02-28 11:55:09.712002-08 benchmark f A100 \N t \N \N \N {} -3470 1193 2025-02-28 11:12:47.050744-08 2025-02-28 11:14:55.313376-08 leaderboard f A100 0.0031209246666666665 t \N \N \N {} -6193 1907 2025-03-11 16:49:57.729617-07 2025-03-11 17:48:28.815284-07 test f T4 \N f \N \N \N {} -6359 1972 2025-03-13 13:07:47.502676-07 2025-03-13 14:27:21.253575-07 test f T4 \N t \N \N \N {} -3472 1193 2025-02-28 10:47:16.91505-08 2025-02-28 11:18:49.513731-08 benchmark t A100 \N t \N \N \N {} -3473 1193 2025-02-28 11:05:06.759594-08 2025-02-28 12:04:22.05689-08 leaderboard t A100 0.003093471 t \N \N \N {} -6194 1907 2025-03-11 17:02:18.345331-07 2025-03-11 16:38:32.775938-07 test t T4 \N f \N \N \N {} -6361 1973 2025-03-13 13:34:46.334274-07 2025-03-13 14:01:13.147474-07 test f T4 \N t \N \N \N {} -6362 1973 2025-03-13 13:37:07.283208-07 2025-03-13 15:08:05.351976-07 benchmark f T4 \N f \N \N \N {} -6518 2020 2025-03-14 11:30:41.762022-07 2025-03-14 11:12:09.117466-07 test f A100 \N t \N \N \N {} -6519 2020 2025-03-14 11:13:39.719926-07 2025-03-14 11:25:21.536972-07 benchmark f A100 \N t \N \N \N {} -6520 2020 2025-03-14 12:06:50.863502-07 2025-03-14 12:44:38.653958-07 leaderboard f A100 0.0025181186666666665 t \N \N \N {} -3476 1196 2025-02-28 10:54:21.072266-08 2025-02-28 11:37:50.81538-08 benchmark t A100 \N t \N \N \N {} -3477 1196 2025-02-28 12:24:50.73577-08 2025-02-28 11:42:01.495948-08 leaderboard t A100 0.0030894043333333336 t \N \N \N {} -6195 1908 2025-03-11 16:33:13.825423-07 2025-03-11 17:00:19.916832-07 test t T4 \N f \N \N \N {} -3484 1195 2025-02-28 11:31:48.964942-08 2025-02-28 11:27:19.249047-08 benchmark t A100 \N f \N \N \N {} -6365 1974 2025-03-13 15:18:06.293763-07 2025-03-13 14:44:47.284631-07 test f T4 \N t \N \N \N {} -3486 1197 2025-02-28 12:26:33.136784-08 2025-02-28 11:24:39.011923-08 benchmark f A100 \N t \N \N \N {} -3487 1197 2025-02-28 11:54:54.56215-08 2025-02-28 12:26:07.45337-08 leaderboard f A100 0.0030942576666666663 t \N \N \N {} -6366 1974 2025-03-13 14:15:36.374645-07 2025-03-13 14:09:23.053095-07 benchmark f T4 \N f \N \N \N {} -6522 2020 2025-03-14 12:18:54.728315-07 2025-03-14 12:44:10.914453-07 benchmark t A100 \N t \N \N \N {} -3489 1197 2025-02-28 11:23:03.004299-08 2025-02-28 11:03:53.165609-08 benchmark t A100 \N t \N \N \N {} -3490 1197 2025-02-28 11:33:31.529975-08 2025-02-28 11:07:28.938258-08 leaderboard t A100 0.0030712816666666667 t \N \N \N {} -6197 1909 2025-03-11 18:17:26.032444-07 2025-03-11 17:19:28.72111-07 test t T4 \N f \N \N \N {} -6368 1974 2025-03-13 14:55:40.171664-07 2025-03-13 14:12:41.864308-07 benchmark t T4 \N f \N \N \N {} -6523 2020 2025-03-14 12:53:35.58379-07 2025-03-14 11:54:41.519798-07 leaderboard t A100 0.002498382 t \N \N \N {} -6524 2021 2025-03-14 12:36:39.402806-07 2025-03-14 11:08:03.626394-07 test t A100 \N t \N \N \N {} -6525 2021 2025-03-14 11:24:50.232214-07 2025-03-14 12:29:14.201976-07 benchmark t A100 \N t \N \N \N {} -6526 2021 2025-03-14 12:39:45.622642-07 2025-03-14 11:23:16.261268-07 leaderboard t A100 0.0025124666666666664 t \N \N \N {} -6612 2047 2025-03-14 13:18:33.788335-07 2025-03-14 13:24:29.736685-07 leaderboard f A100 0.0025605253333333333 t \N \N \N {} -6527 2021 2025-03-14 12:10:21.085092-07 2025-03-14 11:31:54.576597-07 test f A100 \N t \N \N \N {} -6374 1977 2025-03-13 16:02:01.798258-07 2025-03-13 15:38:46.117793-07 benchmark t T4 \N f \N \N \N {} -6536 2023 2025-03-14 12:00:31.556878-07 2025-03-14 12:46:54.788332-07 test t A100 \N t \N \N \N {} -6537 2023 2025-03-14 12:32:44.743863-07 2025-03-14 12:54:06.787263-07 benchmark t A100 \N t \N \N \N {} -6538 2023 2025-03-14 12:49:53.790312-07 2025-03-14 12:00:47.817767-07 leaderboard t A100 0.0025183806666666665 t \N \N \N {} -3507 1200 2025-02-28 12:16:30.548788-08 2025-02-28 11:43:54.947982-08 benchmark t A100 \N t \N \N \N {} -3508 1200 2025-02-28 11:28:38.825541-08 2025-02-28 11:10:09.664227-08 leaderboard t A100 0.003144041 t \N \N \N {} -6203 1912 2025-03-11 17:08:06.730232-07 2025-03-11 17:58:19.821741-07 test f T4 \N f \N \N \N {} -6377 1978 2025-03-13 15:54:45.454946-07 2025-03-13 16:33:05.355765-07 benchmark f T4 \N t \N \N \N {} -6541 2023 2025-03-14 11:06:04.095739-07 2025-03-14 11:57:33.138195-07 leaderboard f A100 0.0024532393333333334 t \N \N \N {} -6618 2048 2025-03-14 12:42:53.553414-07 2025-03-14 13:49:27.329988-07 leaderboard t A100 0.0025746103333333333 t \N \N \N {} -6648 2054 2025-03-14 12:06:53.802343-07 2025-03-14 13:37:02.957155-07 benchmark f A100 \N t \N \N \N {} -6649 2054 2025-03-14 12:36:50.625847-07 2025-03-14 12:35:25.488371-07 leaderboard f A100 0.0024409133333333334 t \N \N \N {} -3513 1201 2025-02-28 12:22:24.212195-08 2025-02-28 12:18:25.901829-08 benchmark t A100 \N t \N \N \N {} -3514 1201 2025-02-28 12:24:03.792716-08 2025-02-28 12:13:02.802944-08 leaderboard t A100 0.0031015336666666664 t \N \N \N {} -6378 1979 2025-03-13 17:23:58.186753-07 2025-03-13 16:06:55.720179-07 benchmark f T4 \N f \N \N \N {} -6543 2025 2025-03-14 12:41:00.561242-07 2025-03-14 11:46:24.982291-07 test t A100 \N t \N \N \N {} -6544 2025 2025-03-14 12:20:54.337724-07 2025-03-14 11:31:16.216919-07 benchmark t A100 \N t \N \N \N {} -6545 2025 2025-03-14 11:25:51.951167-07 2025-03-14 12:23:37.08102-07 leaderboard t A100 0.0025238436666666663 t \N \N \N {} -6620 2048 2025-03-14 12:30:34.657105-07 2025-03-14 12:13:05.701347-07 benchmark f A100 \N t \N \N \N {} -6621 2048 2025-03-14 12:33:05.301709-07 2025-03-14 13:37:25.906329-07 leaderboard f A100 0.002544002 t \N \N \N {} -3519 1202 2025-02-28 11:11:30.376958-08 2025-02-28 11:01:50.55658-08 benchmark t A100 \N t \N \N \N {} -3520 1202 2025-02-28 11:43:16.373234-08 2025-02-28 12:31:24.408715-08 leaderboard t A100 0.003084011 t \N \N \N {} -6207 1914 2025-03-11 17:36:02.063163-07 2025-03-11 18:44:14.758287-07 test f T4 \N f \N \N \N {} -6380 1980 2025-03-13 15:58:51.960023-07 2025-03-13 16:24:59.128925-07 test f T4 \N f \N \N \N {} -6546 2025 2025-03-14 12:53:00.880967-07 2025-03-14 12:23:03.100245-07 test f A100 \N t \N \N \N {} -6547 2025 2025-03-14 11:06:13.671532-07 2025-03-14 11:10:58.899444-07 benchmark f A100 \N t \N \N \N {} -6622 2049 2025-03-14 12:01:06.825573-07 2025-03-14 12:10:07.768513-07 test f A100 \N t \N \N \N {} -6208 1914 2025-03-11 17:46:09.416055-07 2025-03-11 18:43:51.806087-07 test t T4 \N f \N \N \N {} -6381 1981 2025-03-13 17:27:47.16164-07 2025-03-13 17:00:09.810894-07 test t T4 \N f \N \N \N {} -6209 1915 2025-03-11 17:02:30.670995-07 2025-03-11 18:34:35.253755-07 test f T4 \N f \N \N \N {} -6210 1915 2025-03-11 18:19:24.18935-07 2025-03-11 16:56:01.127871-07 test t T4 \N f \N \N \N {} -6212 1916 2025-03-11 18:37:26.217772-07 2025-03-11 18:26:08.828412-07 test t T4 \N f \N \N \N {} -6385 1983 2025-03-13 16:53:42.93878-07 2025-03-13 18:01:17.86942-07 test t T4 \N f \N \N \N {} -6213 1917 2025-03-11 18:49:08.446554-07 2025-03-11 18:23:51.890625-07 test t T4 \N f \N \N \N {} -6386 1983 2025-03-13 16:59:20.147802-07 2025-03-13 17:23:05.485124-07 test f T4 \N f \N \N \N {} -6556 2028 2025-03-14 13:11:22.628618-07 2025-03-14 12:15:44.55829-07 test f A100 \N t \N \N \N {} -6214 1917 2025-03-11 18:57:56.391885-07 2025-03-11 18:40:03.839455-07 test f T4 \N f \N \N \N {} -6387 1984 2025-03-13 17:51:21.236837-07 2025-03-13 16:43:28.14984-07 test t T4 \N f \N \N \N {} -6557 2029 2025-03-14 12:34:22.237304-07 2025-03-14 11:29:44.596311-07 test t T4 \N f \N \N \N {} -6627 2049 2025-03-14 13:14:24.077723-07 2025-03-14 12:11:10.400483-07 leaderboard t A100 0.0031591056666666665 t \N \N \N {} -3545 1207 2025-02-28 11:34:32.128279-08 2025-02-28 10:48:07.092517-08 benchmark f A100 \N t \N \N \N {} -6388 1984 2025-03-13 17:46:54.072499-07 2025-03-13 16:07:38.434852-07 test f T4 \N f \N \N \N {} -6558 2029 2025-03-14 13:24:40.101943-07 2025-03-14 12:23:21.049241-07 test f T4 \N f \N \N \N {} -6628 2050 2025-03-14 13:02:18.585241-07 2025-03-14 12:00:19.719197-07 test f A100 \N t \N \N \N {} -6680 2068 2025-03-14 15:33:14.472771-07 2025-03-14 16:02:51.078416-07 test f T4 \N f \N \N \N {} -3548 1208 2025-02-28 11:47:51.556932-08 2025-02-28 11:49:48.214291-08 benchmark f A100 \N t \N \N \N {} -3549 1208 2025-02-28 12:21:05.688454-08 2025-02-28 11:23:23.692653-08 leaderboard f A100 0.003099421 t \N \N \N {} -6389 1985 2025-03-13 16:57:14.22982-07 2025-03-13 17:30:21.244746-07 test t T4 \N f \N \N \N {} -7055 2184 2025-03-16 20:46:00.165975-07 2025-03-16 21:45:58.753749-07 test f L4 \N f \N \N \N {} -3551 1208 2025-02-28 12:08:51.038645-08 2025-02-28 11:50:46.059027-08 benchmark t A100 \N t \N \N \N {} -3552 1208 2025-02-28 11:11:38.66471-08 2025-02-28 12:18:11.410389-08 leaderboard t A100 0.0030721586666666665 t \N \N \N {} -6390 1985 2025-03-13 16:22:49.557696-07 2025-03-13 17:03:17.529052-07 test f T4 \N f \N \N \N {} -7056 2184 2025-03-16 21:28:43.042415-07 2025-03-16 21:41:54.202912-07 test t L4 \N f \N \N \N {} -3554 1209 2025-02-28 11:42:34.402738-08 2025-02-28 10:58:34.409829-08 benchmark t A100 \N t \N \N \N {} -3555 1209 2025-02-28 12:08:05.685399-08 2025-02-28 12:29:20.539116-08 leaderboard t A100 0.0030725803333333337 t \N \N \N {} -6391 1986 2025-03-13 17:33:05.58602-07 2025-03-13 17:03:31.170452-07 test f T4 \N f \N \N \N {} -6662 2059 2025-03-14 13:23:27.383679-07 2025-03-14 13:38:22.037388-07 test f T4 \N f \N \N \N {} -3557 1209 2025-02-28 10:54:49.669617-08 2025-02-28 12:37:28.504084-08 benchmark f A100 \N t \N \N \N {} -3558 1209 2025-02-28 12:46:50.125484-08 2025-02-28 10:53:05.190346-08 leaderboard f A100 0.003090619 t \N \N \N {} -6392 1986 2025-03-13 18:05:36.821373-07 2025-03-13 16:18:41.422227-07 test t T4 \N f \N \N \N {} -6559 2030 2025-03-14 11:43:33.554172-07 2025-03-14 11:45:17.444192-07 benchmark f A100 \N t \N \N \N {} -3560 1210 2025-02-28 12:25:24.784425-08 2025-02-28 11:26:41.263715-08 benchmark f A100 \N t \N \N \N {} -3561 1210 2025-02-28 10:49:57.408057-08 2025-02-28 10:50:18.625288-08 leaderboard f A100 0.0030909023333333336 t \N \N \N {} -6393 1987 2025-03-13 17:09:40.617657-07 2025-03-13 18:01:53.260744-07 test f T4 \N f \N \N \N {} -3563 1210 2025-02-28 11:47:01.61943-08 2025-02-28 11:03:40.113806-08 benchmark t A100 \N t \N \N \N {} -3592 1229 2025-02-28 14:16:18.378222-08 2025-02-28 14:41:43.485886-08 leaderboard t H100 0.0011789098333333332 t \N \N \N {} -3599 1231 2025-02-28 14:24:45.98762-08 2025-02-28 14:33:38.933844-08 leaderboard t H100 0.0012175512 t \N \N \N {} -3600 1232 2025-02-28 13:31:10.936218-08 2025-02-28 14:19:19.455477-08 test t A100 \N t \N \N \N {} -3601 1232 2025-02-28 14:40:09.495449-08 2025-02-28 13:28:50.540847-08 benchmark t A100 \N t \N \N \N {} -3602 1232 2025-02-28 14:11:02.148305-08 2025-02-28 13:20:25.431282-08 leaderboard t A100 0.0018670176 t \N \N \N {} -3603 1232 2025-02-28 13:32:32.0241-08 2025-02-28 14:31:24.527828-08 test f A100 \N t \N \N \N {} -3604 1232 2025-02-28 13:20:53.784469-08 2025-02-28 14:29:17.100087-08 benchmark f A100 \N t \N \N \N {} -3629 1240 2025-03-01 01:07:27.030682-08 2025-03-01 00:11:34.566732-08 test t A100 \N f \N \N \N {} -3648 1248 2025-03-01 01:50:58.464696-08 2025-03-01 00:44:11.917362-08 test t A100 \N t \N \N \N {} -3605 1232 2025-02-28 14:53:49.887413-08 2025-02-28 15:02:29.191933-08 leaderboard f A100 0.001868504 t \N \N \N {} -3606 1233 2025-02-28 14:00:44.074635-08 2025-02-28 14:09:25.557225-08 benchmark f A100 \N t \N \N \N {} -3607 1234 2025-02-28 14:42:03.73345-08 2025-02-28 15:08:49.030554-08 benchmark f A100 \N f \N \N \N {} -3608 1235 2025-02-28 15:06:39.526344-08 2025-02-28 15:15:55.847946-08 benchmark f A100 \N t \N \N \N {} -3609 1235 2025-02-28 14:36:31.855831-08 2025-02-28 14:45:08.753778-08 benchmark f H100 \N f \N \N \N {} -3610 1236 2025-02-28 14:22:25.170803-08 2025-02-28 15:09:04.222075-08 benchmark f H100 \N t \N \N \N {} -3611 1237 2025-02-28 14:59:01.171066-08 2025-02-28 15:22:44.422251-08 test t H100 \N t \N \N \N {} -3612 1237 2025-02-28 15:35:54.216346-08 2025-02-28 15:38:16.179312-08 benchmark t H100 \N t \N \N \N {} -3613 1237 2025-02-28 14:52:56.878083-08 2025-02-28 14:51:40.998403-08 leaderboard t H100 0.001161027 t \N \N \N {} -3614 1237 2025-02-28 16:12:12.194689-08 2025-02-28 16:07:13.458388-08 test f H100 \N f \N \N \N {} -3615 1238 2025-02-28 16:14:51.726429-08 2025-02-28 16:25:19.334709-08 benchmark f H100 \N t \N \N \N {} -3616 1238 2025-02-28 15:18:16.528728-08 2025-02-28 16:17:03.954782-08 benchmark f A100 \N t \N \N \N {} -3630 1240 2025-03-01 00:33:39.01249-08 2025-03-01 00:07:40.699626-08 test f A100 \N f \N \N \N {} -3617 1239 2025-02-28 15:32:38.229302-08 2025-02-28 15:57:09.263444-08 test t H100 \N t \N \N \N {} -3618 1239 2025-02-28 15:40:41.884458-08 2025-02-28 15:19:49.237295-08 benchmark t H100 \N t \N \N \N {} -3619 1239 2025-02-28 15:26:34.626489-08 2025-02-28 16:48:20.214012-08 leaderboard t H100 0.0011695108 t \N \N \N {} -3620 1239 2025-02-28 15:34:19.352077-08 2025-02-28 15:18:34.628786-08 test f A100 \N t \N \N \N {} -3621 1239 2025-02-28 15:34:17.50586-08 2025-02-28 15:42:25.985409-08 benchmark f A100 \N t \N \N \N {} -3622 1239 2025-02-28 15:22:37.115875-08 2025-02-28 15:55:56.769348-08 leaderboard f A100 0.002215739 t \N \N \N {} -3623 1239 2025-02-28 17:10:36.81778-08 2025-02-28 15:57:08.360846-08 test t A100 \N t \N \N \N {} -3625 1239 2025-02-28 16:44:19.965613-08 2025-02-28 15:49:32.46356-08 leaderboard t A100 0.0020725956666666667 t \N \N \N {} -3626 1239 2025-02-28 15:41:26.021642-08 2025-02-28 15:23:55.7378-08 test f H100 \N t \N \N \N {} -3627 1239 2025-02-28 16:33:01.867796-08 2025-02-28 15:24:47.499545-08 benchmark f H100 \N t \N \N \N {} -3628 1239 2025-02-28 16:49:49.757233-08 2025-02-28 16:35:02.023132-08 leaderboard f H100 0.00116604725 t \N \N \N {} -6234 1922 2025-03-12 00:22:49.917936-07 2025-03-12 00:13:52.488578-07 test t T4 \N f \N \N \N {} -6395 1988 2025-03-13 17:58:47.761639-07 2025-03-13 17:01:13.84194-07 test t T4 \N f \N \N \N {} -6560 2031 2025-03-14 13:05:32.105346-07 2025-03-14 11:29:34.501788-07 benchmark f A100 \N t \N \N \N {} -6629 2050 2025-03-14 12:58:46.09814-07 2025-03-14 12:52:50.872419-07 benchmark f A100 \N t \N \N \N {} -6630 2050 2025-03-14 12:08:39.259423-07 2025-03-14 13:48:19.785521-07 leaderboard f A100 0.002560023 t \N \N \N {} -3638 1243 2025-03-01 00:16:45.170175-08 2025-03-01 00:27:41.072541-08 test t A100 \N f \N \N \N {} -3639 1243 2025-02-28 23:56:11.182103-08 2025-03-01 00:14:59.572871-08 test f A100 \N f \N \N \N {} -6235 1922 2025-03-11 23:57:28.40312-07 2025-03-12 00:24:44.986697-07 test f T4 \N f \N \N \N {} -6396 1988 2025-03-13 16:20:45.984417-07 2025-03-13 16:27:56.674856-07 test f T4 \N f \N \N \N {} -6631 2050 2025-03-14 12:29:57.064337-07 2025-03-14 13:06:46.738173-07 test t A100 \N t \N \N \N {} -3641 1244 2025-03-01 01:27:10.713031-08 2025-03-01 01:18:07.544805-08 benchmark t A100 \N f \N \N \N {} -6236 1923 2025-03-12 01:04:23.501448-07 2025-03-12 00:05:29.54956-07 test t T4 \N f \N \N \N {} -6397 1989 2025-03-13 17:25:39.429146-07 2025-03-13 16:39:56.193057-07 test t T4 \N f \N \N \N {} -6561 2032 2025-03-14 12:04:24.116313-07 2025-03-14 12:06:21.091672-07 benchmark f A100 \N t \N \N \N {} -6632 2050 2025-03-14 12:39:52.486934-07 2025-03-14 13:43:25.534592-07 benchmark t A100 \N t \N \N \N {} -6633 2050 2025-03-14 13:36:06.263877-07 2025-03-14 13:20:17.386606-07 leaderboard t A100 0.002519189 t \N \N \N {} -3643 1244 2025-03-01 01:35:19.685312-08 2025-03-01 00:36:21.094893-08 benchmark f A100 \N f \N \N \N {} -3644 1245 2025-03-01 00:26:06.119792-08 2025-03-01 01:35:27.012514-08 test t A100 \N f \N \N \N {} -3645 1245 2025-03-01 00:43:41.46218-08 2025-02-28 23:55:12.934017-08 test f A100 \N f \N \N \N {} -3646 1246 2025-03-01 00:53:33.727311-08 2025-03-01 02:15:48.757393-08 benchmark f A100 \N f \N \N \N {} -3647 1247 2025-03-01 02:33:55.98627-08 2025-03-01 02:31:22.439703-08 benchmark f A100 \N t \N \N \N {} -6237 1923 2025-03-12 00:58:02.114277-07 2025-03-12 01:54:20.094067-07 test f T4 \N f \N \N \N {} -3649 1248 2025-03-01 01:25:05.925773-08 2025-03-01 01:09:17.783085-08 benchmark t A100 \N t \N \N \N {} -3651 1248 2025-03-01 02:17:03.556096-08 2025-03-01 02:09:10.130241-08 test f A100 \N t \N \N \N {} -3653 1248 2025-03-01 02:26:58.343458-08 2025-03-01 00:43:43.03041-08 leaderboard f A100 0.0031272273333333334 t \N \N \N {} -3654 1249 2025-03-01 02:12:30.998096-08 2025-03-01 00:50:24.715655-08 test f A100 \N t \N \N \N {} -3655 1249 2025-03-01 01:49:55.694137-08 2025-03-01 01:29:49.718531-08 benchmark f A100 \N t \N \N \N {} -3656 1249 2025-03-01 01:38:29.821048-08 2025-03-01 01:46:03.662966-08 leaderboard f A100 0.0031039466666666665 t \N \N \N {} -3889 1297 2025-03-01 02:52:44.650492-08 2025-03-01 03:23:37.6224-08 test t A100 \N t \N \N \N {} -3657 1249 2025-03-01 01:50:49.813103-08 2025-03-01 02:00:22.394174-08 test t A100 \N t \N \N \N {} -3669 1251 2025-03-01 01:21:22.679753-08 2025-03-01 02:00:57.562983-08 test f A100 \N t \N \N \N {} -3670 1251 2025-03-01 02:27:13.746914-08 2025-03-01 01:53:26.460343-08 benchmark f A100 \N t \N \N \N {} -3671 1251 2025-03-01 00:57:08.998138-08 2025-03-01 00:58:17.33663-08 leaderboard f A100 0.0031081036666666664 t \N \N \N {} -3672 1252 2025-03-01 01:11:34.754783-08 2025-03-01 00:52:36.138674-08 test f A100 \N t \N \N \N {} -3681 1253 2025-03-01 01:03:11.979635-08 2025-03-01 02:40:30.398743-08 test f A100 \N t \N \N \N {} -3910 1301 2025-03-01 03:46:47.874489-08 2025-03-01 03:33:33.484922-08 test f A100 \N f \N \N \N {} -3673 1252 2025-03-01 02:02:20.431435-08 2025-03-01 01:05:47.013515-08 benchmark f A100 \N t \N \N \N {} -3674 1252 2025-03-01 02:12:52.290378-08 2025-03-01 01:46:02.154693-08 leaderboard f A100 0.0031048003333333336 t \N \N \N {} -3676 1252 2025-03-01 01:03:12.897497-08 2025-03-01 01:26:39.062279-08 benchmark t A100 \N t \N \N \N {} -3677 1252 2025-03-01 01:38:06.493139-08 2025-03-01 02:09:28.677674-08 leaderboard t A100 0.0031104436666666664 t \N \N \N {} -3678 1253 2025-03-01 02:06:10.058974-08 2025-03-01 01:30:07.280628-08 test t A100 \N t \N \N \N {} -3679 1253 2025-03-01 02:03:05.966122-08 2025-03-01 00:56:13.504406-08 benchmark t A100 \N t \N \N \N {} -3680 1253 2025-03-01 02:45:57.725737-08 2025-03-01 02:34:27.95218-08 leaderboard t A100 0.0031090656666666666 t \N \N \N {} -3682 1253 2025-03-01 02:05:39.373314-08 2025-03-01 01:20:42.226463-08 benchmark f A100 \N t \N \N \N {} -3683 1253 2025-03-01 01:49:07.509476-08 2025-03-01 01:57:14.444777-08 leaderboard f A100 0.0031048203333333335 t \N \N \N {} -3690 1260 2025-03-01 01:35:12.029569-08 2025-03-01 01:45:52.929787-08 benchmark f A100 \N t \N \N \N {} -3691 1261 2025-03-01 01:38:16.278326-08 2025-03-01 01:36:09.051317-08 test t A100 \N t \N \N \N {} -3692 1261 2025-03-01 02:49:47.45514-08 2025-03-01 03:17:46.327605-08 benchmark t A100 \N t \N \N \N {} -3693 1261 2025-03-01 02:16:44.503505-08 2025-03-01 02:01:57.713906-08 leaderboard t A100 0.0030880283333333336 t \N \N \N {} -3694 1261 2025-03-01 02:18:25.475537-08 2025-03-01 02:21:41.927369-08 test f A100 \N t \N \N \N {} -3712 1264 2025-03-01 02:56:03.049752-08 2025-03-01 01:43:52.163817-08 test t A100 \N t \N \N \N {} -3697 1262 2025-03-01 03:19:09.843073-08 2025-03-01 01:30:49.179646-08 test t A100 \N t \N \N \N {} -3698 1262 2025-03-01 03:23:21.681606-08 2025-03-01 02:15:56.73891-08 benchmark t A100 \N t \N \N \N {} -3699 1262 2025-03-01 02:06:27.293399-08 2025-03-01 02:01:32.569519-08 leaderboard t A100 0.0030787253333333336 t \N \N \N {} -3700 1262 2025-03-01 03:02:04.699757-08 2025-03-01 01:27:30.191004-08 test f A100 \N t \N \N \N {} -3701 1262 2025-03-01 01:41:08.419018-08 2025-03-01 02:33:50.455395-08 benchmark f A100 \N t \N \N \N {} -3702 1262 2025-03-01 03:18:19.489873-08 2025-03-01 02:32:54.172929-08 leaderboard f A100 0.0030775323333333336 t \N \N \N {} -3919 1304 2025-03-01 05:14:59.512003-08 2025-03-01 04:15:31.250903-08 test f A100 \N t \N \N \N {} -3704 1263 2025-03-01 03:03:11.700889-08 2025-03-01 02:34:12.760558-08 benchmark f A100 \N t \N \N \N {} -3721 1266 2025-03-01 03:29:58.775718-08 2025-03-01 02:01:14.58848-08 benchmark f A100 \N t \N \N \N {} -3722 1267 2025-03-01 02:00:41.613096-08 2025-03-01 02:39:28.789934-08 test t A100 \N t \N \N \N {} -3723 1267 2025-03-01 02:08:23.198399-08 2025-03-01 02:24:17.413187-08 benchmark t A100 \N t \N \N \N {} -3724 1267 2025-03-01 02:58:51.225291-08 2025-03-01 01:53:44.033391-08 leaderboard t A100 0.0030839456666666666 t \N \N \N {} -3725 1267 2025-03-01 02:45:10.109595-08 2025-03-01 02:41:38.775413-08 test f A100 \N t \N \N \N {} -3928 1305 2025-03-01 05:09:48.841292-08 2025-03-01 05:01:55.591158-08 test f A100 \N t \N \N \N {} -3729 1268 2025-03-01 03:05:42.17627-08 2025-03-01 02:39:10.249741-08 benchmark t A100 \N t \N \N \N {} -3730 1268 2025-03-01 01:47:00.767793-08 2025-03-01 02:57:06.798112-08 leaderboard t A100 0.0031026586666666666 t \N \N \N {} -3731 1268 2025-03-01 02:41:00.300122-08 2025-03-01 03:30:29.20737-08 test f A100 \N t \N \N \N {} -3732 1268 2025-03-01 03:04:23.203277-08 2025-03-01 01:43:41.695278-08 benchmark f A100 \N t \N \N \N {} -3733 1268 2025-03-01 01:51:37.215302-08 2025-03-01 02:38:14.824507-08 leaderboard f A100 0.0030949656666666666 t \N \N \N {} -3734 1269 2025-03-01 02:58:59.331439-08 2025-03-01 02:02:26.027818-08 test t A100 \N t \N \N \N {} -3735 1269 2025-03-01 03:35:14.847212-08 2025-03-01 02:02:44.717076-08 benchmark t A100 \N t \N \N \N {} -3736 1269 2025-03-01 01:53:25.567276-08 2025-03-01 02:33:01.154365-08 leaderboard t A100 0.0031152113333333337 t \N \N \N {} -3737 1269 2025-03-01 02:30:37.175149-08 2025-03-01 02:34:29.93941-08 test f A100 \N t \N \N \N {} -3738 1269 2025-03-01 03:04:08.336127-08 2025-03-01 02:39:05.51646-08 benchmark f A100 \N t \N \N \N {} -3739 1269 2025-03-01 03:26:54.522181-08 2025-03-01 02:30:29.400292-08 leaderboard f A100 0.003116479 t \N \N \N {} -3752 1272 2025-03-01 03:35:25.32574-08 2025-03-01 03:42:34.897532-08 leaderboard t A100 0.003070418 t \N \N \N {} -3964 1321 2025-03-01 04:25:29.824066-08 2025-03-01 05:01:10.21614-08 test t A100 \N t \N \N \N {} -3753 1273 2025-03-01 01:58:03.710454-08 2025-03-01 03:21:45.806033-08 test f A100 \N t \N \N \N {} -3754 1273 2025-03-01 03:22:29.292453-08 2025-03-01 03:42:54.655064-08 benchmark f A100 \N t \N \N \N {} -3755 1273 2025-03-01 02:32:56.792637-08 2025-03-01 03:04:59.058769-08 leaderboard f A100 0.0031262986666666663 t \N \N \N {} -3768 1275 2025-03-01 02:46:08.326312-08 2025-03-01 02:22:21.610827-08 test f A100 \N t \N \N \N {} -3777 1277 2025-03-01 02:21:07.979234-08 2025-03-01 03:47:24.50701-08 test t A100 \N t \N \N \N {} -3775 1276 2025-03-01 01:55:51.302209-08 2025-03-01 02:01:49.922507-08 benchmark t A100 \N t \N \N \N {} -3776 1276 2025-03-01 02:08:35.267407-08 2025-03-01 01:54:26.716726-08 leaderboard t A100 0.0030983713333333335 t \N \N \N {} -3778 1277 2025-03-01 03:32:26.499294-08 2025-03-01 03:46:45.069319-08 benchmark t A100 \N t \N \N \N {} -3779 1277 2025-03-01 03:38:23.774189-08 2025-03-01 02:35:30.930272-08 leaderboard t A100 0.003084539 t \N \N \N {} -3780 1277 2025-03-01 03:22:55.155356-08 2025-03-01 02:43:56.628934-08 test f A100 \N t \N \N \N {} -3784 1278 2025-03-01 02:48:23.56591-08 2025-03-01 02:33:17.354544-08 benchmark t A100 \N t \N \N \N {} -3785 1278 2025-03-01 02:39:45.41231-08 2025-03-01 02:40:12.61527-08 leaderboard t A100 0.0031055783333333335 t \N \N \N {} -3789 1279 2025-03-01 02:01:53.626081-08 2025-03-01 02:47:46.086211-08 test f A100 \N t \N \N \N {} -3786 1278 2025-03-01 02:03:03.53279-08 2025-03-01 03:47:19.946226-08 test f A100 \N t \N \N \N {} -3787 1278 2025-03-01 03:29:33.298347-08 2025-03-01 03:34:20.277338-08 benchmark f A100 \N t \N \N \N {} -3788 1278 2025-03-01 03:00:05.752988-08 2025-03-01 01:56:16.762213-08 leaderboard f A100 0.003097914 t \N \N \N {} -6238 1924 2025-03-12 01:21:46.601814-07 2025-03-12 01:32:26.514257-07 test t T4 \N f \N \N \N {} -6398 1989 2025-03-13 17:08:21.233687-07 2025-03-13 17:06:06.404998-07 test f T4 \N f \N \N \N {} -6562 2033 2025-03-14 13:12:39.948827-07 2025-03-14 13:23:53.347312-07 benchmark f A100 \N t \N \N \N {} -6634 2051 2025-03-14 12:30:34.721734-07 2025-03-14 12:30:27.296794-07 test t A100 \N t \N \N \N {} -3790 1279 2025-03-01 03:32:19.769184-08 2025-03-01 03:50:26.829557-08 benchmark f A100 \N t \N \N \N {} -3791 1279 2025-03-01 02:54:07.655455-08 2025-03-01 02:19:13.748311-08 leaderboard f A100 0.0030992133333333336 t \N \N \N {} -6399 1990 2025-03-13 17:50:57.628826-07 2025-03-13 16:49:18.239868-07 test t T4 \N f \N \N \N {} -6563 2034 2025-03-14 12:07:55.631644-07 2025-03-14 12:27:16.784091-07 benchmark f A100 \N t \N \N \N {} -7059 2185 2025-03-16 22:03:27.033571-07 2025-03-16 21:36:29.376537-07 leaderboard f L4 0.00011869082456140351 t \N \N \N {} -3793 1279 2025-03-01 03:42:52.339097-08 2025-03-01 03:50:15.909886-08 benchmark t A100 \N t \N \N \N {} -3794 1279 2025-03-01 02:12:29.931818-08 2025-03-01 03:14:22.177181-08 leaderboard t A100 0.0030700836666666667 t \N \N \N {} -6240 1925 2025-03-12 01:13:55.791064-07 2025-03-12 00:29:24.037503-07 test f T4 \N t \N \N \N {} -6244 1925 2025-03-12 01:24:08.247115-07 2025-03-12 00:16:50.797071-07 benchmark t T4 \N t \N \N \N {} -6245 1925 2025-03-12 00:39:44.345705-07 2025-03-12 01:03:34.532126-07 leaderboard t T4 3.565283209 t \N \N \N {} -6401 1991 2025-03-13 18:14:55.765999-07 2025-03-13 16:54:11.131116-07 test f T4 \N t \N \N \N {} -6250 1926 2025-03-12 02:15:10.273238-07 2025-03-12 01:01:04.027885-07 benchmark f T4 \N t \N \N \N {} -6251 1926 2025-03-12 00:50:43.580637-07 2025-03-12 00:57:06.425599-07 leaderboard f T4 3.682825032 t \N \N \N {} -6404 1991 2025-03-13 18:23:53.350354-07 2025-03-13 17:41:06.459187-07 test t T4 \N t \N \N \N {} -6405 1991 2025-03-13 18:45:41.07329-07 2025-03-13 17:03:11.875615-07 benchmark t T4 \N t \N \N \N {} -3805 1281 2025-03-01 02:40:30.487878-08 2025-03-01 02:47:22.690155-08 benchmark t A100 \N t \N \N \N {} -3806 1281 2025-03-01 03:18:03.1251-08 2025-03-01 02:07:31.048245-08 leaderboard t A100 0.0030762023333333337 t \N \N \N {} -6406 1991 2025-03-13 16:56:16.674442-07 2025-03-13 17:32:22.195013-07 leaderboard t T4 0.01198132082 t \N \N \N {} -6565 2036 2025-03-14 13:26:34.659744-07 2025-03-14 12:28:45.901302-07 benchmark f A100 \N t \N \N \N {} -6635 2051 2025-03-14 12:12:47.169659-07 2025-03-14 12:30:19.462198-07 benchmark t A100 \N t \N \N \N {} -6636 2051 2025-03-14 13:22:32.747423-07 2025-03-14 13:46:45.493463-07 leaderboard t A100 0.0025760326666666666 t \N \N \N {} -3808 1283 2025-03-01 03:57:31.233254-08 2025-03-01 03:30:41.035131-08 benchmark t A100 \N t \N \N \N {} -3809 1283 2025-03-01 02:53:01.039251-08 2025-03-01 03:11:15.949496-08 leaderboard t A100 0.0030891856666666667 t \N \N \N {} -3810 1282 2025-03-01 02:47:29.668011-08 2025-03-01 03:06:52.828571-08 benchmark f A100 \N t \N \N \N {} -3811 1282 2025-03-01 02:30:35.994805-08 2025-03-01 02:24:05.263461-08 benchmark f H100 \N t \N \N \N {} -6407 1992 2025-03-13 18:08:55.397985-07 2025-03-13 17:16:33.413217-07 test t T4 \N f \N \N \N {} -6566 2037 2025-03-14 12:48:47.844486-07 2025-03-14 12:08:55.718904-07 test f A100 \N f \N \N \N {} -6637 2051 2025-03-14 12:40:08.734201-07 2025-03-14 12:40:23.426756-07 test f A100 \N t \N \N \N {} -3813 1283 2025-03-01 04:00:42.588176-08 2025-03-01 02:20:16.080762-08 benchmark f A100 \N t \N \N \N {} -3814 1283 2025-03-01 03:40:07.853236-08 2025-03-01 03:29:58.258385-08 leaderboard f A100 0.003101622 t \N \N \N {} -3815 1284 2025-03-01 02:25:33.95253-08 2025-03-01 02:28:22.525197-08 test t A100 \N f \N \N \N {} -3816 1284 2025-03-01 03:57:37.041465-08 2025-03-01 02:25:56.015805-08 test f A100 \N f \N \N \N {} -6408 1992 2025-03-13 19:09:40.136801-07 2025-03-13 17:14:34.611515-07 test f T4 \N f \N \N \N {} -6567 2037 2025-03-14 12:36:50.459533-07 2025-03-14 12:05:58.31517-07 test t A100 \N f \N \N \N {} -3818 1285 2025-03-01 02:34:29.255307-08 2025-03-01 02:41:27.552585-08 benchmark f A100 \N t \N \N \N {} -3819 1285 2025-03-01 03:02:33.900221-08 2025-03-01 02:39:16.471311-08 leaderboard f A100 0.003196064 t \N \N \N {} -6409 1993 2025-03-13 17:37:09.987891-07 2025-03-13 17:44:10.720084-07 test t T4 \N f \N \N \N {} -6568 2038 2025-03-14 13:27:33.060334-07 2025-03-14 11:46:55.719973-07 test t A100 \N f \N \N \N {} -6638 2051 2025-03-14 14:00:30.84485-07 2025-03-14 12:56:57.304408-07 benchmark f A100 \N t \N \N \N {} -6639 2051 2025-03-14 13:35:20.196957-07 2025-03-14 12:03:45.180778-07 leaderboard f A100 0.002520242 t \N \N \N {} -3821 1285 2025-03-01 03:35:11.06709-08 2025-03-01 03:50:33.861385-08 benchmark t A100 \N t \N \N \N {} -3822 1285 2025-03-01 04:05:45.187777-08 2025-03-01 03:19:06.671204-08 leaderboard t A100 0.003205114 t \N \N \N {} -6410 1993 2025-03-13 18:31:14.592561-07 2025-03-13 18:03:31.587714-07 test f T4 \N f \N \N \N {} -6569 2038 2025-03-14 11:54:10.44922-07 2025-03-14 12:14:26.005828-07 test f A100 \N f \N \N \N {} -6640 2052 2025-03-14 13:20:11.68013-07 2025-03-14 13:54:08.817793-07 benchmark f A100 \N t \N \N \N {} -6663 2059 2025-03-14 13:43:14.834484-07 2025-03-14 13:10:23.01354-07 test t T4 \N f \N \N \N {} -3824 1286 2025-03-01 03:48:51.460228-08 2025-03-01 03:05:00.868889-08 benchmark t A100 \N t \N \N \N {} -3825 1286 2025-03-01 03:13:52.902037-08 2025-03-01 03:59:39.680247-08 leaderboard t A100 0.0032052243333333336 t \N \N \N {} -6257 1933 2025-03-12 06:29:52.272103-07 2025-03-12 06:33:58.837779-07 benchmark f A100 \N t \N \N \N {} -6411 1994 2025-03-13 17:31:54.500515-07 2025-03-13 17:50:40.571354-07 test t T4 \N f \N \N \N {} -6570 2039 2025-03-14 12:43:54.474387-07 2025-03-14 11:46:53.304327-07 test f A100 \N t \N \N \N {} -6571 2039 2025-03-14 13:36:58.274258-07 2025-03-14 11:48:23.338015-07 benchmark f A100 \N t \N \N \N {} -6572 2039 2025-03-14 12:38:10.932323-07 2025-03-14 12:39:34.102441-07 leaderboard f A100 0.0025144643333333337 t \N \N \N {} -6259 1936 2025-03-12 07:33:01.179724-07 2025-03-12 08:00:02.311465-07 benchmark f A100 \N t \N \N \N {} -6413 1995 2025-03-13 18:23:54.614105-07 2025-03-13 19:13:53.587183-07 test t T4 \N f \N \N \N {} -6576 2040 2025-03-14 12:03:45.01229-07 2025-03-14 12:44:15.413186-07 test f A100 \N t \N \N \N {} -6577 2040 2025-03-14 11:54:53.53347-07 2025-03-14 12:54:34.179949-07 benchmark f A100 \N t \N \N \N {} -6578 2040 2025-03-14 13:10:32.037477-07 2025-03-14 12:17:48.109262-07 leaderboard f A100 0.002512107 t \N \N \N {} -3833 1287 2025-03-01 02:36:47.925069-08 2025-03-01 03:26:45.302835-08 benchmark t A100 \N t \N \N \N {} -3834 1287 2025-03-01 02:32:30.809129-08 2025-03-01 03:12:32.781049-08 leaderboard t A100 0.0030916056666666666 t \N \N \N {} -6260 1937 2025-03-12 07:23:06.50947-07 2025-03-12 08:23:19.792542-07 benchmark f A100 \N f \N \N \N {} -6261 1938 2025-03-12 08:35:16.313743-07 2025-03-12 08:05:05.626744-07 benchmark f A100 \N t \N \N \N {} -6415 1996 2025-03-13 17:38:08.027955-07 2025-03-13 18:18:31.168297-07 test f T4 \N f \N \N \N {} -6582 2041 2025-03-14 13:35:12.454784-07 2025-03-14 11:47:41.393176-07 test f A100 \N t \N \N \N {} -6583 2041 2025-03-14 13:00:57.639982-07 2025-03-14 12:50:35.650726-07 benchmark f A100 \N t \N \N \N {} -3839 1288 2025-03-01 02:49:11.125792-08 2025-03-01 03:21:23.235824-08 benchmark t A100 \N t \N \N \N {} -6262 1939 2025-03-12 07:09:38.373161-07 2025-03-12 08:48:50.014584-07 test f A100 \N t \N \N \N {} -6417 1997 2025-03-13 20:13:42.139063-07 2025-03-13 19:48:53.847265-07 test t T4 \N f \N \N \N {} -6268 1940 2025-03-12 08:48:23.158902-07 2025-03-12 07:34:48.110256-07 test f H100 \N t \N \N \N {} -6271 1940 2025-03-12 08:39:14.809321-07 2025-03-12 08:06:26.839197-07 test t H100 \N t \N \N \N {} -6272 1940 2025-03-12 08:53:57.603406-07 2025-03-12 08:56:46.092949-07 benchmark t H100 \N t \N \N \N {} -6273 1940 2025-03-12 08:30:27.185852-07 2025-03-12 07:53:33.36497-07 leaderboard t H100 0.00004756101351351351 t \N \N \N {} -6584 2041 2025-03-14 11:48:11.361349-07 2025-03-14 13:07:20.826594-07 leaderboard f A100 0.00251554 t \N \N \N {} -3851 1290 2025-03-01 02:42:29.071194-08 2025-03-01 03:06:56.033212-08 benchmark t A100 \N t \N \N \N {} -3852 1290 2025-03-01 03:17:02.156523-08 2025-03-01 02:24:26.38537-08 leaderboard t A100 0.0030752533333333336 t \N \N \N {} -6274 1941 2025-03-12 08:49:13.036616-07 2025-03-12 09:04:52.719316-07 benchmark f T4 \N t \N \N \N {} -6419 1998 2025-03-14 05:01:43.261969-07 2025-03-14 04:51:13.385755-07 benchmark f A100 \N t \N \N \N {} -6585 2041 2025-03-14 12:16:33.083304-07 2025-03-14 11:57:07.432446-07 test t A100 \N t \N \N \N {} -6586 2041 2025-03-14 12:55:15.401484-07 2025-03-14 13:17:33.140416-07 benchmark t A100 \N t \N \N \N {} -3854 1291 2025-03-01 03:39:45.47163-08 2025-03-01 04:08:17.677385-08 benchmark t A100 \N t \N \N \N {} -3855 1291 2025-03-01 04:06:18.714922-08 2025-03-01 04:05:24.645808-08 leaderboard t A100 0.0030727556666666667 t \N \N \N {} -6275 1942 2025-03-12 08:13:42.287391-07 2025-03-12 08:06:35.694977-07 test f T4 \N t \N \N \N {} -6276 1942 2025-03-12 08:50:04.371499-07 2025-03-12 07:30:05.01053-07 benchmark f T4 \N t \N \N \N {} -6277 1942 2025-03-12 08:59:14.826076-07 2025-03-12 08:14:40.196742-07 leaderboard f T4 0.00013039858 t \N \N \N {} -6420 1999 2025-03-14 05:43:54.110159-07 2025-03-14 04:25:26.908913-07 test f A100 \N t \N \N \N {} -3858 1291 2025-03-01 02:47:40.294969-08 2025-03-01 03:21:35.997134-08 leaderboard f A100 0.0030834156666666667 t \N \N \N {} -6278 1942 2025-03-12 07:53:39.313803-07 2025-03-12 08:52:00.346671-07 test t T4 \N t \N \N \N {} -6279 1942 2025-03-12 07:54:27.029152-07 2025-03-12 08:11:56.444947-07 benchmark t T4 \N t \N \N \N {} -6280 1942 2025-03-12 09:01:51.96126-07 2025-03-12 07:48:32.954993-07 leaderboard t T4 0.00017005301923076922 t \N \N \N {} -6588 2042 2025-03-14 12:41:32.755275-07 2025-03-14 13:27:47.933076-07 test t A100 \N t \N \N \N {} -3860 1292 2025-03-01 03:44:44.046571-08 2025-03-01 02:59:32.783521-08 benchmark f A100 \N t \N \N \N {} -3861 1292 2025-03-01 02:54:03.416387-08 2025-03-01 04:17:02.090714-08 leaderboard f A100 0.0030798846666666665 t \N \N \N {} -3864 1292 2025-03-01 03:01:12.811119-08 2025-03-01 03:57:53.126582-08 leaderboard t A100 0.0030685383333333336 t \N \N \N {} -6284 1943 2025-03-12 07:37:15.328744-07 2025-03-12 08:00:32.114323-07 test f L4 \N t \N \N \N {} -6285 1943 2025-03-12 09:15:51.836775-07 2025-03-12 08:18:34.03295-07 benchmark f L4 \N t \N \N \N {} -6286 1943 2025-03-12 07:18:39.964262-07 2025-03-12 08:42:15.886255-07 leaderboard f L4 0.00009671902499999999 t \N \N \N {} -6589 2042 2025-03-14 13:28:36.680878-07 2025-03-14 12:42:33.70216-07 benchmark t A100 \N t \N \N \N {} -3866 1293 2025-03-01 03:45:51.800351-08 2025-03-01 04:07:15.03461-08 benchmark f A100 \N t \N \N \N {} -3867 1293 2025-03-01 03:54:05.969256-08 2025-03-01 03:52:25.243297-08 leaderboard f A100 0.0030785423333333337 t \N \N \N {} -3869 1293 2025-03-01 03:05:36.772437-08 2025-03-01 03:41:52.212108-08 benchmark t A100 \N t \N \N \N {} -3870 1293 2025-03-01 03:43:37.103283-08 2025-03-01 03:30:28.463215-08 leaderboard t A100 0.0030768713333333333 t \N \N \N {} -6290 1944 2025-03-12 10:57:21.104648-07 2025-03-12 10:29:58.980609-07 test t T4 \N t \N \N \N {} -3872 1294 2025-03-01 02:49:18.306021-08 2025-03-01 03:07:55.017888-08 benchmark f A100 \N t \N \N \N {} -3873 1294 2025-03-01 03:18:49.888527-08 2025-03-01 02:34:53.156434-08 leaderboard f A100 0.0030735693333333335 t \N \N \N {} -6293 1945 2025-03-12 11:27:15.263361-07 2025-03-12 10:44:27.72857-07 test t T4 \N f \N \N \N {} -6425 1999 2025-03-14 04:01:43.503576-07 2025-03-14 04:06:07.830224-07 leaderboard t A100 0.0024771706666666666 t \N \N \N {} -6590 2042 2025-03-14 12:07:33.510979-07 2025-03-14 12:55:00.902598-07 leaderboard t A100 0.0031459183333333333 t \N \N \N {} -6642 2053 2025-03-14 13:53:44.288829-07 2025-03-14 13:50:05.960849-07 benchmark t A100 \N t \N \N \N {} -3875 1294 2025-03-01 03:43:43.782738-08 2025-03-01 04:24:15.592112-08 benchmark t A100 \N t \N \N \N {} -3878 1295 2025-03-01 04:12:08.121664-08 2025-03-01 04:27:53.16111-08 benchmark f A100 \N t \N \N \N {} -3879 1295 2025-03-01 03:26:16.469666-08 2025-03-01 03:58:31.78643-08 leaderboard f A100 0.0030710493333333333 t \N \N \N {} -6429 2000 2025-03-14 06:05:54.518003-07 2025-03-14 07:37:02.202589-07 test t H100 \N t \N \N \N {} -6430 2000 2025-03-14 06:35:29.934346-07 2025-03-14 07:22:55.35725-07 benchmark t H100 \N t \N \N \N {} -6431 2000 2025-03-14 06:03:20.051179-07 2025-03-14 06:00:31.398891-07 leaderboard t H100 0.00140618875 t \N \N \N {} -6592 2042 2025-03-14 12:08:29.246929-07 2025-03-14 13:42:58.5473-07 benchmark f A100 \N t \N \N \N {} -6593 2042 2025-03-14 12:19:09.742168-07 2025-03-14 13:19:22.260046-07 leaderboard f A100 0.0038880753333333335 t \N \N \N {} -3881 1295 2025-03-01 03:58:42.298448-08 2025-03-01 04:33:00.759565-08 benchmark t A100 \N t \N \N \N {} -3882 1295 2025-03-01 04:11:32.308003-08 2025-03-01 03:45:03.260733-08 leaderboard t A100 0.0030748093333333336 t \N \N \N {} -6432 2001 2025-03-14 06:20:47.16994-07 2025-03-14 06:30:53.126756-07 test f A100 \N t \N \N \N {} -6433 2001 2025-03-14 06:43:34.047031-07 2025-03-14 05:58:41.550022-07 benchmark f A100 \N t \N \N \N {} -6434 2001 2025-03-14 07:28:13.286534-07 2025-03-14 05:58:23.458927-07 leaderboard f A100 0.0025180643333333336 t \N \N \N {} -6594 2043 2025-03-14 12:13:34.198838-07 2025-03-14 13:41:30.590742-07 test f A100 \N f \N \N \N {} -5073 1550 2025-03-04 16:13:34.029481-08 2025-03-04 17:53:09.887209-08 test f H100 \N f \N \N \N {} -3884 1296 2025-03-01 03:12:53.853598-08 2025-03-01 03:21:33.361057-08 benchmark t A100 \N t \N \N \N {} -3885 1296 2025-03-01 02:52:45.802668-08 2025-03-01 04:33:27.422507-08 leaderboard t A100 0.003072869 t \N \N \N {} -6297 1948 2025-03-12 11:29:51.801512-07 2025-03-12 10:25:13.155691-07 test f T4 \N t \N \N \N {} -6298 1948 2025-03-12 10:35:30.201717-07 2025-03-12 10:27:15.46981-07 benchmark f T4 \N f \N \N \N {} -6435 2001 2025-03-14 07:04:41.915018-07 2025-03-14 07:08:00.946033-07 test t A100 \N t \N \N \N {} -6439 2002 2025-03-14 06:51:44.898139-07 2025-03-14 07:04:48.007592-07 benchmark t A100 \N t \N \N \N {} -6440 2002 2025-03-14 06:58:37.268782-07 2025-03-14 07:30:47.849941-07 leaderboard t A100 0.0025299696666666667 t \N \N \N {} -3890 1297 2025-03-01 04:08:21.671557-08 2025-03-01 03:21:10.925412-08 benchmark t A100 \N t \N \N \N {} -6441 2002 2025-03-14 06:48:37.342621-07 2025-03-14 07:23:19.449449-07 test f A100 \N t \N \N \N {} -6442 2002 2025-03-14 07:08:21.810282-07 2025-03-14 06:12:36.932788-07 benchmark f A100 \N t \N \N \N {} -3893 1297 2025-03-01 03:27:32.585689-08 2025-03-01 03:40:51.676057-08 benchmark f A100 \N t \N \N \N {} -3900 1299 2025-03-01 03:40:55.395999-08 2025-03-01 03:53:19.378987-08 benchmark f A100 \N f \N \N \N {} -3901 1301 2025-03-01 03:23:52.831997-08 2025-03-01 03:24:06.231506-08 test t A100 \N t \N \N \N {} -3906 1301 2025-03-01 04:15:07.380839-08 2025-03-01 03:58:42.535677-08 leaderboard f H100 0.000903244375 t \N \N \N {} -3907 1301 2025-03-01 03:58:13.621695-08 2025-03-01 03:35:37.1111-08 test t H100 \N t \N \N \N {} -3908 1301 2025-03-01 04:58:15.504982-08 2025-03-01 04:38:42.581718-08 benchmark t H100 \N t \N \N \N {} -3913 1303 2025-03-01 03:45:26.565256-08 2025-03-01 05:21:39.968163-08 test t A100 \N t \N \N \N {} -3920 1304 2025-03-01 04:39:50.463012-08 2025-03-01 05:01:19.916314-08 benchmark f A100 \N t \N \N \N {} -3921 1304 2025-03-01 04:39:17.669272-08 2025-03-01 03:55:58.328183-08 leaderboard f A100 0.0030727456666666663 t \N \N \N {} -6670 2063 2025-03-14 14:58:48.236812-07 2025-03-14 15:04:51.635193-07 test f T4 \N f \N \N \N {} -3923 1304 2025-03-01 04:23:40.082947-08 2025-03-01 04:52:37.416251-08 benchmark t A100 \N t \N \N \N {} -3924 1304 2025-03-01 05:06:09.579535-08 2025-03-01 04:30:25.040011-08 leaderboard t A100 0.0030747073333333335 t \N \N \N {} -6307 1950 2025-03-12 11:44:15.74066-07 2025-03-12 10:54:32.286656-07 test f T4 \N f \N \N \N {} -6444 2003 2025-03-14 06:32:45.509148-07 2025-03-14 07:18:07.617634-07 test t A100 \N t \N \N \N {} -6445 2003 2025-03-14 06:43:39.064772-07 2025-03-14 07:26:22.701906-07 benchmark t A100 \N t \N \N \N {} -6446 2003 2025-03-14 07:33:49.087001-07 2025-03-14 06:39:43.653159-07 leaderboard t A100 0.00251628 t \N \N \N {} -3926 1305 2025-03-01 05:27:38.253899-08 2025-03-01 04:35:13.150247-08 benchmark t A100 \N t \N \N \N {} -3927 1305 2025-03-01 04:30:10.546633-08 2025-03-01 04:32:49.219013-08 leaderboard t A100 0.003074227 t \N \N \N {} -6308 1950 2025-03-12 12:25:05.113988-07 2025-03-12 12:37:59.249778-07 test t T4 \N f \N \N \N {} -6447 2003 2025-03-14 07:25:35.263987-07 2025-03-14 05:57:18.436435-07 test f A100 \N t \N \N \N {} -6448 2003 2025-03-14 06:22:22.575249-07 2025-03-14 07:19:37.030539-07 benchmark f A100 \N t \N \N \N {} -6449 2003 2025-03-14 07:33:07.159548-07 2025-03-14 07:43:35.889465-07 leaderboard f A100 0.0025153743333333335 t \N \N \N {} -3929 1305 2025-03-01 05:24:53.102248-08 2025-03-01 05:27:25.918489-08 benchmark f A100 \N t \N \N \N {} -3930 1305 2025-03-01 04:56:51.217802-08 2025-03-01 04:41:26.360821-08 leaderboard f A100 0.0030731703333333337 t \N \N \N {} -6309 1951 2025-03-12 12:37:05.375541-07 2025-03-12 12:21:31.848581-07 test t T4 \N f \N \N \N {} -6450 2004 2025-03-14 07:29:50.054077-07 2025-03-14 06:54:48.788265-07 test f A100 \N t \N \N \N {} -6451 2004 2025-03-14 06:47:18.994761-07 2025-03-14 07:41:04.641274-07 benchmark f A100 \N t \N \N \N {} -6452 2004 2025-03-14 07:10:52.271141-07 2025-03-14 07:05:23.598203-07 leaderboard f A100 0.0025149163333333334 t \N \N \N {} -6686 2071 2025-03-14 15:07:32.035959-07 2025-03-14 16:41:36.954155-07 test t T4 \N f \N \N \N {} -6935 2159 2025-03-16 16:02:23.35434-07 2025-03-16 16:42:39.300711-07 test f T4 \N t \N \N \N {} -6311 1952 2025-03-12 11:40:24.149803-07 2025-03-12 11:50:01.838943-07 test t T4 \N f \N \N \N {} -6458 2005 2025-03-14 07:21:25.580439-07 2025-03-14 07:49:47.315286-07 leaderboard f A100 0.004558669333333333 t \N \N \N {} -6687 2071 2025-03-14 15:04:02.106962-07 2025-03-14 16:27:06.289153-07 test f T4 \N f \N \N \N {} -6883 2136 2025-03-15 15:12:53.005577-07 2025-03-15 15:45:03.98046-07 test f T4 \N t \N \N \N {} -3968 1322 2025-03-01 05:31:02.366882-08 2025-03-01 05:15:44.290903-08 benchmark t A100 \N t \N \N \N {} -3969 1322 2025-03-01 05:37:29.486548-08 2025-03-01 05:45:21.369833-08 leaderboard t A100 0.0030774476666666664 t \N \N \N {} -3970 1322 2025-03-01 05:40:01.603582-08 2025-03-01 05:45:11.281035-08 test f A100 \N t \N \N \N {} -3971 1322 2025-03-01 06:09:29.893232-08 2025-03-01 05:02:33.403442-08 benchmark f A100 \N t \N \N \N {} -3972 1322 2025-03-01 05:06:02.207814-08 2025-03-01 05:55:39.737325-08 leaderboard f A100 0.0030752193333333333 t \N \N \N {} -3973 1323 2025-03-01 05:58:27.485607-08 2025-03-01 05:32:24.84737-08 test f A100 \N t \N \N \N {} -3974 1323 2025-03-01 06:16:37.08018-08 2025-03-01 04:46:10.75807-08 benchmark f A100 \N t \N \N \N {} -3975 1323 2025-03-01 06:18:48.328453-08 2025-03-01 05:34:36.581312-08 leaderboard f A100 0.003101147 t \N \N \N {} -3976 1323 2025-03-01 06:03:28.859556-08 2025-03-01 05:45:30.216393-08 test t A100 \N t \N \N \N {} -3977 1323 2025-03-01 06:05:43.149319-08 2025-03-01 06:06:01.600121-08 benchmark t A100 \N t \N \N \N {} -3978 1323 2025-03-01 05:20:14.946-08 2025-03-01 06:09:26.3369-08 leaderboard t A100 0.0030720786666666665 t \N \N \N {} -3979 1324 2025-03-01 05:38:48.032681-08 2025-03-01 05:38:54.242522-08 test t A100 \N f \N \N \N {} -3980 1324 2025-03-01 05:03:07.857476-08 2025-03-01 05:56:18.026019-08 test f A100 \N f \N \N \N {} -3981 1325 2025-03-01 04:30:24.581687-08 2025-03-01 05:30:26.203251-08 test f A100 \N f \N \N \N {} -3982 1325 2025-03-01 04:56:35.77322-08 2025-03-01 05:06:42.117449-08 test t A100 \N f \N \N \N {} -3989 1327 2025-03-01 04:28:09.627783-08 2025-03-01 06:14:03.4485-08 test f A100 \N t \N \N \N {} -3990 1327 2025-03-01 04:35:12.535221-08 2025-03-01 06:16:55.791101-08 benchmark f A100 \N t \N \N \N {} -3998 1328 2025-03-01 05:02:22.872872-08 2025-03-01 05:53:01.111038-08 test t A100 \N t \N \N \N {} -3999 1328 2025-03-01 05:20:30.293816-08 2025-03-01 04:33:16.504511-08 benchmark t A100 \N t \N \N \N {} -4000 1328 2025-03-01 05:24:59.129999-08 2025-03-01 05:11:00.788762-08 leaderboard t A100 0.0030814433333333333 t \N \N \N {} -4006 1329 2025-03-01 05:03:38.237873-08 2025-03-01 05:49:05.125811-08 leaderboard f A100 0.0030727136666666667 t \N \N \N {} -4007 1330 2025-03-01 06:29:07.869426-08 2025-03-01 06:22:44.330161-08 test t A100 \N t \N \N \N {} -4008 1330 2025-03-01 06:01:42.053545-08 2025-03-01 04:58:15.207722-08 benchmark t A100 \N t \N \N \N {} -4009 1330 2025-03-01 05:03:24.750209-08 2025-03-01 05:57:23.562377-08 leaderboard t A100 0.0030764006666666667 t \N \N \N {} -4010 1330 2025-03-01 05:02:18.387184-08 2025-03-01 04:49:51.849261-08 test f A100 \N t \N \N \N {} -4011 1330 2025-03-01 04:34:41.853129-08 2025-03-01 05:27:20.630573-08 benchmark f A100 \N t \N \N \N {} -4012 1330 2025-03-01 04:49:25.539951-08 2025-03-01 05:57:06.277345-08 leaderboard f A100 0.0030913556666666664 t \N \N \N {} -4013 1331 2025-03-01 04:48:50.723394-08 2025-03-01 05:02:12.598261-08 test f A100 \N t \N \N \N {} -4014 1331 2025-03-01 05:46:30.40955-08 2025-03-01 04:34:39.633219-08 benchmark f A100 \N t \N \N \N {} -4015 1331 2025-03-01 05:38:54.065577-08 2025-03-01 05:25:23.854596-08 leaderboard f A100 0.0030761543333333334 t \N \N \N {} -4016 1331 2025-03-01 04:41:26.822773-08 2025-03-01 06:31:54.977434-08 test t A100 \N t \N \N \N {} -4025 1333 2025-03-01 05:50:11.13569-08 2025-03-01 06:05:47.809639-08 test f A100 \N t \N \N \N {} -4034 1334 2025-03-01 05:19:46.050467-08 2025-03-01 06:13:31.802821-08 test t A100 \N t \N \N \N {} -4042 1336 2025-03-01 06:19:43.447341-08 2025-03-01 05:51:53.054293-08 test f A100 \N t \N \N \N {} -4017 1331 2025-03-01 04:46:15.747877-08 2025-03-01 05:40:33.74129-08 benchmark t A100 \N t \N \N \N {} -4018 1331 2025-03-01 04:50:33.824408-08 2025-03-01 04:35:16.517834-08 leaderboard t A100 0.0030775386666666666 t \N \N \N {} -4019 1332 2025-03-01 06:15:51.207238-08 2025-03-01 06:21:40.29258-08 test f A100 \N t \N \N \N {} -4020 1332 2025-03-01 05:54:40.351278-08 2025-03-01 06:07:21.488972-08 benchmark f A100 \N t \N \N \N {} -4021 1332 2025-03-01 05:31:15.028107-08 2025-03-01 06:30:55.794956-08 leaderboard f A100 0.0032326943333333335 t \N \N \N {} -4029 1333 2025-03-01 04:43:11.484999-08 2025-03-01 06:18:08.597188-08 benchmark t A100 \N t \N \N \N {} -4030 1333 2025-03-01 05:38:23.948276-08 2025-03-01 05:39:32.998484-08 leaderboard t A100 0.0030748286666666667 t \N \N \N {} -4031 1334 2025-03-01 05:48:36.681273-08 2025-03-01 05:09:41.33705-08 test f A100 \N t \N \N \N {} -4032 1334 2025-03-01 05:41:35.653199-08 2025-03-01 05:12:26.846467-08 benchmark f A100 \N t \N \N \N {} -4033 1334 2025-03-01 05:50:46.305933-08 2025-03-01 05:16:24.499999-08 leaderboard f A100 0.0031010393333333335 t \N \N \N {} -4035 1334 2025-03-01 06:38:20.344372-08 2025-03-01 05:01:27.901596-08 benchmark t A100 \N t \N \N \N {} -4036 1334 2025-03-01 05:13:10.923007-08 2025-03-01 05:05:10.395318-08 leaderboard t A100 0.0030793333333333337 t \N \N \N {} -4037 1335 2025-03-01 05:22:53.382722-08 2025-03-01 05:25:53.811459-08 test t A100 \N f \N \N \N {} -4045 1337 2025-03-01 06:43:13.512286-08 2025-03-01 05:52:38.084808-08 test t A100 \N t \N \N \N {} -4046 1337 2025-03-01 06:44:42.843151-08 2025-03-01 06:01:44.654233-08 benchmark t A100 \N t \N \N \N {} -4053 1338 2025-03-01 05:49:19.310951-08 2025-03-01 05:36:21.556206-08 leaderboard t A100 0.0030786213333333333 t \N \N \N {} -4054 1338 2025-03-01 06:08:31.2478-08 2025-03-01 05:15:47.918409-08 test f A100 \N t \N \N \N {} -4055 1338 2025-03-01 06:00:55.317206-08 2025-03-01 05:42:35.691206-08 benchmark f A100 \N t \N \N \N {} -4061 1339 2025-03-01 04:55:54.708149-08 2025-03-01 06:20:05.506072-08 benchmark t A100 \N t \N \N \N {} -4062 1339 2025-03-01 06:29:35.459123-08 2025-03-01 06:23:22.438908-08 leaderboard t A100 0.0030713596666666664 t \N \N \N {} -4063 1340 2025-03-01 05:41:13.291902-08 2025-03-01 05:08:42.07891-08 test f A100 \N t \N \N \N {} -4064 1340 2025-03-01 05:33:54.804638-08 2025-03-01 06:49:34.790694-08 benchmark f A100 \N t \N \N \N {} -4065 1340 2025-03-01 06:22:42.948861-08 2025-03-01 06:31:27.337118-08 leaderboard f A100 0.003198704 t \N \N \N {} -4066 1340 2025-03-01 05:53:22.22062-08 2025-03-01 06:34:56.280809-08 test t A100 \N t \N \N \N {} -4075 1342 2025-03-01 06:23:06.476775-08 2025-03-01 06:20:15.612586-08 test f A100 \N t \N \N \N {} -4084 1343 2025-03-01 06:31:52.402656-08 2025-03-01 06:10:01.556153-08 test f A100 \N t \N \N \N {} -4109 1349 2025-03-01 05:50:58.123614-08 2025-03-01 07:02:47.190517-08 test t A100 \N t \N \N \N {} -4067 1340 2025-03-01 05:51:32.857224-08 2025-03-01 05:50:31.869282-08 benchmark t A100 \N t \N \N \N {} -4068 1340 2025-03-01 06:52:04.233379-08 2025-03-01 05:20:00.968896-08 leaderboard t A100 0.0032254873333333336 t \N \N \N {} -4076 1342 2025-03-01 05:52:15.30752-08 2025-03-01 05:16:01.171421-08 benchmark f A100 \N t \N \N \N {} -4077 1342 2025-03-01 06:48:06.395287-08 2025-03-01 06:20:52.98914-08 leaderboard f A100 0.003085711 t \N \N \N {} -4078 1342 2025-03-01 06:38:10.193601-08 2025-03-01 06:54:28.028198-08 test t A100 \N t \N \N \N {} -4085 1343 2025-03-01 06:30:45.755953-08 2025-03-01 05:42:14.015703-08 benchmark f A100 \N t \N \N \N {} -4086 1343 2025-03-01 05:14:21.820912-08 2025-03-01 06:41:27.76024-08 leaderboard f A100 0.0031491675 t \N \N \N {} -4093 1345 2025-03-01 05:54:34.018691-08 2025-03-01 06:48:03.464696-08 test f A100 \N f \N \N \N {} -4094 1345 2025-03-01 06:07:45.020264-08 2025-03-01 05:56:24.473964-08 test t A100 \N f \N \N \N {} -4101 1347 2025-03-01 06:44:45.774365-08 2025-03-01 05:10:23.773951-08 test f A100 \N f \N \N \N {} -4102 1347 2025-03-01 06:21:29.448139-08 2025-03-01 05:16:01.94432-08 test t A100 \N f \N \N \N {} -4110 1349 2025-03-01 06:50:52.832524-08 2025-03-01 06:21:34.412682-08 benchmark t A100 \N t \N \N \N {} -4111 1349 2025-03-01 05:37:38.785374-08 2025-03-01 05:39:45.670246-08 leaderboard t A100 0.0030744786666666666 t \N \N \N {} -4118 1350 2025-03-01 05:53:22.664423-08 2025-03-01 07:01:25.508428-08 test t A100 \N t \N \N \N {} -4119 1350 2025-03-01 07:06:15.020001-08 2025-03-01 05:36:11.046309-08 benchmark t A100 \N t \N \N \N {} -4120 1350 2025-03-01 06:20:34.501205-08 2025-03-01 07:07:39.442371-08 leaderboard t A100 0.003088505 t \N \N \N {} -4126 1351 2025-03-01 05:29:03.034314-08 2025-03-01 06:25:02.133816-08 leaderboard t A100 0.0030791646666666664 t \N \N \N {} -4134 1353 2025-03-01 06:04:43.547155-08 2025-03-01 05:13:34.33261-08 benchmark t A100 \N t \N \N \N {} -4135 1353 2025-03-01 07:05:13.871186-08 2025-03-01 06:41:07.602177-08 leaderboard t A100 0.003071621 t \N \N \N {} -4143 1354 2025-03-01 05:17:03.588452-08 2025-03-01 05:16:11.035298-08 benchmark t A100 \N t \N \N \N {} -4144 1354 2025-03-01 06:01:34.820165-08 2025-03-01 06:34:58.72374-08 leaderboard t A100 0.00309386 t \N \N \N {} -4165 1363 2025-03-01 09:39:07.8542-08 2025-03-01 09:06:36.459295-08 benchmark f A100 \N t \N \N \N {} -4166 1364 2025-03-01 10:45:48.93923-08 2025-03-01 10:33:36.557942-08 test t A100 \N t \N \N \N {} -4167 1364 2025-03-01 09:43:39.289368-08 2025-03-01 09:24:23.903656-08 benchmark t A100 \N t \N \N \N {} -4170 1364 2025-03-01 10:36:49.26316-08 2025-03-01 09:28:08.907873-08 benchmark f A100 \N t \N \N \N {} -4175 1366 2025-03-01 10:48:21.857968-08 2025-03-01 10:57:43.342896-08 leaderboard t A100 0.0030997733333333337 t \N \N \N {} -4176 1366 2025-03-01 10:27:28.925342-08 2025-03-01 09:25:05.826997-08 test f A100 \N t \N \N \N {} -4184 1372 2025-03-01 10:01:34.032339-08 2025-03-01 10:16:18.107861-08 benchmark f A100 \N t \N \N \N {} -4185 1373 2025-03-01 09:15:05.438331-08 2025-03-01 10:28:47.955472-08 benchmark f A100 \N t \N \N \N {} -4191 1380 2025-03-01 11:36:24.556989-08 2025-03-01 11:40:57.219225-08 benchmark f A100 \N t \N \N \N {} -4192 1381 2025-03-01 12:37:28.809727-08 2025-03-01 11:54:48.723704-08 benchmark f A100 \N f \N \N \N {} -4193 1382 2025-03-01 12:05:31.937467-08 2025-03-01 12:39:42.035292-08 benchmark f A100 \N f \N \N \N {} -4194 1383 2025-03-01 12:24:03.28573-08 2025-03-01 12:49:27.141084-08 benchmark f A100 \N f \N \N \N {} -4195 1384 2025-03-01 12:30:23.318874-08 2025-03-01 11:45:28.231808-08 benchmark f A100 \N t \N \N \N {} -4196 1385 2025-03-01 12:10:07.559639-08 2025-03-01 13:20:27.623427-08 benchmark f A100 \N t \N \N \N {} -4197 1386 2025-03-01 13:54:20.599915-08 2025-03-01 13:59:32.398597-08 test f A100 \N t \N \N \N {} -4198 1387 2025-03-01 14:14:19.137435-08 2025-03-01 13:16:59.246757-08 benchmark f A100 \N t \N \N \N {} -4204 1388 2025-03-01 12:23:09.987084-08 2025-03-01 13:22:39.985792-08 leaderboard t A100 0.496474646 t \N \N \N {} -4205 1389 2025-03-01 13:19:47.926437-08 2025-03-01 13:50:43.82625-08 test f H100 \N t \N \N \N {} -4206 1389 2025-03-01 12:57:36.960021-08 2025-03-01 13:42:33.039405-08 benchmark f H100 \N t \N \N \N {} -4224 1394 2025-03-01 16:02:06.026414-08 2025-03-01 15:58:14.959593-08 leaderboard t A100 0.0031078296666666666 t \N \N \N {} -4225 1394 2025-03-01 15:56:49.702024-08 2025-03-01 14:57:49.257679-08 test t H100 \N t \N \N \N {} -4226 1394 2025-03-01 16:23:12.233422-08 2025-03-01 16:09:26.381534-08 benchmark t H100 \N t \N \N \N {} -4227 1394 2025-03-01 15:22:20.9878-08 2025-03-01 15:51:31.402515-08 leaderboard t H100 0.00140471775 t \N \N \N {} -4230 1397 2025-03-01 17:11:50.531769-08 2025-03-01 17:06:47.495163-08 benchmark f H100 \N t \N \N \N {} -4235 1398 2025-03-01 16:50:13.330905-08 2025-03-01 16:05:18.289309-08 benchmark f H100 \N t \N \N \N {} -4240 1398 2025-03-01 15:52:44.51554-08 2025-03-01 15:40:06.215226-08 test f A100 \N f \N \N \N {} -4241 1399 2025-03-01 17:23:26.394202-08 2025-03-01 16:46:15.926611-08 benchmark f H100 \N f \N \N \N {} -4248 1402 2025-03-01 16:50:49.496325-08 2025-03-01 16:38:17.053665-08 benchmark t H100 \N t \N \N \N {} -4249 1402 2025-03-01 17:29:19.46759-08 2025-03-01 17:17:13.325643-08 leaderboard t H100 0.0013931105 t \N \N \N {} -4250 1403 2025-03-01 16:41:21.078394-08 2025-03-01 16:31:20.029427-08 test f H100 \N t \N \N \N {} -4251 1403 2025-03-01 17:01:41.343912-08 2025-03-01 17:09:49.148125-08 benchmark f H100 \N t \N \N \N {} -4252 1403 2025-03-01 16:09:08.072113-08 2025-03-01 16:30:28.83147-08 leaderboard f H100 0.00139343525 t \N \N \N {} -4415 1443 2025-03-01 23:27:54.443718-08 2025-03-01 23:44:48.408471-08 benchmark t L4 \N t \N \N \N {} -4254 1403 2025-03-01 16:28:43.322824-08 2025-03-01 16:12:28.94178-08 benchmark t H100 \N t \N \N \N {} -4255 1403 2025-03-01 16:59:30.637265-08 2025-03-01 16:54:54.864297-08 leaderboard t H100 0.0014084093333333332 t \N \N \N {} -4256 1402 2025-03-01 17:48:26.163752-08 2025-03-01 16:43:09.49428-08 test f H100 \N t \N \N \N {} -4257 1402 2025-03-01 17:31:46.113694-08 2025-03-01 16:07:29.034922-08 benchmark f H100 \N t \N \N \N {} -4258 1402 2025-03-01 16:44:05.583517-08 2025-03-01 16:25:07.353815-08 leaderboard f H100 0.0014460703333333332 t \N \N \N {} -4259 1404 2025-03-01 16:04:19.796606-08 2025-03-01 17:33:34.248936-08 test t H100 \N t \N \N \N {} -4260 1404 2025-03-01 16:49:13.841981-08 2025-03-01 17:51:00.007281-08 benchmark t H100 \N t \N \N \N {} -4261 1404 2025-03-01 17:21:32.526268-08 2025-03-01 16:39:22.981736-08 leaderboard t H100 0.00139580475 t \N \N \N {} -4262 1404 2025-03-01 16:31:24.233879-08 2025-03-01 17:38:25.925363-08 test f H100 \N t \N \N \N {} -4263 1404 2025-03-01 17:31:33.499326-08 2025-03-01 16:34:05.021142-08 benchmark f H100 \N t \N \N \N {} -4379 1433 2025-03-01 21:51:29.505052-08 2025-03-01 21:42:07.436975-08 benchmark f H100 \N t \N \N \N {} -4264 1404 2025-03-01 16:46:11.323414-08 2025-03-01 17:19:04.9435-08 leaderboard f H100 0.0014059293333333331 t \N \N \N {} -4265 1405 2025-03-01 17:04:29.433941-08 2025-03-01 17:19:54.898023-08 test f L4 \N t \N \N \N {} -4266 1405 2025-03-01 17:15:36.867761-08 2025-03-01 16:06:23.499666-08 benchmark f L4 \N t \N \N \N {} -4267 1405 2025-03-01 17:28:05.852436-08 2025-03-01 16:40:49.105619-08 leaderboard f L4 0.017067446 t \N \N \N {} -4268 1405 2025-03-01 17:14:37.360289-08 2025-03-01 16:02:42.08553-08 test t L4 \N t \N \N \N {} -4384 1437 2025-03-01 22:29:30.981358-08 2025-03-01 22:15:31.729879-08 benchmark f H100 \N f \N \N \N {} -4269 1405 2025-03-01 16:26:20.496455-08 2025-03-01 16:15:13.996431-08 benchmark t L4 \N t \N \N \N {} -4270 1405 2025-03-01 16:08:25.687965-08 2025-03-01 17:10:55.36684-08 leaderboard t L4 0.01706846266666667 t \N \N \N {} -4271 1406 2025-03-01 17:35:32.433009-08 2025-03-01 16:47:33.575921-08 test f T4 \N t \N \N \N {} -4272 1406 2025-03-01 16:29:17.767422-08 2025-03-01 16:51:54.661181-08 benchmark f T4 \N t \N \N \N {} -4273 1406 2025-03-01 16:08:36.107841-08 2025-03-01 16:30:12.375768-08 leaderboard f T4 0.009307217333333334 t \N \N \N {} -4385 1438 2025-03-01 22:33:51.295044-08 2025-03-01 22:12:55.046741-08 benchmark f H100 \N f \N \N \N {} -4274 1406 2025-03-01 17:26:10.061376-08 2025-03-01 16:28:55.30488-08 test t T4 \N f \N \N \N {} -4275 1406 2025-03-01 17:24:14.461681-08 2025-03-01 17:32:10.727373-08 test f L4 \N f \N \N \N {} -4276 1406 2025-03-01 16:51:43.765001-08 2025-03-01 17:08:41.224245-08 test t L4 \N f \N \N \N {} -4351 1425 2025-03-01 18:27:28.317296-08 2025-03-01 19:48:34.537155-08 test t A100 \N t \N \N \N {} -4360 1425 2025-03-01 18:55:29.934952-08 2025-03-01 20:05:32.79375-08 test f T4 \N t \N \N \N {} -4726 1479 2025-03-02 07:19:35.258294-08 2025-03-02 07:04:07.889836-08 test f A100 \N t \N \N \N {} -4341 1424 2025-03-01 19:12:19.828159-08 2025-03-01 20:11:36.50049-08 benchmark f H100 \N t \N \N \N {} -4342 1425 2025-03-01 19:30:03.137888-08 2025-03-01 20:13:32.937364-08 test f H100 \N t \N \N \N {} -4735 1480 2025-03-02 06:33:46.379677-08 2025-03-02 07:28:20.847048-08 test f A100 \N t \N \N \N {} -4343 1425 2025-03-01 19:07:06.678388-08 2025-03-01 19:01:17.242899-08 benchmark f H100 \N t \N \N \N {} -4344 1425 2025-03-01 19:36:47.591098-08 2025-03-01 20:05:13.137174-08 leaderboard f H100 0.0015068819090909092 t \N \N \N {} -4345 1425 2025-03-01 19:40:07.817557-08 2025-03-01 19:16:20.820213-08 test t L4 \N t \N \N \N {} -4355 1425 2025-03-01 19:40:23.407312-08 2025-03-01 18:36:43.639689-08 benchmark t T4 \N t \N \N \N {} -4356 1425 2025-03-01 19:34:42.848141-08 2025-03-01 19:21:17.019994-08 leaderboard t T4 0.019392106333333332 t \N \N \N {} -4364 1425 2025-03-01 19:44:49.950465-08 2025-03-01 19:52:00.834651-08 benchmark f L4 \N t \N \N \N {} -4365 1425 2025-03-01 18:56:32.12492-08 2025-03-01 19:36:54.329004-08 leaderboard f L4 0.018389386333333334 t \N \N \N {} -4366 1426 2025-03-01 22:15:53.709448-08 2025-03-01 21:41:09.208205-08 test f H100 \N f \N \N \N {} -4367 1426 2025-03-01 20:32:14.409145-08 2025-03-01 20:49:12.590979-08 test f A100 \N f \N \N \N {} -4368 1427 2025-03-01 22:21:41.20304-08 2025-03-01 21:10:43.83991-08 test f T4 \N f \N \N \N {} -4369 1427 2025-03-01 20:41:14.21784-08 2025-03-01 22:20:23.41131-08 test f L4 \N f \N \N \N {} -4370 1428 2025-03-01 21:22:14.660722-08 2025-03-01 21:39:17.638743-08 test f L4 \N f \N \N \N {} -4371 1429 2025-03-01 20:56:28.569982-08 2025-03-01 21:27:23.848244-08 test f L4 \N f \N \N \N {} -4375 1432 2025-03-01 21:12:25.058278-08 2025-03-01 21:46:33.33486-08 benchmark f H100 \N t \N \N \N {} -4376 1432 2025-03-01 23:10:02.333804-08 2025-03-01 21:49:56.568079-08 benchmark f L4 \N t \N \N \N {} -4377 1432 2025-03-01 22:54:44.213473-08 2025-03-01 21:21:30.556007-08 benchmark f A100 \N t \N \N \N {} -4876 1500 2025-03-03 09:35:56.137495-08 2025-03-03 09:52:48.005654-08 leaderboard t A100 0.003105324 t \N \N \N {} -4380 1434 2025-03-01 22:15:57.892727-08 2025-03-01 23:36:46.04608-08 benchmark f H100 \N t \N \N \N {} -4381 1435 2025-03-01 22:24:08.366722-08 2025-03-01 22:29:42.067258-08 benchmark f H100 \N t \N \N \N {} -4382 1436 2025-03-01 23:39:19.061091-08 2025-03-01 22:54:39.475489-08 benchmark f H100 \N f \N \N \N {} -4383 1437 2025-03-01 22:35:16.757658-08 2025-03-01 22:50:02.811266-08 benchmark f A100 \N t \N \N \N {} -4386 1439 2025-03-02 00:07:36.475999-08 2025-03-01 23:44:26.098199-08 benchmark f H100 \N t \N \N \N {} -4387 1440 2025-03-02 00:02:57.861102-08 2025-03-02 00:00:32.358477-08 test f H100 \N f \N \N \N {} -4414 1443 2025-03-01 23:54:21.846815-08 2025-03-01 23:32:53.796678-08 test t L4 \N t \N \N \N {} -4388 1440 2025-03-01 23:11:27.786009-08 2025-03-01 23:02:42.012476-08 test t A100 \N f \N \N \N {} -4393 1440 2025-03-01 23:15:26.635065-08 2025-03-01 23:08:51.999345-08 test t L4 \N f \N \N \N {} -4398 1441 2025-03-01 23:17:23.847393-08 2025-03-02 00:04:54.13285-08 test f A100 \N f \N \N \N {} -4403 1442 2025-03-01 22:53:39.143776-08 2025-03-01 23:56:44.359809-08 test t L4 \N f \N \N \N {} -4404 1442 2025-03-01 22:33:19.623771-08 2025-03-01 23:47:56.133494-08 test t H100 \N f \N \N \N {} -4405 1442 2025-03-02 00:04:31.80069-08 2025-03-01 23:26:12.331721-08 test f L4 \N f \N \N \N {} -4406 1442 2025-03-01 23:27:53.503574-08 2025-03-01 22:47:29.500397-08 test f H100 \N f \N \N \N {} -4407 1442 2025-03-02 00:07:32.568134-08 2025-03-01 23:00:08.696649-08 test t T4 \N f \N \N \N {} -4408 1442 2025-03-01 23:29:07.086459-08 2025-03-01 23:23:37.989186-08 test t A100 \N f \N \N \N {} -4409 1442 2025-03-01 22:51:12.097545-08 2025-03-01 22:56:56.425507-08 test f A100 \N f \N \N \N {} -4410 1442 2025-03-01 23:47:29.737111-08 2025-03-02 00:21:02.822694-08 test f T4 \N f \N \N \N {} -4416 1443 2025-03-01 23:58:25.027917-08 2025-03-01 23:05:04.128044-08 leaderboard t L4 0.009129896 t \N \N \N {} -4417 1443 2025-03-01 23:26:40.751758-08 2025-03-02 00:27:51.073423-08 test t A100 \N t \N \N \N {} -4423 1443 2025-03-02 00:04:09.394316-08 2025-03-02 00:13:48.201959-08 test f H100 \N t \N \N \N {} -4424 1443 2025-03-01 23:36:08.305635-08 2025-03-02 00:47:29.293128-08 benchmark f H100 \N t \N \N \N {} -4425 1443 2025-03-01 23:30:11.887008-08 2025-03-02 00:21:43.679513-08 leaderboard f H100 0.0015019263333333332 t \N \N \N {} -4437 1444 2025-03-01 23:22:12.599414-08 2025-03-02 00:09:39.877934-08 leaderboard f A100 0.0021369876666666667 t \N \N \N {} -4438 1444 2025-03-01 23:59:01.602753-08 2025-03-02 00:52:25.788903-08 test f T4 \N t \N \N \N {} -4444 1444 2025-03-01 23:27:58.287274-08 2025-03-01 23:34:31.637125-08 test t T4 \N t \N \N \N {} -4445 1444 2025-03-01 23:28:25.653915-08 2025-03-02 00:46:20.886868-08 benchmark t T4 \N t \N \N \N {} -4446 1444 2025-03-01 23:44:46.267002-08 2025-03-02 00:46:20.218889-08 leaderboard t T4 0.009888984666666666 t \N \N \N {} -4458 1444 2025-03-02 00:52:06.841349-08 2025-03-02 00:45:13.235451-08 leaderboard t L4 0.009366314666666667 t \N \N \N {} -4459 1445 2025-03-02 00:17:32.146735-08 2025-03-02 00:27:53.179825-08 test f A100 \N t \N \N \N {} -4465 1445 2025-03-01 23:25:17.275973-08 2025-03-01 23:51:30.13378-08 test f H100 \N t \N \N \N {} -4466 1445 2025-03-02 00:51:37.846267-08 2025-03-02 00:39:47.26155-08 benchmark f H100 \N t \N \N \N {} -4467 1445 2025-03-01 23:12:50.976391-08 2025-03-02 00:06:29.223376-08 leaderboard f H100 0.0010895946666666667 t \N \N \N {} -4479 1445 2025-03-02 00:18:32.388575-08 2025-03-01 23:33:06.524137-08 leaderboard f L4 0.009311576333333333 t \N \N \N {} -4480 1445 2025-03-02 00:23:10.771659-08 2025-03-02 00:25:16.79615-08 test t L4 \N t \N \N \N {} -4481 1445 2025-03-02 01:06:15.185297-08 2025-03-02 00:56:41.356199-08 benchmark t L4 \N t \N \N \N {} -4486 1447 2025-03-01 23:44:19.043343-08 2025-03-02 00:34:17.729776-08 test t A100 \N t \N \N \N {} -4500 1448 2025-03-01 23:26:04.292969-08 2025-03-02 00:24:20.318749-08 test f A100 \N t \N \N \N {} -4501 1448 2025-03-01 23:52:25.86203-08 2025-03-02 00:46:12.033575-08 benchmark f A100 \N t \N \N \N {} -4502 1448 2025-03-02 00:50:48.885087-08 2025-03-02 00:58:56.374508-08 leaderboard f A100 0.005635142019999999 t \N \N \N {} -4503 1448 2025-03-02 00:36:34.265162-08 2025-03-01 23:44:52.402347-08 test t A100 \N t \N \N \N {} -4514 1450 2025-03-02 00:08:27.757645-08 2025-03-02 00:02:03.781381-08 leaderboard t H100 0.00110452 t \N \N \N {} -4515 1450 2025-03-02 00:14:30.121063-08 2025-03-02 00:04:30.11163-08 test t L4 \N t \N \N \N {} -4516 1450 2025-03-02 00:00:00.221666-08 2025-03-01 23:29:45.13959-08 benchmark t L4 \N t \N \N \N {} -4521 1450 2025-03-02 00:02:47.686536-08 2025-03-01 23:56:26.155139-08 test f H100 \N t \N \N \N {} -4522 1450 2025-03-01 23:44:32.403338-08 2025-03-02 01:02:56.194528-08 benchmark f H100 \N t \N \N \N {} -4523 1450 2025-03-02 00:07:11.12717-08 2025-03-01 23:55:23.35683-08 leaderboard f H100 0.0010997553333333332 t \N \N \N {} -4535 1450 2025-03-02 00:15:33.808245-08 2025-03-02 00:39:03.802273-08 leaderboard f L4 0.009153131 t \N \N \N {} -4536 1451 2025-03-02 01:18:52.170604-08 2025-03-02 01:11:40.804771-08 test t H100 \N t \N \N \N {} -4900 1508 2025-03-03 12:44:25.017899-08 2025-03-03 13:29:37.01192-08 test f H100 \N t \N \N \N {} -4759 1485 2025-03-02 13:16:14.223315-08 2025-03-02 12:01:21.108531-08 benchmark f H100 \N t \N \N \N {} -4905 1511 2025-03-03 14:17:16.597745-08 2025-03-03 13:19:16.395123-08 test t T4 \N f \N \N \N {} -4623 1458 2025-03-02 06:38:45.060119-08 2025-03-02 05:30:45.356488-08 test t A100 \N t \N \N \N {} -6716 2082 2025-03-15 03:44:36.086784-07 2025-03-15 03:23:26.694084-07 test t A100 \N t \N \N \N {} -4632 1460 2025-03-02 06:51:07.426669-08 2025-03-02 06:04:55.597062-08 test t A100 \N t \N \N \N {} -6719 2082 2025-03-15 03:16:11.184399-07 2025-03-15 03:09:17.233425-07 test f A100 \N t \N \N \N {} -4641 1461 2025-03-02 05:59:49.230938-08 2025-03-02 06:17:49.715674-08 test f A100 \N t \N \N \N {} -6722 2083 2025-03-15 03:29:49.696607-07 2025-03-15 04:02:55.058125-07 test f A100 \N t \N \N \N {} -4650 1463 2025-03-02 06:51:19.039666-08 2025-03-02 05:32:00.49752-08 test t A100 \N t \N \N \N {} -6725 2083 2025-03-15 04:23:08.720635-07 2025-03-15 04:21:03.715823-07 test t A100 \N t \N \N \N {} -4659 1464 2025-03-02 07:24:45.335423-08 2025-03-02 05:47:41.040187-08 test f A100 \N t \N \N \N {} -6728 2084 2025-03-15 03:50:48.746611-07 2025-03-15 03:49:46.374297-07 test t A100 \N t \N \N \N {} -4668 1466 2025-03-02 07:09:02.875102-08 2025-03-02 05:37:12.011229-08 test t A100 \N t \N \N \N {} -6731 2084 2025-03-15 02:46:52.42645-07 2025-03-15 04:11:18.181663-07 test f A100 \N t \N \N \N {} -4620 1458 2025-03-02 07:19:02.28608-08 2025-03-02 07:06:35.957428-08 test f A100 \N t \N \N \N {} -4621 1458 2025-03-02 06:23:47.065884-08 2025-03-02 07:03:30.070237-08 benchmark f A100 \N t \N \N \N {} -4622 1458 2025-03-02 07:11:03.17852-08 2025-03-02 06:48:19.70623-08 leaderboard f A100 0.0030958753333333337 t \N \N \N {} -4624 1458 2025-03-02 06:26:45.847704-08 2025-03-02 05:52:44.331704-08 benchmark t A100 \N t \N \N \N {} -4625 1458 2025-03-02 06:51:27.902184-08 2025-03-02 06:23:56.736147-08 leaderboard t A100 0.003088996 t \N \N \N {} -4633 1460 2025-03-02 06:16:38.8786-08 2025-03-02 07:11:21.936323-08 benchmark t A100 \N t \N \N \N {} -4634 1460 2025-03-02 07:04:41.283428-08 2025-03-02 07:16:21.369857-08 leaderboard t A100 0.003092899 t \N \N \N {} -4642 1461 2025-03-02 06:09:41.284959-08 2025-03-02 06:22:42.202445-08 benchmark f A100 \N t \N \N \N {} -4643 1461 2025-03-02 06:39:57.63533-08 2025-03-02 06:46:22.813146-08 leaderboard f A100 0.0030704853333333336 t \N \N \N {} -4651 1463 2025-03-02 06:26:49.901591-08 2025-03-02 06:01:32.015488-08 benchmark t A100 \N t \N \N \N {} -4653 1463 2025-03-02 07:29:04.708213-08 2025-03-02 07:18:09.192245-08 test f A100 \N t \N \N \N {} -4660 1464 2025-03-02 05:32:51.24243-08 2025-03-02 06:13:17.003468-08 benchmark f A100 \N t \N \N \N {} -4661 1464 2025-03-02 07:16:41.081997-08 2025-03-02 05:48:25.892059-08 leaderboard f A100 0.0030758066666666667 t \N \N \N {} -4669 1466 2025-03-02 07:06:29.137455-08 2025-03-02 06:45:12.645699-08 benchmark t A100 \N t \N \N \N {} -4670 1466 2025-03-02 06:40:31.210834-08 2025-03-02 05:42:40.735546-08 leaderboard t A100 0.0030799263333333333 t \N \N \N {} -4678 1467 2025-03-02 06:32:38.464263-08 2025-03-02 06:16:47.589364-08 benchmark f A100 \N t \N \N \N {} -4680 1468 2025-03-02 06:22:28.004766-08 2025-03-02 07:35:00.897908-08 test f A100 \N t \N \N \N {} -4687 1469 2025-03-02 06:37:34.057103-08 2025-03-02 05:51:59.584284-08 benchmark f A100 \N t \N \N \N {} -4688 1469 2025-03-02 06:22:01.378836-08 2025-03-02 06:13:47.362128-08 leaderboard f A100 0.00307773 t \N \N \N {} -4696 1470 2025-03-02 05:54:10.720598-08 2025-03-02 06:58:21.045687-08 benchmark f A100 \N t \N \N \N {} -4697 1470 2025-03-02 06:56:21.605195-08 2025-03-02 07:04:27.428497-08 leaderboard f A100 0.003323739428571429 t \N \N \N {} -4704 1472 2025-03-02 06:51:21.142542-08 2025-03-02 07:19:12.17164-08 benchmark f A100 \N t \N \N \N {} -4705 1473 2025-03-02 06:59:02.14179-08 2025-03-02 06:18:41.378415-08 benchmark f A100 \N t \N \N \N {} -4706 1474 2025-03-02 06:32:28.531936-08 2025-03-02 07:28:17.021525-08 benchmark f A100 \N t \N \N \N {} -4707 1475 2025-03-02 07:43:56.768427-08 2025-03-02 07:18:05.844238-08 benchmark f A100 \N t \N \N \N {} -4709 1476 2025-03-02 07:29:45.620972-08 2025-03-02 06:53:02.850347-08 benchmark t A100 \N t \N \N \N {} -4710 1476 2025-03-02 06:48:36.930559-08 2025-03-02 07:09:21.284629-08 leaderboard t A100 0.003082332 t \N \N \N {} -4718 1477 2025-03-02 07:14:49.953547-08 2025-03-02 07:01:10.444489-08 benchmark f A100 \N t \N \N \N {} -4719 1477 2025-03-02 07:13:14.737688-08 2025-03-02 07:58:55.056874-08 leaderboard f A100 0.003080525 t \N \N \N {} -4727 1479 2025-03-02 08:08:16.530944-08 2025-03-02 06:25:59.616763-08 benchmark f A100 \N t \N \N \N {} -4736 1480 2025-03-02 07:47:58.351644-08 2025-03-02 06:49:23.461444-08 benchmark f A100 \N t \N \N \N {} -4737 1480 2025-03-02 08:02:53.103575-08 2025-03-02 08:13:30.327361-08 leaderboard f A100 0.0030852583333333336 t \N \N \N {} -4752 1485 2025-03-02 11:53:55.657606-08 2025-03-02 11:29:49.203485-08 test f A100 \N t \N \N \N {} -4753 1485 2025-03-02 11:58:43.743381-08 2025-03-02 12:04:12.552688-08 benchmark f A100 \N t \N \N \N {} -4754 1485 2025-03-02 12:59:42.33895-08 2025-03-02 12:36:29.8219-08 leaderboard f A100 0.0017007568181818182 t \N \N \N {} -4760 1485 2025-03-02 13:02:43.584587-08 2025-03-02 11:40:22.88528-08 leaderboard f H100 0.001025282 t \N \N \N {} -4761 1485 2025-03-02 11:54:09.417727-08 2025-03-02 13:05:23.177947-08 test t H100 \N t \N \N \N {} -4764 1485 2025-03-02 12:19:37.770033-08 2025-03-02 11:40:52.739277-08 test f T4 \N t \N \N \N {} -4767 1485 2025-03-02 11:34:48.399203-08 2025-03-02 11:27:10.480623-08 test f L4 \N t \N \N \N {} -4768 1485 2025-03-02 12:21:36.330234-08 2025-03-02 13:16:33.964583-08 benchmark f L4 \N t \N \N \N {} -4769 1485 2025-03-02 11:54:00.372056-08 2025-03-02 12:11:51.743238-08 leaderboard f L4 0.009055283666666665 t \N \N \N {} -4780 1486 2025-03-02 13:32:04.884765-08 2025-03-02 13:19:13.880894-08 test t L4 \N t \N \N \N {} -4788 1486 2025-03-02 12:38:25.692798-08 2025-03-02 12:40:07.438573-08 test f T4 \N t \N \N \N {} -4795 1486 2025-03-02 11:54:55.018029-08 2025-03-02 12:21:15.163724-08 leaderboard f L4 0.013281347 t \N \N \N {} -4827 1490 2025-03-02 14:04:50.955427-08 2025-03-02 14:34:08.371455-08 benchmark t A100 \N t \N \N \N {} -4828 1490 2025-03-02 14:33:00.642669-08 2025-03-02 14:35:15.849785-08 leaderboard t A100 0.0017312735 t \N \N \N {} -4829 1490 2025-03-02 15:19:06.654379-08 2025-03-02 14:54:43.856752-08 test f A100 \N t \N \N \N {} -4830 1490 2025-03-02 13:56:46.528544-08 2025-03-02 15:10:08.582329-08 benchmark f A100 \N t \N \N \N {} -4831 1490 2025-03-02 13:52:33.720668-08 2025-03-02 13:42:04.183005-08 leaderboard f A100 0.0017395216666666667 t \N \N \N {} -4832 1491 2025-03-02 13:31:50.812204-08 2025-03-02 15:31:56.835867-08 test t A100 \N t \N \N \N {} -4833 1491 2025-03-02 15:09:34.132728-08 2025-03-02 13:54:04.869362-08 benchmark t A100 \N t \N \N \N {} -4834 1491 2025-03-02 14:20:30.701842-08 2025-03-02 13:59:22.648224-08 leaderboard t A100 0.0017047336000000001 t \N \N \N {} -4835 1491 2025-03-02 14:45:24.03934-08 2025-03-02 15:10:20.533866-08 test f A100 \N t \N \N \N {} -4836 1491 2025-03-02 14:20:31.244621-08 2025-03-02 15:06:36.572076-08 benchmark f A100 \N t \N \N \N {} -4837 1491 2025-03-02 15:29:45.897744-08 2025-03-02 13:54:34.350922-08 leaderboard f A100 0.0017131206666666668 t \N \N \N {} -6758 2094 2025-03-15 03:45:32.92736-07 2025-03-15 04:07:37.272427-07 test f A100 \N t \N \N \N {} -4844 1493 2025-03-02 13:46:35.98177-08 2025-03-02 14:48:05.043588-08 test f A100 \N t \N \N \N {} -4845 1493 2025-03-02 14:00:59.163247-08 2025-03-02 14:35:29.575037-08 benchmark f A100 \N t \N \N \N {} -6761 2094 2025-03-15 03:27:08.411109-07 2025-03-15 04:01:20.079415-07 test t A100 \N t \N \N \N {} -4852 1494 2025-03-02 15:08:24.34101-08 2025-03-02 14:19:40.913664-08 leaderboard f L4 0.009482677 t \N \N \N {} -4853 1494 2025-03-02 13:50:54.659903-08 2025-03-02 13:57:25.615542-08 test t L4 \N t \N \N \N {} -4854 1494 2025-03-02 14:27:36.134951-08 2025-03-02 13:47:01.295492-08 benchmark t L4 \N t \N \N \N {} -4855 1494 2025-03-02 15:05:50.23843-08 2025-03-02 15:31:13.138166-08 leaderboard t L4 0.009465309666666666 t \N \N \N {} -4856 1494 2025-03-02 14:59:15.831079-08 2025-03-02 15:20:52.999421-08 test t A100 \N t \N \N \N {} -4857 1494 2025-03-02 14:03:52.80639-08 2025-03-02 14:22:25.374431-08 benchmark t A100 \N t \N \N \N {} -4858 1494 2025-03-02 14:26:35.800808-08 2025-03-02 14:28:06.519575-08 leaderboard t A100 0.0017436125 t \N \N \N {} -4859 1494 2025-03-02 15:29:00.03614-08 2025-03-02 14:28:22.752305-08 test f A100 \N t \N \N \N {} -4860 1494 2025-03-02 14:27:41.758642-08 2025-03-02 14:55:37.506929-08 benchmark f A100 \N t \N \N \N {} -4861 1494 2025-03-02 15:13:30.285322-08 2025-03-02 13:38:55.585292-08 leaderboard f A100 0.0017438063333333332 t \N \N \N {} -4862 1494 2025-03-02 15:33:33.758009-08 2025-03-02 14:29:38.081435-08 test f T4 \N f \N \N \N {} -4869 1495 2025-03-02 15:00:43.386949-08 2025-03-02 15:19:19.029978-08 leaderboard f A100 0.0017183623 t \N \N \N {} -4863 1494 2025-03-02 13:48:00.216726-08 2025-03-02 14:43:10.705646-08 test t T4 \N f \N \N \N {} -4864 1495 2025-03-02 14:14:21.117209-08 2025-03-02 14:50:17.471125-08 test t A100 \N t \N \N \N {} -4868 1495 2025-03-02 13:56:41.498396-08 2025-03-02 15:19:57.661184-08 benchmark f A100 \N t \N \N \N {} -4870 1496 2025-03-03 05:33:22.851475-08 2025-03-03 07:04:29.985456-08 test f T4 \N f \N \N \N {} -4872 1498 2025-03-03 10:02:30.253679-08 2025-03-03 10:00:42.925801-08 benchmark f A100 \N t \N \N \N {} -4873 1499 2025-03-03 08:49:19.901664-08 2025-03-03 10:04:25.898649-08 benchmark f A100 \N t \N \N \N {} -4874 1500 2025-03-03 10:15:34.816738-08 2025-03-03 09:04:15.56701-08 test t A100 \N t \N \N \N {} -4875 1500 2025-03-03 09:58:02.568244-08 2025-03-03 10:16:40.069607-08 benchmark t A100 \N t \N \N \N {} -4878 1500 2025-03-03 10:05:00.25862-08 2025-03-03 09:37:20.75377-08 benchmark f A100 \N t \N \N \N {} -4879 1500 2025-03-03 09:50:57.496245-08 2025-03-03 08:44:48.833862-08 leaderboard f A100 0.003117381 t \N \N \N {} -4882 1503 2025-03-03 10:01:52.486048-08 2025-03-03 10:04:44.602379-08 benchmark f A100 \N t \N \N \N {} -4883 1504 2025-03-03 09:35:17.850935-08 2025-03-03 10:09:25.356439-08 benchmark f A100 \N t \N \N \N {} -4885 1506 2025-03-03 13:09:09.154292-08 2025-03-03 13:05:14.933666-08 test f H100 \N t \N \N \N {} -4886 1506 2025-03-03 12:41:09.063313-08 2025-03-03 12:33:34.096179-08 benchmark f H100 \N t \N \N \N {} -4887 1506 2025-03-03 13:59:05.903613-08 2025-03-03 13:48:05.240853-08 leaderboard f H100 0.007589034 t \N \N \N {} -4888 1506 2025-03-03 13:41:18.845874-08 2025-03-03 13:57:20.398849-08 test t H100 \N t \N \N \N {} -4889 1506 2025-03-03 13:43:47.027609-08 2025-03-03 13:34:31.478831-08 benchmark t H100 \N t \N \N \N {} -4890 1506 2025-03-03 14:32:13.712927-08 2025-03-03 13:02:10.756259-08 leaderboard t H100 0.007640819666666667 t \N \N \N {} -4892 1507 2025-03-03 14:08:09.124545-08 2025-03-03 13:52:34.536612-08 benchmark t A100 \N t \N \N \N {} -4893 1507 2025-03-03 14:13:34.686495-08 2025-03-03 13:52:41.545312-08 leaderboard t A100 0.023358184 t \N \N \N {} -4897 1508 2025-03-03 14:08:08.277234-08 2025-03-03 12:42:40.071166-08 test t H100 \N t \N \N \N {} -4898 1508 2025-03-03 13:11:16.067265-08 2025-03-03 13:52:02.911783-08 benchmark t H100 \N t \N \N \N {} -4901 1508 2025-03-03 12:59:43.830263-08 2025-03-03 13:02:51.227677-08 benchmark f H100 \N t \N \N \N {} -4903 1510 2025-03-03 13:17:23.292196-08 2025-03-03 14:30:50.919066-08 test t T4 \N f \N \N \N {} -6766 2097 2025-03-15 03:49:11.850159-07 2025-03-15 04:26:16.901571-07 test t A100 \N t \N \N \N {} -5252 1613 2025-03-06 07:03:50.875686-08 2025-03-06 06:49:34.383024-08 leaderboard t L4 0.017109318 t \N \N \N {} -5253 1613 2025-03-06 07:08:11.029391-08 2025-03-06 07:01:52.953606-08 test f L4 \N t \N \N \N {} -4937 1521 2025-03-03 14:44:11.876868-08 2025-03-03 14:10:59.325044-08 leaderboard f A100 0.0014066483684210525 t \N \N \N {} -5254 1613 2025-03-06 07:19:32.162924-08 2025-03-06 08:10:28.115423-08 benchmark f L4 \N t \N \N \N {} -4938 1521 2025-03-03 15:24:12.517619-08 2025-03-03 14:20:06.658751-08 test t A100 \N t \N \N \N {} -4935 1521 2025-03-03 14:48:27.263394-08 2025-03-03 15:21:43.699775-08 test f A100 \N t \N \N \N {} -4936 1521 2025-03-03 14:31:08.393947-08 2025-03-03 14:45:16.89233-08 benchmark f A100 \N t \N \N \N {} -4939 1521 2025-03-03 15:07:14.225011-08 2025-03-03 14:04:11.041623-08 benchmark t A100 \N t \N \N \N {} -4940 1521 2025-03-03 15:16:14.240133-08 2025-03-03 14:50:14.153588-08 leaderboard t A100 0.001405596387755102 t \N \N \N {} -4941 1522 2025-03-03 15:05:35.069177-08 2025-03-03 14:28:42.867479-08 test f H100 \N t \N \N \N {} -4945 1522 2025-03-03 14:50:55.354936-08 2025-03-03 15:01:22.261798-08 benchmark t H100 \N t \N \N \N {} -4947 1523 2025-03-03 15:51:18.302976-08 2025-03-03 15:33:42.474228-08 benchmark f L4 \N f \N \N \N {} -4950 1524 2025-03-03 14:20:11.679881-08 2025-03-03 15:49:02.916005-08 leaderboard f A100 0.0025553103333333335 t \N \N \N {} -4951 1524 2025-03-03 14:08:29.207972-08 2025-03-03 14:22:21.528102-08 test t H100 \N t \N \N \N {} -4952 1524 2025-03-03 14:38:24.77646-08 2025-03-03 15:05:24.221642-08 benchmark t H100 \N t \N \N \N {} -4953 1524 2025-03-03 14:46:04.864855-08 2025-03-03 15:51:25.144507-08 leaderboard t H100 0.001393842 t \N \N \N {} -4954 1524 2025-03-03 14:30:39.536975-08 2025-03-03 14:17:44.438638-08 test t A100 \N t \N \N \N {} -4955 1524 2025-03-03 14:40:56.30983-08 2025-03-03 14:38:06.121372-08 benchmark t A100 \N t \N \N \N {} -4956 1524 2025-03-03 15:09:02.97402-08 2025-03-03 15:42:51.195857-08 leaderboard t A100 0.0025125443333333334 t \N \N \N {} -5003 1534 2025-03-03 23:49:46.367273-08 2025-03-03 22:04:46.20686-08 benchmark f T4 \N t \N \N \N {} -5256 1613 2025-03-06 07:45:45.096126-08 2025-03-06 08:18:22.747974-08 test t T4 \N t \N \N \N {} -4968 1533 2025-03-03 22:51:29.622688-08 2025-03-03 23:00:37.622546-08 test t A100 \N t \N \N \N {} -4969 1533 2025-03-03 21:55:51.670102-08 2025-03-03 22:25:04.572539-08 benchmark t A100 \N f \N \N \N {} -4970 1533 2025-03-03 22:42:07.031457-08 2025-03-03 23:50:25.299897-08 test f A100 \N t \N \N \N {} -4971 1533 2025-03-03 22:38:20.171762-08 2025-03-03 23:13:31.324985-08 benchmark f A100 \N f \N \N \N {} -4972 1533 2025-03-03 22:03:32.238956-08 2025-03-03 22:00:26.809337-08 test t H100 \N t \N \N \N {} -4973 1533 2025-03-03 23:37:08.619928-08 2025-03-03 22:52:08.648968-08 benchmark t H100 \N f \N \N \N {} -4975 1533 2025-03-03 22:42:28.912392-08 2025-03-03 22:08:16.911585-08 benchmark f H100 \N f \N \N \N {} -4976 1533 2025-03-03 22:57:31.735787-08 2025-03-03 22:02:40.663491-08 test t T4 \N t \N \N \N {} -4977 1533 2025-03-03 23:18:01.411543-08 2025-03-03 22:17:36.143751-08 benchmark t T4 \N f \N \N \N {} -4978 1533 2025-03-03 23:47:51.751613-08 2025-03-03 22:21:29.994607-08 test f T4 \N t \N \N \N {} -4979 1533 2025-03-03 23:38:54.802767-08 2025-03-03 23:19:22.073079-08 benchmark f T4 \N f \N \N \N {} -4980 1533 2025-03-03 22:10:43.459126-08 2025-03-03 23:21:01.183959-08 test f L4 \N t \N \N \N {} -4987 1534 2025-03-03 22:29:57.882848-08 2025-03-03 22:02:34.344571-08 test f A100 \N t \N \N \N {} -4988 1534 2025-03-03 23:17:09.194997-08 2025-03-03 22:00:28.272124-08 benchmark f A100 \N t \N \N \N {} -6772 2098 2025-03-15 05:05:11.153225-07 2025-03-15 03:30:05.714156-07 test t A100 \N t \N \N \N {} -4989 1534 2025-03-03 23:03:04.470836-08 2025-03-03 22:15:42.701149-08 leaderboard f A100 0.001491699 t \N \N \N {} -4990 1534 2025-03-03 23:01:12.271656-08 2025-03-03 23:25:12.093163-08 test f H100 \N t \N \N \N {} -4991 1534 2025-03-03 23:48:25.304808-08 2025-03-03 23:01:02.444844-08 benchmark f H100 \N t \N \N \N {} -4992 1534 2025-03-03 23:36:40.312813-08 2025-03-03 23:14:16.51261-08 leaderboard f H100 0.001046992 t \N \N \N {} -4993 1534 2025-03-03 22:43:08.623067-08 2025-03-03 23:34:08.683584-08 test f L4 \N t \N \N \N {} -4994 1534 2025-03-03 22:04:15.889744-08 2025-03-03 22:26:35.523219-08 benchmark f L4 \N t \N \N \N {} -4995 1534 2025-03-03 22:41:49.509135-08 2025-03-03 23:39:33.494802-08 leaderboard f L4 0.009082157 t \N \N \N {} -4996 1534 2025-03-03 23:19:47.000677-08 2025-03-03 23:51:43.362821-08 test t H100 \N t \N \N \N {} -5000 1534 2025-03-03 22:10:50.514317-08 2025-03-03 23:01:22.855314-08 benchmark t T4 \N t \N \N \N {} -5001 1534 2025-03-03 23:23:50.275013-08 2025-03-03 22:04:37.625675-08 leaderboard t T4 0.013584824666666667 t \N \N \N {} -5002 1534 2025-03-03 22:16:59.138409-08 2025-03-03 22:04:29.040324-08 test f T4 \N t \N \N \N {} -6775 2098 2025-03-15 04:57:02.889001-07 2025-03-15 04:18:28.535969-07 test f A100 \N t \N \N \N {} -5011 1539 2025-03-04 07:31:37.161177-08 2025-03-04 07:58:56.890059-08 test f T4 \N t \N \N \N {} -5012 1539 2025-03-04 08:16:38.896588-08 2025-03-04 07:18:18.877124-08 benchmark f T4 \N t \N \N \N {} -5013 1539 2025-03-04 08:20:34.258391-08 2025-03-04 07:24:16.514602-08 leaderboard f T4 0.009332833333333334 t \N \N \N {} -5014 1539 2025-03-04 06:44:13.355504-08 2025-03-04 07:28:03.052048-08 test t T4 \N t \N \N \N {} -5015 1539 2025-03-04 08:06:37.290401-08 2025-03-04 06:41:47.643875-08 benchmark t T4 \N t \N \N \N {} -5016 1539 2025-03-04 06:59:22.532084-08 2025-03-04 06:53:54.381226-08 leaderboard t T4 0.009299085333333333 t \N \N \N {} -5017 1540 2025-03-04 08:59:30.670557-08 2025-03-04 07:55:07.055003-08 test f A100 \N f \N \N \N {} -5027 1545 2025-03-04 14:11:42.47821-08 2025-03-04 13:44:28.274936-08 leaderboard f A100 0.00004787177777777778 t \N \N \N {} -5032 1545 2025-03-04 14:00:04.919683-08 2025-03-04 13:52:34.794499-08 benchmark f H100 \N t \N \N \N {} -5033 1545 2025-03-04 15:24:15.587724-08 2025-03-04 15:27:11.189949-08 leaderboard f H100 0.00004866111 t \N \N \N {} -5035 1545 2025-03-04 15:30:36.223103-08 2025-03-04 14:27:02.560929-08 benchmark t T4 \N t \N \N \N {} -5036 1545 2025-03-04 15:07:24.286467-08 2025-03-04 14:51:48.245831-08 leaderboard t T4 0.00023255714285714288 t \N \N \N {} -5257 1613 2025-03-06 06:53:37.987844-08 2025-03-06 08:14:43.956638-08 benchmark t T4 \N t \N \N \N {} -5037 1545 2025-03-04 14:19:47.708086-08 2025-03-04 15:37:55.178413-08 test f L4 \N f \N \N \N {} -5038 1545 2025-03-04 13:49:27.790553-08 2025-03-04 15:32:18.011451-08 test t L4 \N f \N \N \N {} -5039 1545 2025-03-04 15:35:59.738048-08 2025-03-04 14:26:26.882713-08 test f T4 \N f \N \N \N {} -5072 1549 2025-03-04 18:02:14.774593-08 2025-03-04 16:24:38.762227-08 test f H100 \N f \N \N \N {} -5075 1552 2025-03-04 16:26:06.712061-08 2025-03-04 17:05:50.91046-08 benchmark f H100 \N t \N \N \N {} -5076 1553 2025-03-04 17:19:12.024738-08 2025-03-04 17:13:44.312105-08 test t H100 \N t \N \N \N {} -5077 1553 2025-03-04 17:19:42.564719-08 2025-03-04 17:12:42.482126-08 benchmark t H100 \N t \N \N \N {} -5078 1553 2025-03-04 18:03:27.829582-08 2025-03-04 16:30:29.070429-08 leaderboard t H100 0.001479608 t \N \N \N {} -5079 1553 2025-03-04 16:43:40.87693-08 2025-03-04 17:28:08.937479-08 test f H100 \N t \N \N \N {} -5080 1553 2025-03-04 16:51:53.271335-08 2025-03-04 16:21:38.563765-08 benchmark f H100 \N t \N \N \N {} -5084 1556 2025-03-04 16:54:52.472201-08 2025-03-04 17:45:47.245193-08 test f A100 \N f \N \N \N {} -5085 1557 2025-03-04 18:28:01.276786-08 2025-03-04 17:44:42.993298-08 test f A100 \N f \N \N \N {} -5086 1558 2025-03-04 18:11:17.534507-08 2025-03-04 17:29:06.539472-08 test f A100 \N f \N \N \N {} -6790 2101 2025-03-15 03:49:21.74589-07 2025-03-15 04:16:48.818713-07 test f A100 \N t \N \N \N {} -5093 1560 2025-03-04 18:23:35.895658-08 2025-03-04 17:04:19.58105-08 test f H100 \N f \N \N \N {} -5094 1561 2025-03-04 18:05:24.892968-08 2025-03-04 18:46:27.229976-08 test f H100 \N t \N \N \N {} -5095 1562 2025-03-04 18:36:54.224297-08 2025-03-04 18:32:29.172193-08 test f A100 \N t \N \N \N {} -5096 1563 2025-03-04 18:52:59.239172-08 2025-03-04 18:15:38.074894-08 test f H100 \N t \N \N \N {} -5097 1563 2025-03-04 18:49:59.0744-08 2025-03-04 17:26:17.186125-08 benchmark f H100 \N t \N \N \N {} -5098 1563 2025-03-04 17:39:36.278703-08 2025-03-04 17:57:36.746337-08 leaderboard f H100 0.001524286 t \N \N \N {} -5099 1564 2025-03-04 18:14:59.270131-08 2025-03-04 18:51:49.99979-08 benchmark f A100 \N t \N \N \N {} -5100 1563 2025-03-04 18:12:08.43731-08 2025-03-04 17:24:24.25148-08 test t H100 \N t \N \N \N {} -5101 1563 2025-03-04 17:22:01.762045-08 2025-03-04 18:00:18.365591-08 benchmark t H100 \N t \N \N \N {} -5102 1563 2025-03-04 17:14:24.254997-08 2025-03-04 18:03:10.882073-08 leaderboard t H100 0.0015306753333333332 t \N \N \N {} -5104 1565 2025-03-04 18:29:07.767766-08 2025-03-04 17:23:19.898042-08 test t A100 \N t \N \N \N {} -5105 1565 2025-03-04 18:37:16.219751-08 2025-03-04 17:10:25.551997-08 benchmark t A100 \N t \N \N \N {} -5108 1565 2025-03-04 18:35:43.150886-08 2025-03-04 18:46:33.299334-08 benchmark f A100 \N t \N \N \N {} -5109 1565 2025-03-04 17:46:26.10294-08 2025-03-04 17:32:00.135585-08 leaderboard f A100 0.006325797333333333 t \N \N \N {} -5117 1569 2025-03-04 22:14:53.928421-08 2025-03-04 22:40:51.332237-08 test f A100 \N t \N \N \N {} -5118 1569 2025-03-04 22:24:33.021103-08 2025-03-04 21:34:40.758739-08 benchmark f A100 \N t \N \N \N {} -5119 1569 2025-03-04 22:58:48.215389-08 2025-03-04 22:50:16.105009-08 leaderboard f A100 0.0016024253333333334 t \N \N \N {} -5131 1569 2025-03-04 22:47:53.005147-08 2025-03-04 21:52:01.609325-08 leaderboard t H100 0.0010646286666666667 t \N \N \N {} -5132 1569 2025-03-04 23:21:58.974923-08 2025-03-04 21:27:57.937643-08 test f T4 \N t \N \N \N {} -5133 1569 2025-03-04 21:35:43.347052-08 2025-03-04 22:03:01.621549-08 benchmark f T4 \N t \N \N \N {} -5138 1569 2025-03-04 22:37:43.97357-08 2025-03-04 23:16:28.792385-08 test f L4 \N t \N \N \N {} -5139 1569 2025-03-04 21:55:16.016933-08 2025-03-04 23:05:51.261929-08 benchmark f L4 \N t \N \N \N {} -5140 1569 2025-03-04 23:05:20.733299-08 2025-03-04 22:29:48.217612-08 leaderboard f L4 0.009060729666666666 t \N \N \N {} -5152 1570 2025-03-04 22:28:21.72317-08 2025-03-04 23:05:56.534035-08 leaderboard f H100 0.0011025386666666667 t \N \N \N {} -5153 1571 2025-03-04 23:27:03.934327-08 2025-03-04 22:25:50.087612-08 test t A100 \N t \N \N \N {} -5171 1574 2025-03-04 22:56:14.353869-08 2025-03-04 23:19:35.268597-08 test t A100 \N f \N \N \N {} -5172 1574 2025-03-04 21:59:17.605165-08 2025-03-04 22:14:53.169362-08 test f A100 \N f \N \N \N {} -5173 1575 2025-03-04 22:04:24.384305-08 2025-03-04 22:14:55.882584-08 test f H100 \N t \N \N \N {} -5174 1575 2025-03-04 23:09:21.089194-08 2025-03-04 22:12:41.373209-08 benchmark f H100 \N t \N \N \N {} -5185 1578 2025-03-05 04:32:15.216004-08 2025-03-05 04:30:27.093417-08 benchmark f H100 \N t \N \N \N {} -5186 1578 2025-03-05 05:12:33.674089-08 2025-03-05 05:32:04.699339-08 leaderboard f H100 0.0014253056666666667 t \N \N \N {} -5187 1579 2025-03-05 05:33:08.325393-08 2025-03-05 04:46:50.693654-08 test f H100 \N t \N \N \N {} -5188 1580 2025-03-05 04:39:23.334662-08 2025-03-05 04:55:15.840654-08 test f H100 \N f \N \N \N {} -5189 1581 2025-03-05 05:09:09.30193-08 2025-03-05 04:30:58.000235-08 test f H100 \N f \N \N \N {} -5191 1583 2025-03-05 06:03:36.083056-08 2025-03-05 05:52:58.768087-08 test f H100 \N f \N \N \N {} -5192 1584 2025-03-05 04:53:13.978814-08 2025-03-05 06:01:29.534419-08 test f H100 \N f \N \N \N {} -5193 1585 2025-03-05 05:16:23.384842-08 2025-03-05 04:40:47.567792-08 test f H100 \N f \N \N \N {} -5194 1586 2025-03-05 05:55:30.771046-08 2025-03-05 04:56:37.274029-08 test f H100 \N f \N \N \N {} -5195 1587 2025-03-05 05:49:50.339689-08 2025-03-05 05:58:41.349328-08 test f H100 \N f \N \N \N {} -5241 1613 2025-03-06 08:16:58.083166-08 2025-03-06 07:04:03.546846-08 test t A100 \N t \N \N \N {} -5196 1588 2025-03-05 05:52:45.188962-08 2025-03-05 06:10:15.320706-08 test f H100 \N f \N \N \N {} -5198 1590 2025-03-05 06:35:32.564142-08 2025-03-05 05:59:51.088599-08 test f H100 \N f \N \N \N {} -5211 1604 2025-03-05 23:01:07.467624-08 2025-03-06 00:15:28.333028-08 benchmark f A100 \N t \N \N \N {} -5209 1600 2025-03-05 16:08:44.73907-08 2025-03-05 16:32:02.657585-08 test f T4 \N f \N \N \N {} -5210 1601 2025-03-05 17:19:12.616402-08 2025-03-05 16:16:23.001998-08 test f T4 \N f \N \N \N {} -5212 1605 2025-03-05 22:59:21.454362-08 2025-03-05 22:28:41.565705-08 test f A100 \N t \N \N \N {} -5213 1605 2025-03-05 23:20:06.529966-08 2025-03-05 23:11:34.874411-08 benchmark f A100 \N t \N \N \N {} -5214 1605 2025-03-05 22:21:25.7678-08 2025-03-05 22:43:21.731097-08 leaderboard f A100 0.00240375 t \N \N \N {} -5218 1606 2025-03-05 22:50:16.943212-08 2025-03-06 00:17:26.530067-08 test f H100 \N t \N \N \N {} -5219 1606 2025-03-05 23:56:01.568548-08 2025-03-05 23:45:15.745008-08 benchmark f H100 \N t \N \N \N {} -5220 1606 2025-03-05 23:02:21.7458-08 2025-03-06 00:12:54.212376-08 leaderboard f H100 0.001836295 t \N \N \N {} -5221 1606 2025-03-05 23:07:53.314931-08 2025-03-05 23:19:58.450174-08 test t H100 \N t \N \N \N {} -5222 1606 2025-03-05 22:21:37.603066-08 2025-03-05 23:33:48.373334-08 benchmark t H100 \N t \N \N \N {} -5223 1606 2025-03-05 23:56:17.695563-08 2025-03-05 23:31:36.226457-08 leaderboard t H100 0.001844128 t \N \N \N {} -5224 1608 2025-03-06 06:41:32.825228-08 2025-03-06 07:35:28.239062-08 test f T4 \N f \N \N \N {} -5226 1610 2025-03-06 07:18:42.460735-08 2025-03-06 06:35:06.778387-08 test t T4 \N t \N \N \N {} -5250 1613 2025-03-06 08:07:54.141701-08 2025-03-06 07:24:21.635977-08 test t L4 \N t \N \N \N {} -5227 1610 2025-03-06 07:38:46.614128-08 2025-03-06 06:58:40.087618-08 benchmark t T4 \N t \N \N \N {} -5228 1610 2025-03-06 07:51:50.188241-08 2025-03-06 06:58:42.083894-08 leaderboard t T4 0.017143785666666668 t \N \N \N {} -5229 1610 2025-03-06 06:31:06.739357-08 2025-03-06 06:37:17.472387-08 test f T4 \N t \N \N \N {} -5230 1610 2025-03-06 07:22:42.771965-08 2025-03-06 07:33:54.418655-08 benchmark f T4 \N t \N \N \N {} -5231 1610 2025-03-06 06:16:53.919416-08 2025-03-06 06:36:47.083912-08 leaderboard f T4 0.01720559766666667 t \N \N \N {} -5232 1611 2025-03-06 08:16:50.440029-08 2025-03-06 07:49:49.111467-08 test t T4 \N f \N \N \N {} -6784 2100 2025-03-15 04:40:09.589806-07 2025-03-15 04:00:29.456068-07 test f A100 \N t \N \N \N {} -5234 1612 2025-03-06 07:34:10.797478-08 2025-03-06 06:55:41.824879-08 benchmark f L4 \N t \N \N \N {} -5235 1612 2025-03-06 07:42:17.581429-08 2025-03-06 07:04:34.801114-08 benchmark f A100 \N t \N \N \N {} -5236 1612 2025-03-06 07:29:27.298493-08 2025-03-06 08:18:54.173232-08 benchmark f H100 \N t \N \N \N {} -5237 1612 2025-03-06 07:45:18.830048-08 2025-03-06 07:09:08.875473-08 benchmark f T4 \N t \N \N \N {} -5238 1613 2025-03-06 07:02:42.383443-08 2025-03-06 06:58:40.326617-08 test t H100 \N t \N \N \N {} -5239 1613 2025-03-06 06:58:41.561231-08 2025-03-06 07:26:52.163278-08 benchmark t H100 \N t \N \N \N {} -5240 1613 2025-03-06 08:17:32.781179-08 2025-03-06 07:19:03.090424-08 leaderboard t H100 0.0014283363333333333 t \N \N \N {} -5242 1613 2025-03-06 07:50:24.304771-08 2025-03-06 06:37:12.655596-08 benchmark t A100 \N t \N \N \N {} -5243 1613 2025-03-06 07:52:14.675021-08 2025-03-06 07:13:22.297664-08 leaderboard t A100 0.0025773366666666667 t \N \N \N {} -5260 1613 2025-03-06 08:18:47.261863-08 2025-03-06 06:59:45.373237-08 benchmark f T4 \N t \N \N \N {} -5261 1613 2025-03-06 06:55:11.400354-08 2025-03-06 07:22:29.939541-08 leaderboard f T4 0.016297266666666668 t \N \N \N {} -5262 1614 2025-03-06 07:10:48.778261-08 2025-03-06 07:33:58.158012-08 test t A100 \N t \N \N \N {} -5263 1614 2025-03-06 07:57:06.330893-08 2025-03-06 07:32:20.266324-08 benchmark t A100 \N t \N \N \N {} -5264 1614 2025-03-06 07:34:46.398663-08 2025-03-06 07:48:31.150592-08 leaderboard t A100 0.002518239 t \N \N \N {} -5265 1614 2025-03-06 08:22:10.296115-08 2025-03-06 08:10:01.676777-08 test f A100 \N t \N \N \N {} -5266 1614 2025-03-06 07:36:30.354753-08 2025-03-06 08:06:26.122608-08 benchmark f A100 \N t \N \N \N {} -6793 2101 2025-03-15 04:34:05.070783-07 2025-03-15 03:38:46.857954-07 test t A100 \N t \N \N \N {} -5267 1614 2025-03-06 08:17:25.703839-08 2025-03-06 07:44:04.763072-08 leaderboard f A100 0.0025364223333333336 t \N \N \N {} -5268 1614 2025-03-06 07:54:07.764514-08 2025-03-06 06:53:43.516588-08 test f L4 \N t \N \N \N {} -5269 1614 2025-03-06 06:49:11.954531-08 2025-03-06 08:09:48.887046-08 benchmark f L4 \N t \N \N \N {} -5270 1614 2025-03-06 07:58:12.691983-08 2025-03-06 07:16:48.763534-08 leaderboard f L4 0.01703991833333333 t \N \N \N {} -5271 1614 2025-03-06 07:29:09.154149-08 2025-03-06 06:51:28.330755-08 test t L4 \N t \N \N \N {} -5272 1614 2025-03-06 08:11:29.656544-08 2025-03-06 07:59:29.594078-08 benchmark t L4 \N t \N \N \N {} -5273 1614 2025-03-06 08:35:09.612272-08 2025-03-06 07:01:28.409958-08 leaderboard t L4 0.01704863566666667 t \N \N \N {} -5274 1614 2025-03-06 07:29:25.840467-08 2025-03-06 08:40:01.921695-08 test f H100 \N t \N \N \N {} -5275 1614 2025-03-06 07:16:27.972728-08 2025-03-06 07:22:01.065139-08 benchmark f H100 \N t \N \N \N {} -5282 1617 2025-03-06 11:49:00.194719-08 2025-03-06 12:03:16.273518-08 test t L4 \N f \N \N \N {} -5283 1616 2025-03-06 12:12:36.448946-08 2025-03-06 11:07:02.723415-08 test f T4 \N t \N \N \N {} -5284 1616 2025-03-06 12:25:43.203938-08 2025-03-06 10:58:59.548248-08 benchmark f T4 \N t \N \N \N {} -5285 1616 2025-03-06 12:14:49.086319-08 2025-03-06 11:05:00.869471-08 leaderboard f T4 0.009402661909090908 t \N \N \N {} -5288 1616 2025-03-06 11:15:57.5079-08 2025-03-06 12:34:26.281327-08 leaderboard t T4 0.009402624535714285 t \N \N \N {} -5289 1617 2025-03-06 11:04:48.91978-08 2025-03-06 11:41:19.26857-08 test f T4 \N f \N \N \N {} -5290 1617 2025-03-06 11:31:11.14058-08 2025-03-06 11:07:15.510427-08 test t T4 \N f \N \N \N {} -5291 1616 2025-03-06 11:20:35.3437-08 2025-03-06 11:41:25.791263-08 test t L4 \N t \N \N \N {} -5292 1616 2025-03-06 11:17:21.149159-08 2025-03-06 11:36:57.390953-08 benchmark t L4 \N t \N \N \N {} -5293 1616 2025-03-06 11:23:09.088018-08 2025-03-06 10:56:39.864963-08 leaderboard t L4 0.009685256699999999 t \N \N \N {} -5294 1616 2025-03-06 11:46:28.551612-08 2025-03-06 10:50:17.314532-08 test f L4 \N t \N \N \N {} -5295 1616 2025-03-06 11:20:19.815835-08 2025-03-06 11:22:30.677937-08 benchmark f L4 \N t \N \N \N {} -5296 1616 2025-03-06 11:38:50.355641-08 2025-03-06 11:50:15.455809-08 leaderboard f L4 0.009650051153846154 t \N \N \N {} -5309 1620 2025-03-06 12:17:08.411894-08 2025-03-06 11:23:40.680848-08 test t L4 \N t \N \N \N {} -6796 2102 2025-03-15 03:47:18.073866-07 2025-03-15 04:58:31.457625-07 test f A100 \N t \N \N \N {} -6799 2102 2025-03-15 03:49:39.535334-07 2025-03-15 04:47:47.163839-07 test t A100 \N t \N \N \N {} -5310 1620 2025-03-06 11:47:19.342547-08 2025-03-06 11:29:52.276452-08 benchmark t L4 \N t \N \N \N {} -5311 1620 2025-03-06 11:30:56.626007-08 2025-03-06 11:54:39.587711-08 leaderboard t L4 0.017108037 t \N \N \N {} -5315 1621 2025-03-06 12:21:24.243905-08 2025-03-06 11:09:28.475948-08 test t L4 \N f \N \N \N {} -5316 1621 2025-03-06 12:05:13.275699-08 2025-03-06 12:39:19.771477-08 test f L4 \N f \N \N \N {} -5591 1709 2025-03-09 08:38:21.814972-07 2025-03-09 08:31:41.454653-07 leaderboard f T4 0.017528556666666667 t \N \N \N {} -6819 2120 2025-03-15 09:28:09.69146-07 2025-03-15 10:53:19.142071-07 test f A100 \N t \N \N \N {} -5592 1709 2025-03-09 09:45:13.630538-07 2025-03-09 08:29:21.654913-07 test t T4 \N t \N \N \N {} -5637 1719 2025-03-09 10:37:18.14285-07 2025-03-09 09:06:06.681007-07 test f T4 \N t \N \N \N {} -5646 1720 2025-03-09 10:27:41.86582-07 2025-03-09 09:54:42.03058-07 test t T4 \N t \N \N \N {} -5665 1722 2025-03-09 09:32:47.436929-07 2025-03-09 09:26:45.271601-07 benchmark f T4 \N t \N \N \N {} -6822 2120 2025-03-15 10:43:09.477007-07 2025-03-15 09:32:55.270251-07 test t A100 \N t \N \N \N {} -6825 2121 2025-03-15 10:03:06.648384-07 2025-03-15 09:27:27.809952-07 test t A100 \N t \N \N \N {} -5666 1722 2025-03-09 09:52:43.506841-07 2025-03-09 10:58:18.20445-07 leaderboard f T4 0.016603317666666666 t \N \N \N {} -6828 2121 2025-03-15 09:33:59.003711-07 2025-03-15 10:39:36.450576-07 test f A100 \N t \N \N \N {} -5667 1724 2025-03-09 10:44:35.101417-07 2025-03-09 09:13:35.191271-07 test f A100 \N t \N \N \N {} -5655 1721 2025-03-09 10:15:26.439475-07 2025-03-09 09:13:07.504617-07 test f T4 \N t \N \N \N {} -5664 1722 2025-03-09 09:28:08.995402-07 2025-03-09 10:57:10.770407-07 test f T4 \N t \N \N \N {} -6831 2122 2025-03-15 09:55:36.80771-07 2025-03-15 09:14:46.211485-07 test t A100 \N t \N \N \N {} -5668 1724 2025-03-09 10:39:56.890456-07 2025-03-09 11:00:53.761033-07 benchmark f A100 \N t \N \N \N {} -5669 1724 2025-03-09 09:11:31.886775-07 2025-03-09 10:51:26.751854-07 leaderboard f A100 0.0026062246666666666 t \N \N \N {} -5673 1724 2025-03-09 10:17:36.739347-07 2025-03-09 09:34:01.324901-07 test t A100 \N t \N \N \N {} -5670 1725 2025-03-09 09:56:06.327259-07 2025-03-09 10:35:57.538152-07 test f H100 \N t \N \N \N {} -5682 1726 2025-03-09 09:24:13.10994-07 2025-03-09 09:44:04.129165-07 test f L4 \N t \N \N \N {} -5671 1725 2025-03-09 09:58:59.213519-07 2025-03-09 09:39:17.681959-07 benchmark f H100 \N t \N \N \N {} -6834 2122 2025-03-15 09:41:59.559997-07 2025-03-15 10:55:52.113033-07 test f A100 \N t \N \N \N {} -5672 1725 2025-03-09 09:41:17.366352-07 2025-03-09 09:41:18.800653-07 leaderboard f H100 0.0014690293333333333 t \N \N \N {} -6864 2127 2025-03-15 09:44:16.174491-07 2025-03-15 10:46:22.719823-07 test t A100 \N t \N \N \N {} -6837 2123 2025-03-15 11:04:53.025505-07 2025-03-15 10:47:38.423663-07 test f A100 \N t \N \N \N {} -5691 1728 2025-03-09 09:50:18.197376-07 2025-03-09 09:21:08.916986-07 test f A100 \N t \N \N \N {} -6840 2123 2025-03-15 09:53:02.595875-07 2025-03-15 09:54:52.862532-07 test t A100 \N t \N \N \N {} -6843 2124 2025-03-15 10:08:06.005806-07 2025-03-15 10:57:35.474858-07 test f A100 \N t \N \N \N {} -5674 1724 2025-03-09 09:30:02.411383-07 2025-03-09 09:10:48.029672-07 benchmark t A100 \N t \N \N \N {} -6846 2124 2025-03-15 09:34:42.825301-07 2025-03-15 10:21:34.275136-07 test t A100 \N t \N \N \N {} -5563 1700 2025-03-08 17:19:44.52692-08 2025-03-08 16:34:26.7346-08 test t H100 \N f \N \N \N {} -6849 2125 2025-03-15 09:45:34.633967-07 2025-03-15 09:27:25.430422-07 test f A100 \N t \N \N \N {} -5564 1700 2025-03-08 17:51:09.804861-08 2025-03-08 16:57:41.781475-08 test f H100 \N f \N \N \N {} -5547 1692 2025-03-08 16:29:46.214302-08 2025-03-08 16:20:04.501518-08 test f H100 \N f \N \N \N {} -5548 1692 2025-03-08 15:58:09.465006-08 2025-03-08 16:18:52.498393-08 test t H100 \N f \N \N \N {} -5549 1693 2025-03-08 16:52:18.575178-08 2025-03-08 17:37:26.953067-08 test t H100 \N f \N \N \N {} -5567 1702 2025-03-08 16:31:44.29566-08 2025-03-08 17:20:51.940485-08 test t H100 \N f \N \N \N {} -5554 1695 2025-03-08 16:24:17.191755-08 2025-03-08 18:01:05.802736-08 test f H100 \N f \N \N \N {} -5555 1696 2025-03-08 17:11:18.772884-08 2025-03-08 17:09:32.444232-08 test t H100 \N f \N \N \N {} -5556 1696 2025-03-08 17:57:34.2415-08 2025-03-08 17:37:04.496604-08 test f H100 \N f \N \N \N {} -5581 1707 2025-03-09 09:14:09.834023-07 2025-03-09 08:25:50.569029-07 test f T4 \N t \N \N \N {} -5585 1707 2025-03-09 09:38:59.880211-07 2025-03-09 08:38:21.050436-07 benchmark t T4 \N t \N \N \N {} -5587 1708 2025-03-09 09:24:39.948132-07 2025-03-09 08:27:28.337093-07 test f T4 \N f \N \N \N {} -5593 1709 2025-03-09 08:46:11.00316-07 2025-03-09 09:09:14.41678-07 benchmark t T4 \N t \N \N \N {} -5594 1709 2025-03-09 09:13:43.175362-07 2025-03-09 09:11:53.850526-07 leaderboard t T4 0.017543475333333333 t \N \N \N {} -5595 1710 2025-03-09 09:43:56.816043-07 2025-03-09 10:16:02.768519-07 test t T4 \N t \N \N \N {} -5596 1710 2025-03-09 08:28:21.825411-07 2025-03-09 09:26:32.812995-07 benchmark t T4 \N t \N \N \N {} -5597 1710 2025-03-09 09:14:02.814794-07 2025-03-09 09:20:14.126574-07 leaderboard t T4 0.016824632333333332 t \N \N \N {} -6858 2126 2025-03-15 10:43:39.789025-07 2025-03-15 09:34:45.620404-07 test t A100 \N t \N \N \N {} -5601 1711 2025-03-09 09:09:47.896726-07 2025-03-09 09:02:44.866972-07 test t T4 \N f \N \N \N {} -5602 1711 2025-03-09 10:23:41.056545-07 2025-03-09 09:36:00.891836-07 test f T4 \N f \N \N \N {} -5700 1729 2025-03-09 10:19:32.974097-07 2025-03-09 09:54:46.566098-07 test f H100 \N t \N \N \N {} -5607 1712 2025-03-09 10:24:03.045125-07 2025-03-09 09:51:21.11739-07 benchmark t T4 \N t \N \N \N {} -5608 1712 2025-03-09 08:34:08.27444-07 2025-03-09 10:22:37.533877-07 leaderboard t T4 0.01711488933333333 t \N \N \N {} -5613 1714 2025-03-09 09:30:39.959969-07 2025-03-09 10:16:30.712056-07 test t T4 \N t \N \N \N {} -5614 1714 2025-03-09 09:55:01.657622-07 2025-03-09 09:16:09.636467-07 benchmark t T4 \N f \N \N \N {} -5781 1751 2025-03-09 22:17:14.433037-07 2025-03-09 21:49:17.871196-07 test t L4 \N t \N \N \N {} -5629 1717 2025-03-09 08:54:48.324259-07 2025-03-09 10:08:29.038511-07 benchmark t T4 \N t \N \N \N {} -5630 1717 2025-03-09 10:42:26.967084-07 2025-03-09 09:41:58.61599-07 leaderboard t T4 0.016894572333333333 t \N \N \N {} -5631 1718 2025-03-09 09:27:03.051502-07 2025-03-09 10:10:12.243584-07 test t T4 \N t \N \N \N {} -5632 1718 2025-03-09 09:39:46.422345-07 2025-03-09 10:18:05.599135-07 benchmark t T4 \N t \N \N \N {} -5636 1718 2025-03-09 09:51:06.131421-07 2025-03-09 09:42:47.922533-07 leaderboard f T4 0.021235485666666668 t \N \N \N {} -5638 1719 2025-03-09 10:26:54.407345-07 2025-03-09 09:15:13.06042-07 benchmark f T4 \N t \N \N \N {} -5639 1719 2025-03-09 09:29:44.472627-07 2025-03-09 09:37:56.334139-07 leaderboard f T4 0.016808872333333332 t \N \N \N {} -5640 1719 2025-03-09 10:32:20.431198-07 2025-03-09 09:39:18.18504-07 test t T4 \N t \N \N \N {} -5645 1720 2025-03-09 09:55:57.197808-07 2025-03-09 09:40:52.349852-07 leaderboard f T4 0.016596006666666666 t \N \N \N {} -5647 1720 2025-03-09 10:17:27.374148-07 2025-03-09 10:50:14.82081-07 benchmark t T4 \N t \N \N \N {} -5648 1720 2025-03-09 09:16:50.615819-07 2025-03-09 10:21:27.988216-07 leaderboard t T4 0.016557458666666667 t \N \N \N {} -5649 1721 2025-03-09 09:42:37.64406-07 2025-03-09 10:22:42.1259-07 test t T4 \N t \N \N \N {} -5652 1722 2025-03-09 10:58:24.342857-07 2025-03-09 09:19:31.766123-07 test t T4 \N t \N \N \N {} -5654 1722 2025-03-09 10:21:05.107452-07 2025-03-09 09:25:54.295116-07 leaderboard t T4 0.016595996333333335 t \N \N \N {} -5656 1721 2025-03-09 09:56:16.90649-07 2025-03-09 09:16:05.410514-07 benchmark f T4 \N t \N \N \N {} -5657 1721 2025-03-09 09:29:05.785369-07 2025-03-09 10:35:50.223531-07 leaderboard f T4 0.016588513666666665 t \N \N \N {} -5658 1723 2025-03-09 09:19:55.392332-07 2025-03-09 09:53:19.722057-07 test f T4 \N t \N \N \N {} -5677 1725 2025-03-09 09:28:24.172642-07 2025-03-09 10:22:11.236381-07 benchmark t H100 \N t \N \N \N {} -5678 1725 2025-03-09 09:26:29.522614-07 2025-03-09 10:22:55.539844-07 leaderboard t H100 0.001474356 t \N \N \N {} -5679 1726 2025-03-09 10:03:52.222717-07 2025-03-09 09:33:44.331339-07 test t L4 \N t \N \N \N {} -5680 1726 2025-03-09 10:46:04.610414-07 2025-03-09 09:20:34.148072-07 benchmark t L4 \N t \N \N \N {} -5681 1726 2025-03-09 09:37:57.602752-07 2025-03-09 10:22:08.448087-07 leaderboard t L4 0.017242522333333333 t \N \N \N {} -5686 1727 2025-03-09 10:05:30.828856-07 2025-03-09 09:43:47.571121-07 benchmark f L4 \N t \N \N \N {} -5687 1727 2025-03-09 11:07:15.116394-07 2025-03-09 10:13:13.37959-07 leaderboard f L4 0.017209885666666667 t \N \N \N {} -5688 1727 2025-03-09 10:18:00.314232-07 2025-03-09 10:57:21.614871-07 test t L4 \N t \N \N \N {} -5689 1727 2025-03-09 10:01:49.26376-07 2025-03-09 09:25:21.45197-07 benchmark t L4 \N t \N \N \N {} -5690 1727 2025-03-09 10:45:23.99607-07 2025-03-09 09:43:33.892968-07 leaderboard t L4 0.017194495333333334 t \N \N \N {} -5695 1729 2025-03-09 11:12:01.892957-07 2025-03-09 10:53:34.36104-07 benchmark t H100 \N t \N \N \N {} -5696 1729 2025-03-09 10:25:09.517125-07 2025-03-09 10:31:41.836024-07 leaderboard t H100 0.00151662 t \N \N \N {} -5697 1728 2025-03-09 10:48:03.533453-07 2025-03-09 09:49:32.322572-07 test t A100 \N t \N \N \N {} -5698 1728 2025-03-09 10:47:46.442739-07 2025-03-09 10:50:32.307978-07 benchmark t A100 \N t \N \N \N {} -5699 1728 2025-03-09 11:11:38.205953-07 2025-03-09 09:57:38.117077-07 leaderboard t A100 0.0027480226666666665 t \N \N \N {} -5701 1729 2025-03-09 10:46:43.436224-07 2025-03-09 10:08:36.297082-07 benchmark f H100 \N t \N \N \N {} -5702 1729 2025-03-09 09:53:35.832862-07 2025-03-09 10:24:24.298067-07 leaderboard f H100 0.001514298 t \N \N \N {} -5790 1752 2025-03-09 21:29:49.172353-07 2025-03-09 21:20:16.735724-07 test t T4 \N t \N \N \N {} -5775 1749 2025-03-09 22:00:09.902505-07 2025-03-09 22:13:49.54977-07 test f A100 \N t \N \N \N {} -5776 1749 2025-03-09 23:05:49.965781-07 2025-03-09 22:51:52.170132-07 benchmark f A100 \N t \N \N \N {} -5777 1749 2025-03-09 21:24:00.00801-07 2025-03-09 22:47:05.154155-07 leaderboard f A100 0.002640859 t \N \N \N {} -5778 1750 2025-03-09 23:04:17.164077-07 2025-03-09 23:12:50.24992-07 test t H100 \N t \N \N \N {} -5779 1750 2025-03-09 21:43:55.396805-07 2025-03-09 22:41:37.388888-07 benchmark t H100 \N t \N \N \N {} -5782 1751 2025-03-09 21:58:32.972565-07 2025-03-09 22:18:54.173023-07 benchmark t L4 \N t \N \N \N {} -5784 1750 2025-03-09 22:33:29.942446-07 2025-03-09 22:47:34.687352-07 test f H100 \N t \N \N \N {} -5785 1750 2025-03-09 22:57:51.36389-07 2025-03-09 23:11:33.809283-07 benchmark f H100 \N t \N \N \N {} -5786 1750 2025-03-09 22:55:32.663609-07 2025-03-09 21:59:50.521514-07 leaderboard f H100 0.001517079 t \N \N \N {} -5787 1752 2025-03-09 21:22:25.235791-07 2025-03-09 22:13:25.850939-07 test f T4 \N t \N \N \N {} -5788 1752 2025-03-09 23:01:38.584323-07 2025-03-09 21:41:17.038278-07 benchmark f T4 \N t \N \N \N {} -5793 1749 2025-03-09 21:33:50.661901-07 2025-03-09 22:14:05.438845-07 test t A100 \N t \N \N \N {} -5794 1749 2025-03-09 23:11:26.826051-07 2025-03-09 22:00:03.831654-07 benchmark t A100 \N t \N \N \N {} -5795 1749 2025-03-09 21:37:34.837957-07 2025-03-09 22:12:24.604584-07 leaderboard t A100 0.0027676723333333337 t \N \N \N {} -5796 1751 2025-03-09 23:08:13.975436-07 2025-03-09 22:41:10.744916-07 test f L4 \N t \N \N \N {} -5797 1751 2025-03-09 22:51:47.852548-07 2025-03-09 21:35:35.48834-07 benchmark f L4 \N t \N \N \N {} -5798 1751 2025-03-09 22:37:22.743083-07 2025-03-09 21:36:38.099972-07 leaderboard f L4 0.017245750666666667 t \N \N \N {} -5997 1823 2025-03-10 13:44:13.361199-07 2025-03-10 14:17:08.320572-07 test f H100 \N t \N \N \N {} -6111 1849 2025-03-11 00:45:42.466684-07 2025-03-11 02:08:01.224061-07 benchmark f A100 \N f \N \N \N {} -5887 1782 2025-03-10 12:48:56.366283-07 2025-03-10 11:26:28.502927-07 test t T4 \N f \N \N \N {} -5888 1782 2025-03-10 11:20:36.181976-07 2025-03-10 11:07:40.511412-07 test f T4 \N f \N \N \N {} -6006 1824 2025-03-10 14:29:01.568504-07 2025-03-10 13:17:50.880972-07 test t L4 \N t \N \N \N {} -5931 1804 2025-03-10 13:24:49.874112-07 2025-03-10 13:12:59.214864-07 test t T4 \N f \N \N \N {} -5932 1804 2025-03-10 13:11:51.554353-07 2025-03-10 12:36:24.247696-07 test f T4 \N f \N \N \N {} -5933 1805 2025-03-10 12:33:56.206912-07 2025-03-10 12:22:23.193916-07 test t T4 \N f \N \N \N {} -5934 1805 2025-03-10 12:16:39.318057-07 2025-03-10 13:04:51.698492-07 test f T4 \N f \N \N \N {} -5885 1781 2025-03-10 10:56:08.127207-07 2025-03-10 12:40:40.827896-07 test f T4 \N f \N \N \N {} -5886 1781 2025-03-10 11:37:13.25838-07 2025-03-10 11:18:09.354031-07 test t T4 \N f \N \N \N {} -5889 1783 2025-03-10 12:26:17.975854-07 2025-03-10 11:35:06.825449-07 test t T4 \N f \N \N \N {} -5893 1785 2025-03-10 12:49:26.520475-07 2025-03-10 12:05:07.923004-07 test t T4 \N f \N \N \N {} -5894 1785 2025-03-10 11:21:13.907922-07 2025-03-10 12:13:55.484547-07 test f T4 \N f \N \N \N {} -5895 1786 2025-03-10 12:53:04.60948-07 2025-03-10 12:56:23.335782-07 test t T4 \N f \N \N \N {} -5896 1786 2025-03-10 11:52:35.543265-07 2025-03-10 12:47:37.44292-07 test f T4 \N f \N \N \N {} -5897 1787 2025-03-10 12:19:59.35342-07 2025-03-10 12:03:09.756897-07 test t T4 \N f \N \N \N {} -5898 1787 2025-03-10 12:26:38.384347-07 2025-03-10 12:38:55.659544-07 test f T4 \N f \N \N \N {} -5904 1790 2025-03-10 13:07:41.471622-07 2025-03-10 11:29:54.088642-07 test f T4 \N f \N \N \N {} -5905 1791 2025-03-10 12:11:05.954905-07 2025-03-10 11:56:04.212348-07 test t T4 \N f \N \N \N {} -5906 1791 2025-03-10 12:06:20.464482-07 2025-03-10 13:05:37.047802-07 test f T4 \N f \N \N \N {} -5936 1806 2025-03-10 12:25:19.66528-07 2025-03-10 13:25:54.809235-07 test f T4 \N f \N \N \N {} -5907 1792 2025-03-10 11:41:05.031269-07 2025-03-10 12:31:10.781699-07 test f T4 \N f \N \N \N {} -5908 1792 2025-03-10 13:24:19.84949-07 2025-03-10 12:05:30.566627-07 test t T4 \N f \N \N \N {} -5909 1793 2025-03-10 13:10:29.314729-07 2025-03-10 12:27:22.377821-07 test f T4 \N f \N \N \N {} -5910 1793 2025-03-10 11:56:32.644344-07 2025-03-10 12:01:46.701558-07 test t T4 \N f \N \N \N {} -5911 1794 2025-03-10 11:51:38.865118-07 2025-03-10 11:51:19.334553-07 test t T4 \N f \N \N \N {} -5943 1809 2025-03-10 14:18:40.194531-07 2025-03-10 13:13:02.518142-07 leaderboard f A100 0.0026386466666666664 t \N \N \N {} -5912 1794 2025-03-10 12:22:55.695011-07 2025-03-10 13:18:30.719097-07 test f T4 \N f \N \N \N {} -5913 1795 2025-03-10 12:18:06.516563-07 2025-03-10 11:38:14.754524-07 test f T4 \N f \N \N \N {} -5914 1795 2025-03-10 12:36:00.298953-07 2025-03-10 12:55:22.229312-07 test t T4 \N f \N \N \N {} -5923 1800 2025-03-10 11:55:59.030364-07 2025-03-10 12:53:27.690509-07 test f T4 \N f \N \N \N {} -5924 1800 2025-03-10 12:07:20.544617-07 2025-03-10 12:44:06.621555-07 test t T4 \N f \N \N \N {} -5925 1801 2025-03-10 12:06:30.111263-07 2025-03-10 12:53:12.669353-07 test t T4 \N f \N \N \N {} -5926 1801 2025-03-10 13:31:32.380898-07 2025-03-10 13:00:06.56229-07 test f T4 \N f \N \N \N {} -5946 1809 2025-03-10 13:29:37.323879-07 2025-03-10 13:21:30.63564-07 leaderboard t A100 0.002635759 t \N \N \N {} -5937 1807 2025-03-10 12:41:08.079867-07 2025-03-10 12:35:49.297728-07 test f T4 \N f \N \N \N {} -5938 1807 2025-03-10 13:35:12.048803-07 2025-03-10 12:54:21.189668-07 test t T4 \N f \N \N \N {} -5939 1808 2025-03-10 13:59:07.441287-07 2025-03-10 13:33:08.669422-07 test t T4 \N f \N \N \N {} -5940 1808 2025-03-10 14:15:23.166819-07 2025-03-10 13:28:24.134307-07 test f T4 \N f \N \N \N {} -5941 1809 2025-03-10 14:13:38.207591-07 2025-03-10 13:49:12.972948-07 test f A100 \N t \N \N \N {} -5942 1809 2025-03-10 13:54:14.281293-07 2025-03-10 13:15:36.646021-07 benchmark f A100 \N t \N \N \N {} -5953 1811 2025-03-10 13:30:04.564524-07 2025-03-10 12:55:33.446063-07 test t T4 \N f \N \N \N {} -5954 1811 2025-03-10 14:13:48.338956-07 2025-03-10 12:46:33.424518-07 test f T4 \N f \N \N \N {} -5955 1812 2025-03-10 12:31:35.951286-07 2025-03-10 14:17:08.111846-07 test f T4 \N f \N \N \N {} -5956 1812 2025-03-10 13:45:55.348085-07 2025-03-10 13:37:16.626985-07 test t T4 \N f \N \N \N {} -5957 1813 2025-03-10 14:19:03.474874-07 2025-03-10 12:40:48.028667-07 test f T4 \N f \N \N \N {} -5958 1813 2025-03-10 13:09:32.274357-07 2025-03-10 12:58:22.955703-07 test t T4 \N f \N \N \N {} -5960 1814 2025-03-10 13:48:21.644188-07 2025-03-10 13:41:37.383129-07 test t T4 \N f \N \N \N {} -5961 1815 2025-03-10 14:25:53.892843-07 2025-03-10 14:27:52.516911-07 test f T4 \N f \N \N \N {} -5962 1815 2025-03-10 13:39:34.9964-07 2025-03-10 14:26:34.410119-07 test t T4 \N f \N \N \N {} -5963 1816 2025-03-10 12:54:03.964782-07 2025-03-10 13:19:23.51163-07 test t T4 \N f \N \N \N {} -5965 1817 2025-03-10 14:33:23.123369-07 2025-03-10 13:20:30.62665-07 test t T4 \N f \N \N \N {} -5966 1817 2025-03-10 14:24:33.780553-07 2025-03-10 13:09:05.912817-07 test f T4 \N f \N \N \N {} -5967 1818 2025-03-10 12:58:03.382174-07 2025-03-10 13:01:07.893499-07 test f T4 \N t \N \N \N {} -5971 1818 2025-03-10 13:56:03.131162-07 2025-03-10 13:29:13.996141-07 benchmark t T4 \N t \N \N \N {} -5989 1821 2025-03-10 14:34:10.506362-07 2025-03-10 13:16:40.681009-07 benchmark t L4 \N t \N \N \N {} -5990 1821 2025-03-10 14:14:40.84149-07 2025-03-10 13:59:04.607394-07 leaderboard t L4 0.0023410633333333336 t \N \N \N {} -5998 1823 2025-03-10 14:14:38.086577-07 2025-03-10 14:04:27.238303-07 benchmark f H100 \N t \N \N \N {} -5999 1823 2025-03-10 13:02:04.67764-07 2025-03-10 13:38:00.727394-07 leaderboard f H100 0.006278458 t \N \N \N {} -6000 1823 2025-03-10 14:25:36.878836-07 2025-03-10 13:56:47.954868-07 test t H100 \N t \N \N \N {} -6001 1823 2025-03-10 14:33:56.104952-07 2025-03-10 13:27:44.07615-07 benchmark t H100 \N t \N \N \N {} -6002 1823 2025-03-10 13:55:54.754715-07 2025-03-10 14:28:21.040943-07 leaderboard t H100 0.006274267666666667 t \N \N \N {} -6007 1824 2025-03-10 14:28:35.829178-07 2025-03-10 14:56:43.794426-07 benchmark t L4 \N t \N \N \N {} -6008 1824 2025-03-10 14:47:33.073271-07 2025-03-10 13:02:30.850136-07 leaderboard t L4 0.07900226333333332 t \N \N \N {} -6009 1825 2025-03-10 13:08:30.376545-07 2025-03-10 14:58:22.56048-07 test t T4 \N t \N \N \N {} -6010 1825 2025-03-10 13:10:54.018143-07 2025-03-10 14:06:35.263338-07 benchmark t T4 \N t \N \N \N {} -6012 1825 2025-03-10 14:42:31.922476-07 2025-03-10 14:59:03.540136-07 test f T4 \N t \N \N \N {} -6093 1844 2025-03-10 22:33:26.258836-07 2025-03-10 23:34:33.63838-07 leaderboard f A100 0.023116982 t \N \N \N {} -6075 1838 2025-03-10 16:41:59.664667-07 2025-03-10 17:22:08.116083-07 test t T4 \N f \N \N \N {} -6076 1838 2025-03-10 17:26:55.475959-07 2025-03-10 17:59:15.681447-07 test f T4 \N f \N \N \N {} -6077 1839 2025-03-10 17:21:38.821552-07 2025-03-10 17:51:32.223604-07 test f T4 \N f \N \N \N {} -6078 1839 2025-03-10 16:49:21.182853-07 2025-03-10 17:57:15.491956-07 test t T4 \N f \N \N \N {} -6079 1840 2025-03-10 18:26:53.2575-07 2025-03-10 18:21:22.708514-07 test t T4 \N f \N \N \N {} -6080 1840 2025-03-10 16:58:04.162955-07 2025-03-10 16:54:29.133532-07 test f T4 \N f \N \N \N {} -6094 1844 2025-03-10 22:39:58.995586-07 2025-03-10 23:17:13.977931-07 test t A100 \N t \N \N \N {} -6081 1841 2025-03-10 17:37:46.462074-07 2025-03-10 17:23:29.564865-07 test f T4 \N f \N \N \N {} -6082 1841 2025-03-10 17:01:30.436669-07 2025-03-10 17:54:05.772936-07 test t T4 \N f \N \N \N {} -6083 1842 2025-03-10 17:25:05.119241-07 2025-03-10 17:23:54.474002-07 test t T4 \N f \N \N \N {} -6084 1842 2025-03-10 17:50:14.778236-07 2025-03-10 17:38:41.04272-07 test f T4 \N f \N \N \N {} -6085 1843 2025-03-10 23:26:01.96551-07 2025-03-10 22:45:08.618919-07 test f T4 \N t \N \N \N {} -6102 1845 2025-03-10 23:18:03.684504-07 2025-03-10 22:13:42.532972-07 leaderboard t H100 0.007583517333333333 t \N \N \N {} -6086 1843 2025-03-10 23:47:15.122747-07 2025-03-10 23:21:01.982853-07 benchmark f T4 \N t \N \N \N {} -6087 1843 2025-03-10 23:09:56.514137-07 2025-03-10 23:41:36.80451-07 leaderboard f T4 1.0437126283333333 t \N \N \N {} -6088 1843 2025-03-10 22:13:21.923671-07 2025-03-10 23:11:52.790625-07 test t T4 \N t \N \N \N {} -6089 1843 2025-03-10 23:06:17.150635-07 2025-03-10 21:59:26.386221-07 benchmark t T4 \N t \N \N \N {} -6090 1843 2025-03-10 22:53:29.015752-07 2025-03-10 22:55:46.841799-07 leaderboard t T4 0.9874019316666667 t \N \N \N {} -6091 1844 2025-03-10 22:58:35.828002-07 2025-03-10 22:04:49.298779-07 test f A100 \N t \N \N \N {} -6092 1844 2025-03-10 23:35:22.877612-07 2025-03-10 22:06:59.964007-07 benchmark f A100 \N t \N \N \N {} -6101 1845 2025-03-10 22:20:24.248784-07 2025-03-10 23:42:44.548166-07 benchmark t H100 \N t \N \N \N {} -6113 1852 2025-03-11 01:44:33.106318-07 2025-03-11 00:54:18.834861-07 test f A100 \N f \N \N \N {} -6114 1853 2025-03-11 02:21:59.844098-07 2025-03-11 00:42:46.671243-07 test f A100 \N t \N \N \N {} -6115 1854 2025-03-11 02:28:36.156334-07 2025-03-11 00:46:06.202296-07 benchmark f A100 \N t \N \N \N {} -6886 2139 2025-03-16 11:38:46.212648-07 2025-03-16 11:28:03.154972-07 test f H100 \N f \N \N \N {} -6312 1952 2025-03-12 12:28:15.470511-07 2025-03-12 11:10:36.793829-07 test f T4 \N f \N \N \N {} -6460 2005 2025-03-14 06:31:39.099707-07 2025-03-14 07:51:37.583527-07 benchmark t A100 \N t \N \N \N {} -6461 2005 2025-03-14 06:46:54.111465-07 2025-03-14 07:07:11.64108-07 leaderboard t A100 0.004546970333333333 t \N \N \N {} -6596 2044 2025-03-14 13:39:50.152918-07 2025-03-14 12:42:49.661409-07 test f T4 \N f \N \N \N {} -6887 2140 2025-03-16 11:24:30.571404-07 2025-03-16 10:17:15.135954-07 test f T4 \N t \N \N \N {} -6651 2054 2025-03-14 12:23:27.508379-07 2025-03-14 12:07:07.961699-07 benchmark t A100 \N t \N \N \N {} -6652 2054 2025-03-14 13:00:27.647397-07 2025-03-14 13:25:59.68305-07 leaderboard t A100 0.0024835623333333335 t \N \N \N {} -6653 2055 2025-03-14 13:40:44.971801-07 2025-03-14 12:14:34.48949-07 benchmark f A100 \N t \N \N \N {} -6654 2056 2025-03-14 12:23:48.644957-07 2025-03-14 13:52:44.535234-07 benchmark f A100 \N t \N \N \N {} -6657 2058 2025-03-14 13:30:54.732584-07 2025-03-14 12:53:01.468344-07 benchmark t A100 \N t \N \N \N {} -6658 2058 2025-03-14 13:18:02.16859-07 2025-03-14 13:30:04.076635-07 leaderboard t A100 0.002514722 t \N \N \N {} -6660 2058 2025-03-14 12:19:31.91691-07 2025-03-14 12:23:40.05458-07 benchmark f A100 \N t \N \N \N {} -6678 2067 2025-03-14 14:51:27.080477-07 2025-03-14 15:45:50.453303-07 test t T4 \N f \N \N \N {} -6679 2067 2025-03-14 16:00:05.640981-07 2025-03-14 15:45:05.704388-07 test f T4 \N f \N \N \N {} -6681 2068 2025-03-14 15:59:19.410815-07 2025-03-14 15:47:46.277659-07 test t T4 \N f \N \N \N {} -6682 2069 2025-03-14 14:50:20.787072-07 2025-03-14 16:19:00.44313-07 test t T4 \N f \N \N \N {} -6683 2069 2025-03-14 15:33:20.870757-07 2025-03-14 16:21:01.08373-07 test f T4 \N f \N \N \N {} -6688 2072 2025-03-14 16:45:06.05283-07 2025-03-14 15:41:49.353865-07 test f T4 \N f \N \N \N {} -6689 2072 2025-03-14 15:20:50.731476-07 2025-03-14 15:36:51.584067-07 test t T4 \N f \N \N \N {} -6888 2141 2025-03-16 11:25:39.687485-07 2025-03-16 10:00:50.121381-07 test f H100 \N t \N \N \N {} -6690 2073 2025-03-14 17:13:14.556768-07 2025-03-14 16:38:50.501123-07 test t T4 \N f \N \N \N {} -6691 2073 2025-03-14 16:16:09.058399-07 2025-03-14 16:49:34.474312-07 test f T4 \N f \N \N \N {} -6692 2074 2025-03-14 16:06:22.83602-07 2025-03-14 16:32:58.584801-07 test t T4 \N f \N \N \N {} -6695 2075 2025-03-14 16:07:22.505809-07 2025-03-14 16:41:15.832579-07 test t T4 \N f \N \N \N {} -6696 2076 2025-03-14 17:00:09.730989-07 2025-03-14 17:45:40.086325-07 test f T4 \N f \N \N \N {} -6697 2076 2025-03-14 16:27:20.361782-07 2025-03-14 17:11:01.011478-07 test t T4 \N f \N \N \N {} -6698 2077 2025-03-14 17:12:38.530745-07 2025-03-14 17:22:03.682351-07 test t T4 \N f \N \N \N {} -6699 2077 2025-03-14 17:57:47.071236-07 2025-03-14 17:32:18.120911-07 test f T4 \N f \N \N \N {} -6700 2078 2025-03-14 17:18:24.324855-07 2025-03-14 16:55:54.52059-07 test t T4 \N f \N \N \N {} -6701 2078 2025-03-14 16:20:14.880505-07 2025-03-14 16:15:25.529956-07 test f T4 \N f \N \N \N {} -6702 2079 2025-03-14 16:16:56.772414-07 2025-03-14 16:16:13.557071-07 test t T4 \N f \N \N \N {} -6703 2079 2025-03-14 16:04:46.914169-07 2025-03-14 17:47:21.761297-07 test f T4 \N f \N \N \N {} -6705 2080 2025-03-15 03:22:21.602325-07 2025-03-15 03:51:58.457278-07 benchmark t A100 \N t \N \N \N {} -6706 2080 2025-03-15 02:53:01.372004-07 2025-03-15 04:22:27.007219-07 leaderboard t A100 0.002516597 t \N \N \N {} -6712 2081 2025-03-15 02:40:30.575706-07 2025-03-15 03:31:55.583471-07 leaderboard f A100 0.0025119373333333337 t \N \N \N {} -6714 2081 2025-03-15 04:01:30.92787-07 2025-03-15 04:10:50.24876-07 benchmark t A100 \N t \N \N \N {} -6715 2081 2025-03-15 03:13:12.177222-07 2025-03-15 04:31:33.220267-07 leaderboard t A100 0.0025109593333333337 t \N \N \N {} -6717 2082 2025-03-15 04:18:09.428811-07 2025-03-15 04:24:16.122702-07 benchmark t A100 \N t \N \N \N {} -6718 2082 2025-03-15 02:49:24.552314-07 2025-03-15 02:42:15.529163-07 leaderboard t A100 0.0025148316666666667 t \N \N \N {} -6720 2082 2025-03-15 04:08:25.063554-07 2025-03-15 03:27:30.400048-07 benchmark f A100 \N t \N \N \N {} -6723 2083 2025-03-15 03:03:30.27571-07 2025-03-15 03:18:11.663245-07 benchmark f A100 \N t \N \N \N {} -6724 2083 2025-03-15 03:42:23.801801-07 2025-03-15 03:28:29.653584-07 leaderboard f A100 0.002519684 t \N \N \N {} -6726 2083 2025-03-15 04:21:45.565499-07 2025-03-15 04:16:51.069546-07 benchmark t A100 \N t \N \N \N {} -6727 2083 2025-03-15 04:07:46.449197-07 2025-03-15 04:25:25.23021-07 leaderboard t A100 0.002565036 t \N \N \N {} -6729 2084 2025-03-15 03:39:18.979856-07 2025-03-15 03:30:24.318469-07 benchmark t A100 \N t \N \N \N {} -6730 2084 2025-03-15 04:36:55.579596-07 2025-03-15 02:45:20.602588-07 leaderboard t A100 0.0025688876666666665 t \N \N \N {} -6732 2084 2025-03-15 03:14:12.489414-07 2025-03-15 02:51:03.824773-07 benchmark f A100 \N t \N \N \N {} -6733 2084 2025-03-15 03:22:20.346621-07 2025-03-15 03:11:13.814391-07 leaderboard f A100 0.00251581 t \N \N \N {} -6734 2085 2025-03-15 04:20:48.927946-07 2025-03-15 04:46:33.035612-07 benchmark f A100 \N t \N \N \N {} -6736 2086 2025-03-15 04:16:49.706067-07 2025-03-15 03:09:15.471405-07 benchmark t A100 \N t \N \N \N {} -6737 2086 2025-03-15 04:03:35.988704-07 2025-03-15 03:48:00.494296-07 leaderboard t A100 0.0025361823333333337 t \N \N \N {} -6739 2086 2025-03-15 03:15:14.837866-07 2025-03-15 04:10:52.660769-07 benchmark f A100 \N t \N \N \N {} -6740 2086 2025-03-15 03:44:14.062436-07 2025-03-15 04:17:31.002026-07 leaderboard f A100 0.0025177116666666665 t \N \N \N {} -6742 2087 2025-03-15 03:11:35.820391-07 2025-03-15 03:29:33.061134-07 benchmark t A100 \N t \N \N \N {} -6743 2087 2025-03-15 04:11:17.86842-07 2025-03-15 04:57:34.429343-07 leaderboard t A100 0.0025124136666666665 t \N \N \N {} -6745 2087 2025-03-15 04:40:18.217533-07 2025-03-15 04:45:54.791513-07 benchmark f A100 \N t \N \N \N {} -6746 2087 2025-03-15 03:26:08.981404-07 2025-03-15 03:26:01.506723-07 leaderboard f A100 0.0025170136666666666 t \N \N \N {} -6747 2088 2025-03-15 03:45:17.641686-07 2025-03-15 03:59:01.555077-07 benchmark f A100 \N t \N \N \N {} -6748 2089 2025-03-15 04:38:05.216274-07 2025-03-15 03:11:47.405569-07 benchmark f A100 \N t \N \N \N {} -6749 2090 2025-03-15 03:38:24.37619-07 2025-03-15 03:54:27.64572-07 benchmark f A100 \N t \N \N \N {} -6750 2091 2025-03-15 03:40:25.330357-07 2025-03-15 05:05:56.066665-07 benchmark f A100 \N t \N \N \N {} -6751 2092 2025-03-15 03:14:13.338645-07 2025-03-15 03:23:06.85116-07 benchmark f A100 \N t \N \N \N {} -6753 2093 2025-03-15 03:42:38.171903-07 2025-03-15 04:13:43.823972-07 benchmark f A100 \N t \N \N \N {} -6754 2093 2025-03-15 04:16:08.4764-07 2025-03-15 04:59:47.179425-07 leaderboard f A100 0.0025626343333333334 t \N \N \N {} -6756 2093 2025-03-15 03:19:26.506082-07 2025-03-15 03:33:18.400847-07 benchmark t A100 \N t \N \N \N {} -6757 2093 2025-03-15 03:50:47.54997-07 2025-03-15 04:41:08.193634-07 leaderboard t A100 0.0025123326666666667 t \N \N \N {} -6759 2094 2025-03-15 04:41:28.996534-07 2025-03-15 03:59:31.091742-07 benchmark f A100 \N t \N \N \N {} -6760 2094 2025-03-15 03:19:48.97712-07 2025-03-15 04:52:46.927488-07 leaderboard f A100 0.002587383 t \N \N \N {} -6762 2094 2025-03-15 03:21:46.748837-07 2025-03-15 03:42:35.174807-07 benchmark t A100 \N t \N \N \N {} -6763 2094 2025-03-15 04:43:41.011534-07 2025-03-15 04:19:37.015055-07 leaderboard t A100 0.0025829123333333333 t \N \N \N {} -6764 2095 2025-03-15 03:22:19.084993-07 2025-03-15 04:23:32.846181-07 benchmark f A100 \N t \N \N \N {} -6765 2096 2025-03-15 04:20:43.869135-07 2025-03-15 03:47:49.996079-07 benchmark f A100 \N t \N \N \N {} -6767 2097 2025-03-15 05:10:33.784233-07 2025-03-15 05:11:32.173991-07 benchmark t A100 \N t \N \N \N {} -6768 2097 2025-03-15 05:07:59.890466-07 2025-03-15 04:39:03.8974-07 leaderboard t A100 0.0024414366666666667 t \N \N \N {} -6770 2097 2025-03-15 04:36:24.390754-07 2025-03-15 04:02:31.398882-07 benchmark f A100 \N t \N \N \N {} -6771 2097 2025-03-15 04:12:36.697244-07 2025-03-15 03:55:22.254114-07 leaderboard f A100 0.0025765703333333334 t \N \N \N {} -6773 2098 2025-03-15 05:10:57.321488-07 2025-03-15 04:50:43.920081-07 benchmark t A100 \N t \N \N \N {} -6774 2098 2025-03-15 04:14:30.497647-07 2025-03-15 04:53:30.016091-07 leaderboard t A100 0.002577079 t \N \N \N {} -6776 2098 2025-03-15 05:09:55.996121-07 2025-03-15 03:56:57.017989-07 benchmark f A100 \N t \N \N \N {} -6777 2098 2025-03-15 04:11:54.507468-07 2025-03-15 03:43:49.234308-07 leaderboard f A100 0.002441317 t \N \N \N {} -6779 2099 2025-03-15 03:45:03.883023-07 2025-03-15 05:13:30.957419-07 benchmark f A100 \N t \N \N \N {} -6780 2099 2025-03-15 03:28:47.790669-07 2025-03-15 04:06:54.990019-07 leaderboard f A100 0.002574019 t \N \N \N {} -6782 2099 2025-03-15 05:16:01.58112-07 2025-03-15 04:49:37.793655-07 benchmark t A100 \N t \N \N \N {} -6783 2099 2025-03-15 04:17:13.541413-07 2025-03-15 05:01:49.023637-07 leaderboard t A100 0.0024406096666666666 t \N \N \N {} -6785 2100 2025-03-15 05:11:53.175254-07 2025-03-15 04:00:48.036801-07 benchmark f A100 \N t \N \N \N {} -6786 2100 2025-03-15 04:50:11.427307-07 2025-03-15 04:59:24.114334-07 leaderboard f A100 0.0025154266666666666 t \N \N \N {} -6788 2100 2025-03-15 03:58:09.913043-07 2025-03-15 04:10:04.670157-07 benchmark t A100 \N t \N \N \N {} -6789 2100 2025-03-15 04:27:52.506737-07 2025-03-15 05:09:40.763406-07 leaderboard t A100 0.0025164226666666667 t \N \N \N {} -6791 2101 2025-03-15 03:51:55.571727-07 2025-03-15 04:36:50.58781-07 benchmark f A100 \N t \N \N \N {} -6792 2101 2025-03-15 05:02:04.728096-07 2025-03-15 03:47:35.278382-07 leaderboard f A100 0.002513957 t \N \N \N {} -6794 2101 2025-03-15 04:13:17.847293-07 2025-03-15 05:12:57.162997-07 benchmark t A100 \N t \N \N \N {} -6795 2101 2025-03-15 04:32:46.623254-07 2025-03-15 04:14:33.053587-07 leaderboard t A100 0.0025157153333333336 t \N \N \N {} -6797 2102 2025-03-15 04:32:32.324876-07 2025-03-15 05:04:55.352884-07 benchmark f A100 \N t \N \N \N {} -6798 2102 2025-03-15 05:21:36.583203-07 2025-03-15 04:18:22.362049-07 leaderboard f A100 0.002519632 t \N \N \N {} -6800 2102 2025-03-15 04:46:37.876337-07 2025-03-15 05:18:37.22304-07 benchmark t A100 \N t \N \N \N {} -6801 2102 2025-03-15 04:37:43.517293-07 2025-03-15 04:33:17.157515-07 leaderboard t A100 0.0025164303333333336 t \N \N \N {} -6802 2103 2025-03-15 04:28:54.778992-07 2025-03-15 04:05:58.240583-07 benchmark f A100 \N t \N \N \N {} -6803 2104 2025-03-15 04:17:37.393026-07 2025-03-15 04:32:32.169131-07 benchmark f A100 \N t \N \N \N {} -6805 2106 2025-03-15 04:31:28.892695-07 2025-03-15 05:14:03.534628-07 benchmark f A100 \N t \N \N \N {} -6809 2110 2025-03-15 05:36:30.308746-07 2025-03-15 05:16:01.72196-07 benchmark f A100 \N f \N \N \N {} -6810 2111 2025-03-15 05:40:36.907231-07 2025-03-15 04:27:34.338625-07 benchmark f A100 \N t \N \N \N {} -6811 2112 2025-03-15 04:14:08.830754-07 2025-03-15 04:10:52.990865-07 benchmark f A100 \N t \N \N \N {} -6812 2113 2025-03-15 04:18:14.027105-07 2025-03-15 04:27:38.546365-07 benchmark f A100 \N t \N \N \N {} -6892 2142 2025-03-16 11:16:36.721079-07 2025-03-16 10:23:08.981733-07 benchmark f H100 \N f \N \N \N {} -6813 2114 2025-03-15 05:32:29.886643-07 2025-03-15 04:16:37.097189-07 benchmark f A100 \N f \N \N \N {} -6814 2115 2025-03-15 04:20:01.707962-07 2025-03-15 04:47:01.479607-07 benchmark f A100 \N t \N \N \N {} -6815 2116 2025-03-15 09:41:27.374128-07 2025-03-15 10:42:21.527333-07 benchmark f A100 \N f \N \N \N {} -6817 2118 2025-03-15 09:34:00.585802-07 2025-03-15 09:31:57.810416-07 benchmark f A100 \N t \N \N \N {} -6818 2119 2025-03-15 09:01:24.585482-07 2025-03-15 09:06:44.508444-07 benchmark f A100 \N t \N \N \N {} -6820 2120 2025-03-15 09:11:32.950497-07 2025-03-15 10:49:33.106269-07 benchmark f A100 \N t \N \N \N {} -6821 2120 2025-03-15 10:40:24.613571-07 2025-03-15 09:19:20.415657-07 leaderboard f A100 0.002443316 t \N \N \N {} -6824 2120 2025-03-15 09:48:57.993341-07 2025-03-15 10:57:27.863934-07 leaderboard t A100 0.0025090733333333333 t \N \N \N {} -6826 2121 2025-03-15 10:59:21.842552-07 2025-03-15 09:27:40.540091-07 benchmark t A100 \N t \N \N \N {} -6827 2121 2025-03-15 11:01:43.071093-07 2025-03-15 09:36:34.551049-07 leaderboard t A100 0.0025179616666666663 t \N \N \N {} -6829 2121 2025-03-15 10:06:55.697797-07 2025-03-15 09:52:44.863131-07 benchmark f A100 \N t \N \N \N {} -6830 2121 2025-03-15 10:04:37.365154-07 2025-03-15 10:01:47.995774-07 leaderboard f A100 0.00258409 t \N \N \N {} -6832 2122 2025-03-15 09:19:20.004818-07 2025-03-15 10:37:41.259917-07 benchmark t A100 \N t \N \N \N {} -6833 2122 2025-03-15 10:30:16.383706-07 2025-03-15 10:47:04.167114-07 leaderboard t A100 0.002514938 t \N \N \N {} -6835 2122 2025-03-15 09:23:17.985207-07 2025-03-15 10:19:25.750256-07 benchmark f A100 \N t \N \N \N {} -6836 2122 2025-03-15 09:33:25.125545-07 2025-03-15 09:07:15.935099-07 leaderboard f A100 0.002580424 t \N \N \N {} -6838 2123 2025-03-15 09:19:08.433444-07 2025-03-15 10:51:34.7913-07 benchmark f A100 \N t \N \N \N {} -6839 2123 2025-03-15 10:48:27.578624-07 2025-03-15 10:18:07.6516-07 leaderboard f A100 0.0025640053333333334 t \N \N \N {} -6841 2123 2025-03-15 09:57:51.450683-07 2025-03-15 11:08:21.326212-07 benchmark t A100 \N t \N \N \N {} -6842 2123 2025-03-15 10:58:54.403466-07 2025-03-15 10:55:26.439192-07 leaderboard t A100 0.0024363643333333335 t \N \N \N {} -6844 2124 2025-03-15 10:59:45.752999-07 2025-03-15 11:08:50.594752-07 benchmark f A100 \N t \N \N \N {} -6845 2124 2025-03-15 10:30:51.054594-07 2025-03-15 09:50:58.327798-07 leaderboard f A100 0.0025648486666666665 t \N \N \N {} -6847 2124 2025-03-15 10:17:13.326454-07 2025-03-15 11:08:25.750753-07 benchmark t A100 \N t \N \N \N {} -6848 2124 2025-03-15 10:50:00.948662-07 2025-03-15 09:49:49.033815-07 leaderboard t A100 0.0024406783333333335 t \N \N \N {} -6850 2125 2025-03-15 10:24:17.864597-07 2025-03-15 11:03:08.565844-07 benchmark f A100 \N t \N \N \N {} -6851 2125 2025-03-15 10:43:35.545505-07 2025-03-15 09:58:24.113231-07 leaderboard f A100 0.0025163956666666666 t \N \N \N {} -6853 2125 2025-03-15 10:00:18.694621-07 2025-03-15 09:47:13.923293-07 benchmark t A100 \N t \N \N \N {} -6854 2125 2025-03-15 09:28:35.589355-07 2025-03-15 09:48:08.961608-07 leaderboard t A100 0.0024417513333333334 t \N \N \N {} -6856 2126 2025-03-15 11:02:27.462138-07 2025-03-15 09:43:02.319651-07 benchmark f A100 \N t \N \N \N {} -6857 2126 2025-03-15 10:34:45.228313-07 2025-03-15 10:59:38.816203-07 leaderboard f A100 0.0025180366666666667 t \N \N \N {} -6859 2126 2025-03-15 10:50:57.421372-07 2025-03-15 10:57:16.908572-07 benchmark t A100 \N t \N \N \N {} -6860 2126 2025-03-15 09:48:05.47393-07 2025-03-15 11:11:47.912597-07 leaderboard t A100 0.0024680626666666663 t \N \N \N {} -6862 2127 2025-03-15 10:49:46.799765-07 2025-03-15 11:16:21.43097-07 benchmark f A100 \N t \N \N \N {} -6863 2127 2025-03-15 09:54:25.438285-07 2025-03-15 09:33:00.814065-07 leaderboard f A100 0.0025179106666666667 t \N \N \N {} -6865 2127 2025-03-15 09:30:50.965569-07 2025-03-15 10:48:04.990717-07 benchmark t A100 \N t \N \N \N {} -6866 2127 2025-03-15 10:18:20.707038-07 2025-03-15 11:07:56.251823-07 leaderboard t A100 0.00251993 t \N \N \N {} -6867 2128 2025-03-15 12:31:03.821683-07 2025-03-15 13:36:09.213881-07 test t T4 \N f \N \N \N {} -6873 2131 2025-03-15 13:22:04.183948-07 2025-03-15 14:24:48.619022-07 test f T4 \N f \N \N \N {} -6879 2134 2025-03-15 13:51:24.499821-07 2025-03-15 13:59:05.032478-07 test f T4 \N f \N \N \N {} -6880 2134 2025-03-15 13:38:48.357103-07 2025-03-15 13:25:18.378546-07 test t T4 \N f \N \N \N {} -6881 2135 2025-03-15 13:08:37.869004-07 2025-03-15 14:28:23.740745-07 test t T4 \N f \N \N \N {} -6882 2135 2025-03-15 14:19:21.411146-07 2025-03-15 13:47:56.79824-07 test f T4 \N f \N \N \N {} -6885 2138 2025-03-16 09:39:05.307836-07 2025-03-16 09:57:12.148158-07 test f H100 \N f \N \N \N {} -6898 2147 2025-03-16 12:12:19.701328-07 2025-03-16 12:45:49.124926-07 benchmark f T4 \N t \N \N \N {} -6900 2148 2025-03-16 12:59:18.78657-07 2025-03-16 12:12:58.201063-07 benchmark t T4 \N t \N \N \N {} -6901 2148 2025-03-16 13:01:51.449428-07 2025-03-16 13:37:59.705354-07 leaderboard t T4 0.04730965533333334 t \N \N \N {} -6903 2148 2025-03-16 12:58:16.904312-07 2025-03-16 12:26:52.007968-07 benchmark f T4 \N t \N \N \N {} -6904 2148 2025-03-16 13:30:00.224366-07 2025-03-16 12:52:44.892037-07 leaderboard f T4 0.04833626233333334 t \N \N \N {} -6908 2152 2025-03-16 15:44:14.854646-07 2025-03-16 16:27:57.619851-07 test t T4 \N f \N \N \N {} -6937 2159 2025-03-16 16:22:15.506089-07 2025-03-16 15:10:41.848628-07 test t T4 \N t \N \N \N {} -6909 2152 2025-03-16 16:12:17.885519-07 2025-03-16 16:07:41.651133-07 test f T4 \N f \N \N \N {} -6910 2153 2025-03-16 16:06:39.076718-07 2025-03-16 16:34:36.042625-07 test f T4 \N f \N \N \N {} -6911 2153 2025-03-16 16:28:04.258089-07 2025-03-16 15:53:40.235028-07 test t T4 \N f \N \N \N {} -6917 2156 2025-03-16 16:39:30.967226-07 2025-03-16 16:15:32.585871-07 benchmark f T4 \N t \N \N \N {} -6918 2156 2025-03-16 16:54:16.751464-07 2025-03-16 16:03:12.107749-07 leaderboard f T4 0.00024644342857142856 t \N \N \N {} -6920 2156 2025-03-16 14:58:37.646855-07 2025-03-16 15:33:11.455364-07 benchmark t T4 \N t \N \N \N {} -6921 2156 2025-03-16 15:16:42.101122-07 2025-03-16 16:12:37.070857-07 leaderboard t T4 0.00027644572222222224 t \N \N \N {} -6936 2159 2025-03-16 15:05:11.186583-07 2025-03-16 15:35:42.129511-07 benchmark f T4 \N f \N \N \N {} -6938 2159 2025-03-16 16:42:58.618407-07 2025-03-16 15:47:12.763026-07 benchmark t T4 \N f \N \N \N {} -6939 2161 2025-03-16 17:02:27.026204-07 2025-03-16 16:28:43.910502-07 test t T4 \N t \N \N \N {} -6940 2161 2025-03-16 16:06:50.499036-07 2025-03-16 15:50:15.342936-07 benchmark t T4 \N t \N \N \N {} -6944 2161 2025-03-16 16:05:29.503727-07 2025-03-16 16:16:37.57484-07 leaderboard f T4 0.00036897348214285714 t \N \N \N {} -6958 2165 2025-03-16 15:32:28.811938-07 2025-03-16 15:44:31.566608-07 test t T4 \N f \N \N \N {} -6959 2165 2025-03-16 15:57:30.46348-07 2025-03-16 17:11:51.584129-07 test f T4 \N f \N \N \N {} -6967 2168 2025-03-16 19:51:44.930831-07 2025-03-16 19:38:02.035729-07 test t T4 \N f \N \N \N {} -6968 2168 2025-03-16 19:59:18.445681-07 2025-03-16 20:46:51.847725-07 test f T4 \N f \N \N \N {} -6969 2169 2025-03-16 20:01:37.505709-07 2025-03-16 19:53:20.526714-07 test t T4 \N t \N \N \N {} -6970 2169 2025-03-16 18:55:53.752614-07 2025-03-16 19:43:27.875834-07 benchmark t T4 \N t \N \N \N {} -6972 2169 2025-03-16 20:01:43.044-07 2025-03-16 20:16:29.475064-07 test f T4 \N t \N \N \N {} -6973 2169 2025-03-16 20:12:25.898132-07 2025-03-16 20:12:59.112672-07 benchmark f T4 \N t \N \N \N {} -6975 2170 2025-03-16 20:55:02.810795-07 2025-03-16 19:05:21.943366-07 test f T4 \N t \N \N \N {} -6982 2171 2025-03-16 20:34:15.139307-07 2025-03-16 19:39:38.074903-07 benchmark t T4 \N t \N \N \N {} -6988 2172 2025-03-16 21:11:02.554591-07 2025-03-16 20:23:24.725167-07 benchmark t T4 \N t \N \N \N {} -6989 2172 2025-03-16 19:23:38.557607-07 2025-03-16 19:25:56.789059-07 leaderboard t T4 0.0008529994 t \N \N \N {} -6990 2172 2025-03-16 20:55:25.236794-07 2025-03-16 20:01:21.258987-07 test f T4 \N t \N \N \N {} -6991 2172 2025-03-16 20:19:48.287171-07 2025-03-16 19:24:39.531799-07 benchmark f T4 \N t \N \N \N {} -6992 2172 2025-03-16 20:07:21.615115-07 2025-03-16 20:13:42.640692-07 leaderboard f T4 0.0005998246363636364 t \N \N \N {} -6993 2173 2025-03-16 19:27:19.561439-07 2025-03-16 20:33:05.60488-07 test t T4 \N t \N \N \N {} -6994 2173 2025-03-16 20:10:47.945369-07 2025-03-16 19:25:09.306754-07 benchmark t T4 \N t \N \N \N {} -7006 2175 2025-03-16 20:10:27.360853-07 2025-03-16 20:32:19.626531-07 leaderboard f T4 0.00022696372727272726 t \N \N \N {} -7007 2176 2025-03-16 20:27:39.388715-07 2025-03-16 21:13:20.266181-07 test t T4 \N t \N \N \N {} -7013 2177 2025-03-16 20:52:50.82818-07 2025-03-16 20:52:32.181629-07 test t T4 \N t \N \N \N {} -7020 2178 2025-03-16 20:56:15.870132-07 2025-03-16 21:37:33.135945-07 benchmark f T4 \N t \N \N \N {} -7021 2178 2025-03-16 20:46:26.93908-07 2025-03-16 20:15:43.470303-07 leaderboard f T4 0.00023776616 t \N \N \N {} -7022 2178 2025-03-16 20:29:51.881141-07 2025-03-16 20:49:15.652665-07 test t T4 \N t \N \N \N {} -7023 2178 2025-03-16 19:44:17.51868-07 2025-03-16 20:29:57.33345-07 benchmark t T4 \N t \N \N \N {} -7024 2178 2025-03-16 20:46:38.473464-07 2025-03-16 19:46:05.014633-07 leaderboard t T4 0.00023401815384615384 t \N \N \N {} -7025 2179 2025-03-16 20:43:42.53621-07 2025-03-16 20:37:35.619442-07 test t T4 \N t \N \N \N {} -7026 2179 2025-03-16 20:01:15.232212-07 2025-03-16 20:15:45.919775-07 benchmark t T4 \N t \N \N \N {} -7027 2179 2025-03-16 20:05:15.564236-07 2025-03-16 20:03:37.77276-07 leaderboard t T4 0.00023105845833333333 t \N \N \N {} -7028 2179 2025-03-16 21:07:58.380332-07 2025-03-16 20:38:16.446317-07 test f T4 \N t \N \N \N {} -7029 2179 2025-03-16 21:12:36.483147-07 2025-03-16 20:50:41.542771-07 benchmark f T4 \N t \N \N \N {} -7030 2179 2025-03-16 21:41:50.345063-07 2025-03-16 20:30:53.023148-07 leaderboard f T4 0.0002410815 t \N \N \N {} -7031 2180 2025-03-16 20:01:10.433977-07 2025-03-16 21:48:38.104837-07 test f T4 \N t \N \N \N {} -7032 2180 2025-03-16 20:42:55.307852-07 2025-03-16 19:56:48.747118-07 benchmark f T4 \N t \N \N \N {} -7033 2180 2025-03-16 19:54:54.43732-07 2025-03-16 21:43:31.624944-07 leaderboard f T4 0.0010320996666666666 t \N \N \N {} -7034 2180 2025-03-16 21:08:01.480549-07 2025-03-16 20:04:50.559362-07 test t T4 \N t \N \N \N {} -7035 2180 2025-03-16 20:17:30.013044-07 2025-03-16 21:24:22.031756-07 benchmark t T4 \N t \N \N \N {} -7036 2180 2025-03-16 21:41:47.373413-07 2025-03-16 21:32:11.525076-07 leaderboard t T4 0.0010306484 t \N \N \N {} -7037 2181 2025-03-16 21:22:46.486398-07 2025-03-16 20:29:47.484582-07 test f A100 \N t \N \N \N 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345678901234567 1643 2025-03-15 13:08:41.987409-07 t -2132 342 kernel.py 901234567890123 1644 2025-03-15 15:40:51.471691-07 t -2133 342 impl.py 789012345678901 1645 2025-03-15 15:31:01.440834-07 t -\. - - --- --- Data for Name: user_info; Type: TABLE DATA; Schema: leaderboard; Owner: - --- - -COPY leaderboard.user_info (id, user_name) FROM stdin; -123456789012345 Alice -234567890123456 Bob -345678901234567 Charlie -456789012345678 Dave -567890123456789 Eve -678901234567890 Frank -789012345678901 Grace -890123456789012 Heidi -901234567890123 Ivan -012345678901234 James -\. - - --- --- Name: code_files_id_seq; Type: SEQUENCE SET; Schema: leaderboard; Owner: - --- - -SELECT pg_catalog.setval('leaderboard.code_files_id_seq', 1687, true); - - --- --- Name: leaderboard_id_seq; Type: SEQUENCE SET; Schema: leaderboard; Owner: - --- - -SELECT pg_catalog.setval('leaderboard.leaderboard_id_seq', 365, true); - - --- --- Name: runs_id_seq; Type: SEQUENCE SET; Schema: leaderboard; Owner: - --- - -SELECT pg_catalog.setval('leaderboard.runs_id_seq', 7078, true); - - --- --- Name: submission_id_seq; Type: SEQUENCE SET; Schema: leaderboard; Owner: - --- - -SELECT pg_catalog.setval('leaderboard.submission_id_seq', 2188, true); - - --- --- Name: code_files code_files_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.code_files - ADD CONSTRAINT code_files_pkey PRIMARY KEY (id); - - --- --- Name: gpu_type gpu_type_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.gpu_type - ADD CONSTRAINT gpu_type_pkey PRIMARY KEY (leaderboard_id, gpu_type); - - --- --- Name: leaderboard leaderboard_name_key; Type: CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.leaderboard - ADD CONSTRAINT leaderboard_name_key UNIQUE (name); - - --- --- Name: leaderboard leaderboard_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.leaderboard - ADD CONSTRAINT leaderboard_pkey PRIMARY KEY (id); - - --- --- Name: runs runs_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.runs - ADD CONSTRAINT runs_pkey PRIMARY KEY (id); - - --- --- Name: submission submission_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.submission - ADD CONSTRAINT submission_pkey PRIMARY KEY (id); - - --- --- Name: user_info user_info_pkey; Type: CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.user_info - ADD CONSTRAINT user_info_pkey PRIMARY KEY (id); - - --- --- Name: submission fk_user_info; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.submission - ADD CONSTRAINT fk_user_info FOREIGN KEY (user_id) REFERENCES leaderboard.user_info(id); - - --- --- Name: gpu_type gpu_type_leaderboard_id_fkey; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.gpu_type - ADD CONSTRAINT gpu_type_leaderboard_id_fkey FOREIGN KEY (leaderboard_id) REFERENCES leaderboard.leaderboard(id) ON DELETE CASCADE; - - --- --- Name: runs runs_submission_id_fkey; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.runs - ADD CONSTRAINT runs_submission_id_fkey FOREIGN KEY (submission_id) REFERENCES leaderboard.submission(id); - - --- --- Name: submission submission_code_id_fkey; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.submission - ADD CONSTRAINT submission_code_id_fkey FOREIGN KEY (code_id) REFERENCES leaderboard.code_files(id); - - --- --- Name: submission submission_leaderboard_id_fkey; Type: FK CONSTRAINT; Schema: leaderboard; Owner: - --- - -ALTER TABLE ONLY leaderboard.submission - ADD CONSTRAINT submission_leaderboard_id_fkey FOREIGN KEY (leaderboard_id) REFERENCES leaderboard.leaderboard(id); - - --- --- PostgreSQL database dump complete --- - From 2c61c8dd0b9ee66c27b8497f6bcbb2bcff4dab93 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 16:57:10 -0700 Subject: [PATCH 20/28] echo variables --- src/libkernelbot/background_submission_manager.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/src/libkernelbot/background_submission_manager.py b/src/libkernelbot/background_submission_manager.py index 692bc999..1d78eab3 100644 --- a/src/libkernelbot/background_submission_manager.py +++ b/src/libkernelbot/background_submission_manager.py @@ -118,7 +118,7 @@ async def _worker_loop(self): self.queue.get(), timeout=self.idle_seconds ) logger.info( - "[Background Job] worker %r got submission job %s", id(asyncio.current_task()), item.sub_id + "[Background Job][worker %r] pick the submission job `%s`", id(asyncio.current_task()), item.sub_id ) except asyncio.TimeoutError: async with self._state_lock: @@ -130,7 +130,7 @@ async def _worker_loop(self): try: self._workers.remove(me) logger.info( - "[Background Job] scale down: worker %r exiting; workers=%d", + "[Background Job][worker %r] idle too long, scale down; existing workers=%d", me.get_name() if hasattr(me, "get_name") else id(me), @@ -143,11 +143,15 @@ async def _worker_loop(self): continue t = asyncio.create_task(self._run_one(item), name=f"submision-job-{item.sub_id}") + async with self._state_lock: self._live_tasks.add(t) try: await t # wait submission job to finish finally: + logger.info( + "[Background Job][worker %r] finishes the submission job `%s`", id(asyncio.current_task()), item.sub_id + ) async with self._state_lock: self._live_tasks.discard(t) self.queue.task_done() @@ -191,7 +195,7 @@ async def heartbeat(): timeout=HARD_TIMEOUT_SEC, ) ts = dt.datetime.now(dt.timezone.utc) - logger.info("[Background Job] run submission %s succeeded", sub_id) + logger.info("[Background Job] submission %s succeeded", sub_id) with self.backend.db as db: db.upsert_submission_job_status( sub_id, status="succeeded", last_heartbeat=ts @@ -214,7 +218,7 @@ async def heartbeat(): sub_id, status="failed", last_heartbeat=ts, error=str(e) ) except Exception: - logger.error("Failed to write failed status for submission %s", sub_id) + logger.error("[Background Job] Failed to write failed status for submission %s", sub_id) finally: stop_heartbeat.set() hb_task.cancel() From ce611d1185b1188969f424be1b45d79d5aaa4442 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 17:18:18 -0700 Subject: [PATCH 21/28] echo variables --- src/kernelbot/api/main.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/kernelbot/api/main.py b/src/kernelbot/api/main.py index 2eeef55b..7b706682 100644 --- a/src/kernelbot/api/main.py +++ b/src/kernelbot/api/main.py @@ -379,7 +379,7 @@ async def run_submission( # noqa: C901 ) return StreamingResponse(generator, media_type="text/event-stream") -async def start_detached_run( +async def enqueue_background_job( req: ProcessedSubmissionRequest, mode: SubmissionMode, backend: KernelBackend, @@ -443,8 +443,8 @@ async def run_submission_v2( if not req.gpus or len(req.gpus) != 1: raise HTTPException(status_code=400, detail="Invalid GPU type") - # start the submission - sub_id,job_id = await start_detached_run( + # put submission request to background manager to run in background + sub_id,job_id = await enqueue_background_job( req, submission_mode_enum, backend_instance, background_submission_manager ) return JSONResponse( From 8dbd54c7ea72d08c016df8ad0a28cce6efef34fd Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sat, 23 Aug 2025 17:33:47 -0700 Subject: [PATCH 22/28] echo variables --- src/libkernelbot/background_submission_manager.py | 2 +- src/libkernelbot/leaderboard_db.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/libkernelbot/background_submission_manager.py b/src/libkernelbot/background_submission_manager.py index 1d78eab3..9eb7154e 100644 --- a/src/libkernelbot/background_submission_manager.py +++ b/src/libkernelbot/background_submission_manager.py @@ -53,7 +53,7 @@ def __init__( self.queue: asyncio.Queue[JobItem] = asyncio.Queue() self._workers: list[asyncio.Task] = [] # workers currently running self._live_tasks: set[asyncio.Task] = set() # tasks currently processing - self.idle_seconds = idle_seconds + self.idle_seconds = idle_seconds # idle_seconds for each worker before scale down # state variables self._state_lock = asyncio.Lock() diff --git a/src/libkernelbot/leaderboard_db.py b/src/libkernelbot/leaderboard_db.py index 83094c6a..15c01b08 100644 --- a/src/libkernelbot/leaderboard_db.py +++ b/src/libkernelbot/leaderboard_db.py @@ -418,7 +418,7 @@ def upsert_submission_job_status( status = COALESCE(EXCLUDED.status, s.status), error = COALESCE(EXCLUDED.error, s.error), last_heartbeat = COALESCE(EXCLUDED.last_heartbeat, s.last_heartbeat) - RETURNING submission_id; + RETURNING id; """, (sub_id, status, error, last_heartbeat), ) From 7bf840222e3440be320aeade9633aeb4cc01ba25 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Sun, 24 Aug 2025 18:44:18 -0700 Subject: [PATCH 23/28] echo variables --- src/kernelbot/api/main.py | 56 ++++++++++++++++++++++++--------------- 1 file changed, 35 insertions(+), 21 deletions(-) diff --git a/src/kernelbot/api/main.py b/src/kernelbot/api/main.py index 7b706682..5cc42f92 100644 --- a/src/kernelbot/api/main.py +++ b/src/kernelbot/api/main.py @@ -20,7 +20,7 @@ SubmissionRequest, prepare_submission, ) -from libkernelbot.utils import KernelBotError +from libkernelbot.utils import KernelBotError, setup_logging from .api_utils import ( _handle_discord_oauth, @@ -28,6 +28,7 @@ to_submit_info, _run_submission, ) +logger = setup_logging(__name__) # yes, we do want ... = Depends() in function signatures # ruff: noqa: B008 @@ -426,31 +427,44 @@ async def run_submission_v2( Returns: JSONResponse: A JSON response containing job_id and and submission_id for the client to poll for status. """ + try: - await simple_rate_limit() + await simple_rate_limit() - submission_request, submission_mode_enum = await to_submit_info( - user_info, submission_mode, file, leaderboard_name, gpu_type, db_context - ) + submission_request, submission_mode_enum = await to_submit_info( + user_info, submission_mode, file, leaderboard_name, gpu_type, db_context + ) - # throw error if submission request is invalid - try: - req = prepare_submission(submission_request, backend_instance) - except Exception as e: - raise HTTPException(status_code=400, detail=str(e)) from e + # throw error if submission request is invalid + try: + req = prepare_submission(submission_request, backend_instance) + except Exception as e: + raise HTTPException(status_code=400, detail=str(e)) from e - # prepare submission request before the submission is started - if not req.gpus or len(req.gpus) != 1: - raise HTTPException(status_code=400, detail="Invalid GPU type") + # prepare submission request before the submission is started + if not req.gpus or len(req.gpus) != 1: + raise HTTPException(status_code=400, detail="Invalid GPU type") - # put submission request to background manager to run in background - sub_id,job_id = await enqueue_background_job( - req, submission_mode_enum, backend_instance, background_submission_manager - ) - return JSONResponse( - status_code=202, - content={"id": sub_id, "job_id": job_id, "status": "accepted"}, - ) + # put submission request to background manager to run in background + sub_id,job_id = await enqueue_background_job( + req, submission_mode_enum, backend_instance, background_submission_manager + ) + return JSONResponse( + status_code=202, + content={"id": sub_id, "job_id": job_id, "status": "accepted"}, + ) + # Preserve FastAPI HTTPException as-is + except HTTPException: + raise + + # Your custom sanitized error + except KernelBotError as e: + raise HTTPException(status_code=getattr(e, "http_code", 400), detail=str(e)) from e + # All other unexpected errors → 500 + except Exception as e: + # logger.exception("Unexpected error in run_submission_v2") + logger.error(f"Unexpected error in api submissoin: {e}") + raise HTTPException(status_code=500, detail="Internal server error") from e @app.get("/leaderboards") async def get_leaderboards(db_context=Depends(get_db)): From 08f320b31f4c42fe268b45d1fa9b4b54d26f1ba0 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Mon, 25 Aug 2025 12:26:41 -0700 Subject: [PATCH 24/28] echo variables --- src/kernelbot/api/main.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/src/kernelbot/api/main.py b/src/kernelbot/api/main.py index 5cc42f92..3e466b94 100644 --- a/src/kernelbot/api/main.py +++ b/src/kernelbot/api/main.py @@ -430,28 +430,31 @@ async def run_submission_v2( try: await simple_rate_limit() + logger.info(f"Received submission request for {leaderboard_name} {gpu_type} {submission_mode}") - submission_request, submission_mode_enum = await to_submit_info( - user_info, submission_mode, file, leaderboard_name, gpu_type, db_context - ) # throw error if submission request is invalid try: + submission_request, submission_mode_enum = await to_submit_info( + user_info, submission_mode, file, leaderboard_name, gpu_type, db_context + ) + req = prepare_submission(submission_request, backend_instance) except Exception as e: - raise HTTPException(status_code=400, detail=str(e)) from e + raise HTTPException(status_code=400, detail=f"failed to prepare submission request: {str(e)}") # prepare submission request before the submission is started if not req.gpus or len(req.gpus) != 1: raise HTTPException(status_code=400, detail="Invalid GPU type") # put submission request to background manager to run in background - sub_id,job_id = await enqueue_background_job( + sub_id,job_status_id = await enqueue_background_job( req, submission_mode_enum, backend_instance, background_submission_manager ) + return JSONResponse( status_code=202, - content={"id": sub_id, "job_id": job_id, "status": "accepted"}, + content={"details":{"id": sub_id, "job_status_id": job_status_id}, "status": "accepted"}, ) # Preserve FastAPI HTTPException as-is except HTTPException: From 811f1c1c9c8339fafe26f2e0e0e14d39cf50c284 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Mon, 25 Aug 2025 14:05:28 -0700 Subject: [PATCH 25/28] echo variables --- src/kernelbot/api/main.py | 3 +-- src/libkernelbot/background_submission_manager.py | 2 +- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/src/kernelbot/api/main.py b/src/kernelbot/api/main.py index 3e466b94..fa07c112 100644 --- a/src/kernelbot/api/main.py +++ b/src/kernelbot/api/main.py @@ -403,7 +403,7 @@ async def enqueue_background_job( return sub_id,job_id @app.post("/submission/{leaderboard_name}/{gpu_type}/{submission_mode}") -async def run_submission_v2( +async def run_submission_async( leaderboard_name: str, gpu_type: str, submission_mode: str, @@ -423,7 +423,6 @@ async def run_submission_v2( user_id (str): The validated user ID obtained from the X-Popcorn-Cli-Id header. Raises: HTTPException: If the kernelbot is not initialized, or header/input is invalid. - Returns: JSONResponse: A JSON response containing job_id and and submission_id for the client to poll for status. """ diff --git a/src/libkernelbot/background_submission_manager.py b/src/libkernelbot/background_submission_manager.py index 9eb7154e..a7df3419 100644 --- a/src/libkernelbot/background_submission_manager.py +++ b/src/libkernelbot/background_submission_manager.py @@ -24,7 +24,7 @@ class JobItem: # Periodicaly update the last heartbeat time for the submission job in submission_job_status table HEARTBEAT_SEC = 15 # heartbeat interval 15s # HARD_TIMEOUT_SEC [3hours]:if a submission is not completed within the hard timeout, it will be marked as failed in submission_job_status table -HARD_TIMEOUT_SEC = 60 * 60 * 3 # hard timeout 3 hours +HARD_TIMEOUT_SEC = 60 * 30 # hard timeout 30 mins class BackgroundSubmissionManager: From 24c066d366d56e50213f4fc56f63c328e2cbfafd Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Wed, 27 Aug 2025 12:26:09 -0700 Subject: [PATCH 26/28] add workflow to dispatch --- src/kernelbot/api/api_utils.py | 10 +++++++--- src/kernelbot/api/main.py | 3 ++- src/libkernelbot/leaderboard_db.py | 8 +++++++- tests/conftest.py | 1 + tests/test_background_submission_manager.py | 2 +- tests/test_validate_user_header.py | 4 +++- 6 files changed, 21 insertions(+), 7 deletions(-) diff --git a/src/kernelbot/api/api_utils.py b/src/kernelbot/api/api_utils.py index 1cce7dfa..253f8bf0 100644 --- a/src/kernelbot/api/api_utils.py +++ b/src/kernelbot/api/api_utils.py @@ -1,10 +1,12 @@ from typing import Any + import requests -from fastapi import HTTPException,UploadFile +from fastapi import HTTPException, UploadFile from kernelbot.env import env from libkernelbot.backend import KernelBackend from libkernelbot.consts import SubmissionMode +from libkernelbot.leaderboard_db import LeaderboardDB from libkernelbot.report import ( Log, MultiProgressReporter, @@ -12,8 +14,10 @@ RunResultReport, Text, ) -from libkernelbot.submission import ProcessedSubmissionRequest, SubmissionRequest, prepare_submission -from libkernelbot.leaderboard_db import LeaderboardDB +from libkernelbot.submission import ( + SubmissionRequest, + prepare_submission, +) async def _handle_discord_oauth(code: str, redirect_uri: str) -> tuple[str, str]: diff --git a/src/kernelbot/api/main.py b/src/kernelbot/api/main.py index fa07c112..b2f62cdf 100644 --- a/src/kernelbot/api/main.py +++ b/src/kernelbot/api/main.py @@ -25,9 +25,10 @@ from .api_utils import ( _handle_discord_oauth, _handle_github_oauth, - to_submit_info, _run_submission, + to_submit_info, ) + logger = setup_logging(__name__) # yes, we do want ... = Depends() in function signatures diff --git a/src/libkernelbot/leaderboard_db.py b/src/libkernelbot/leaderboard_db.py index 2c883ffd..5c9552cc 100644 --- a/src/libkernelbot/leaderboard_db.py +++ b/src/libkernelbot/leaderboard_db.py @@ -5,7 +5,13 @@ import psycopg2 -from libkernelbot.db_types import IdentityType, LeaderboardItem, LeaderboardRankedEntry, RunItem, SubmissionItem +from libkernelbot.db_types import ( + IdentityType, + LeaderboardItem, + LeaderboardRankedEntry, + RunItem, + SubmissionItem, +) from libkernelbot.run_eval import CompileResult, RunResult, SystemInfo from libkernelbot.task import LeaderboardDefinition, LeaderboardTask from libkernelbot.utils import ( diff --git a/tests/conftest.py b/tests/conftest.py index 4eff7652..f296bbc9 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -2,6 +2,7 @@ import subprocess import time from pathlib import Path + import pytest REQUIRED = { diff --git a/tests/test_background_submission_manager.py b/tests/test_background_submission_manager.py index d4032534..ac038972 100644 --- a/tests/test_background_submission_manager.py +++ b/tests/test_background_submission_manager.py @@ -53,7 +53,7 @@ async def test_enqueue_and_run_job(mock_backend): db_context = mock_backend.db db_context.upsert_submission_job_status = mock.Mock( side_effect=lambda *a, **k: a[0] - ) + ) db_context.update_heartbeat_if_active = mock.Mock() # mock submit_full diff --git a/tests/test_validate_user_header.py b/tests/test_validate_user_header.py index df916b00..54ab484d 100644 --- a/tests/test_validate_user_header.py +++ b/tests/test_validate_user_header.py @@ -1,8 +1,10 @@ from typing import Optional + +import pytest from fastapi import HTTPException + from kernelbot.api.main import validate_user_header from libkernelbot.db_types import IdentityType -import pytest class DummyDBCtx: From 5ccd64d717ab7ca218a6a98aa43329d11679314a Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Wed, 27 Aug 2025 12:31:00 -0700 Subject: [PATCH 27/28] add workflow to dispatch --- .../background_submission_manager.py | 80 ++++++++++++++----- .../20250822_01_UtXzl-website-submission.py | 2 +- 2 files changed, 62 insertions(+), 20 deletions(-) diff --git a/src/libkernelbot/background_submission_manager.py b/src/libkernelbot/background_submission_manager.py index a7df3419..0f50b9fa 100644 --- a/src/libkernelbot/background_submission_manager.py +++ b/src/libkernelbot/background_submission_manager.py @@ -23,7 +23,8 @@ class JobItem: # Periodicaly update the last heartbeat time for the submission job in submission_job_status table HEARTBEAT_SEC = 15 # heartbeat interval 15s -# HARD_TIMEOUT_SEC [3hours]:if a submission is not completed within the hard timeout, it will be marked as failed in submission_job_status table +# HARD_TIMEOUT_SEC [3hours]:if a submission is not completed within the hard timeout, +# it will be marked as failed in submission_job_status table HARD_TIMEOUT_SEC = 60 * 30 # hard timeout 30 mins @@ -52,8 +53,12 @@ def __init__( self.backend = backend self.queue: asyncio.Queue[JobItem] = asyncio.Queue() self._workers: list[asyncio.Task] = [] # workers currently running - self._live_tasks: set[asyncio.Task] = set() # tasks currently processing - self.idle_seconds = idle_seconds # idle_seconds for each worker before scale down + self._live_tasks: set[ + asyncio.Task + ] = set() # tasks currently processing + self.idle_seconds = ( + idle_seconds # idle_seconds for each worker before scale down + ) # state variables self._state_lock = asyncio.Lock() @@ -72,7 +77,9 @@ async def start(self): self._workers.append(t) async def stop(self): - logger.info("[Background Job] stopping background submission manager...") + logger.info( + "[Background Job] stopping background submission manager..." + ) async with self._state_lock: self._accepting = False workers = list(self._workers) @@ -82,15 +89,21 @@ async def stop(self): for t in workers: with contextlib.suppress(asyncio.CancelledError): await t - logger.info("[Background Job] ...stopped all background submission manager") + logger.info( + "[Background Job] ...stopped all background submission manager" + ) async def enqueue( - self, req: ProcessedSubmissionRequest, mode: SubmissionMode, sub_id: int + self, + req: ProcessedSubmissionRequest, + mode: SubmissionMode, + sub_id: int, ) -> tuple[int, int]: async with self._state_lock: if not self._accepting: raise RuntimeError( - "[Background Job] Background Submission Manager is not accepting new jobs right now" + "[Background Job] Background Submission Manager is not" + "accepting new jobs right now" ) logger.info("enqueueing submission %s", sub_id) now = dt.datetime.now(dt.timezone.utc) @@ -100,7 +113,9 @@ async def enqueue( status="pending", last_heartbeat=now, ) - await self.queue.put(JobItem(job_id=job_id, sub_id=sub_id, req=req, mode=mode)) + await self.queue.put( + JobItem(job_id=job_id, sub_id=sub_id, req=req, mode=mode) + ) # if we have no workers and it does not hit maximum, start one await self._autoscale_up() return job_id, sub_id @@ -118,7 +133,9 @@ async def _worker_loop(self): self.queue.get(), timeout=self.idle_seconds ) logger.info( - "[Background Job][worker %r] pick the submission job `%s`", id(asyncio.current_task()), item.sub_id + "[Background Job][worker %r] pick the submission job `%s`", + id(asyncio.current_task()), + item.sub_id, ) except asyncio.TimeoutError: async with self._state_lock: @@ -130,7 +147,8 @@ async def _worker_loop(self): try: self._workers.remove(me) logger.info( - "[Background Job][worker %r] idle too long, scale down; existing workers=%d", + "[Background Job][worker %r] idle too long," + "scale down; existing workers=%d", me.get_name() if hasattr(me, "get_name") else id(me), @@ -142,7 +160,9 @@ async def _worker_loop(self): continue - t = asyncio.create_task(self._run_one(item), name=f"submision-job-{item.sub_id}") + t = asyncio.create_task( + self._run_one(item), name=f"submision-job-{item.sub_id}" + ) async with self._state_lock: self._live_tasks.add(t) @@ -150,7 +170,9 @@ async def _worker_loop(self): await t # wait submission job to finish finally: logger.info( - "[Background Job][worker %r] finishes the submission job `%s`", id(asyncio.current_task()), item.sub_id + "[Background Job][worker %r] finishes the submission job `%s`", + id(asyncio.current_task()), + item.sub_id, ) async with self._state_lock: self._live_tasks.discard(t) @@ -191,7 +213,9 @@ async def heartbeat(): try: reporter = MultiProgressReporterAPI() await asyncio.wait_for( - self.backend.submit_full(item.req, item.mode, reporter, sub_id), + self.backend.submit_full( + item.req, item.mode, reporter, sub_id + ), timeout=HARD_TIMEOUT_SEC, ) ts = dt.datetime.now(dt.timezone.utc) @@ -211,14 +235,24 @@ async def heartbeat(): ) except Exception as e: ts = dt.datetime.now(dt.timezone.utc) - logger.error("[Background Job] submission job %s failed", sub_id, exc_info=True) + logger.error( + "[Background Job] submission job %s failed", + sub_id, + exc_info=True, + ) try: with self.backend.db as db: db.upsert_submission_job_status( - sub_id, status="failed", last_heartbeat=ts, error=str(e) + sub_id, + status="failed", + last_heartbeat=ts, + error=str(e), ) except Exception: - logger.error("[Background Job] Failed to write failed status for submission %s", sub_id) + logger.error( + "[Background Job] Failed to write failed status for submission %s", + sub_id, + ) finally: stop_heartbeat.set() hb_task.cancel() @@ -231,15 +265,23 @@ async def _autoscale_up(self): workers = len(self._workers) qsize = self.queue.qsize() - desired = min(self.max_workers, max(self.min_workers, running + qsize)) + desired = min( + self.max_workers, max(self.min_workers, running + qsize) + ) need = desired - workers to_add = max(0, need) logger.info( "[Background Job] autoscale plan: add %d workers " "(max=%d, busy=%d, active=%s, enqueue=%d)", - to_add, self.max_workers, running, workers, qsize, + to_add, + self.max_workers, + running, + workers, + qsize, ) for _ in range(to_add): - logging.info("[Background Job] scale up: starting a new worker") + logging.info( + "[Background Job] scale up: starting a new worker" + ) t = asyncio.create_task(self._worker_loop(), name="bg-worker") self._workers.append(t) diff --git a/src/migrations/20250822_01_UtXzl-website-submission.py b/src/migrations/20250822_01_UtXzl-website-submission.py index 9f27f52a..a4f3c4eb 100644 --- a/src/migrations/20250822_01_UtXzl-website-submission.py +++ b/src/migrations/20250822_01_UtXzl-website-submission.py @@ -18,7 +18,7 @@ submission_id INTEGER NOT NULL REFERENCES leaderboard.submission(id) ON DELETE CASCADE, - status VARCHAR(255) DEFAULT NULL, -- pending | running | succeeded | failed | timed_out + status VARCHAR(255) DEFAULT NULL, -- status of the job error TEXT DEFAULT NULL, -- error details if failed created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(), -- creation timestamp last_heartbeat TIMESTAMPTZ DEFAULT NULL, -- updated periodically by worker From 7de476f32bf4c473d1706b9fe6d79a4555f40a24 Mon Sep 17 00:00:00 2001 From: Yang Wang Date: Wed, 27 Aug 2025 12:37:50 -0700 Subject: [PATCH 28/28] add workflow to dispatch --- .github/workflows/lint.yml | 8 ++++---- src/kernelbot/api/api_utils.py | 2 +- src/kernelbot/api/main.py | 3 ++- 3 files changed, 7 insertions(+), 6 deletions(-) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index cf064d63..f6640176 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -15,16 +15,16 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 - + - name: Set up Python uses: actions/setup-python@v4 with: python-version: '3.10' - + - name: Install dependencies run: | pip install ruff - + - name: Run Ruff check run: | - ruff check . --exclude examples/ + ruff check . --exclude examples/ --line-length 120 diff --git a/src/kernelbot/api/api_utils.py b/src/kernelbot/api/api_utils.py index 253f8bf0..4082108e 100644 --- a/src/kernelbot/api/api_utils.py +++ b/src/kernelbot/api/api_utils.py @@ -189,7 +189,7 @@ async def display_report(self, title: str, report: RunResultReport): elif isinstance(part, Log): self.long_report += f"\n\n## {part.header}:\n" self.long_report += f"```\n{part.content}```" - +# ruff: noqa: C901 async def to_submit_info( user_info: Any, submission_mode: str, diff --git a/src/kernelbot/api/main.py b/src/kernelbot/api/main.py index b2f62cdf..d9d0ae9b 100644 --- a/src/kernelbot/api/main.py +++ b/src/kernelbot/api/main.py @@ -440,8 +440,9 @@ async def run_submission_async( ) req = prepare_submission(submission_request, backend_instance) + except Exception as e: - raise HTTPException(status_code=400, detail=f"failed to prepare submission request: {str(e)}") + raise HTTPException(status_code=400, detail=f"failed to prepare submission request: {str(e)}") from e # prepare submission request before the submission is started if not req.gpus or len(req.gpus) != 1: